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WP36: audit, preregistration, pipeline, models A–F, AMLI, validation, figures/tables, manuscript v1.0 draft

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
Simon-Pierre Boucher committed (Nov 1, 2026) Browse files

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194 changed files
Unified Split
added .gitignore +50 −0
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1+# Raw & processed data — never committed (see data/raw/README.md for provenance)
2+data/raw/*
3+!data/raw/README.md
4+data/processed/*
5+!data/processed/.gitkeep
6+
7+# Python
8+__pycache__/
9+*.pyc
10+.pytest_cache/
11+.venv/
12+*.egg-info/
13+
14+# LaTeX build artifacts (paper/main.pdf IS committed)
15+paper/*.aux
16+paper/*.log
17+paper/*.out
18+paper/*.toc
19+paper/*.lof
20+paper/*.lot
21+paper/*.fls
22+paper/*.fdb_latexmk
23+paper/*.bbl
24+paper/*.blg
25+paper/*.spl
26+paper/*.synctex.gz
27+paper/*.run.xml
28+paper/*.bcf
29+
30+# Logs
31+logs/
32+*.log
33+
34+# macOS
35+.DS_Store
36+
37+# WP36 — restricted / regenerable data layers
38+data/raw/*
39+!data/raw/README.md
40+!data/raw/*.sha256
41+data/interim/*
42+!data/interim/manifest.json
43+data/processed/*.parquet
44+data/synthetic/*.db
45+data/raw/external/*
46+!data/raw/external/README.md
47+results/**/*.parquet
48+catboost_info/
49+logs/*.log
50+paper/main_anon.pdf
added CLAIMS_LEDGER.csv +40 −0
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1+claim_id,section,claim,evidence_type,evidence_ref,status,notes
2+C-001,data,"Panel covers 2026-08-12 12:30 UTC to 2026-11-01 06:49 UTC (81 days, 586 crawl cycles), 273 265 listings, 62 821 active at snapshot",computation,results/descriptives/summary.json; data/interim/manifest.json,verified,
3+C-002,data,"AutoTrader (2 000 sliding window) and CarGurus (500 cap) are sampled portals excluded from S1/S2",documentation+computation,data/raw/registry/docs/connecteurs/{autotrader,cargurus}.md; reports/data_access_audit.md §5,verified,
4+C-003,design,"Listing exit = observed disappearance confirmed by >= 7 days of absence; never a verified sale",design,DECISIONS.md D-004; src/ucml/cleaning.py,verified,
5+C-004,data,"Crawl cycle 3.3 h mean; 7.3 observations per listing-day; max same-source gap 13.6 h",computation,results/descriptives/summary.json coverage,verified,
6+C-005,data,"S1 = 136 393; S2 = 80 856; S2L = 128 091; 47 235 left-censored (day-1 + source start-up)",computation,data/processed/sample_construction.csv; cleaning_stats.json,verified,D-014
7+C-006,data,"2 164 placeholder prices <= $1; 3 451 oscillating uids; 70 excluded with >= 3 transients",computation,reports/data_access_audit.md §4-5; cleaning_stats.json,verified,
8+C-007,data,"Price log: 76 261 sustained cuts, 26 486 rises (all sources); among non-portal sources cuts ≈ 9x rises",computation,results/descriptives (events by kind, cleaning validation log E-003),verified,
9+C-008,data,"Median S1 entry price 25 627 $, mileage 68 629 km, age 5.7 y; SUV 56.7 %, sedan 20.8 %, pickup 10.6 %; BEV 6.1 %",computation,results/descriptives/summary.json; paper/tables/tab03_descriptives.tex,verified,
10+C-009,data,"S2 completed spells: median 8.9 d (IQR 1.0–20.6), 44 % within 7 d, 87 % within 30 d; 46 % right-censored",computation,results/descriptives/summary.json dynamics,verified,
11+C-010,data,"19.7 % of S2 listings have >= 1 sustained cut; 6.6 % >= 2; median first cut 2.8 % (mean 4.6 %)",computation,results/descriptives/summary.json dynamics,verified,
12+C-011,prices,"H1: nonlinear age/km terms jointly significant chi2(12)=664; tensor chi2(49)=667; age x powertrain chi2(35)=312",computation,results/hedonic/summary.json tests,verified,
13+C-012,prices,"Grouped-dealer CV RMSE: linear 0.176, splines 0.171 (−2.8 %), tensor 0.172, trim FE 0.161 (57 % coverage), LightGBM 0.159",computation,results/hedonic/cv_grouped_dealer.json,verified,
14+C-013,prices,"Linear benchmark: −8.5 % per year of age, −3.2 % per 10 000 km (A1)",computation,results/hedonic/coefs_A1_linear_mm.csv,verified,
15+C-014,prices,"Surface: vs 1 y/10 000 km, price −14 % (1 y, 50k km), −27 % (3 y), −37 % (5 y), −57 % (10 y); at 5 y from 10k to 200k km: −28 % to −59 %",computation,results/hedonic/surface_A2t.csv,verified,
16+C-015,prices,"H4: region effects jointly insignificant chi2(16)=14.9 p=0.53; premia −1.0 % (Lanaudière) to +3.7 % (Bas-Saint-Laurent); Outaouais +2.1 % p=0.14",computation,results/hedonic/region_premia_A4.csv; summary.json tests,verified,null result
17+C-016,prices,"In-sample RMSE 0.167 (A1) → 0.162 (A2) → 0.107 (A4 trim FE) → 0.091 (A5 dealer FE)",computation,results/hedonic/summary.json fits,verified,
18+C-017,dynamics,"First-markdown empirical hazard 0.7 %/day (days 1–3) → 1.1 % (day 7) → 1.4–1.5 % (weeks 2–4)",computation,results/hazards/empirical_hazard_first_markdown.csv,verified,
19+C-018,dynamics,"H2: listing-age bins jointly significant chi2(8)=170 in cloglog B; coefficients rise 0.79 → 1.68",computation,results/hazards/cloglog_B_first_markdown.csv; summary.json,verified,
20+C-019,dynamics,"Cox markdown (dealer-clustered): resid 0.716 (p=5e-4), log density 0.114 (p<1e-5), price position 0.356 (p=0.002); cloglog same signs but insignificant",computation,results/hazards/cox_first_markdown.csv; cloglog_B_first_markdown.csv,verified,
21+C-020,dynamics,"Cut size: median 2.7 %; +10 % overpricing → +22 % larger cut; declines with listing age",computation,results/hazards/cutsize_ols.csv; summary.json B_cut_size,verified,
22+C-021,competition,"Daily base rate of own cut 1.44 %; same-day competitor-cut coefficient +0.89 pp (SE 0.21); lags 1–6 ≈ 0; peaks at k=±7, +14",computation,results/competition/eventstudy_main.csv; summary.json,verified,association only
23+C-022,competition,"Leads jointly significant p<1e-4; placebo permuted within make-model: all ≈ 0 (joint p=0.18)",computation,results/competition/summary.json,verified,
24+C-023,competition,"Dealer x weekday FE: k=0 falls to 0.55 pp; k=7 0.17; k=14 0.33",computation,results/competition/summary_dow.json,verified,
25+C-024,competition,"First-markdown risk set: k=0 +2.6 pp vs base 4.9 %; negative leads",computation,results/competition/eventstudy_firstcut.csv,verified,
26+C-025,exit,"Exit hazard 13 % on day 1, 2 % days 2–3, ≈3 % weeks 2–3, <1 % beyond 60 d",computation,results/hazards/empirical_hazard_exit.csv,verified,
27+C-026,exit,"H5 not supported: entry residual in exit hazard 0.18 (cloglog p=0.42), 0.13 (Cox p=0.54) with dealer clusters; mileage −0.49, price −0.26 (Cox)",computation,results/hazards/cloglog_C_exit.csv; cox_exit.csv,verified,null result
28+C-027,validation,"17 898 overlapping dealer-site/otogo VIN pairs; of 8 541 both-exited pairs 80 % within 1 d, 94 % within 7 d, median gap 0.4 d",computation,results/validation/exit_validation.json,verified,
29+C-028,validation,"11.4 % of dealer-site exits still listed on otogo 7 d later; 2.8 % the reverse",computation,results/validation/exit_validation.json,verified,
30+C-029,validation,"Of 47 398 exits observable 30 d: 10.2 % relisted within 30 d, 5.8 % > 5 km away; transfer gap median 1.1 d, q75 8.7 d; 10 139 of 95 968 VINs at 2+ locations > 5 km",computation,results/validation/exit_validation.json,verified,
31+C-030,prediction,"Chronological split: LightGBM log loss 0.405 markdown (AUC 0.785), 0.585 exit (AUC 0.727); ECE < 0.02; dealer/region holdouts AUC 0.58–0.61",computation,results/prediction/metrics.json; paper/tables/tab08_prediction.tex,verified,
32+C-031,prediction,"Dealer target-encoded rate = top LightGBM feature (≈35 % gain markdown, 30 % exit); Cox exit C-index 0.658 on later cohort",computation,results/prediction/feature_importance_*.csv; metrics.json,verified,
33+C-032,amli,"PCA first component explains 56 % (region x week, weeks 2–10); loadings exit +0.41, mdfreq +0.35, depth −0.51, disp(rev) +0.52, arrival +0.44",computation,results/amli/summary.json region.weights,verified,sign reversal on depth
34+C-033,amli,"Component correlations: exit–arrival 0.62, exit–depth −0.35, exit–disp(rev) 0.44, depth–disp 0.85",computation,results/amli/summary.json component_corr,verified,
35+C-034,amli,"Held-out validation: leave-exit-out index → next-week exit 0.97 pp [0.69, 1.24], n=68, R2 0.66; with lagged exit 1.03 [0.70, 1.35]",computation,results/amli/summary.json region_validation,verified,
36+C-035,amli,"Leave-one-component-out correlations 0.91–0.95; corr(equal,PCA)=0.85; corr(equal,supervised)=0.68; segments PCA 44–46 %, slopes 0.46 pp (body CI [0.06, 0.86]; tier CI covers 0)",computation,results/amli/summary.json,verified,
37+C-036,amli,"No external forecasting test possible: no weekly Quebec used-car benchmark exists for Aug–Oct 2026",documentation,reports/external_benchmarks_inventory.md,verified,
38+C-037,lit,"172 references verified via Crossref DOI or publisher URL; no retracted items",documentation,literature/evidence_matrix.csv; search_protocol.md,verified,
39+C-038,background,"Quebec new vehicle sales Aug 2026 = 41 657 units (StatCan 20-10-0085-01); BoC hike to 2.50 % on 2026-10-28",external,reports/external_benchmarks_inventory.md,verified,
40+C-039,robustness,"Holm adjustment leaves H1, H2, H3, H6 significant and H4, H5 unrejected",computation,results/robustness/holm_primary_tests.csv,pending,agent running
added DECISIONS.md +18 −0
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1+# DECISIONS — methodological decisions and rejected alternatives (WP36)
2+
3+| ID | Date | Decision | Alternatives rejected | Rationale / evidence |
4+|---|---|---|---|---|
5+| D-001 | 2026-11-01 | Use an owner-authorised read-only `.backup` of the production SQLite DB as the single raw source; no scraping, no public-API use. | Re-crawling dealer sites; calling `/api/*` | Direct authorised export is strictly richer (price_log, sync_log) and lawful; robots.txt disallows `/api/`. |
6+| D-002 | 2026-11-01 | Drop the `users` table on the production node before transfer; treat VIN, URL, images, description, exact GPS as restricted (never committed/released). | Keep full copy | Personal-information minimisation (Phase 0 §6 governance). |
7+| D-003 | 2026-11-01 | Dynamic analyses (H2, H3, H5, AMLI) use the **dynamic sample** = direct dealer sites + full-inventory portals (otogo, autousagee) + banners, excluding sampled portals (autotrader, cargurus), the private-seller marketplace (kijiji), flickering/failed connectors (leprixdugros, albioccasion, baiecomeaugm, septilesgm, stjeromeautodepot, excelmoto) and uid `classeauto:-98`. | Use all sources | Sampled portals create spurious exits (78 % of cargurus exits < 1 day); flicker shares 0.52–0.94 for excluded connectors (audit §5). |
8+| D-004 | 2026-11-01 | Listing exit = last observation followed by ≥ 7 days of absence before the snapshot (else right-censored at last_seen). Exit is labelled "observed disappearance", never "sale". | 2-miss rule of the platform (≈ 7 h) | Reactivation share up to 0.44 on otogo; a 7-day confirmation window makes flicker negligible; sale is unobserved. |
9+| D-005 | 2026-11-01 | Sustained price paths: a price level lasting < 48 h that reverts to the immediately preceding price is removed (A→B→A transient); uids with ≥ 3 such reversions are flagged "oscillating" and excluded from markdown analyses (kept in hedonics with first sustained price). | Use raw price_log | Oscillation artefacts (e.g. otogo:1694844 alternating twice a day) would be counted as markdowns and rises. |
10+| D-006 | 2026-11-01 | H3 is estimated as an association (event-study / distributed lags with market×time and product×time controls) and explicitly not causal. | Causal claims via IV | No credible exogenous variation in competitor repricing; reflection problem. |
11+| D-007 | 2026-11-01 | H6: AMLI is constructed and validated internally (component coherence, leave-one-component-out predictive checks, bootstrap CIs, regional cross-sections) ; external forecasting tests are declared infeasible for a 81-day panel. | Regress AMLI on monthly StatCan series | ≤ 3 monthly overlapping points; any "forecasting power" claim would be unsupported. |
12+| D-008 | 2026-11-01 | Left-censored stock (listings already active at the first crawl, 2026-08-12) is excluded from duration-based hazards (unknown entry date) but kept for hedonics; sensitivity: include with entry = first_seen (lower bound). | Treat first_seen as entry for all | Day-1 stock (≈ 46 k) has unknown listing age. |
13+| D-009 | 2026-11-01 | Vehicle age measured in years as (listing date − (model year − 1, Sept 1)) ... see analysis plan; mileage in 10 000 km; splines for both. | Model year dummies only | Continuous age needed for the depreciation surface. |
14+| D-010 | 2026-11-01 | Hedonic quality fixed effects are make×model (A2) and make×model×trim (A4), **not** make×model×model-year: with an 81-day window, model-year FE would absorb vehicle age (age varies only with entry date within a model-year cell). Age is identified across model years. | make-model-year cohort FE as written in the plan | Collinearity of age and model-year FE in a short panel (hedonic.py docstring). Amendment to analysis_plan §3 Model A. |
15+| D-011 | 2026-11-01 | Model D (competitor response) is estimated as a linear probability model with market×week and make-model-year×week fixed effects (pyfixest), SEs clustered by dealer (primary) and region (secondary); cloglog with coarse FE as robustness. | cloglog with high-dimensional FE | Nonlinear models with ~10⁴ FE cells on 2.9 M listing-days are not estimable with available tools (incidental-parameter and compute); daily base rate ≈ 1 % so LPM is a reasonable local approximation. |
16+| D-012 | 2026-11-01 | Anonymised identifiers use 10-hex-character SHA-1 prefixes (listings) and 8 (dealers) after a collision was detected at 7 characters (139 expected collisions among 273 k uids). | 7-character ids | Birthday-bound collisions produced duplicate listing rows in the risk set (E-005). |
17+| D-014 | 2026-11-01 | Left-censoring is defined **per source**: a listing is left-censored if it entered before 2026-08-13 00:00 UTC **or within 24 h of its source's first non-empty crawl** (otogo started on day 6 with 24 565 listings that day, autousagee on day 11 with 11 925). | Global day-1 cut-off only (plan v1.0) | Descriptives agent found 33 224 start-up listings (29 % of S2) whose "entry" was the connector start date; durations and hazards would be biased. Amendment to analysis_plan §1 (S2). |
18+| D-015 | 2026-11-01 | AMLI market-week panel starts at panel week 2 (days 14–76): weeks 0–1 are contaminated by the portal start-ups (otogo day 6, autousagee day 11). Supervised weights fitted on weeks 2–5, evaluated on weeks 6–10. Coordinates outside the Quebec bounding box (lat 44–63, lng −80 to −56) are treated as missing. | Keep weeks 0–1 | Province components in week 0 are outliers (start-up stock composition), see figure 8 draft. |
added EXPERIMENTS.md +22 −0
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1+# EXPERIMENTS — commands, versions, data snapshots, results, failures (WP36)
2+
3+Environment: macOS 27.0 (Apple M5 Max, 18 cores, 48 GB), Python 3.12.13 via uv (`requirements-lock.txt`), R 4.6.1, TeX Live 2026, SQLite 3.54.
4+
5+| ID | Date | Command | Data snapshot | Result / artefact | Outcome |
6+|---|---|---|---|---|---|
7+| E-000 | 2026-11-01 | `sqlite3 .backup` on M4M64b + drop users + VACUUM + SHA-256 + zstd + scp | `data/raw/autoka-snapshot-20261101.db` (1 348 825 088 bytes) | checksum OK | success |
8+| E-001 | 2026-11-01 | ad-hoc SQL audit queries (reports/data_access_audit.md) | snapshot 2026-11-01 | audit tables | success |
9+| E-002 | 2026-11-01 | `scripts/01_ingest.py` | snapshot sha256 65c3dad2… | `data/interim/*.parquet`, manifest (vehicles 273 265, price_log 428 484, sync_log 83 531; sync ts 2026-08-12 12:30:35 → 2026-11-01 06:49:42 UTC) | success (2.8 s) |
10+| E-003 | 2026-11-01 | `scripts/02_clean.py` | idem | `data/processed/listings.parquet`, `price_events.parquet` (entry 270 909, cut 76 261, rise 26 486, minor 27 915), `exclusions.csv`, `sample_construction.csv` (S0 273 265 → S1 136 393 → S2 114 767; S2L 128 091); region recovered 13 073; powertrain recovered 84 753; coords from dealer 5 582 | success (≈4 s) |
11+| E-004 | 2026-11-01 | `scripts/03_features.py` | idem | `data/processed/risk_set.parquet` 3 470 045 listing-days, 125 606 listings (S2L with coords), 2 377 make-model groups, exits 76 262, first cuts 34 103 | success (5.5 s) |
12+| E-005 | 2026-11-01 | risk-set sanity check | idem | duplicate listing rows (max 3 exits per listing_id) → id collision at 7 hex chars; fixed (D-012) and E-003/E-004 rerun | failure → fixed |
13+| E-006 | 2026-11-01 | `scripts/04_hedonic.py` | idem | `results/hedonic/`: A1–A5 fits (N 136 393; R² 0.923 → 0.977), Wald H1 nonlinear joint χ²(12)=664 p<1e-100, tensor χ²(49)=667, age×powertrain χ²(35)=312; H4 region joint χ²(16)=14.9 **p=0.53** (null); grouped-dealer CV RMSE: A1 0.1756, A2 0.1706, A2t 0.1724, A4 0.1614 (coverage 57 %), LightGBM 0.1588 | success (2 min 38 s) |
14+| E-007 | 2026-11-01 | D-014 fix: `02_clean` + `03_features` rerun | idem | S2 114 767 → **80 856** (start-up stock of otogo/autousagee, 33 224 listings, now left-censored); risk set 3 470 045 listing-days / 125 606 listings (S2L) | success |
15+| E-008 | 2026-11-01 | `scripts/06_hazards.py` (v3: no-competitor flag, Cox dealer-clustered SEs) | idem | B cloglog 318 323 listing-days / 15 692 events, age bins χ²(8)=170; C cloglog 1 663 764 / 43 311, χ²(8)=104; Cox concordance 0.575 (cut) / 0.648 (exit); PH violated for 5 / 9 covariates | success (≈12 min) |
16+| E-009 | 2026-11-01 | `scripts/07_competition.py` + `07b` | idem | main 25 km n=1 663 764, k=0 +0.89 pp; placebo null; dealer×weekday k=0 +0.55 pp | success |
17+| E-010 | 2026-11-01 | `scripts/08_prediction.py` (agent) | idem | metrics.json; chrono LightGBM log loss 0.405 / 0.585 | success (133 s) |
18+| E-011 | 2026-11-01 | `scripts/09_amli.py 200` (weeks 2–10, D-015) | idem | PCA 56 %; held-out slope 0.97 pp [0.69, 1.24]; 200 dealer-bootstrap reps | success (≈10 min) |
19+| E-012 | 2026-11-01 | `scripts/10_validation.py` | idem | exit_validation.json (94 % within 7 d; 10.2 % relisted) | success |
20+| E-013 | 2026-11-01 | first hazards run (v1/v2) | idem | v1 KeyError e_day (merge); v2 dropped listing-days without competitors (pricepos NaN) → B n_listings = n_events; both fixed in v3 | failures → fixed |
21+| E-014 | 2026-11-01 | first AMLI run (weeks 0–10) | idem | week-0 province components were start-up outliers → D-015 | superseded |
22+| E-015 | 2026-11-01 | `paper/`: latexmk | — | main.pdf 45 pp.; bib `$` escape fixed in busse2006cashback | success |
added Makefile +37 −0
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1+# UQO WP36 — end-to-end pipeline (see REPRODUCE.md)
2+PY := .venv/bin/python
3+.PHONY: all env data models figures tables paper test clean
4+
5+all: data models tables figures paper
6+
7+env:
8+ uv venv -p 3.12 .venv && uv pip install -e ".[dev]"
9+
10+data: ## 01 ingest -> 02 clean -> 03 risk set
11+ $(PY) scripts/01_ingest.py && $(PY) scripts/02_clean.py && $(PY) scripts/03_features.py
12+
13+models: ## 04 hedonic, 05 descriptives, 06 hazards, 07 competition (+07b), 08 prediction, 09 AMLI, 10 validation, 12 robustness
14+ $(PY) scripts/04_hedonic.py > logs/04_hedonic.log 2>&1
15+ $(PY) scripts/05_descriptives.py > logs/05_descriptives.log 2>&1
16+ $(PY) scripts/06_hazards.py > logs/06_hazards.log 2>&1
17+ $(PY) scripts/07_competition.py > logs/07_competition.log 2>&1
18+ $(PY) scripts/07b_competition_dow.py > logs/07b_competition_dow.log 2>&1
19+ $(PY) scripts/08_prediction.py > logs/08_prediction.log 2>&1
20+ $(PY) scripts/09_amli.py 200 > logs/09_amli.log 2>&1
21+ $(PY) scripts/10_validation.py > logs/10_validation.log 2>&1
22+ $(PY) scripts/12_robustness.py > logs/12_robustness.log 2>&1
23+
24+figures:
25+ $(PY) scripts/11_figures.py
26+
27+tables:
28+ $(PY) scripts/13_tables.py
29+
30+paper:
31+ cd paper && latexmk -pdf -interaction=nonstopmode main.tex
32+
33+test:
34+ $(PY) -m pytest -q tests
35+
36+clean:
37+ cd paper && latexmk -C
added README.md +81 −0
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1+# The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior
2+
3+**UQO Working Paper No. 36** — *Evidence from a Daily Panel of 273,265 Quebec Listings*
4+
5+![Paper](https://img.shields.io/badge/paper-PDF-NavyBlue) ![Python](https://img.shields.io/badge/Python-3.12-3776ab) ![Data](https://img.shields.io/badge/data-Auto--Ka%20snapshot%202026--11--01-ff5a2a) ![Reproducible](https://img.shields.io/badge/reproducible-make%20all-green) ![UQO](https://img.shields.io/badge/UQO-DSA-blue) ![Author](https://img.shields.io/badge/author-S.--P.%20Boucher-lightgrey)
6+
7+## TL;DR
8+- Daily panel of every used-vehicle listing on 131 Quebec dealer websites, 2 full-inventory portals and 5 dealer groups, 12 Aug–1 Nov 2026 (273,265 listings, 428,484 price-log rows, 586 crawl cycles), from the aggregator Auto-Ka.
9+- Depreciation of asking prices is strongly nonlinear in age and mileage (H1 supported); regional quality-adjusted premia are a precisely estimated null (joint p = 0.53).
10+- 20 % of listings get a sustained markdown (median 2.8 %); the markdown hazard rises over three weeks; overpriced vehicles are cut sooner and more deeply.
11+- Competitor price cuts co-move the same day (+0.9 pp vs a 1.4 % base rate) with no lagged response; placebo null — association, not causation.
12+- Listing exit ≠ sale: 94 % of exits coincide across two sources within a week, but ≥ 10 % are followed by a relisting.
13+- A five-component Automobile Market Liquidity Index (AMLI) explains 56 % of common variation and its non-exit components predict next-week exits; deep markdowns load as illiquidity. No external forecasting claim.
14+
15+## Status
16+Version 1.0, November 2026. Not yet submitted (SSRN: not yet submitted). Preregistered plan: `research/analysis_plan.md` (amendments in `DECISIONS.md`).
17+
18+## What this paper does
19+Audits an aggregator database, builds a reproducible listing-day panel with auditable exclusions, estimates hedonic, hazard, event-study and prediction models under a preregistered plan, validates listing exit as a sale proxy, and constructs and internally validates a market-level liquidity index.
20+
21+## Headline results
22+| Hypothesis | Result | Evidence |
23+|---|---|---|
24+| H1 nonlinear depreciation | Supported (χ²(12)=664; CV RMSE −2.8 %) | `results/hedonic/` |
25+| H2 markdown hazard ↑ listing age | Supported (χ²(8)=170); competition/relative-price effects significant in Cox, imprecise in cloglog | `results/hazards/` |
26+| H3 competitor response | Same-day co-movement only; no lagged response; placebo null | `results/competition/` |
27+| H4 regional premia | Null (p = 0.53; all |premia| < 4 %) | `results/hedonic/region_premia_A4.csv` |
28+| H5 relative price → exit | Not supported (p = 0.42/0.54); exit validated across sources (94 % within 7 d) | `results/hazards/`, `results/validation/` |
29+| H6 AMLI | PCA 56 %; held-out slope 0.97 pp [0.69, 1.24]; no external test possible | `results/amli/` |
30+
31+## Repository layout
32+```
33+paper/ main.tex, sections/, appendix/, tables/, figures/, references.bib, main.pdf
34+src/ucml/ ingest, pricepaths, cleaning, features, hedonic, hazards, competition, prediction, amli, validation, robustness, descriptives, figures, tables, plotstyle, synthetic
35+scripts/ 01_ingest … 13_tables (numbered pipeline)
36+tests/ pytest suite (synthetic fixture, no restricted data)
37+research/ questions.md, analysis_plan.md (preregistration)
38+literature/ references.bib (172 verified), evidence_matrix.csv, novelty_memo.md, search_protocol.md
39+reports/ data_access_audit.md, external_benchmarks_inventory.md
40+data/ raw/ (restricted, not committed), interim/, processed/, synthetic/
41+results/ machine-readable outputs per model
42+STATUS.md DECISIONS.md EXPERIMENTS.md CLAIMS_LEDGER.csv REPRODUCE.md
43+```
44+
45+## Quick start
46+```bash
47+make env && make test
48+make all # data → models → tables → figures → paper (≈45 min; needs the restricted snapshot)
49+cd paper && make # PDF only
50+```
51+
52+## Data
53+Owner-authorised read-only snapshot of the Auto-Ka production database (SQLite, 1.35 GB, SHA-256 in `data/raw/*.sha256`), user table dropped before transfer; VINs, URLs, free text and exact coordinates restricted to `data/raw` and `data/interim`. Not redistributed. See `reports/data_access_audit.md`.
54+
55+## Methodology
56+Hedonic splines with make–model(–trim) and dealer FE (pyfixest); discrete-time cloglog and Cox hazards with dealer-clustered SEs (statsmodels, lifelines); linear-probability event study with market×week and model-year×week FE and a within-make–model placebo; LightGBM/logistic prediction with chronological, dealer and region holdouts; five-component AMLI with equal/PCA/supervised weights, dealer block bootstrap and held-out-component validation.
57+
58+## Figures & tables inventory
59+Figures 1–10 (`figures/`, vector PDF + PNG): coverage, depreciation surface, durations/cuts, price paths, hazards, event study, regional map, AMLI series, AMLI validation, prediction. Tables 1–9 (`paper/tables/`): sources, sample construction, descriptives, hedonic (+tests), hazards, competition (+region premia), AMLI, prediction, robustness.
60+
61+## Limitations
62+Asking prices, not transactions; exit = observed disappearance (≥ 10 % relistings); 81-day window; registry is not a random sample of dealers; H3 is association only; AMLI validated internally only.
63+
64+## Citation
65+```bibtex
66+@techreport{boucher2026usedcars,
67+ author = {Boucher, Simon-Pierre},
68+ title = {The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior},
69+ institution = {Université du Québec en Outaouais, Département des sciences administratives},
70+ type = {UQO Working Paper},
71+ number = {36},
72+ year = {2026},
73+ month = {November}
74+}
75+```
76+
77+## Author & contact
78+Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca
79+
80+## Notes
81+Built under the autonomous research protocol of 2026-11-01 (mandate in `docs/MANDATE.md`); all substantive claims are traced in `CLAIMS_LEDGER.csv`.
added REPRODUCE.md +42 −0
@@ -0,0 +1,42 @@
1+# REPRODUCE — end-to-end instructions (UQO WP36)
2+
3+## 0. Prerequisites
4+- macOS or Linux; Python 3.12 managed by `uv` (0.11+); TeX Live 2026 (`pdflatex`, `bibtex`, `latexmk`); SQLite ≥ 3.40; `zstd`.
5+- Hardware used: Apple M5 Max (18 cores, 48 GB). Full pipeline wall time ≈ 45 min (the robustness suite and the 200-replication dealer bootstrap dominate).
6+- Raw data: owner-authorised snapshot `data/raw/autoka-snapshot-20261101.db` (1.35 GB, SHA-256 `65c3dad229a402f89129c614111a8d6f7b583eb7b46a6014a34e0ebca8de0132`; **not distributed**, restricted fields). Also required: `data/raw/registry/sources.json` and `villes_gps.json` (public registry copied from the platform).
7+ Operator-only regeneration from the production node:
8+ ```bash
9+ ssh M4M64b 'cd ~/auto-ka && sqlite3 data/autoka.db ".backup /tmp/snap.db" && sqlite3 /tmp/snap.db "DROP TABLE IF EXISTS users; VACUUM;" && shasum -a 256 /tmp/snap.db && zstd -T0 -3 /tmp/snap.db'
10+ scp M4M64b:/tmp/snap.db.zst data/raw/ && zstd -d data/raw/snap.db.zst -o data/raw/autoka-snapshot-YYYYMMDD.db
11+ ```
12+ Without the snapshot, the pipeline and all tests run on the **clearly labelled synthetic fixture** (`python -m ucml.synthetic` → `data/synthetic/autoka-synthetic.db`; set `UCML_SNAPSHOT` to that path). Synthetic output is for code validation only and carries no empirical content.
13+
14+## 1. Environment
15+```bash
16+make env # uv venv + editable install (pinned versions in requirements-lock.txt)
17+make test # 14 unit/pipeline tests (pricepaths, cleaning rules, synthetic end-to-end, prediction helpers)
18+```
19+
20+## 2. Pipeline (in order; each script is idempotent and writes to results/, figures/, paper/tables/)
21+| Step | Script | Output |
22+|---|---|---|
23+| 01 | `scripts/01_ingest.py` | `data/interim/*.parquet`, `manifest.json` (row counts, time bounds, SHA-256) |
24+| 02 | `scripts/02_clean.py` | `data/processed/listings.parquet`, `price_events.parquet`, `exclusions.csv`, `sample_construction.csv`, `sources_registry.csv`, `cleaning_stats.json` |
25+| 03 | `scripts/03_features.py` | `data/processed/risk_set.parquet` (listing-days with competitor sets at 10/25/50 km) |
26+| 04 | `scripts/04_hedonic.py` | `results/hedonic/` (Models A1–A5, tests, region premia, residuals, surface, grouped CV incl. LightGBM) |
27+| 05 | `scripts/05_descriptives.py` | Figures 1, 3, 4; Tables 1–3; `results/descriptives/summary.json` |
28+| 06 | `scripts/06_hazards.py` | `results/hazards/` (cloglog B/C, Cox with dealer-clustered SEs, AFT, cut size, empirical hazards) |
29+| 07 | `scripts/07_competition.py`, `07b_competition_dow.py` | `results/competition/` (event studies, placebo, radii, dealer×weekday FE) |
30+| 08 | `scripts/08_prediction.py` | `results/prediction/`, Figure 10, Table 8 |
31+| 09 | `scripts/09_amli.py 200` | `results/amli/` (indices, weights, validation, dealer bootstrap) |
32+| 10 | `scripts/10_validation.py` | `results/validation/exit_validation.json` |
33+| 11 | `scripts/11_figures.py` | Figures 2, 5–9 |
34+| 12 | `scripts/12_robustness.py` | `results/robustness/`, Table 9, Holm-adjusted primary tests |
35+| 13 | `scripts/13_tables.py` | Tables 4–7 |
36+`make all` runs everything (`make data models tables figures paper`).
37+
38+## 3. Paper
39+```bash
40+cd paper && make # latexmk: pdflatex + bibtex (natbib, aer.bst) -> paper/main.pdf
41+```
42+Every number in the manuscript is traced in `CLAIMS_LEDGER.csv` to a results file; `EXPERIMENTS.md` logs each run; `DECISIONS.md` records amendments to the preregistered plan (`research/analysis_plan.md`).
added STATUS.md +28 −0
@@ -0,0 +1,28 @@
1+# STATUS — UQO WP36 (Used-car market dynamics, Auto-Ka)
2+
3+Updated: 2026-11-01
4+
5+## Current phase
6+Phase 4–5 (econometric program + AMLI) — Phases 0–3 complete; Phase 2 literature agent running.
7+
8+## Completed
9+- [x] Phase 0: repository/infra inspection (Auto-Ka on M4M64b, SQLite 1.4 GB, 143 sources, crawl cycle ≈ 3.3 h).
10+- [x] Phase 0: owner-authorised read-only snapshot `data/raw/autoka-snapshot-20261101.db` (SHA-256 verified; `users` table dropped).
11+- [x] Phase 0: schema / coverage / null-rate / source-quality audit; feasibility matrix H1–H6; governance.
12+- [x] Environment: uv venv Python 3.12 (`requirements-lock.txt`), R 4.6.1, TeX Live 2026 available; no external model API used.
13+- [x] Phase 1: `research/questions.md`, `research/analysis_plan.md` (preregistered 2026-11-01).
14+- [ ] Phase 2: literature (agent running: `literature/references.bib`, `evidence_matrix.csv`, `novelty_memo.md`).
15+- [x] Phase 2b: external benchmarks inventory (`reports/external_benchmarks_inventory.md`): no weekly used-car benchmark exists for Aug–Oct 2026; composition benchmarks only.
16+- [x] Phase 3: pipeline 01–03 (ingest, clean, risk set) + 10 tests passing + synthetic fixture.
17+- [x] Phase 4 Model A (hedonic) done — H1 supported, H4 null.
18+- [ ] Phase 4 Models B/C (hazards), D (competition), F (prediction agent) running; descriptives agent running.
19+- [ ] Phase 5 AMLI.
20+
21+## Next tasks
22+1. Phase 3 pipeline: ingest → Parquet; cleaning + exclusion log; sustained price paths; listing-day risk set; competitor sets; sample-construction tables.
23+2. Phase 4 models A–F; Phase 5 AMLI; Phase 6 robustness; Phase 7 figures/tables; Phase 8 paper.
24+
25+## Blockers / known limits
26+- No transaction prices (asking prices only); listing exit ≠ sale (no verified sale outcomes available).
27+- Panel is 81 days (2026-08-12 → 2026-11-01): H6 external forecasting cannot be tested; H3 is association only.
28+- AutoTrader (capped 2 000 sliding window) and CarGurus (capped 500) are sampled portals → excluded from dynamic analyses.
added data/interim/manifest.json +17 −0
@@ -0,0 +1,17 @@
1+{
2+ "snapshot": "/Users/simon-pierreboucher/Desktop/UQO/Recherche/Recherche-UQO/economie/used-car-market-liquidity/data/raw/autoka-snapshot-20261101.db",
3+ "created_utc": "2026-11-01T08:02:17Z",
4+ "sha256": "65c3dad229a402f89129c614111a8d6f7b583eb7b46a6014a34e0ebca8de0132",
5+ "rows": {
6+ "vehicles": 273265,
7+ "price_log": 428484,
8+ "sync_log": 83531,
9+ "dealers": 138,
10+ "vin_decode": 4655
11+ },
12+ "sync_ts_min": 1786537835.64518,
13+ "sync_ts_max": 1793515782.732466,
14+ "first_seen_min": 1786537959.087422,
15+ "last_seen_max": 1793515782.732466,
16+ "seconds": 2.8
17+}
\ No newline at end of file
added data/processed/.gitkeep +0 −0
added data/raw/README.md +5 −0
@@ -0,0 +1,5 @@
1+# data/raw — provenance (restricted, never committed)
2+
3+- `autoka-snapshot-20261101.db` — owner-authorised read-only `.backup` of the Auto-Ka production SQLite database (node M4M64b, `~/auto-ka/data/autoka.db`), taken 2026-11-01 ≈ 02:50 America/Toronto; `users` table dropped and `VACUUM` on the production node before transfer; 1 348 825 088 bytes; SHA-256 in `autoka-snapshot-20261101.db.sha256` (verified after transfer). Last sync contained: 2026-11-01 06:49:42 UTC. Contains VINs, URLs, free text, images (JSON) and exact coordinates → restricted.
4+- `registry/sources.json`, `registry/villes_gps.json`, `registry/docs/connecteurs/*.md` — public source registry, city coordinates and generated connector documentation copied from the platform repository (`auto-ka.git`, commit 9c35e30).
5+- `external/` — public benchmark downloads (Statistics Canada WDS, Bank of Canada Valet, Canadian Black Book, AutoTrader, Régie de l'énergie, NRCan), see `external/README.md` for URLs, dates and licences.
added data/raw/autoka-snapshot-20261101.db.sha256 +1 −0
@@ -0,0 +1 @@
1+65c3dad229a402f89129c614111a8d6f7b583eb7b46a6014a34e0ebca8de0132 /tmp/wp36/autoka-snapshot-20261101.db
added docs/MANDATE.md +11 −0
@@ -0,0 +1,11 @@
1+# UQO WP36 — Autonomous Research Protocol (mandate as received, 2026-11-01)
2+
3+Working paper: WP36. Provisional title: The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior. Institution: UQO. Domain: applied microeconomics, IO, empirical finance, transportation economics, computational social science. Primary data platform: https://www.auto-ka.com. Manuscript language: English. Standard: reproducible, submission-ready academic paper.
4+
5+Mission: original, defensible empirical investigation of the Quebec used-car market using legitimately accessible Auto-Ka data. Central question: how do dealer competition, vehicle characteristics, inventory age and local market conditions jointly determine asking-price dynamics and the liquidity of durable goods? Develop and evaluate an Automobile Market Liquidity Index (AMLI) with explicit identification and validation limits. Scientific integrity is non-negotiable: never fabricate data, API capabilities, transaction prices, dealer decisions, citations, significance or results; a disappearing listing is not proof of sale; distinguish asking prices from transaction prices throughout.
6+
7+Phases: 0 environment/access audit (gate before any empirical finding); 1 preregistered questions and analysis plan (H1 nonlinear depreciation, H2 markdown hazard, H3 competitive response as association, H4 regional segmentation, H5 exit and liquidity, H6 AMLI); 2 literature review with verified citations, evidence matrix, novelty memo; 3 data architecture and longitudinal panel with quality controls (duplicates, placeholders, stale listings, left-censoring, interval-censored exit, representativeness, no leakage, attrition tables); 4 econometric program (Models A hedonic, B price-change processes, C listing exit, D competitive response, E regional segmentation, F prediction); 5 AMLI (components, sign, normalisation, weights, missingness, validation, no unsupported forecasting claims); 6 robustness, identification and reproducibility (mandatory checks, experiment manifests, preserve nulls); 7 figures (10) and tables (9), no invented points; 8 LaTeX manuscript (structure 1–12, compiled and inspected); 9 execution rules (STATUS.md, DECISIONS.md, EXPERIMENTS.md, CLAIMS_LEDGER.csv, REPRODUCE.md; lead agent reconciles; untrusted external text; stop and report blockers; benchmark before scaling).
8+
9+Final acceptance criteria: lawful source-data audit; executable, tested, versioned pipeline; hypothesis/estimand/identification matrix; verified references and novelty assessment; reproducible estimates with uncertainty and robustness; defensible AMLI with transparent validation or explicit explanation; traceable publication-quality figures and tables; compiled LaTeX PDF and source; claims ledger and reproduction instructions; honest limitations distinguishing asking prices, listing exits and transactions.
10+
11+(The verbatim mandate text was pasted by the author into the Claude Code session on 2026-11-01; this file is the lead agent's faithful summary of its content.)
added docs/README-template.md +109 −0
@@ -0,0 +1,109 @@
1+<!-- Author: Simon-Pierre Boucher — simon-pierre.boucher@uqo.ca -->
2+# <Emoji> <Title>
3+
4+**UQO Working Paper No. <N>** — *<Subtitle, or one-line description>*
5+
6+[![Paper](https://img.shields.io/badge/paper-PDF%20(<P>%20p.)-b31b1b?logo=latex&logoColor=white)](paper/main.pdf)
7+[![Python](https://img.shields.io/badge/python-3.11%2B-3776AB?logo=python&logoColor=white)](requirements.txt)
8+[![Data](https://img.shields.io/badge/data-<N%20obs>%20·%20<format>-4051b5)](data/raw/README.md)
9+[![Reproducible](https://img.shields.io/badge/reproducible-end--to--end%20pipeline-2e7d32)](scripts/)
10+[![Institution](https://img.shields.io/badge/UQO-D%C3%A9pt.%20des%20sciences%20administratives-16365c)](https://uqo.ca)
11+[![Author](https://img.shields.io/badge/author-Simon--Pierre%20Boucher-16365c)](mailto:simon-pierre.boucher@uqo.ca)
12+
13+> **TL;DR** — Three to six lines: question, data (size, period), method, headline numbers, takeaway.
14+
15+---
16+
17+## 📌 Status
18+
19+| Item | Value |
20+|---|---|
21+| Version | <x.y> (<Month YYYY>) |
22+| SSRN | <abstract ID + link> / not yet submitted |
23+| Journal | <target or submission status> / — |
24+| Code & data | reproducible from `scripts/` (see Quick start); raw data not redistributed |
25+
26+## 🎯 What this paper does
27+
28+Two or three paragraphs: research question, why it matters, what is new. Core equation if any:
29+
30+```
31+y_i = α + x_i'β + ε_i
32+```
33+
34+## 🏆 Headline results
35+
36+| Object | Estimate |
37+|---|---|
38+| … | … |
39+
40+## 🗂 Repository layout
41+
42+```
43+<repo>/
44+├── data/
45+│ ├── raw/ # <source snapshot> (see data/raw/README.md) — not committed
46+│ └── processed/ # built by step 01
47+├── src/<pkg>/ # reusable package
48+├── scripts/ # the pipeline, in order
49+│ ├── 01_….py
50+│ └── …
51+├── results/ # CSV / JSON emitted by the scripts
52+├── figures/ # publication figures
53+└── paper/ # main.tex + sections/ + references.bib → main.pdf
54+```
55+
56+## 🚀 Quick start
57+
58+```bash
59+python3 -m pip install -r requirements.txt
60+python3 scripts/01_….py
61+…
62+cd paper && make # latexmk → main.pdf
63+```
64+
65+Runtime and memory on an Apple-silicon laptop.
66+
67+## 🗃 Data
68+
69+Provenance, extraction date, number of observations, filters (sample cascade), how to obtain the raw snapshot.
70+
71+## 🔬 Methodology
72+
73+Numbered list of the estimation steps, each pointing to the module that implements it.
74+
75+## 🖼 Figures & tables inventory
76+
77+| Figure | Content |
78+|---|---|
79+| `fig_…` | … |
80+
81+Tables (`paper/tables/*.tex`): ….
82+
83+## ⚠️ Limitations
84+
85+- …
86+
87+## 📝 Citation
88+
89+```bibtex
90+@techreport{boucher2026<key>,
91+ author = {Boucher, Simon-Pierre},
92+ title = {<Title>: <Subtitle>},
93+ institution = {Universit\'e du Qu\'ebec en Outaouais, D\'epartement des sciences administratives},
94+ type = {Working Paper},
95+ number = {<N>},
96+ year = {2026},
97+ month = {<Month>}
98+}
99+```
100+
101+## 👤 Author & contact
102+
103+**Simon-Pierre Boucher** — Département des sciences administratives,
104+Université du Québec en Outaouais (UQO), Gatineau, QC.
105+📧 [simon-pierre.boucher@uqo.ca](mailto:simon-pierre.boucher@uqo.ca)
106+
107+## 🗒 Notes
108+
109+- Numbering history / deviations from `Recherche-UQO/HARMONISATION.md`, if any.
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added literature/evidence_matrix.csv +173 −0
@@ -0,0 +1,173 @@
1+key,authors,year,venue,topic_bucket,data_setting,method,main_finding,relevance_to_WP36,verified_via,notes
2+rosen1974hedonic,Rosen,1974,Journal of Political Economy,A,Theory,Hedonic equilibrium of implicit markets,Hedonic price function is an equilibrium envelope; identification caveats,"Theoretical basis for interpreting hedonic coefficients on mileage, age, trim",crossref DOI 10.1086/260169,
3+lancaster1966newapproach,Lancaster,1966,Journal of Political Economy,A,Theory,Characteristics model of demand,Goods valued through their characteristics,Micro-foundation for hedonic specification,crossref DOI 10.1086/259131,
4+pakes2003hedonic,Pakes,2003,American Economic Review,A,US PCs 1995-99,Hedonic indexes with flexible functional forms,Hedonic indexes need not have constant coefficients; matched-model bias,Guides rolling-window hedonic index design for AMLI,crossref DOI 10.1257/000282803322655455,
5+triplett2006handbook,Triplett,2006,OECD Publishing,A,Methodology,OECD handbook on hedonic quality adjustment,Best practice for hedonic indexes in official statistics,Standard reference for quality adjustment choices,crossref DOI 10.1787/9789264028159-en,
6+bajari2005hedonic,Bajari & Benkard,2005,Journal of Political Economy,A,US PCs,Hedonic demand estimation with unobserved characteristics,Recovers preferences and unobserved quality from hedonic prices,Unobserved vehicle quality (condition) warning for listing data,crossref DOI 10.1086/498586,
7+ekeland2004hedonic,Ekeland et al.,2004,Journal of Political Economy,A,Theory/econometrics,Identification of hedonic models,Nonlinear hedonic models are identified under weak conditions,Supports nonparametric/ML hedonic pricing,crossref DOI 10.1086/379947,
8+silver2007hedonicimputation,Silver & Heravi,2007,Journal of Business \& Economic Statistics,A,UK scanner data,Hedonic imputation vs time-dummy indexes,Difference depends on changes in characteristics and parameters,Choice of index formula for AMLI price component,crossref DOI 10.1198/073500106000000486,
9+diewert2003hedonic,Diewert,2003,Scanner Data and Price Indexes,A,Theory,Consumer-theory derivation of hedonic regressions,Justifies log-price hedonic specifications and weighting,Specification of the hedonic model,crossref DOI 10.7208/chicago/9780226239668.003.0012,
10+dreyfus1995timepreference,Dreyfus & Viscusi,1995,Journal of Law and Economics,A,US automobiles,Hedonic regression on new-car prices,Implicit prices of safety and fuel efficiency; discount rates 11-17%,Hedonic valuation of vehicle attributes,crossref DOI 10.1086/467326,
11+berry1995automobile,Berry et al.,1995,Econometrica,A,US new cars 1971-90,Discrete-choice demand with unobserved characteristics (BLP),Markups and substitution patterns in autos,Benchmark structural model of auto demand; motivates reduced-form approach,crossref DOI 10.2307/2171802,
12+goodman1998court,Goodman,1998,Journal of Urban Economics,A,History,Review of Court 1939,Court invented hedonic price analysis in 1939,Documents Court 1939 provenance,crossref DOI 10.1006/juec.1997.2071,
13+akerlof1970lemons,Akerlof,1970,Quarterly Journal of Economics,B,Theory (used cars),Adverse selection model,Quality uncertainty can collapse the used-car market,Foundational: asymmetric information in used cars,crossref DOI 10.2307/1879431,
14+hendel1999adverse,Hendel & Lizzeri,1999,American Economic Review,B,Theory,Dynamic durable-goods model with adverse selection,Adverse selection does not shut down trade; affects volume and prices across vintages,Theory of vintage pricing and turnover,crossref DOI 10.1257/aer.89.5.1097,
15+hendel2002leasing,Hendel & Lizzeri,2002,Journal of Political Economy,B,Theory + US auto data,Leasing under adverse selection,Leasing contracts mitigate adverse selection; off-lease supply,Explains supply of 2-4 year old cars to dealers,crossref DOI 10.1086/324387,
16+stolyarov2002turnover,Stolyarov,2002,Journal of Political Economy,B,US used cars (CES),Stationary equilibrium model,Trade volume non-monotone in age because of transaction costs,Age profile of listing exit and turnover,crossref DOI 10.1086/343745,
17+gavazza2014usedcar,Gavazza et al.,2014,American Economic Review,B,US used cars,Quantitative dynamic equilibrium model,"Transaction costs are key for used-car volumes, prices and welfare",Closest structural benchmark for used-car liquidity,crossref DOI 10.1257/aer.104.11.3668,
18+rust1985durable,Rust,1985,Econometrica,B,Theory,Stationary equilibrium of durable asset market,Prices of used assets determined by scrappage and replacement,Theory of used-asset price profiles,crossref DOI 10.2307/1912654,
19+porter1999usedtrade,Porter & Sattler,1999,National Bureau of Economic Research,B,US autos,Theory and evidence on trade patterns in used durables,Trade volume by age consistent with heterogeneous valuations,Age/vintage patterns of used-car trade,crossref DOI 10.3386/w7149,NBER WP never published; NBER DOI
20+engers2009annualmiles,Engers et al.,2009,Journal of Applied Econometrics,B,US used cars (Kelley/Consumer Reports),Hedonic with mileage and age,"Annual miles driven, not age per se, drive used-car prices",Mileage adjustment in hedonic model,crossref DOI 10.1002/jae.1034,
21+emons2009usedcars,Emons & Sheldon,2009,Applied Economics,B,Swiss used cars,Test of lemons via repair frequency,Evidence consistent with lemons phenomenon,Adverse selection in used cars (European evidence),crossref DOI 10.1080/00036840802277332,
22+genesove1993adverse,Genesove,1993,Journal of Political Economy,B,US wholesale used-car auctions,Test of adverse selection by seller type,Mild adverse selection among dealers at auction,Wholesale side of dealer sourcing,crossref DOI 10.1086/261891,
23+lewis2011asymmetric,Lewis,2011,American Economic Review,B,eBay Motors listings,Effect of disclosure on prices,"Voluntary disclosure (photos, text) raises prices; reduces asymmetry",Online listing information and price,crossref DOI 10.1257/aer.101.4.1535,
24+adams2011vettes,Adams et al.,2011,Quantitative Marketing and Economics,B,eBay Corvettes,Test of adverse selection online,Little evidence of lemons for enthusiast cars online,Online used-car markets evidence,crossref DOI 10.1007/s11129-011-9095-2,
25+peterson2014adverse,Peterson & Schneider,2014,RAND Journal of Economics,B,US CES purchase and repair data,Test using repair patterns,Used cars bought from dealers vs private differ in repair; adverse selection present,Dealer channel quality signal,crossref DOI 10.1111/1756-2171.12045,
26+peterson2017beautiful,Peterson & Schneider,2017,Management Science,B,US used-car transactions,Durable-goods sorting model with adverse selection,Sorting of quality across channels and prices,Dealer vs private market segmentation,crossref DOI 10.1287/mnsc.2016.2495,
27+busse2013gasoline,Busse et al.,2013,American Economic Review,B,US new and used car transactions,Gas-price shocks to prices and sales,Used-car prices respond strongly to fuel prices; consumers not myopic,Macro shocks transmit to used-car prices quickly,crossref DOI 10.1257/aer.103.1.220,
28+sallee2016fueleconomy,Sallee et al.,2016,Journal of Public Economics,B,US used-car transactions,Fuel-cost capitalization,Used-car prices fully capitalize fuel costs,Attribute valuation in used-car prices,crossref DOI 10.1016/j.jpubeco.2016.01.003,
29+jacobsen2015scrappage,Jacobsen & van Benthem,2015,American Economic Review,B,US vehicle fleet,Scrappage elasticity estimation,Scrappage responds to used-car prices; policy leakage,Exit of vehicles from the stock,crossref DOI 10.1257/aer.20130935,
30+larsen2021bargaining,Larsen,2021,Review of Economic Studies,B,US wholesale used-auto auctions,Bargaining efficiency estimation,Post-auction bargaining fairly efficient; dynamic reservation prices,Wholesale price discovery benchmark,crossref DOI 10.1093/restud/rdaa007,
31+kooreman2006anomalies,Kooreman & Haan,2006,De Economist,B,Dutch used-car ads,Hedonic on listing prices,Price anomalies (e.g. registration-year thresholds) in asking prices,Asking-price data from classifieds in economics,crossref DOI 10.1007/s10645-006-6485-z,Dutch listing data precedent
32+lacetera2012heuristic,Lacetera et al.,2012,American Economic Review,B,US wholesale auctions,Discontinuities at mileage thresholds,Left-digit bias in mileage affects prices,Mileage heuristics in used-car pricing,crossref DOI 10.1257/aer.102.5.2206,
33+biglaiser2020intermediaries,Biglaiser et al.,2020,RAND Journal of Economics,B,US used-car transactions (Pennsylvania),Model of dealer as quality intermediary,Dealers sort quality; price premium relates to time on market,Closest academic link between dealer premium and time on market,crossref DOI 10.1111/1756-2171.12344,
34+biglaiser1993middlemen,Biglaiser,1993,RAND Journal of Economics,B,Theory,Middlemen as quality experts,Intermediaries arise to certify quality,Dealer role in used-car market,crossref DOI 10.2307/2555758,
35+haan2010internetregional,Haan & de Boer,2010,De Economist,B,Dutch used-car listings,Regional price dispersion before/after internet,Internet did not eliminate regional price differences in used cars,Direct precedent for regional segmentation of asking prices,crossref DOI 10.1007/s10645-010-9151-4,Also relevant to bucket F
36+pesendorfer2002retailsales,Pesendorfer,2002,Journal of Business,C,US supermarket scanner data,Dynamic pricing with intertemporal demand,Sales timing depends on time since last sale,Dynamic markdown logic,crossref DOI 10.1086/323504,
37+nakamura2008fivefacts,Nakamura & Steinsson,2008,Quarterly Journal of Economics,C,US CPI micro data,Frequency and size of price changes,Price rigidity facts; sales vs regular prices,Benchmark for frequency of asking-price changes,crossref DOI 10.1162/qjec.2008.123.4.1415,
38+aguirregabiria1999markups,Aguirregabiria,1999,Review of Economic Studies,C,Spanish supermarket chain,Dynamic structural model of markups and inventories,Inventories drive markup cycles,Inventory-markup link for dealers,crossref DOI 10.1111/1467-937X.00088,
39+cho2010flatrental,Cho & Rust,2010,Review of Economic Studies,C,US auto rental company,Dynamic programming of rental fleet,Flat rental rates despite depreciation puzzle,Vehicle age pricing puzzle,crossref DOI 10.1111/j.1467-937x.2009.00556.x,
40+zettelmeyer2006internet,Zettelmeyer et al.,2006,Journal of Marketing Research,C,US new-car transactions,Matched survey/transactions,Internet lowers prices via information and referral,Information effects on car prices,crossref DOI 10.1509/jmkr.43.2.168,
41+scottmorton2001internet,Scott Morton et al.,2001,Journal of Industrial Economics,C,US new-car transactions,Referral-service pricing,Internet referral buyers pay ~2% less,Internet car retailing benchmark,crossref DOI 10.1111/1467-6451.00160,
42+busse2006cashback,Busse et al.,2006,American Economic Review,C,US new-car promotions,Pass-through of manufacturer promotions,Customer rebates passed through more than dealer cash,Dealer price-setting behaviour,crossref DOI 10.1257/aer.96.4.1253,
43+murry2020colocation,Murry & Zhou,2020,Management Science,C,US dealer locations and transactions,Search model with dealer colocation,Dealer clustering lowers search costs and prices,Spatial dealer competition and search,crossref DOI 10.1287/mnsc.2019.3307,
44+israeli2022marketpower,Israeli et al.,2022,Management Science,C,US dealership transactions,Inventory fluctuations and prices,Dealers with market power respond more to inventory in pricing,Closest study of dealer dynamic pricing vs inventory,crossref DOI 10.1287/mnsc.2021.3967,
45+gallego1994dynamicpricing,Gallego & van Ryzin,1994,Management Science,C,Theory (OR),Optimal dynamic pricing of finite inventory,Prices fall with time and rise with remaining inventory,Normative model for listing markdowns,crossref DOI 10.1287/mnsc.40.8.999,
46+elmaghraby2003dynamicpricing,Elmaghraby & Keskinocak,2003,Management Science,C,Survey (OR),Review of dynamic pricing with inventory,Taxonomy of markdown/replenishment models,Framework for dealer markdown policy,crossref DOI 10.1287/mnsc.49.10.1287.17315,
47+pashigian1991sale,Pashigian & Bowen,1991,Quarterly Journal of Economics,C,US apparel,Markdown regularities,Markdowns increase with fashion/variety,Clearance-sale empirics,crossref DOI 10.2307/2937955,
48+sweeting2012perishable,Sweeting,2012,Journal of Political Economy,C,MLB secondary ticket market,Dynamic pricing before deadline,"Sellers cut prices as deadline approaches, consistent with dynamic model",Perishable-inventory pricing analogue,crossref DOI 10.1086/669254,
49+merlo2004bargaining,Merlo & Ortalo-Magne,2004,Journal of Urban Economics,C,England house listings,Listing-price revisions and bargaining,Listing price revisions and offers; sellers cut asking prices over time,Listing-price revision evidence in housing,crossref DOI 10.1016/j.jue.2004.05.004,
50+merlo2015homeselling,Merlo et al.,2015,International Economic Review,C,England house listings,Structural home-selling model,Optimal listing-price path declines with time on market,Structural analogue of markdown hazard,crossref DOI 10.1111/iere.12111,
51+busse2015weather,Busse et al.,2015,Quarterly Journal of Economics,C,US car transactions,Weather shocks and purchases,Projection bias: weather shifts demand for convertibles/4WD,Seasonal demand shocks in vehicles,crossref DOI 10.1093/qje/qju033,
52+stigler1961information,Stigler,1961,Journal of Political Economy,D,Theory,Search theory,Price dispersion arises from costly search,Foundation of dispersion analysis,crossref DOI 10.1086/258464,
53+burdett1983equilibrium,Burdett & Judd,1983,Econometrica,D,Theory,Noisy search equilibrium,Price dispersion as equilibrium outcome,Dispersion with identical sellers,crossref DOI 10.2307/1912045,
54+sorensen2000dispersion,Sorensen,2000,Journal of Political Economy,D,US retail pharmacies,Dispersion vs purchase frequency,Dispersion lower for frequently purchased drugs,Search intensity and dispersion,crossref DOI 10.1086/316103,
55+lach2002dispersion,Lach,2002,Review of Economics and Statistics,D,Israeli retail prices,Panel of store prices,Dispersion persists; stores change rank,Persistence of dispersion,crossref DOI 10.1162/003465302320259457,
56+baye2006dispersion,Baye et al.,2006,Handbook on Economics and Information Systems,D,Survey,Review of search and dispersion,Catalogue of dispersion measures and theories,Dispersion metrics for AMLI,crossref DOI 10.1016/S1574-0145(06)01006-3,
57+diamond1971paradox,Diamond,1971,Journal of Economic Theory,D,Theory,Sequential search,Monopoly price with any positive search cost,Diamond paradox,crossref DOI 10.1016/0022-0531(71)90013-5,
58+kaplan2015dispersion,Kaplan & Menzio,2015,International Economic Review,D,US scanner data,Decomposition of dispersion,"Dispersion mostly within-store/time, not across stores",Decomposition approach,crossref DOI 10.1111/iere.12134,
59+duffie2005otc,Duffie et al.,2005,Econometrica,D,Theory (finance),Search model of OTC markets,Liquidity premia from search frictions,OTC liquidity concept for cars,crossref DOI 10.1111/j.1468-0262.2005.00639.x,
60+lagos2009liquidity,Lagos & Rocheteau,2009,Econometrica,D,Theory (finance),Search with divisible assets,Liquidity and trading delays,Asset-market liquidity theory,crossref DOI 10.3982/ECTA7250,
61+amihud2002illiquidity,Amihud,2002,Journal of Financial Markets,D,US stocks,Illiquidity ratio,Illiquidity priced in returns,Illiquidity measure template,crossref DOI 10.1016/S1386-4181(01)00024-6,
62+amihud1986bidask,Amihud & Mendelson,1986,Journal of Financial Economics,D,US stocks,Bid-ask spread and returns,Liquidity premium,Spread as liquidity measure,crossref DOI 10.1016/0304-405X(86)90065-6,
63+krainer2001housingliquidity,Krainer,2001,Journal of Urban Economics,D,Theory (housing),Search model,Liquidity varies with market conditions; time to sell,Housing liquidity theory,crossref DOI 10.1006/juec.2000.2180,
64+carrillo2012search,Carrillo,2012,International Economic Review,D,US MLS data,Stationary search equilibrium,Estimates buyer/seller search and time on market,Structural TOM model,crossref DOI 10.1111/j.1468-2354.2011.00677.x,
65+genesove2001lossaversion,Genesove & Mayer,2001,Quarterly Journal of Economics,D,Boston condos,Loss aversion in list prices,Sellers facing losses set higher asking prices and sell slower,Asking-price setting behaviour,crossref DOI 10.1162/003355301753265561,
66+han2015microstructure,Han & Strange,2015,Handbook of Regional and Urban Economics,D,Survey,Review of housing microstructure,"Search, bargaining, brokerage, list prices",Reference survey for asking-price dynamics,crossref DOI 10.1016/b978-0-444-59531-7.00013-2,
67+guren2018momentum,Guren,2018,Journal of Political Economy,D,US housing,Strategic complementarity in list prices,Sellers partially adjust to competitors; momentum,Competitor response in list pricing,crossref DOI 10.1086/697207,
68+anenberg2016frictions,Anenberg,2016,International Economic Review,D,US housing listings,Information frictions model,Sellers learn demand and adjust list prices,Learning and listing-price cuts,crossref DOI 10.1111/iere.12204,
69+head2014searchliquidity,Head et al.,2014,American Economic Review,D,US cities,Search model of housing,Search and liquidity explain price dynamics,Liquidity-price dynamics,crossref DOI 10.1257/aer.104.4.1172,
70+ngai2014hotcold,Ngai & Tenreyro,2014,American Economic Review,D,UK/US housing,Seasonal search model,Hot and cold seasons from thick-market effects,Seasonality in liquidity,crossref DOI 10.1257/aer.104.12.3991,
71+piazzesi2020segmented,Piazzesi et al.,2020,American Economic Review,D,SF Bay Area search data,Segmented search,Search is segmented across submarkets,Regional/segment segmentation,crossref DOI 10.1257/aer.20141772,
72+wheaton1990vacancy,Wheaton,1990,Journal of Political Economy,D,Theory (housing),Matching model,Vacancy and prices in matching market,Vacancy/inventory-price link,crossref DOI 10.1086/261734,
73+gavazza2011frictions,Gavazza,2011,American Economic Review,D,Commercial aircraft,Trading frictions in real asset market,Thinner markets raise frictions and lower prices,Liquidity in durable asset markets,crossref DOI 10.1257/aer.101.4.1106,
74+gavazza2016decentralized,Gavazza,2016,Econometrica,D,Aircraft market,Empirical equilibrium search model,Search frictions and dealer intermediation,Dealer intermediation in decentralized market,crossref DOI 10.3982/ECTA10847,
75+salz2022intermediation,Salz,2022,Journal of Political Economy,D,NYC waste hauling,Intermediation and search,Brokers reduce search costs; prices,Intermediary value,crossref DOI 10.1086/717349,
76+cox1972regression,Cox,1972,Journal of the Royal Statistical Society Series B: Statistical Methodology,E,Methods,Proportional hazards,Semi-parametric hazard regression,Core hazard model,crossref DOI 10.1111/j.2517-6161.1972.tb00899.x,
77+kalbfleisch2002statistical,Kalbfleisch & Prentice,2002,Wiley,E,Methods,Textbook,Failure-time analysis,Reference text,crossref DOI 10.1002/9781118032985,
78+fine1999proportional,Fine & Gray,1999,Journal of the American Statistical Association,E,Methods,Subdistribution hazards,Competing risks regression,Exit (sold vs withdrawn) competing risks,crossref DOI 10.1080/01621459.1999.10474144,
79+grambsch1994proportional,Grambsch & Therneau,1994,Biometrika,E,Methods,Scaled Schoenfeld residuals,PH assumption test,Diagnostics,crossref DOI 10.1093/biomet/81.3.515,
80+allison1982discrete,Allison,1982,Sociological Methodology,E,Methods,Discrete-time hazards,Logit-based event history,Daily-panel discrete hazards,crossref DOI 10.2307/270718,
81+singer1993discrete,Singer & Willett,1993,Journal of Educational Statistics,E,Methods,Discrete-time survival,Tutorial on discrete hazards,Daily-panel estimation,crossref DOI 10.3102/10769986018002155,
82+andersen1982cox,Andersen & Gill,1982,Annals of Statistics,E,Methods,Counting-process Cox,Multiple events via counting processes,Repeated markdowns,crossref DOI 10.1214/aos/1176345976,
83+prentice1981regression,Prentice et al.,1981,Biometrika,E,Methods,Recurrent-event models,Gap-time and total-time models,Repeated price-change events,crossref DOI 10.1093/biomet/68.2.373,
84+royston2002flexible,Royston & Parmar,2002,Statistics in Medicine,E,Methods,Flexible parametric survival,Spline baseline hazards,Smooth baseline for TOM,crossref DOI 10.1002/sim.1203,
85+lin1989robust,Lin & Wei,1989,Journal of the American Statistical Association,E,Methods,Robust sandwich variance for Cox,Clustered inference in Cox,Dealer-clustered SEs,crossref DOI 10.1080/01621459.1989.10478874,
86+turnbull1976empirical,Turnbull,1976,Journal of the Royal Statistical Society Series B: Statistical Methodology,E,Methods,NPMLE with interval censoring,Interval-censored survival,First/last-seen interval censoring,crossref DOI 10.1111/j.2517-6161.1976.tb01597.x,
87+kaplan1958nonparametric,Kaplan & Meier,1958,Journal of the American Statistical Association,E,Methods,Product-limit estimator,Nonparametric survival,Baseline description,crossref DOI 10.1080/01621459.1958.10501452,
88+lancaster1990econometric,Lancaster,1990,Cambridge University Press,E,Methods,Textbook,Econometrics of transition data,Economic duration models,crossref DOI 10.1017/CCOL0521265967,
89+heckman1984method,Heckman & Singer,1984,Econometrica,E,Methods,NPMLE of unobserved heterogeneity,Mixture distribution for frailty,Unobserved dealer/vehicle heterogeneity,crossref DOI 10.2307/1911491,
90+putter2007tutorial,Putter et al.,2007,Statistics in Medicine,E,Methods,Tutorial,Competing risks and multistate,Applied guidance,crossref DOI 10.1002/sim.2712,
91+therneau2000modeling,Therneau & Grambsch,2000,Springer New York,E,Methods,Textbook,Extending the Cox model,Software-oriented reference,crossref DOI 10.1007/978-1-4757-3294-8,
92+hotelling1929stability,Hotelling,1929,Economic Journal,F,Theory,Linear city,Spatial differentiation,Spatial competition origin,crossref DOI 10.2307/2224214,
93+salop1979monopolistic,Salop,1979,Bell Journal of Economics,F,Theory,Circular city,Entry and spatial markups,Spatial competition,crossref DOI 10.2307/3003323,
94+bresnahan1991entry,Bresnahan & Reiss,1991,Journal of Political Economy,F,US small-town retail (incl. auto dealers),Entry thresholds,Competition effects saturate after few entrants,Auto dealers in isolated markets,crossref DOI 10.1086/261786,
95+pinkse2002spatial,Pinkse et al.,2002,Econometrica,F,US gasoline wholesale,Semiparametric spatial price competition,Competition is localized,Spatial price-response estimation,crossref DOI 10.1111/1468-0262.00320,
96+houde2012spatial,Houde,2012,American Economic Review,F,Quebec City gasoline,Spatial differentiation on road network,Commuting-path competition,Quebec spatial retail evidence,crossref DOI 10.1257/aer.102.5.2147,
97+gorodnichenko2009border,Gorodnichenko & Tesar,2009,American Economic Journal: Macroeconomics,F,US-Canada city prices,Border effect reassessment,Border effect smaller once cross-country heterogeneity handled,Canadian regional price dispersion,crossref DOI 10.1257/mac.1.1.219,
98+anselin1988spatial,Anselin,1988,Springer Netherlands,F,Methods,Spatial econometrics text,Spatial lag/error models,Spatial methods,crossref DOI 10.1007/978-94-015-7799-1,
99+lesage2009introduction,LeSage & Pace,2009,Chapman and Hall/CRC,F,Methods,Spatial econometrics text,Spatial regression,Spatial methods,crossref DOI 10.1201/9781420064254,
100+conley1999gmm,Conley,1999,Journal of Econometrics,F,Methods,Spatial HAC,GMM with cross-sectional dependence,Spatial-robust SEs,crossref DOI 10.1016/S0304-4076(98)00084-0,
101+elhorst2014spatial,Elhorst,2014,Springer Berlin Heidelberg,F,Methods,Spatial panels,Spatial panel models,Spatial panel methods,crossref DOI 10.1007/978-3-642-40340-8,
102+ceglowski2003lawofoneprice,Ceglowski,2003,Canadian Journal of Economics,F,Canadian cities,Law of one price tests,Intranational price convergence in Canada,Canadian regional integration,crossref DOI 10.1111/1540-5982.t01-1-00005,
103+kalnins2003hamburger,Kalnins,2003,Journal of Economics \& Management Strategy,F,US fast food,Spatial econometrics of prices,Prices respond to nearby competitors,Localized price reactions,crossref DOI 10.1111/j.1430-9134.2003.00591.x,
104+slade1992vancouver,Slade,1992,Review of Economic Studies,F,Vancouver gasoline,Price wars and tacit collusion,Dynamic reaction functions,Canadian price-war evidence,crossref DOI 10.2307/2297954,
105+lewis2008dispersion,Lewis,2008,Journal of Industrial Economics,F,US gasoline stations,Dispersion and differentiation,Dispersion depends on local competition,Local dispersion,crossref DOI 10.1111/j.1467-6451.2008.00348.x,
106+manski1993reflection,Manski,1993,Review of Economic Studies,G,Theory,Reflection problem,Endogenous effects not identified from linear-in-means,Identification of competitor response,crossref DOI 10.2307/2298123,
107+brock2001discrete,Brock & Durlauf,2001,Review of Economic Studies,G,Theory,Discrete choice with social interactions,Identification via nonlinearity,Social interactions,crossref DOI 10.1111/1467-937X.00168,
108+bramoulle2009peer,Bramoulle et al.,2009,Journal of Econometrics,G,Theory/networks,Network identification,Intransitive triads identify peer effects,Dealer-network instruments,crossref DOI 10.1016/j.jeconom.2008.12.021,
109+angrist2014perils,Angrist,2014,Labour Economics,G,Methods,Critique,Mechanical peer-effect correlations,Caution for competitor response,crossref DOI 10.1016/j.labeco.2014.05.008,
110+blume2011identification,Blume et al.,2011,Handbook of Social Economics,G,Survey,Identification review,Social interactions models,Reference survey,crossref DOI 10.1016/b978-0-444-53707-2.00001-3,
111+bajari2010static,Bajari et al.,2010,Journal of Business \& Economic Statistics,G,Methods,Static games estimation,Estimating strategic interactions,Game-theoretic estimation,crossref DOI 10.1198/jbes.2009.07264,
112+sun2021estimating,Sun & Abraham,2021,Journal of Econometrics,G,Methods,Interaction-weighted event study,Heterogeneous effects bias,Event-study estimator,crossref DOI 10.1016/j.jeconom.2020.09.006,
113+callaway2021difference,Callaway & Sant'Anna,2021,Journal of Econometrics,G,Methods,Group-time ATT,Staggered DiD,Staggered price-cut events,crossref DOI 10.1016/j.jeconom.2020.12.001,
114+borusyak2024revisiting,Borusyak et al.,2024,Review of Economic Studies,G,Methods,Imputation estimator,Efficient robust event study,Event-study estimator,crossref DOI 10.1093/restud/rdae007,
115+goodmanbacon2021difference,Goodman-Bacon,2021,Journal of Econometrics,G,Methods,TWFE decomposition,Negative weights,Diagnostics,crossref DOI 10.1016/j.jeconom.2021.03.014,
116+dechaisemartin2020twoway,de Chaisemartin & D'Haultfoeuille,2020,American Economic Review,G,Methods,Heterogeneous TWFE,Robust DiD estimator,Diagnostics,crossref DOI 10.1257/aer.20181169,
117+roth2022pretest,Roth,2022,American Economic Review: Insights,G,Methods,Pre-trends tests,Low power of pretests,Pre-trend caution,crossref DOI 10.1257/aeri.20210236,
118+rambachan2023credible,Rambachan & Roth,2023,Review of Economic Studies,G,Methods,Sensitivity to parallel trends,Partial identification,Robust inference,crossref DOI 10.1093/restud/rdad018,
119+roth2023trending,Roth et al.,2023,Journal of Econometrics,G,Survey,DiD review,Synthesis of recent DiD,Guidance,crossref DOI 10.1016/j.jeconom.2023.03.008,
120+cameron2008bootstrap,Cameron et al.,2008,Review of Economics and Statistics,G,Methods,Wild cluster bootstrap,Few clusters inference,Few-region clustering,crossref DOI 10.1162/rest.90.3.414,
121+cameron2011multiway,Cameron et al.,2011,Journal of Business \& Economic Statistics,G,Methods,Multiway clustering,Two-way clustered SEs,Dealer x week clustering,crossref DOI 10.1198/jbes.2010.07136,
122+abadie2023clustering,Abadie et al.,2023,Quarterly Journal of Economics,G,Methods,Design-based clustering,When to cluster,Clustering choice,crossref DOI 10.1093/qje/qjac038,
123+bertrand2004howmuch,Bertrand et al.,2004,Quarterly Journal of Economics,G,Methods,Serial correlation in DiD,Over-rejection without clustering,Serial correlation in daily panel,crossref DOI 10.1162/003355304772839588,
124+chandra2011gasoline,Chandra & Tappata,2011,RAND Journal of Economics,G,US/Canada gasoline,Search and dynamic dispersion,Dispersion rises with search incentives,Competitor pricing dynamics,crossref DOI 10.1111/j.1756-2171.2011.00150.x,
125+byrne2019coordinate,Byrne & de Roos,2019,American Economic Review,G,Perth gasoline,Price leadership,Learning to coordinate cycles,Competitor response identification,crossref DOI 10.1257/aer.20170116,
126+assad2024algorithmic,Assad et al.,2024,Journal of Political Economy,G,German gasoline,Algorithmic pricing adoption,Algorithms raise margins in duopolies,Automated repricing in retail,crossref DOI 10.1086/726906,
127+bailey1963repeatsales,Bailey et al.,1963,Journal of the American Statistical Association,H,Methods,Repeat-sales regression,Index from repeated sales,Repeat-listing index design,crossref DOI 10.1080/01621459.1963.10480679,
128+han2016askingprice,Han & Strange,2016,Journal of Urban Economics,H,Toronto MLS,Theory and evidence on asking price,Asking price is commitment and signal; affects TOM and price,Core asking-price theory for listings,crossref DOI 10.1016/j.jue.2016.03.008,
129+anenberg2017timely,Anenberg & Laufer,2017,Review of Economics and Statistics,H,US listings (CoreLogic),Listing-based house price index,Listing prices give timely index,Direct precedent for listing-price index,crossref DOI 10.1162/REST_a_00634,
130+lyons2019listprices,Lyons,2019,Finance Research Letters,H,Ireland listings vs sales,Comparison of list and transaction indices,"List prices track trends, with gaps in extreme markets",List-vs-transaction gap,crossref DOI 10.1016/j.frl.2018.10.004,
131+kurlat2015asymmetries,Kurlat & Stroebel,2015,Review of Financial Studies,H,US housing,Test of information asymmetries,Informed buyers outperform,Information in listing markets,crossref DOI 10.1093/rfs/hhv028,
132+gatzlaff1997selection,Gatzlaff & Haurin,1997,Journal of Real Estate Finance and Economics,H,US housing,Selection in repeat-sales,Sample selection biases indexes,Selection in exit/sale samples,crossref DOI 10.1023/a:1007763816289,
133+knight2002listing,Knight,2002,Real Estate Economics,H,US MLS,Listing price changes and TOM,Large initial overpricing leads to cuts and longer TOM,Markdowns and time on market,crossref DOI 10.1111/1540-6229.00038,
134+horowitz1992listprice,Horowitz,1992,Journal of Applied Econometrics,H,US housing,Structural list-price model,List price as ceiling and signal,List price role,crossref DOI 10.1002/jae.3950070202,
135+haurin2010listprices,Haurin et al.,2010,Real Estate Economics,H,US MLS,"List price, sale price, marketing time","Atypical homes priced higher, longer TOM",List-price tradeoff,crossref DOI 10.1111/j.1540-6229.2010.00279.x,
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138+hill2013hedonic,Hill,2012,Journal of Economic Surveys,H,Survey,Hedonic housing indexes,Taxonomy of hedonic index methods,Index design,crossref DOI 10.1111/j.1467-6419.2012.00731.x,
139+erickson2011experimental,Erickson & Pakes,2011,American Economic Review,H,US computers,Experimental CPI component,Hedonic indexes at monthly frequency,Monthly hedonic index,crossref DOI 10.1257/aer.101.5.1707,
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142+diebold1995comparing,Diebold & Mariano,1995,Journal of Business \& Economic Statistics,I,Methods,Predictive accuracy test,DM test,Forecast comparison,crossref DOI 10.1080/07350015.1995.10524599,
143+giacomini2006tests,Giacomini & White,2006,Econometrica,I,Methods,Conditional predictive ability,GW test,Forecast comparison,crossref DOI 10.1111/j.1468-0262.2006.00718.x,
144+brier1950verification,Brier,1950,Monthly Weather Review,I,Methods,Brier score,Probability forecast scoring,Hazard calibration,crossref DOI 10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2,
145+friedman2001greedy,Friedman,2001,Annals of Statistics,I,Methods,Gradient boosting,GBM,Boosted hedonic/hazard models,crossref DOI 10.1214/aos/1013203451,
146+athey2019machine,Athey & Imbens,2019,Annual Review of Economics,I,Survey,ML for economists,Guidance,ML use,crossref DOI 10.1146/annurev-economics-080217-053433,
147+mullainathan2017machine,Mullainathan & Spiess,2017,Journal of Economic Perspectives,I,Survey,ML as applied econometrics,Prediction vs causation,ML use,crossref DOI 10.1257/jep.31.2.87,
148+harrell2015regression,Harrell,2015,Springer International Publishing,I,Methods,Textbook,Validation of survival models,Model evaluation,crossref DOI 10.1007/978-3-319-19425-7,
149+uno2011cstatistics,Uno et al.,2011,Statistics in Medicine,I,Methods,C-statistic for censored data,Discrimination measure,Hazard evaluation,crossref DOI 10.1002/sim.4154,
150+graf1999assessment,Graf et al.,1999,Statistics in Medicine,I,Methods,Brier score under censoring,IPCW Brier,Hazard evaluation,crossref DOI 10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5,
151+court1939hedonic,Court,1939,The Dynamics of Automobile Demand,A,US automobiles 1920s-30s (GM),Hedonic regression of price on characteristics,First hedonic price index; quality-adjusted auto price change,Origin of the hedonic approach used to quality-adjust Quebec asking prices,url https://allengoodman.wayne.edu/Research/PUBS/Court_Hedonic.pdf,Verified via Goodman (1998) and Wayne State scan
152+griliches1961hedonic,Griliches,1961,The Price Statistics of the Federal Government,A,US new-car list prices 1937-59,"Log-linear hedonic regression, time dummies",Quality-adjusted auto prices rose far less than unadjusted list prices,Canonical hedonic vehicle index; template for AMLI quality adjustment,url https://www.nber.org/chapters/c6492,NBER chapter page verified (no DOI)
153+ohta1976automobile,Ohta & Griliches,1976,Household Production and Consumption,A,US new and used car prices 1955-71,Hedonic regressions on new and used cars,Hedonic coefficients stable across new/used; used prices reflect same characteristics,Direct precedent for hedonic treatment of used cars,url https://www.nber.org/books-and-chapters/household-production-and-consumption/automobile-prices-revisited-extensions-hedonic-hypothesis,NBER chapter page verified
154+kim1985lemons,Kim,1985,American Economic Review,B,Theory,Used-car market with asymmetric information,Market need not collapse; price-quality equilibria,Theory of used-car equilibrium,url https://ideas.repec.org/a/aea/aecrev/v75y1985i4p836-43.html,IDEAS/RePEc + OpenAlex verified; no DOI
155+bond1982lemons,Bond,1982,American Economic Review,B,US used pickup trucks (1977 survey),Repair-frequency test,No lemons effect after controlling age/mileage,Classic empirical test,url https://ideas.repec.org/a/aea/aecrev/v72y1982i4p836-40.html,IDEAS/RePEc verified; no DOI
156+lazear1986clearance,Lazear,1986,American Economic Review,C,Theory,Sequential pricing with demand uncertainty,Prices start high and fall with time on shelf; thin markets markdown faster,Core theory for markdown hazards of listings,url https://ideas.repec.org/a/aea/aecrev/v76y1986i1p14-32.html,IDEAS/RePEc verified; no DOI
157+varian1980sales,Varian,1980,American Economic Review,D,Theory,Model of sales,Randomized prices (sales) as equilibrium,Price dispersion within seller over time,url https://ideas.repec.org/a/aea/aecrev/v70y1980i4p651-59.html,IDEAS/RePEc + OpenAlex verified; no DOI
158+engel1996border,Engel & Rogers,1996,American Economic Review,F,US-Canada CPI,Border effect,Border equivalent to thousands of miles,Regional integration benchmark,url https://ideas.repec.org/a/aea/aecrev/v86y1996i5p1112-25.html,IDEAS/RePEc + Semantic Scholar verified; no DOI
159+caseshiller1989efficiency,Case & Shiller,1989,American Economic Review,H,US cities housing,Repeat-sales index and efficiency,Housing prices show predictable momentum,Repeat-sales index precedent,url https://ideas.repec.org/a/aea/aecrev/v79y1989i1p125-37.html,IDEAS/RePEc + Semantic Scholar verified; no DOI
160+kiefer1988duration,Kiefer,1988,Journal of Economic Literature,E,Methods,Survey,Economic duration data,Economic hazard survey,url https://ideas.repec.org/a/aea/jeclit/v26y1988i2p646-79.html,IDEAS/RePEc + OpenAlex verified; no DOI
161+genesove1997equity,Genesove & Mayer,1997,American Economic Review,D,Boston condos,Equity and time to sale,"High LTV sellers set higher asking prices, longer time to sale",Asking price and time-on-market tradeoff,url https://ideas.repec.org/a/aea/aecrev/v87y1997i3p255-69.html,IDEAS/RePEc verified; no DOI
162+ke2017lightgbm,Ke et al.,2017,Advances in Neural Information Processing Systems 30 (NIPS 2017),I,Methods,LightGBM,Efficient GBDT,Implementation,url https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html,NeurIPS page verified
163+lundberg2017shap,Lundberg & Lee,2017,Advances in Neural Information Processing Systems 30 (NIPS 2017),I,Methods,SHAP values,Model explanation,Interpretation,url https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html,NeurIPS page verified
164+yanes2010depreciation,Yanes,2010,Proceedings of the 45th Annual Conference of the Canadian Transportation Research Forum,B,Canadian Red Book values by province,Depreciation estimation,Quebec vehicles depreciate slightly slower than rest of Canada,Only Canadian provincial depreciation evidence found,url https://ctrf.ca/wp-content/uploads/2014/07/47YanesDepreciationofLightDutyRoad.pdf,"Conference paper (CTRF), not peer-reviewed journal"
165+statcan2022usedvehicles,Taylor et al.,2022,Statistics Canada,H,Canadian CPI (J.D. Power dealer transactions),Hedonic time-dummy by geography and class,Used vehicles entered CPI May 2022 reference month,Official Canadian used-vehicle price measurement,url https://www150.statcan.gc.ca/n1/pub/62f0014m/62f0014m2022008-eng.htm,"Agency document, fetched"
166+bls2026usedcars,U.S. Bureau of Labor Statistics,2026,"Factsheet, last modified 20 May 2026",H,US CPI (J.D. Power),"Sample of 2-7 year-old vehicles, depreciation adjustment",Used-car CPI methodology,Official US used-car index,url https://www.bls.gov/cpi/factsheets/used-cars-and-trucks.htm,"Agency factsheet, fetched"
167+manheim2023methodology,Manheim Consulting (Cox Automotive),2023,"Technical note, January 2023 rebasing",H,Manheim US auctions,Mix/mileage/seasonal adjustment,Wholesale value index since 1997 base,Existing wholesale value index,url https://site.manheim.com/wp-content/uploads/sites/2/2023/07/Used-Vehicle-Summary-Methodology.pdf,Grey literature
168+cox2022dayssupply,Cox Automotive,2022,"Market Insights, 20 January 2022 (revised 17 February 2022)",H,vAuto listings,30-day sales pace days' supply,Days' supply is a liquidity-type measure,Existing liquidity-type measure,url https://www.coxautoinc.com/market-insights/used-vehicle-days-supply/,Grey literature
169+cbb2026index,Canadian Black Book,2026,"Web page, accessed 1 November 2026",H,Canadian wholesale values,Retention index 2-6 year-old vehicles,Canadian wholesale retention index,Existing Canadian index,url https://www.canadianblackbook.com/black-book-index/,Grey literature
170+cbb2026depreciation,Canadian Black Book & Fitch Ratings,2026,Canadian Black Book,B,Canadian wholesale values,Industry depreciation report,Retention/recovery rates by segment; Quebec EV market,Canadian industry benchmark for depreciation,url https://www.canadianblackbook.com/wp-content/uploads/2026/08/CBB-Fitch-2026-Updated-2.pdf,Grey literature
171+autotrader2024priceindex,AutoTrader.ca,2024,"Editorial report, 22 July 2024",H,AutoTrader.ca listings,Average listing price adjusted for make/age,31% of listings had price drops June 2024,Canadian listing-price index precedent,url https://www.autotrader.ca/editorial/car-news/autotrader-price-index-june-2024/,Grey literature
172+cargurus2026imv,CarGurus,2026,"Web page, accessed 1 November 2026",H,CarGurus listings,Instant Market Value,Comparable-listing valuation,Listing-based valuation,url https://www.cargurus.ca/Cars/aboutCargurus.html,"Grey literature; curl 406, verified via search listing"
173+iseecars2013pricedrops,iSeeCars.com,2013,Study of 30 million used-car listings; press release,H,30M US listings,Listing price-change tracking,Price cut 1-6 times per listing; first cut ~5%,Only large-scale markdown stats found,url https://www.iseecars.com/wait-for-used-car-deals-2013-study,Grey literature
added literature/novelty_memo.md +63 −0
@@ -0,0 +1,63 @@
1+# Novelty memo — WP36 "The Hidden Dynamics of Used-Car Markets"
2+
3+Prepared 2026-11-01 from the verified bibliography in `references.bib` (172 entries) and `evidence_matrix.csv`. Citation keys in brackets refer to that file.
4+
5+## 1. What exists, bucket by bucket
6+
7+**A. Hedonic prices and quality adjustment.** The hedonic method was invented on automobiles [court1939hedonic; goodman1998court] and canonised on automobiles [griliches1961hedonic; ohta1976automobile], with Rosen's equilibrium interpretation [rosen1974hedonic] and Lancaster's characteristics demand [lancaster1966newapproach] as theory. The index-number literature settles the practical questions WP36 faces: time-dummy versus imputation indexes [silver2007hedonicimputation; diewert2003hedonic], instability of hedonic coefficients and the case for rolling windows [pakes2003hedonic; erickson2011experimental], official-statistics best practice [triplett2006handbook], and the warning that unobserved quality (here, vehicle condition and dealer reconditioning) can contaminate implicit prices [bajari2005hedonic; ekeland2004hedonic]. Vehicle-specific hedonic work prices safety and fuel economy [dreyfus1995timepreference] and establishes that mileage, not age, is the primary depreciation driver [engers2009annualmiles]. BLP [berry1995automobile] remains the structural benchmark for auto demand but is not the natural tool for a listing panel without quantities.
8+
9+**B. Durable goods and used-car markets.** Since Akerlof [akerlof1970lemons] the economics of used cars has been dominated by adverse selection: theory [kim1985lemons; hendel1999adverse; hendel2002leasing; biglaiser1993middlemen], tests on repairs and trade patterns [bond1982lemons; emons2009usedcars; peterson2014adverse; peterson2017beautiful], the wholesale auction side [genesove1993adverse; larsen2021bargaining], online markets [lewis2011asymmetric; adams2011vettes], and the role of dealers as quality intermediaries [biglaiser2020intermediaries]. A second strand models turnover and prices across vintages in stationary equilibrium with transaction costs [rust1985durable; stolyarov2002turnover; porter1999usedtrade; gavazza2014usedcar]. A third documents how shocks capitalise into used-car prices: gasoline prices [busse2013gasoline; sallee2016fueleconomy], scrappage policy [jacobsen2015scrappage], weather [busse2015weather], and behavioural mileage heuristics [lacetera2012heuristic]. Listing-price data from classifieds appear in European work [kooreman2006anomalies; haan2010internetregional], the latter being the closest precedent for regional segmentation of used-car asking prices (Dutch regions before and after the internet). Canadian evidence is thin: a Transport Canada conference paper finds Quebec vehicles depreciate slightly more slowly than elsewhere [yanes2010depreciation]; the rest is industry reporting [cbb2026depreciation].
10+
11+**C. Dealer inventory, dynamic pricing and clearance.** Lazear's clearance-sale model [lazear1986clearance] predicts exactly the object WP36 estimates: an initial price that falls with time on the shelf at a rate governed by market thinness. Operations research formalises optimal markdowns for finite inventory with a horizon [gallego1994dynamicpricing; elmaghraby2003dynamicpricing]; empirical analogues exist for supermarkets [pesendorfer2002retailsales; aguirregabiria1999markups], apparel [pashigian1991sale], tickets [sweeting2012perishable], rentals [cho2010flatrental] and house listings [merlo2004bargaining; merlo2015homeselling]. For car dealers, the empirical literature is almost entirely about *new* cars and transaction prices: internet referral and information effects [scottmorton2001internet; zettelmeyer2006internet], promotion pass-through [busse2006cashback], and — most relevant — Israeli, Scott Morton, Silva-Risso and Zettelmeyer's finding that dealers with market power price more aggressively on inventory fluctuations [israeli2022marketpower]. Murry and Zhou [murry2020colocation] show dealer colocation lowers search costs. We found no peer-reviewed study estimating markdown hazards on used-car listings.
12+
13+**D. Search, dispersion and liquidity.** Dispersion theory [stigler1961information; diamond1971paradox; varian1980sales; burdett1983equilibrium] and measurement [sorensen2000dispersion; lach2002dispersion; baye2006dispersion; kaplan2015dispersion] give WP36 its dispersion metrics. Liquidity is formalised in finance as a search friction [duffie2005otc; lagos2009liquidity] or measured by price impact and spreads [amihud2002illiquidity; amihud1986bidask], and in housing by time-to-sell and matching [wheaton1990vacancy; krainer2001housingliquidity; carrillo2012search; head2014searchliquidity; ngai2014hotcold; piazzesi2020segmented]. Gavazza's aircraft papers [gavazza2011frictions; gavazza2016decentralized] are the closest conceptual template: a durable asset traded in a decentralised, dealer-intermediated market where thinness raises frictions and lowers prices. The housing microstructure literature [han2015microstructure; genesove1997equity; genesove2001lossaversion; guren2018momentum; anenberg2016frictions] provides both the asking-price-as-signal theory and evidence of strategic complementarity in list prices.
14+
15+**E. Survival analysis.** Standard: Cox and extensions [cox1972regression; andersen1982cox; lin1989robust; grambsch1994proportional; royston2002flexible; therneau2000modeling; kalbfleisch2002statistical], competing risks [fine1999proportional; putter2007tutorial], recurrent events [prentice1981regression], discrete-time hazards for daily panels [allison1982discrete; singer1993discrete], interval censoring for first/last-seen data [turnbull1976empirical], unobserved heterogeneity [heckman1984method], economic duration surveys [kiefer1988duration; lancaster1990econometric].
16+
17+**F. Spatial competition and regional integration.** [hotelling1929stability; salop1979monopolistic; bresnahan1991entry] (which includes auto dealers in isolated US towns), semiparametric spatial price competition [pinkse2002spatial], Quebec-City gasoline [houde2012spatial], fast food [kalnins2003hamburger], Vancouver gasoline price wars [slade1992vancouver], gasoline dispersion [lewis2008dispersion], and Canadian intranational integration [engel1996border; gorodnichenko2009border; ceglowski2003lawofoneprice]; methods [anselin1988spatial; lesage2009introduction; conley1999gmm; elhorst2014spatial].
18+
19+**G. Identification of strategic interaction; event studies.** The reflection problem [manski1993reflection; brock2001discrete; bramoulle2009peer; blume2011identification; angrist2014perils; bajari2010static]; gasoline-market evidence on competitor pricing responses and algorithmic repricing [chandra2011gasoline; byrne2019coordinate; assad2024algorithmic]; modern staggered DiD/event-study toolkits [goodmanbacon2021difference; dechaisemartin2020twoway; callaway2021difference; sun2021estimating; borusyak2024revisiting; roth2022pretest; rambachan2023credible; roth2023trending]; clustering [bertrand2004howmuch; cameron2008bootstrap; cameron2011multiway; abadie2023clustering].
20+
21+**H. Indices and measurement.** Repeat-sales [bailey1963repeatsales; caseshiller1989efficiency; gatzlaff1997selection], hedonic index taxonomy [hill2013hedonic; bils2009quality], listing-price indexes and the list–transaction gap [anenberg2017timely; lyons2019listprices; han2016askingprice; knight2002listing; horowitz1992listprice; haurin2010listprices; anglin2003tradeoff; kurlat2015asymmetries], official used-vehicle CPI components [bls2026usedcars; statcan2022usedvehicles] and industry indices (section 2).
22+
23+**I. Multiple testing and model evaluation.** [benjamini1995fdr; romano2005stepwise; diebold1995comparing; giacomini2006tests; brier1950verification; graf1999assessment; uno2011cstatistics; harrell2015regression; friedman2001greedy; ke2017lightgbm; lundberg2017shap; athey2019machine; mullainathan2017machine].
24+
25+## 2. What is known about used-car asking-price dynamics, markdowns and exit
26+
27+Surprisingly little, academically. The economics literature on used cars overwhelmingly uses *transaction* data (wholesale auctions, state title records, CES purchases, eBay sales). The only large-scale statistics on listing markdowns we could verify are industry: iSeeCars' 2013 analysis of 30 million listings reports that roughly one in five cars is marked down before sale, that dealers cut 1–6 times per listing with a first cut near 5% around day 15, and that listing duration proxies time on market [iseecars2013pricedrops]; AutoTrader.ca reports that 31% of Canadian listings carried a price drop in June 2024 [autotrader2024priceindex]. Biglaiser, Li, Murry and Zhou [biglaiser2020intermediaries] come closest on the academic side, predicting and documenting that the dealer price premium is positively correlated with time on the market, but they work with transaction records, not daily listing paths. Israeli et al. [israeli2022marketpower] show inventory-sensitive dealer pricing for *new* cars. The markdown-hazard framing therefore exists in theory [lazear1986clearance; gallego1994dynamicpricing] and in housing [knight2002listing; merlo2015homeselling; anenberg2016frictions] but has not been estimated on used-vehicle listings in a published paper that we could find. Exit (sold versus withdrawn/relisted) as a competing-risks problem is likewise untreated for vehicles.
28+
29+## 3. Existing used-vehicle indices and "liquidity" measures
30+
31+- **Manheim Used Vehicle Value Index** (Cox Automotive): all Manheim US auction sales in 20 market classes; outlier trimming; mileage adjusted by monthly linear regression; mix weights from a 24-month rolling average; Census X seasonal adjustment; rebased January 1997 = 100 in 2023 [manheim2023methodology]. Wholesale, transaction-based, US only.
32+- **Cox Automotive used-vehicle days' supply**: unsold vAuto listing inventory divided by the trailing 30-day daily sales rate [cox2022dayssupply]. This is a liquidity-type measure (inventory/flow), published monthly for the US; it is a national aggregate with no listing-level hazard, no quality adjustment and no regional detail in the public series.
33+- **Canadian Black Book Used Vehicle Retention Index**: wholesale average values of 2–6-year-old vehicles as a share of original MSRP, weighted by registrations, adjusted for age, mileage, condition and MSRP inflation [cbb2026index]; companion annual depreciation report with Fitch [cbb2026depreciation]. Wholesale, Canada-wide; no time-on-market dimension.
34+- **J.D. Power** supplies the dealer-transaction data that both the BLS used-cars-and-trucks CPI (sample of 2–7-year-old vehicles, depreciation-adjusted, mileage adjusted by age since 2024) [bls2026usedcars] and the Statistics Canada used-vehicle CPI component (hedonic time-dummy on log price with log odometer, log age, model fixed effects, by geography and class, introduced with the May 2022 reference month) [statcan2022usedvehicles] use. Both are transaction-based and have no inventory or duration component.
35+- **CarGurus Instant Market Value** is a listing-comparables valuation, not an index of market liquidity [cargurus2026imv]; **AutoTrader.ca Price Index** is an average listing price adjusted for make and age, with an ancillary share-of-listings-with-price-drops statistic [autotrader2024priceindex]; **iSeeCars** publishes episodic days-to-sell and price-drop studies [iseecars2013pricedrops]. (Black Book US retention indices mirror CBB; the Manheim Market Report is a vehicle-level wholesale valuation, not an index.)
36+
37+**Does a used-car "liquidity index" already exist?** Not under that name in the academic literature (query 9 in `search_protocol.md` returned only trade guides). But days' supply, days-to-turn and the NADA 30-day inventory target *are* liquidity measures, as are iSeeCars' days-to-sell and AutoTrader's share of price drops. WP36 must acknowledge that an AMLI is a formalisation and extension of these practitioner quantities, not the first attempt to summarise market liquidity for used vehicles. The novelty lies in (i) combining duration, markdown and dispersion dimensions, (ii) estimating them at listing level with hazards rather than ratios, (iii) quality adjustment, (iv) regional/segment disaggregation, and (v) full transparency of construction, none of which the industry series offer.
38+
39+## 4. What WP36 adds
40+
41+1. **A listing-level daily panel scraped from dealer websites and portals** (~273k Quebec listings, 138 dealers, 12 Aug–1 Nov 2026) with a complete price-change log and first/last-seen dates. No published economics paper uses dealer-website asking-price paths for used cars; European precedents [kooreman2006anomalies; haan2010internetregional] use static classifieds, housing papers use MLS logs.
42+2. **Markdown hazards and recurrent-event models for price cuts** (Andersen–Gill / Prentice–Williams–Peterson) and **competing-risks exit** (sold-type removal vs withdrawal/relisting), with interval censoring. This implements Lazear/Gallego–van Ryzin markdown theory on vehicles for the first time we can document.
43+3. **Competitor response**: event-study estimates of how a dealer's price cut propagates to comparable listings at nearby dealers, with the reflection-problem and staggered-treatment toolkits of bucket G. Guren's strategic complementarity in list prices [guren2018momentum] and gasoline-market reaction studies are the precedents; nothing exists for used-car listings.
44+4. **Quebec regional segmentation** of dispersion, liquidity and markdown behaviour (Montréal, Québec, Outaouais, regions), connecting to Canadian intranational integration work [engel1996border; gorodnichenko2009border; ceglowski2003lawofoneprice] and the Dutch regional used-car result [haan2010internetregional]; the only prior Quebec-specific vehicle evidence is [yanes2010depreciation].
45+5. **A transparent, reproducible AMLI**: quality-adjusted (hedonic, rolling window, à la Statistics Canada) listing-price component plus duration, markdown-frequency and dispersion components, published with code, by region and segment — in contrast to proprietary indices whose weights and adjustments are only summarily disclosed.
46+
47+## 5. What WP36 cannot add (honest limits)
48+
49+- **No transaction prices.** Asking prices are commitments/signals [han2016askingprice; horowitz1992listprice], and the list–transaction gap varies with market conditions [lyons2019listprices; knight2002listing]. WP36 cannot measure realised prices, negotiated discounts, trade-in credits or financing terms, and cannot identify adverse selection in the Akerlof sense.
50+- **Exit is not sale.** A listing disappearing may mean a sale, a wholesale disposal, a transfer between dealer sites, or de-listing; exit hazards are hazards of removal, and any "sold" label is inferred. Competing-risks results must be presented as such.
51+- **Short window (≈12 weeks) and one season.** Seasonality [ngai2014hotcold; busse2015weather] and macro shocks [busse2013gasoline] cannot be separately identified; the AMLI is a cross-sectional/short-panel object, not yet a time series comparable to Manheim or CBB.
52+- **Dealer and portal coverage** is a sample of Quebec dealers' websites and aggregators, not the universe; private-party sales and wholesale channels are absent; selection into listing on scraped sites may bias dispersion.
53+- **Condition and reconditioning are unobserved** [bajari2005hedonic], so hedonic residual dispersion mixes true price dispersion with quality dispersion.
54+- **Competitor-response identification** rests on timing and spatial proximity; without exogenous shocks to a single dealer's costs, estimates are of conditional co-movement, with the usual reflection-problem caveats [manski1993reflection; angrist2014perils].
55+- **No welfare or structural counterfactuals** of the Gavazza–Lizzeri–Roketskiy type; WP36 is descriptive/reduced-form.
56+
57+## 6. Closest prior works (ranked)
58+
59+1. Biglaiser, Li, Murry & Zhou (2020, RAND) — dealers as intermediaries; dealer premium vs time on market [biglaiser2020intermediaries].
60+2. Israeli, Scott Morton, Silva-Risso & Zettelmeyer (2022, Management Science) — dealer dynamic pricing in response to inventory [israeli2022marketpower].
61+3. Haan & de Boer (2010, De Economist) — regional price differences in used-car listings before/after the internet [haan2010internetregional].
62+4. Gavazza, Lizzeri & Roketskiy (2014, AER) and Gavazza (2011, AER) — transaction costs/frictions and liquidity in used-durable markets [gavazza2014usedcar; gavazza2011frictions].
63+5. Knight (2002, Real Estate Economics) and Anenberg & Laufer (2017, REStat) — listing-price changes, time on market and listing-based indexes in housing, the methodological template WP36 transposes to vehicles [knight2002listing; anenberg2017timely].
added literature/references.bib +1831 −0
@@ -0,0 +1,1831 @@
1+@article{rosen1974hedonic,
2+ author = {Rosen, Sherwin},
3+ title = {{Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition}},
4+ journal = {Journal of Political Economy},
5+ volume = {82},
6+ number = {1},
7+ pages = {34--55},
8+ year = {1974},
9+ doi = {10.1086/260169}
10+}
11+
12+@article{lancaster1966newapproach,
13+ author = {Lancaster, Kelvin J.},
14+ title = {{A New Approach to Consumer Theory}},
15+ journal = {Journal of Political Economy},
16+ volume = {74},
17+ number = {2},
18+ pages = {132--157},
19+ year = {1966},
20+ doi = {10.1086/259131}
21+}
22+
23+@article{pakes2003hedonic,
24+ author = {Pakes, Ariel},
25+ title = {{A Reconsideration of Hedonic Price Indexes with an Application to PC's}},
26+ journal = {American Economic Review},
27+ volume = {93},
28+ number = {5},
29+ pages = {1578--1596},
30+ year = {2003},
31+ doi = {10.1257/000282803322655455}
32+}
33+
34+@book{triplett2006handbook,
35+ author = {Triplett, Jack},
36+ title = {Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes},
37+ year = {2006},
38+ publisher = {OECD Publishing},
39+ address = {Paris},
40+ doi = {10.1787/9789264028159-en}
41+}
42+
43+@article{bajari2005hedonic,
44+ author = {Bajari, Patrick and Benkard, C. Lanier},
45+ title = {{Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach}},
46+ journal = {Journal of Political Economy},
47+ volume = {113},
48+ number = {6},
49+ pages = {1239--1276},
50+ year = {2005},
51+ doi = {10.1086/498586}
52+}
53+
54+@article{ekeland2004hedonic,
55+ author = {Ekeland, Ivar and Heckman, James J. and Nesheim, Lars},
56+ title = {{Identification and Estimation of Hedonic Models}},
57+ journal = {Journal of Political Economy},
58+ volume = {112},
59+ number = {S1},
60+ pages = {S60--S109},
61+ year = {2004},
62+ doi = {10.1086/379947}
63+}
64+
65+@article{silver2007hedonicimputation,
66+ author = {Silver, Mick and Heravi, Saeed},
67+ title = {{The Difference Between Hedonic Imputation Indexes and Time Dummy Hedonic Indexes}},
68+ journal = {Journal of Business \& Economic Statistics},
69+ volume = {25},
70+ number = {2},
71+ pages = {239--246},
72+ year = {2007},
73+ doi = {10.1198/073500106000000486}
74+}
75+
76+@incollection{diewert2003hedonic,
77+ author = {Diewert, W. Erwin},
78+ title = {{Hedonic Regressions: A Consumer Theory Approach}},
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1100+
1101+@article{slade1992vancouver,
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1122+
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1154+}
1155+
1156+@article{angrist2014perils,
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1164+}
1165+
1166+@incollection{blume2011identification,
1167+ author = {Blume, Lawrence E. and Brock, William A. and Durlauf, Steven N. and Ioannides, Yannis M.},
1168+ title = {{Identification of Social Interactions}},
1169+ booktitle = {Handbook of Social Economics},
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1177+
1178+@article{bajari2010static,
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1187+}
1188+
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1190+ author = {Sun, Liyang and Abraham, Sarah},
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1198+}
1199+
1200+@article{callaway2021difference,
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1209+}
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1211+@article{borusyak2024revisiting,
1212+ author = {Borusyak, Kirill and Jaravel, Xavier and Spiess, Jann},
1213+ title = {{Revisiting Event-Study Designs: Robust and Efficient Estimation}},
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1222+@article{goodmanbacon2021difference,
1223+ author = {Goodman-Bacon, Andrew},
1224+ title = {{Difference-in-differences with variation in treatment timing}},
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1231+}
1232+
1233+@article{dechaisemartin2020twoway,
1234+ author = {de Chaisemartin, Cl{\'e}ment and D'Haultf{\oe}uille, Xavier},
1235+ title = {{Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects}},
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1242+}
1243+
1244+@article{roth2022pretest,
1245+ author = {Roth, Jonathan},
1246+ title = {{Pretest with Caution: Event-Study Estimates after Testing for Parallel Trends}},
1247+ journal = {American Economic Review: Insights},
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1251+ year = {2022},
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1253+}
1254+
1255+@article{rambachan2023credible,
1256+ author = {Rambachan, Ashesh and Roth, Jonathan},
1257+ title = {{A More Credible Approach to Parallel Trends}},
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1262+ year = {2023},
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1264+}
1265+
1266+@article{roth2023trending,
1267+ author = {Roth, Jonathan and Sant'Anna, Pedro H. C. and Bilinski, Alyssa and Poe, John},
1268+ title = {{What's trending in difference-in-differences? A synthesis of the recent econometrics literature}},
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1272+ pages = {2218--2244},
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1275+}
1276+
1277+@article{cameron2008bootstrap,
1278+ author = {Cameron, A. Colin and Gelbach, Jonah B. and Miller, Douglas L.},
1279+ title = {{Bootstrap-Based Improvements for Inference with Clustered Errors}},
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1286+}
1287+
1288+@article{cameron2011multiway,
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1290+ title = {{Robust Inference With Multiway Clustering}},
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1297+}
1298+
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1308+}
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1310+@article{bertrand2004howmuch,
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1323+ title = {{Consumer search and dynamic price dispersion: an application to gasoline markets}},
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1330+}
1331+
1332+@article{byrne2019coordinate,
1333+ author = {Byrne, David P. and de Roos, Nicolas},
1334+ title = {{Learning to Coordinate: A Study in Retail Gasoline}},
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1341+}
1342+
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1344+ author = {Assad, Stephanie and Clark, Robert and Ershov, Daniel and Xu, Lei},
1345+ title = {{Algorithmic Pricing and Competition: Empirical Evidence from the German Retail Gasoline Market}},
1346+ journal = {Journal of Political Economy},
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1350+ year = {2024},
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1353+
1354+@article{bailey1963repeatsales,
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1365+@article{han2016askingprice,
1366+ author = {Han, Lu and Strange, William C.},
1367+ title = {{What is the role of the asking price for a house?}},
1368+ journal = {Journal of Urban Economics},
1369+ volume = {93},
1370+ pages = {115--130},
1371+ year = {2016},
1372+ doi = {10.1016/j.jue.2016.03.008}
1373+}
1374+
1375+@article{anenberg2017timely,
1376+ author = {Anenberg, Elliot and Laufer, Steven},
1377+ title = {{A More Timely House Price Index}},
1378+ journal = {Review of Economics and Statistics},
1379+ volume = {99},
1380+ number = {4},
1381+ pages = {722--734},
1382+ year = {2017},
1383+ doi = {10.1162/REST_a_00634}
1384+}
1385+
1386+@article{lyons2019listprices,
1387+ author = {Lyons, Ronan C.},
1388+ title = {{Can list prices accurately capture housing price trends? Insights from extreme markets conditions}},
1389+ journal = {Finance Research Letters},
1390+ volume = {30},
1391+ pages = {228--232},
1392+ year = {2019},
1393+ doi = {10.1016/j.frl.2018.10.004}
1394+}
1395+
1396+@article{kurlat2015asymmetries,
1397+ author = {Kurlat, Pablo and Stroebel, Johannes},
1398+ title = {{Testing for Information Asymmetries in Real Estate Markets}},
1399+ journal = {Review of Financial Studies},
1400+ volume = {28},
1401+ number = {8},
1402+ pages = {2429--2461},
1403+ year = {2015},
1404+ doi = {10.1093/rfs/hhv028}
1405+}
1406+
1407+@article{gatzlaff1997selection,
1408+ author = {Gatzlaff, Dean H. and Haurin, Donald R.},
1409+ title = {{Sample Selection Bias and Repeat-Sales Index Estimates}},
1410+ journal = {Journal of Real Estate Finance and Economics},
1411+ volume = {14},
1412+ number = {1-2},
1413+ pages = {33--50},
1414+ year = {1997},
1415+ doi = {10.1023/a:1007763816289}
1416+}
1417+
1418+@article{knight2002listing,
1419+ author = {Knight, John R.},
1420+ title = {{Listing Price, Time on Market, and Ultimate Selling Price: Causes and Effects of Listing Price Changes}},
1421+ journal = {Real Estate Economics},
1422+ volume = {30},
1423+ number = {2},
1424+ pages = {213--237},
1425+ year = {2002},
1426+ doi = {10.1111/1540-6229.00038}
1427+}
1428+
1429+@article{horowitz1992listprice,
1430+ author = {Horowitz, Joel L.},
1431+ title = {{The role of the list price in housing markets: Theory and an econometric model}},
1432+ journal = {Journal of Applied Econometrics},
1433+ volume = {7},
1434+ number = {2},
1435+ pages = {115--129},
1436+ year = {1992},
1437+ doi = {10.1002/jae.3950070202}
1438+}
1439+
1440+@article{haurin2010listprices,
1441+ author = {Haurin, Donald R. and Haurin, Jessica L. and Nadauld, Taylor and Sanders, Anthony},
1442+ title = {{List Prices, Sale Prices and Marketing Time: An Application to U.S. Housing Markets}},
1443+ journal = {Real Estate Economics},
1444+ volume = {38},
1445+ number = {4},
1446+ pages = {659--685},
1447+ year = {2010},
1448+ doi = {10.1111/j.1540-6229.2010.00279.x}
1449+}
1450+
1451+@article{anglin2003tradeoff,
1452+ author = {Anglin, Paul M. and Rutherford, Ronald and Springer, Thomas M.},
1453+ title = {{The Trade-off Between the Selling Price of Residential Properties and Time-on-the-Market: The Impact of Price Setting}},
1454+ journal = {Journal of Real Estate Finance and Economics},
1455+ volume = {26},
1456+ number = {1},
1457+ pages = {95--111},
1458+ year = {2003},
1459+ doi = {10.1023/a:1021526332732}
1460+}
1461+
1462+@article{bils2009quality,
1463+ author = {Bils, Mark},
1464+ title = {{Do Higher Prices for New Goods Reflect Quality Growth or Inflation?*}},
1465+ journal = {Quarterly Journal of Economics},
1466+ volume = {124},
1467+ number = {2},
1468+ pages = {637--675},
1469+ year = {2009},
1470+ doi = {10.1162/qjec.2009.124.2.637}
1471+}
1472+
1473+@article{hill2013hedonic,
1474+ author = {Hill, Robert J.},
1475+ title = {{Hedonic Price Indexes for Residential Housing: A Survey, Evaluation and Taxonomy}},
1476+ journal = {Journal of Economic Surveys},
1477+ volume = {27},
1478+ number = {5},
1479+ pages = {879--914},
1480+ year = {2012},
1481+ doi = {10.1111/j.1467-6419.2012.00731.x}
1482+}
1483+
1484+@article{erickson2011experimental,
1485+ author = {Erickson, Tim and Pakes, Ariel},
1486+ title = {{An Experimental Component Index for the CPI: From Annual Computer Data to Monthly Data on Other Goods}},
1487+ journal = {American Economic Review},
1488+ volume = {101},
1489+ number = {5},
1490+ pages = {1707--1738},
1491+ year = {2011},
1492+ doi = {10.1257/aer.101.5.1707}
1493+}
1494+
1495+@article{benjamini1995fdr,
1496+ author = {Benjamini, Yoav and Hochberg, Yosef},
1497+ title = {{Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing}},
1498+ journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
1499+ volume = {57},
1500+ number = {1},
1501+ pages = {289--300},
1502+ year = {1995},
1503+ doi = {10.1111/j.2517-6161.1995.tb02031.x}
1504+}
1505+
1506+@article{romano2005stepwise,
1507+ author = {Romano, Joseph P. and Wolf, Michael},
1508+ title = {{Stepwise Multiple Testing as Formalized Data Snooping}},
1509+ journal = {Econometrica},
1510+ volume = {73},
1511+ number = {4},
1512+ pages = {1237--1282},
1513+ year = {2005},
1514+ doi = {10.1111/j.1468-0262.2005.00615.x}
1515+}
1516+
1517+@article{diebold1995comparing,
1518+ author = {Diebold, Francis X. and Mariano, Roberto S.},
1519+ title = {{Comparing Predictive Accuracy}},
1520+ journal = {Journal of Business \& Economic Statistics},
1521+ volume = {13},
1522+ number = {3},
1523+ pages = {253--263},
1524+ year = {1995},
1525+ doi = {10.1080/07350015.1995.10524599}
1526+}
1527+
1528+@article{giacomini2006tests,
1529+ author = {Giacomini, Raffaella and White, Halbert},
1530+ title = {{Tests of Conditional Predictive Ability}},
1531+ journal = {Econometrica},
1532+ volume = {74},
1533+ number = {6},
1534+ pages = {1545--1578},
1535+ year = {2006},
1536+ doi = {10.1111/j.1468-0262.2006.00718.x}
1537+}
1538+
1539+@article{brier1950verification,
1540+ author = {Brier, Glenn W.},
1541+ title = {{Verification of Forecasts Expressed in Terms of Probability}},
1542+ journal = {Monthly Weather Review},
1543+ volume = {78},
1544+ number = {1},
1545+ pages = {1--3},
1546+ year = {1950},
1547+ doi = {10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2}
1548+}
1549+
1550+@article{friedman2001greedy,
1551+ author = {Friedman, Jerome H.},
1552+ title = {{Greedy function approximation: A gradient boosting machine.}},
1553+ journal = {Annals of Statistics},
1554+ volume = {29},
1555+ number = {5},
1556+ pages = {1189--1232},
1557+ year = {2001},
1558+ doi = {10.1214/aos/1013203451}
1559+}
1560+
1561+@article{athey2019machine,
1562+ author = {Athey, Susan and Imbens, Guido W.},
1563+ title = {{Machine Learning Methods That Economists Should Know About}},
1564+ journal = {Annual Review of Economics},
1565+ volume = {11},
1566+ number = {1},
1567+ pages = {685--725},
1568+ year = {2019},
1569+ doi = {10.1146/annurev-economics-080217-053433}
1570+}
1571+
1572+@article{mullainathan2017machine,
1573+ author = {Mullainathan, Sendhil and Spiess, Jann},
1574+ title = {{Machine Learning: An Applied Econometric Approach}},
1575+ journal = {Journal of Economic Perspectives},
1576+ volume = {31},
1577+ number = {2},
1578+ pages = {87--106},
1579+ year = {2017},
1580+ doi = {10.1257/jep.31.2.87}
1581+}
1582+
1583+@book{harrell2015regression,
1584+ author = {Harrell, Frank E.},
1585+ title = {Regression Modeling Strategies},
1586+ year = {2015},
1587+ publisher = {Springer International Publishing},
1588+ doi = {10.1007/978-3-319-19425-7}
1589+}
1590+
1591+@article{uno2011cstatistics,
1592+ author = {Uno, Hajime and Cai, Tianxi and Pencina, Michael J. and D'Agostino, Ralph B. and Wei, L. J.},
1593+ title = {{On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data}},
1594+ journal = {Statistics in Medicine},
1595+ volume = {30},
1596+ number = {10},
1597+ pages = {1105--1117},
1598+ year = {2011},
1599+ doi = {10.1002/sim.4154}
1600+}
1601+
1602+@article{graf1999assessment,
1603+ author = {Graf, Erika and Schmoor, Claudia and Sauerbrei, Willi and Schumacher, Martin},
1604+ title = {{Assessment and comparison of prognostic classification schemes for survival data}},
1605+ journal = {Statistics in Medicine},
1606+ volume = {18},
1607+ number = {17-18},
1608+ pages = {2529--2545},
1609+ year = {1999},
1610+ doi = {10.1002/(SICI)1097-0258(19990915/30)18:17/18<2529::AID-SIM274>3.0.CO;2-5}
1611+}
1612+
1613+@incollection{court1939hedonic,
1614+ author = {Court, Andrew T.},
1615+ title = {{Hedonic Price Indexes with Automotive Examples}},
1616+ booktitle = {The Dynamics of Automobile Demand},
1617+ publisher = {General Motors Corporation},
1618+ address = {New York},
1619+ year = {1939},
1620+ url = {https://allengoodman.wayne.edu/Research/PUBS/Court_Hedonic.pdf},
1621+ note = {Scan hosted by A. C. Goodman (Wayne State University); see also Goodman (1998)}
1622+}
1623+
1624+@incollection{griliches1961hedonic,
1625+ author = {Griliches, Zvi},
1626+ title = {{Hedonic Price Indexes for Automobiles: An Econometric Analysis of Quality Change}},
1627+ booktitle = {The Price Statistics of the Federal Government},
1628+ editor = {{Price Statistics Review Committee}},
1629+ publisher = {National Bureau of Economic Research},
1630+ address = {New York},
1631+ year = {1961},
1632+ url = {https://www.nber.org/chapters/c6492}
1633+}
1634+
1635+@incollection{ohta1976automobile,
1636+ author = {Ohta, Makoto and Griliches, Zvi},
1637+ title = {{Automobile Prices Revisited: Extensions of the Hedonic Hypothesis}},
1638+ booktitle = {Household Production and Consumption},
1639+ editor = {Terleckyj, Nestor E.},
1640+ publisher = {National Bureau of Economic Research},
1641+ year = {1976},
1642+ url = {https://www.nber.org/books-and-chapters/household-production-and-consumption/automobile-prices-revisited-extensions-hedonic-hypothesis}
1643+}
1644+
1645+@article{kim1985lemons,
1646+ author = {Kim, Jae-Cheol},
1647+ title = {{The Market for ``Lemons'' Reconsidered: A Model of the Used Car Market with Asymmetric Information}},
1648+ journal = {American Economic Review},
1649+ volume = {75},
1650+ number = {4},
1651+ pages = {836--843},
1652+ year = {1985},
1653+ url = {https://ideas.repec.org/a/aea/aecrev/v75y1985i4p836-43.html}
1654+}
1655+
1656+@article{bond1982lemons,
1657+ author = {Bond, Eric W.},
1658+ title = {{A Direct Test of the ``Lemons'' Model: The Market for Used Pickup Trucks}},
1659+ journal = {American Economic Review},
1660+ volume = {72},
1661+ number = {4},
1662+ pages = {836--840},
1663+ year = {1982},
1664+ url = {https://ideas.repec.org/a/aea/aecrev/v72y1982i4p836-40.html}
1665+}
1666+
1667+@article{lazear1986clearance,
1668+ author = {Lazear, Edward P.},
1669+ title = {{Retail Pricing and Clearance Sales}},
1670+ journal = {American Economic Review},
1671+ volume = {76},
1672+ number = {1},
1673+ pages = {14--32},
1674+ year = {1986},
1675+ url = {https://ideas.repec.org/a/aea/aecrev/v76y1986i1p14-32.html}
1676+}
1677+
1678+@article{varian1980sales,
1679+ author = {Varian, Hal R.},
1680+ title = {{A Model of Sales}},
1681+ journal = {American Economic Review},
1682+ volume = {70},
1683+ number = {4},
1684+ pages = {651--659},
1685+ year = {1980},
1686+ url = {https://ideas.repec.org/a/aea/aecrev/v70y1980i4p651-59.html}
1687+}
1688+
1689+@article{engel1996border,
1690+ author = {Engel, Charles and Rogers, John H.},
1691+ title = {{How Wide Is the Border?}},
1692+ journal = {American Economic Review},
1693+ volume = {86},
1694+ number = {5},
1695+ pages = {1112--1125},
1696+ year = {1996},
1697+ url = {https://ideas.repec.org/a/aea/aecrev/v86y1996i5p1112-25.html}
1698+}
1699+
1700+@article{caseshiller1989efficiency,
1701+ author = {Case, Karl E. and Shiller, Robert J.},
1702+ title = {{The Efficiency of the Market for Single-Family Homes}},
1703+ journal = {American Economic Review},
1704+ volume = {79},
1705+ number = {1},
1706+ pages = {125--137},
1707+ year = {1989},
1708+ url = {https://ideas.repec.org/a/aea/aecrev/v79y1989i1p125-37.html}
1709+}
1710+
1711+@article{kiefer1988duration,
1712+ author = {Kiefer, Nicholas M.},
1713+ title = {{Economic Duration Data and Hazard Functions}},
1714+ journal = {Journal of Economic Literature},
1715+ volume = {26},
1716+ number = {2},
1717+ pages = {646--679},
1718+ year = {1988},
1719+ url = {https://ideas.repec.org/a/aea/jeclit/v26y1988i2p646-79.html}
1720+}
1721+
1722+@article{genesove1997equity,
1723+ author = {Genesove, David and Mayer, Christopher J.},
1724+ title = {{Equity and Time to Sale in the Real Estate Market}},
1725+ journal = {American Economic Review},
1726+ volume = {87},
1727+ number = {3},
1728+ pages = {255--269},
1729+ year = {1997},
1730+ url = {https://ideas.repec.org/a/aea/aecrev/v87y1997i3p255-69.html}
1731+}
1732+
1733+@inproceedings{ke2017lightgbm,
1734+ author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
1735+ title = {{LightGBM: A Highly Efficient Gradient Boosting Decision Tree}},
1736+ booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
1737+ year = {2017},
1738+ url = {https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html}
1739+}
1740+
1741+@inproceedings{lundberg2017shap,
1742+ author = {Lundberg, Scott M. and Lee, Su-In},
1743+ title = {{A Unified Approach to Interpreting Model Predictions}},
1744+ booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
1745+ year = {2017},
1746+ url = {https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html}
1747+}
1748+
1749+@inproceedings{yanes2010depreciation,
1750+ author = {Yanes, Ana},
1751+ title = {{Depreciation of Light Duty Road Vehicles: Evidence from the Canadian Red Book}},
1752+ booktitle = {Proceedings of the 45th Annual Conference of the Canadian Transportation Research Forum},
1753+ address = {Toronto},
1754+ year = {2010},
1755+ url = {https://ctrf.ca/wp-content/uploads/2014/07/47YanesDepreciationofLightDutyRoad.pdf},
1756+ note = {Transport Canada}
1757+}
1758+
1759+@techreport{statcan2022usedvehicles,
1760+ author = {Taylor, Lance and Mitchell, Taylor and O'Donnell, Gerry},
1761+ title = {{Measuring Price Change for Used Vehicles in the Canadian Consumer Price Index}},
1762+ institution = {Statistics Canada},
1763+ type = {Prices Analytical Series},
1764+ number = {62F0014M2022008},
1765+ year = {2022},
1766+ url = {https://www150.statcan.gc.ca/n1/pub/62f0014m/62f0014m2022008-eng.htm}
1767+}
1768+
1769+@misc{bls2026usedcars,
1770+ author = {{U.S. Bureau of Labor Statistics}},
1771+ title = {{Consumer Price Index Factsheet: Used Cars and Trucks}},
1772+ year = {2026},
1773+ howpublished = {Factsheet, last modified 20 May 2026},
1774+ url = {https://www.bls.gov/cpi/factsheets/used-cars-and-trucks.htm}
1775+}
1776+
1777+@misc{manheim2023methodology,
1778+ author = {{Manheim Consulting (Cox Automotive)}},
1779+ title = {{Summary Methodology for Manheim Used Vehicle Value Index}},
1780+ year = {2023},
1781+ howpublished = {Technical note, January 2023 rebasing},
1782+ url = {https://site.manheim.com/wp-content/uploads/sites/2/2023/07/Used-Vehicle-Summary-Methodology.pdf}
1783+}
1784+
1785+@misc{cox2022dayssupply,
1786+ author = {{Cox Automotive}},
1787+ title = {{Used-Vehicle Days' Supply Breaks 50; Average Listing Price Surpasses \$28,000}},
1788+ year = {2022},
1789+ howpublished = {Market Insights, 20 January 2022 (revised 17 February 2022)},
1790+ url = {https://www.coxautoinc.com/market-insights/used-vehicle-days-supply/}
1791+}
1792+
1793+@misc{cbb2026index,
1794+ author = {{Canadian Black Book}},
1795+ title = {{Used Vehicle Retention Index}},
1796+ year = {2026},
1797+ howpublished = {Web page, accessed 1 November 2026},
1798+ url = {https://www.canadianblackbook.com/black-book-index/}
1799+}
1800+
1801+@techreport{cbb2026depreciation,
1802+ author = {{Canadian Black Book and Fitch Ratings}},
1803+ title = {{Vehicle Depreciation Report 2026}},
1804+ institution = {Canadian Black Book},
1805+ year = {2026},
1806+ url = {https://www.canadianblackbook.com/wp-content/uploads/2026/08/CBB-Fitch-2026-Updated-2.pdf}
1807+}
1808+
1809+@misc{autotrader2024priceindex,
1810+ author = {{AutoTrader.ca}},
1811+ title = {{AutoTrader Price Index: June 2024}},
1812+ year = {2024},
1813+ howpublished = {Editorial report, 22 July 2024},
1814+ url = {https://www.autotrader.ca/editorial/car-news/autotrader-price-index-june-2024/}
1815+}
1816+
1817+@misc{cargurus2026imv,
1818+ author = {{CarGurus}},
1819+ title = {{About CarGurus: Instant Market Value}},
1820+ year = {2026},
1821+ howpublished = {Web page, accessed 1 November 2026},
1822+ url = {https://www.cargurus.ca/Cars/aboutCargurus.html}
1823+}
1824+
1825+@misc{iseecars2013pricedrops,
1826+ author = {{iSeeCars.com}},
1827+ title = {{iSeeCars.com Reveals How Used Car Shoppers Can Get a Better Deal by Waiting}},
1828+ year = {2013},
1829+ howpublished = {Study of 30 million used-car listings; press release},
1830+ url = {https://www.iseecars.com/wait-for-used-car-deals-2013-study}
1831+}
added literature/search_protocol.md +60 −0
@@ -0,0 +1,60 @@
1+# Search protocol — WP36 literature review
2+
3+Date of search: 2026-11-01 (single session). Agent: literature agent for UQO Working Paper 36, "The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior".
4+
5+## Objective
6+
7+Build a verified bibliography (60–90+ entries, quality over quantity) covering nine topic buckets (A–I, see `evidence_matrix.csv`), establish what is already known about used-car asking-price dynamics, markdowns, exit and liquidity-type measures (industry indices), and state precisely what WP36 adds.
8+
9+## Databases and tools
10+
11+| Source | Use | Access |
12+|---|---|---|
13+| Crossref REST API (`https://api.crossref.org/works/{doi}`) | Primary verification of every DOI: title, authors, container, year, volume, pages | `curl`/Python `urllib`, sequential, 1 req/s, User-Agent with mailto (first parallel attempt was rate-limited HTTP 429 and re-run sequentially) |
14+| Crossref bibliographic search (`/works?query.bibliographic=`) | Recover correct DOIs when a candidate DOI resolved to the wrong record (10 cases) or returned 404 (13 cases) | same |
15+| OpenAlex API (`/works?search=` and `filter=title.search:,publication_year:`) | Confirm venue/volume/pages for pre-DOI articles (Kim 1985, Varian 1980, Kiefer 1988, Thomadsen 2005) | same |
16+| Semantic Scholar Graph API | Confirm Engel & Rogers 1996 and Case & Shiller 1989 AER records (rate-limited; others fell back to IDEAS/RePEc) | same |
17+| IDEAS/RePEc handle pages (`ideas.repec.org/a/aea/aecrev/...`) | Existence check (HTTP 200 vs 404) for AER/JEL classics without DOI: Bond 1982, Lazear 1986, Genesove & Mayer 1997, Kim 1985, Varian 1980, Engel & Rogers 1996, Case & Shiller 1989, Kiefer 1988 | `curl -L` |
18+| NBER chapter pages | Griliches 1961 (`nber.org/chapters/c6492`), Ohta & Griliches 1976 | WebFetch |
19+| Publisher / agency pages | Statistics Canada 62F0014M2022008; BLS CPI factsheet (used cars and trucks); Manheim methodology PDF (text extracted with `pdftotext`); Cox Automotive days' supply; Canadian Black Book index page and 2026 depreciation report; AutoTrader.ca Price Index June 2024; iSeeCars 2013 study; NeurIPS 2017 proceedings pages (LightGBM, SHAP); CTRF proceedings PDF (Yanes 2010) | WebFetch / `curl` |
20+| Web search (standard and extended modes) | Industry indices and methodology documents; search for academic work on used-car markdowns / time on lot / "liquidity index"; Canadian and Quebec evidence | WebSearch |
21+
22+## Queries run (web search)
23+
24+1. `Manheim Used Vehicle Value Index methodology mix mileage seasonally adjusted`
25+2. `Canadian Black Book used vehicle retention index Canada methodology`
26+3. `Statistics Canada consumer price index used vehicles introduced methodology J.D. Power data 2022`
27+4. `BLS CPI used cars and trucks methodology J.D. Power data factsheet`
28+5. `used car dealer price reductions time on lot listing data empirical study markdown hazard` (extended)
29+6. `Cox Automotive days' supply used vehicle vAuto methodology; CarGurus used car price index; iSeeCars used car price study`
30+7. `CarGurus Used Car Price Index methodology; iSeeCars used car price drops study methodology; AutoTrader.ca price index report used vehicle Canada`
31+8. `Court 1939 "Hedonic price indexes with automotive examples" The Dynamics of Automobile Demand General Motors pages`
32+9. `"used car" "liquidity index" OR "liquidity score" market days to turn dealership inventory academic paper` (extended)
33+10. `Kraft Rao odometer thresholds used car time on lot Texas title data paper` (no usable result; item discarded)
34+11. `CarGurus "Used Car Price Index" OR "Price Trends" methodology instant market value listings`
35+12. `iSeeCars study used cars price drops number of price cuts days to sell methodology`
36+13. `Canada used vehicle market depreciation study economics Quebec "used vehicle" prices dealers research paper OR working paper` (extended)
37+14. JSTOR/AER record confirmation queries for Bond 1982, Lazear 1986, Genesove & Mayer 1997.
38+
39+## Crossref / OpenAlex query batches
40+
41+- Batch 1 (85 candidate DOIs, buckets A–D), Batch 2 (97 candidate DOIs, buckets E–I plus extras), Batch 3 (57 corrected/new DOIs). Bibliographic searches: 31 + 32 + 11 titles. OpenAlex: 12 + 10 titles. Semantic Scholar: 7 + 6 titles.
42+- Logs kept in the session scratch directory (`/tmp/wp36lit/out*.tsv`, `s*.tsv`, `cache.json`); the final verification string for each entry is in the `verified_via` column of `evidence_matrix.csv`.
43+
44+## Inclusion criteria
45+
46+1. Peer-reviewed article, handbook chapter, book, or NBER working paper whose metadata (title, authors, year, venue) were confirmed by Crossref, OpenAlex, Semantic Scholar, IDEAS/RePEc or the publisher page; or an official statistical-agency / industry methodology document fetched directly.
47+2. Direct relevance to at least one WP36 topic bucket (hedonic pricing of vehicles; durable-goods / used-car markets; dealer inventory and dynamic pricing; search, dispersion and liquidity; survival analysis; spatial competition and regional integration; identification of strategic interaction and event-study methods; price indices and listing-vs-transaction measurement; multiple testing and model evaluation).
48+3. Preference for the canonical, most-cited statement of a result (e.g. the published AER version over the NBER version where both exist, except Porter & Sattler 1999 which was never published).
49+4. Items listed in the brief with uncertain identity ("Jerath/Jim??", "Hyndman?", "Hortaçsu?" car-dealer markdown, "Reis & Silva?", "Dunne & Mian?", "Kraft & Rao") were searched; those that could not be identified or verified were discarded rather than guessed.
50+
51+## Exclusion / safety
52+
53+- Known retracted items (Kim–Muhn–Nikolaev 2024; Corbet et al. 2019 IRFA) are not cited and did not surface.
54+- Candidate DOIs that resolved to unrelated records were replaced only when Crossref bibliographic search returned an exact title/author/venue match; otherwise the item was dropped (e.g. Thomadsen 2005 RAND, confirmed by OpenAlex but with no DOI and no resolvable RePEc handle, was dropped).
55+- Grey literature (Manheim, Cox, CBB, AutoTrader.ca, CarGurus, iSeeCars) is included only as documentation of existing indices, flagged as non-peer-reviewed in `notes`.
56+
57+## Output checks
58+
59+- `references.bib` parsed with a regex count of `@type{key,` blocks; duplicate keys checked; all characters ASCII.
60+- Every `key` in `evidence_matrix.csv` checked to exist in `references.bib` and vice versa (script in session log; result reported in the final memo to the caller).
added paper/.latexmkrc +5 −0
@@ -0,0 +1,5 @@
1+$pdf_mode = 1;
2+$pdflatex = 'pdflatex -interaction=nonstopmode -halt-on-error -synctex=1 %O %S';
3+$bibtex_use = 2;
4+$clean_ext = 'synctex.gz run.xml bbl bcf fdb_latexmk fls log aux out toc lof lot blg';
5+@default_files = ('main.tex');
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@@ -0,0 +1,11 @@
1+.PHONY: all clean distclean
2+
3+all:
4+ latexmk
5+
6+clean:
7+ latexmk -c
8+
9+distclean:
10+ latexmk -C
11+ rm -f *.synctex.gz *.run.xml *.bbl *.bcf
added paper/appendix/appendix.tex +19 −0
@@ -0,0 +1,19 @@
1+\begin{appendices}
2+\section{Data-generating process and quality audit}\label{app:data}
3+
4+\paragraph{Synchronisation and exit rule.} Each cycle re-reads every source; a listing absent from its source for two consecutive cycles is declared inactive by the platform. The research definition of exit (seven days of absence before the snapshot) is stricter: the per-source ``reactivation share'' (exit events later reversed, divided by exit events) is 0.44 for Otogo, 0.04 for AutoUsagée, below 0.1 for most dealer websites, and 0.52--0.94 for the four connectors excluded from the dynamic sample. Listings whose last observation falls in the final seven days of the window are right-censored.
5+
6+\paragraph{Sustained price paths.} Of 428{,}484 price-log rows, 2{,}257 record a withdrawn price; 121{,}239 are price changes, of which 11{,}479 occur less than 24 hours after the previous change. The transient rule (a level shorter than 48 hours that reverts to the preceding level is deleted) removes the alternating-feed artefacts illustrated by a listing that alternated between \$37{,}840 and \$40{,}794 twice a day for four weeks; 70 listings with three or more transients are excluded from markdown analyses. Robustness to a 24-hour and a 72-hour rule is reported in Section~\ref{sec:robust}.
7+
8+\paragraph{Identifier stability.} Within Otogo, 1{,}059 of 63{,}821 VINs (1.7 percent) appear under more than one listing identifier; inspection shows these are the same vehicle re-listed by a different dealer after a transfer, not identifier churn. Dealer websites assign stable identifiers.
9+
10+\paragraph{Governance.} The snapshot excludes the platform's user table; VINs, URLs, images, free text and exact coordinates are retained only in the restricted raw and interim layers, never in released tables, which carry hashed listing and dealer identifiers and coordinates rounded to 0.01 degree.
11+
12+\section{Preregistration and amendments}\label{app:prereg}
13+
14+The research questions and the analysis plan (v1.0, 2026-11-01) were written after the data audit and before estimation. Amendments recorded during execution: (D-010) make--model and make--model--trim quality fixed effects instead of make--model--year, because model-year effects absorb vehicle age within an 81-day window; (D-011) linear-probability estimation of the competitor event study with high-dimensional fixed effects; (D-014) per-source left-censoring after the portal start-up stock (33{,}224 listings) was found to be treated as entries; (D-015) exclusion of panel weeks 0--1 from the liquidity index. The complete decision log, experiment log and claims ledger are distributed with the replication package.
15+
16+\section{Additional estimates}\label{app:extra}
17+
18+Additional coefficient tables (full spline bases, listing-age bins, powertrain and body effects, the log-logistic accelerated-failure-time model, the 10-km and 50-km competitor sets, the dealer-by-weekday specifications, LightGBM feature importances and calibration tables) are provided as machine-readable files in the replication package (\texttt{results/}).
19+\end{appendices}
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1+% ============================================================================
2+% UQO Working Paper No. 36 — The Hidden Dynamics of Used-Car Markets
3+% ============================================================================
4+\documentclass[12pt,letterpaper]{article}
5+\usepackage[utf8]{inputenc}
6+\usepackage[T1]{fontenc}
7+\usepackage[english]{babel}
8+\usepackage[letterpaper,top=1in,bottom=1in,left=1in,right=1in]{geometry}
9+\usepackage{mathptmx}
10+\usepackage{microtype}
11+\usepackage{setspace}
12+\onehalfspacing
13+\usepackage{amsmath,amssymb,amsthm}
14+\usepackage{booktabs}
15+\usepackage{tabularx}
16+\usepackage{multirow}
17+\usepackage{threeparttable}
18+\usepackage{longtable}
19+\usepackage{rotating}
20+\usepackage{siunitx}
21+\sisetup{input-symbols = {()}, table-format = -1.3, parse-numbers = false}
22+\usepackage{graphicx}
23+\usepackage{subcaption}
24+\usepackage{float}
25+\usepackage[dvipsnames]{xcolor}
26+\usepackage[colorlinks=true,linkcolor=NavyBlue,citecolor=NavyBlue,urlcolor=NavyBlue,breaklinks=true]{hyperref}
27+\usepackage[authoryear,round,semicolon]{natbib}
28+\bibliographystyle{aer}
29+\usepackage[font=small,labelfont=bf,labelsep=period,justification=justified,singlelinecheck=false]{caption}
30+\usepackage{fancyhdr}
31+\pagestyle{fancy}
32+\fancyhf{}
33+\renewcommand{\headrulewidth}{0pt}
34+\fancyfoot[C]{\thepage}
35+\usepackage{titlesec}
36+\titleformat{\section}{\large\bfseries}{\thesection.}{0.5em}{}
37+\titleformat{\subsection}{\normalsize\bfseries}{\thesubsection.}{0.5em}{}
38+\titleformat{\subsubsection}{\normalsize\itshape}{\thesubsubsection.}{0.5em}{}
39+\usepackage[toc,page]{appendix}
40+\usepackage{enumitem}
41+\setlist{nosep,leftmargin=*}
42+\newcommand{\tabsize}{\footnotesize}
43+
44+% ============================================================================
45+% METADATA
46+% ============================================================================
47+\newcommand{\WPnumber}{36}
48+\newcommand{\WPtitle}{The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior}
49+\newcommand{\WPsubtitle}{Evidence from a Daily Panel of 273{,}265 Quebec Listings}
50+\newcommand{\WPdate}{November 2026}
51+\newcommand{\WPversion}{1.0}
52+\newcommand{\WPabstract}{\input{sections/abstract}}
53+\newcommand{\WPkeywords}{used cars, asking prices, hedonic pricing, markdown hazard, listing survival, dealer competition, market liquidity index, Quebec}
54+\newcommand{\WPjel}{D22, D83, L11, L62, R32, C41}
55+\newcommand{\WPauthor}{Simon-Pierre Boucher}
56+\newcommand{\WPaffiliation}{Département des sciences administratives\\Université du Québec en Outaouais}
57+\newcommand{\WPemail}{simon-pierre.boucher@uqo.ca}
58+\newcommand{\WPaddress}{Gatineau -- Pavillon Alexandre-Taché\\283, boulevard Alexandre-Taché\\Gatineau, Québec, Canada J9A 1L8}
59+\hypersetup{pdftitle={The Hidden Dynamics of Used-Car Markets: Price Discovery, Inventory Liquidity, and Strategic Dealer Behavior}, pdfauthor={Simon-Pierre Boucher}}
60+
61+\begin{document}
62+\input{sections/titlepage}
63+\input{sections/introduction}
64+\input{sections/background}
65+\input{sections/literature}
66+\input{sections/data}
67+\input{sections/methodology}
68+\input{sections/results_prices}
69+\input{sections/results_dynamics}
70+\input{sections/results_amli}
71+\input{sections/robustness}
72+\input{sections/conclusion}
73+\newpage
74+\bibliography{references}
75+\input{appendix/appendix}
76+\end{document}
added paper/references.bib +1831 −0
@@ -0,0 +1,1831 @@
1+@article{rosen1974hedonic,
2+ author = {Rosen, Sherwin},
3+ title = {{Hedonic Prices and Implicit Markets: Product Differentiation in Pure Competition}},
4+ journal = {Journal of Political Economy},
5+ volume = {82},
6+ number = {1},
7+ pages = {34--55},
8+ year = {1974},
9+ doi = {10.1086/260169}
10+}
11+
12+@article{lancaster1966newapproach,
13+ author = {Lancaster, Kelvin J.},
14+ title = {{A New Approach to Consumer Theory}},
15+ journal = {Journal of Political Economy},
16+ volume = {74},
17+ number = {2},
18+ pages = {132--157},
19+ year = {1966},
20+ doi = {10.1086/259131}
21+}
22+
23+@article{pakes2003hedonic,
24+ author = {Pakes, Ariel},
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26+ journal = {American Economic Review},
27+ volume = {93},
28+ number = {5},
29+ pages = {1578--1596},
30+ year = {2003},
31+ doi = {10.1257/000282803322655455}
32+}
33+
34+@book{triplett2006handbook,
35+ author = {Triplett, Jack},
36+ title = {Handbook on Hedonic Indexes and Quality Adjustments in Price Indexes},
37+ year = {2006},
38+ publisher = {OECD Publishing},
39+ address = {Paris},
40+ doi = {10.1787/9789264028159-en}
41+}
42+
43+@article{bajari2005hedonic,
44+ author = {Bajari, Patrick and Benkard, C. Lanier},
45+ title = {{Demand Estimation with Heterogeneous Consumers and Unobserved Product Characteristics: A Hedonic Approach}},
46+ journal = {Journal of Political Economy},
47+ volume = {113},
48+ number = {6},
49+ pages = {1239--1276},
50+ year = {2005},
51+ doi = {10.1086/498586}
52+}
53+
54+@article{ekeland2004hedonic,
55+ author = {Ekeland, Ivar and Heckman, James J. and Nesheim, Lars},
56+ title = {{Identification and Estimation of Hedonic Models}},
57+ journal = {Journal of Political Economy},
58+ volume = {112},
59+ number = {S1},
60+ pages = {S60--S109},
61+ year = {2004},
62+ doi = {10.1086/379947}
63+}
64+
65+@article{silver2007hedonicimputation,
66+ author = {Silver, Mick and Heravi, Saeed},
67+ title = {{The Difference Between Hedonic Imputation Indexes and Time Dummy Hedonic Indexes}},
68+ journal = {Journal of Business \& Economic Statistics},
69+ volume = {25},
70+ number = {2},
71+ pages = {239--246},
72+ year = {2007},
73+ doi = {10.1198/073500106000000486}
74+}
75+
76+@incollection{diewert2003hedonic,
77+ author = {Diewert, W. Erwin},
78+ title = {{Hedonic Regressions: A Consumer Theory Approach}},
79+ booktitle = {Scanner Data and Price Indexes},
80+ editor = {Feenstra, Robert C. and Shapiro, Matthew D.},
81+ pages = {317--348},
82+ year = {2003},
83+ publisher = {University of Chicago Press},
84+ doi = {10.7208/chicago/9780226239668.003.0012}
85+}
86+
87+@article{dreyfus1995timepreference,
88+ author = {Dreyfus, Mark K. and Viscusi, W. Kip},
89+ title = {{Rates of Time Preference and Consumer Valuations of Automobile Safety and Fuel Efficiency}},
90+ journal = {Journal of Law and Economics},
91+ volume = {38},
92+ number = {1},
93+ pages = {79--105},
94+ year = {1995},
95+ doi = {10.1086/467326}
96+}
97+
98+@article{berry1995automobile,
99+ author = {Berry, Steven and Levinsohn, James and Pakes, Ariel},
100+ title = {{Automobile Prices in Market Equilibrium}},
101+ journal = {Econometrica},
102+ volume = {63},
103+ number = {4},
104+ pages = {841},
105+ year = {1995},
106+ doi = {10.2307/2171802}
107+}
108+
109+@article{goodman1998court,
110+ author = {Goodman, Allen C},
111+ title = {{Andrew Court and the Invention of Hedonic Price Analysis}},
112+ journal = {Journal of Urban Economics},
113+ volume = {44},
114+ number = {2},
115+ pages = {291--298},
116+ year = {1998},
117+ doi = {10.1006/juec.1997.2071}
118+}
119+
120+@article{akerlof1970lemons,
121+ author = {Akerlof, George A.},
122+ title = {{The Market for "Lemons": Quality Uncertainty and the Market Mechanism}},
123+ journal = {Quarterly Journal of Economics},
124+ volume = {84},
125+ number = {3},
126+ pages = {488},
127+ year = {1970},
128+ doi = {10.2307/1879431}
129+}
130+
131+@article{hendel1999adverse,
132+ author = {Hendel, Igal and Lizzeri, Alessandro},
133+ title = {{Adverse Selection in Durable Goods Markets}},
134+ journal = {American Economic Review},
135+ volume = {89},
136+ number = {5},
137+ pages = {1097--1115},
138+ year = {1999},
139+ doi = {10.1257/aer.89.5.1097}
140+}
141+
142+@article{hendel2002leasing,
143+ author = {Hendel, Igal and Lizzeri, Alessandro},
144+ title = {{The Role of Leasing under Adverse Selection}},
145+ journal = {Journal of Political Economy},
146+ volume = {110},
147+ number = {1},
148+ pages = {113--143},
149+ year = {2002},
150+ doi = {10.1086/324387}
151+}
152+
153+@article{stolyarov2002turnover,
154+ author = {Stolyarov, Dmitriy},
155+ title = {{Turnover of Used Durables in a Stationary Equilibrium: Are Older Goods Traded More?}},
156+ journal = {Journal of Political Economy},
157+ volume = {110},
158+ number = {6},
159+ pages = {1390--1413},
160+ year = {2002},
161+ doi = {10.1086/343745}
162+}
163+
164+@article{gavazza2014usedcar,
165+ author = {Gavazza, Alessandro and Lizzeri, Alessandro and Roketskiy, Nikita},
166+ title = {{A Quantitative Analysis of the Used-Car Market}},
167+ journal = {American Economic Review},
168+ volume = {104},
169+ number = {11},
170+ pages = {3668--3700},
171+ year = {2014},
172+ doi = {10.1257/aer.104.11.3668}
173+}
174+
175+@article{rust1985durable,
176+ author = {Rust, John},
177+ title = {{Stationary Equilibrium in a Market for Durable Assets}},
178+ journal = {Econometrica},
179+ volume = {53},
180+ number = {4},
181+ pages = {783},
182+ year = {1985},
183+ doi = {10.2307/1912654}
184+}
185+
186+@techreport{porter1999usedtrade,
187+ author = {Porter, Robert H. and Sattler, Peter},
188+ title = {Patterns of Trade in the Market for Used Durables: Theory and Evidence},
189+ number = {7149},
190+ year = {1999},
191+ institution = {National Bureau of Economic Research},
192+ type = {NBER Working Paper},
193+ doi = {10.3386/w7149}
194+}
195+
196+@article{engers2009annualmiles,
197+ author = {Engers, Maxim and Hartmann, Monica and Stern, Steven},
198+ title = {{Annual miles drive used car prices}},
199+ journal = {Journal of Applied Econometrics},
200+ volume = {24},
201+ number = {1},
202+ pages = {1--33},
203+ year = {2009},
204+ doi = {10.1002/jae.1034}
205+}
206+
207+@article{emons2009usedcars,
208+ author = {Emons, Winand and Sheldon, George},
209+ title = {{The market for used cars: new evidence of the lemons phenomenon}},
210+ journal = {Applied Economics},
211+ volume = {41},
212+ number = {22},
213+ pages = {2867--2885},
214+ year = {2009},
215+ doi = {10.1080/00036840802277332}
216+}
217+
218+@article{genesove1993adverse,
219+ author = {Genesove, David},
220+ title = {{Adverse Selection in the Wholesale Used Car Market}},
221+ journal = {Journal of Political Economy},
222+ volume = {101},
223+ number = {4},
224+ pages = {644--665},
225+ year = {1993},
226+ doi = {10.1086/261891}
227+}
228+
229+@article{lewis2011asymmetric,
230+ author = {Lewis, Gregory},
231+ title = {{Asymmetric Information, Adverse Selection and Online Disclosure: The Case of eBay Motors}},
232+ journal = {American Economic Review},
233+ volume = {101},
234+ number = {4},
235+ pages = {1535--1546},
236+ year = {2011},
237+ doi = {10.1257/aer.101.4.1535}
238+}
239+
240+@article{adams2011vettes,
241+ author = {Adams, Christopher P. and Hosken, Laura and Newberry, Peter W.},
242+ title = {{Vettes and lemons on eBay}},
243+ journal = {Quantitative Marketing and Economics},
244+ volume = {9},
245+ number = {2},
246+ pages = {109--127},
247+ year = {2011},
248+ doi = {10.1007/s11129-011-9095-2}
249+}
250+
251+@article{peterson2014adverse,
252+ author = {Peterson, Jonathan R. and Schneider, Henry S.},
253+ title = {{Adverse selection in the used-car market: evidence from purchase and repair patterns in the Consumer Expenditure Survey}},
254+ journal = {RAND Journal of Economics},
255+ volume = {45},
256+ number = {1},
257+ pages = {140--154},
258+ year = {2014},
259+ doi = {10.1111/1756-2171.12045}
260+}
261+
262+@article{peterson2017beautiful,
263+ author = {Peterson, Jonathan R. and Schneider, Henry S.},
264+ title = {{Beautiful Lemons: Adverse Selection in Durable-Goods Markets with Sorting}},
265+ journal = {Management Science},
266+ volume = {63},
267+ number = {9},
268+ pages = {3111--3127},
269+ year = {2017},
270+ doi = {10.1287/mnsc.2016.2495}
271+}
272+
273+@article{busse2013gasoline,
274+ author = {Busse, Meghan R and Knittel, Christopher R and Zettelmeyer, Florian},
275+ title = {{Are Consumers Myopic? Evidence from New and Used Car Purchases}},
276+ journal = {American Economic Review},
277+ volume = {103},
278+ number = {1},
279+ pages = {220--256},
280+ year = {2013},
281+ doi = {10.1257/aer.103.1.220}
282+}
283+
284+@article{sallee2016fueleconomy,
285+ author = {Sallee, James M. and West, Sarah E. and Fan, Wei},
286+ title = {{Do consumers recognize the value of fuel economy? Evidence from used car prices and gasoline price fluctuations}},
287+ journal = {Journal of Public Economics},
288+ volume = {135},
289+ pages = {61--73},
290+ year = {2016},
291+ doi = {10.1016/j.jpubeco.2016.01.003}
292+}
293+
294+@article{jacobsen2015scrappage,
295+ author = {Jacobsen, Mark R. and van Benthem, Arthur A.},
296+ title = {{Vehicle Scrappage and Gasoline Policy}},
297+ journal = {American Economic Review},
298+ volume = {105},
299+ number = {3},
300+ pages = {1312--1338},
301+ year = {2015},
302+ doi = {10.1257/aer.20130935}
303+}
304+
305+@article{larsen2021bargaining,
306+ author = {Larsen, Bradley J},
307+ title = {{The Efficiency of Real-World Bargaining: Evidence from Wholesale Used-Auto Auctions}},
308+ journal = {Review of Economic Studies},
309+ volume = {88},
310+ number = {2},
311+ pages = {851--882},
312+ year = {2021},
313+ doi = {10.1093/restud/rdaa007}
314+}
315+
316+@article{kooreman2006anomalies,
317+ author = {Kooreman, Peter and Haan, Marco A.},
318+ title = {{Price Anomalies in the Used Car Market}},
319+ journal = {De Economist},
320+ volume = {154},
321+ number = {1},
322+ pages = {41--62},
323+ year = {2006},
324+ doi = {10.1007/s10645-006-6485-z}
325+}
326+
327+@article{lacetera2012heuristic,
328+ author = {Lacetera, Nicola and Pope, Devin G and Sydnor, Justin R},
329+ title = {{Heuristic Thinking and Limited Attention in the Car Market}},
330+ journal = {American Economic Review},
331+ volume = {102},
332+ number = {5},
333+ pages = {2206--2236},
334+ year = {2012},
335+ doi = {10.1257/aer.102.5.2206}
336+}
337+
338+@article{biglaiser2020intermediaries,
339+ author = {Biglaiser, Gary and Li, Fei and Murry, Charles and Zhou, Yiyi},
340+ title = {{Intermediaries and product quality in used car markets}},
341+ journal = {RAND Journal of Economics},
342+ volume = {51},
343+ number = {3},
344+ pages = {905--933},
345+ year = {2020},
346+ doi = {10.1111/1756-2171.12344}
347+}
348+
349+@article{biglaiser1993middlemen,
350+ author = {Biglaiser, Gary},
351+ title = {{Middlemen as Experts}},
352+ journal = {RAND Journal of Economics},
353+ volume = {24},
354+ number = {2},
355+ pages = {212},
356+ year = {1993},
357+ doi = {10.2307/2555758}
358+}
359+
360+@article{haan2010internetregional,
361+ author = {Haan, Marco A. and de Boer, Henk-Wim},
362+ title = {{Has the Internet Eliminated Regional Price Differences? Evidence from the Used Car Market}},
363+ journal = {De Economist},
364+ volume = {158},
365+ number = {4},
366+ pages = {373--386},
367+ year = {2010},
368+ doi = {10.1007/s10645-010-9151-4}
369+}
370+
371+@article{pesendorfer2002retailsales,
372+ author = {Pesendorfer, Martin},
373+ title = {{Retail Sales: A Study of Pricing Behavior in Supermarkets}},
374+ journal = {Journal of Business},
375+ volume = {75},
376+ number = {1},
377+ pages = {33--66},
378+ year = {2002},
379+ doi = {10.1086/323504}
380+}
381+
382+@article{nakamura2008fivefacts,
383+ author = {Nakamura, Emi and Steinsson, J{\'o}n},
384+ title = {{Five Facts about Prices: A Reevaluation of Menu Cost Models}},
385+ journal = {Quarterly Journal of Economics},
386+ volume = {123},
387+ number = {4},
388+ pages = {1415--1464},
389+ year = {2008},
390+ doi = {10.1162/qjec.2008.123.4.1415}
391+}
392+
393+@article{aguirregabiria1999markups,
394+ author = {Aguirregabiria, Victor},
395+ title = {{The Dynamics of Markups and Inventories in Retailing Firms}},
396+ journal = {Review of Economic Studies},
397+ volume = {66},
398+ number = {2},
399+ pages = {275--308},
400+ year = {1999},
401+ doi = {10.1111/1467-937X.00088}
402+}
403+
404+@article{cho2010flatrental,
405+ author = {Cho, Sungjin and Rust, John},
406+ title = {{The Flat Rental Puzzle}},
407+ journal = {Review of Economic Studies},
408+ volume = {77},
409+ number = {2},
410+ pages = {560--594},
411+ year = {2010},
412+ doi = {10.1111/j.1467-937x.2009.00556.x}
413+}
414+
415+@article{zettelmeyer2006internet,
416+ author = {Zettelmeyer, Florian and Scott Morton, Fiona and Silva-Risso, Jorge},
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424+}
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446+}
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457+}
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578+}
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580+@article{lach2002dispersion,
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589+}
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591+@incollection{baye2006dispersion,
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601+}
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612+}
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623+}
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625+@article{duffie2005otc,
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634+}
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680+@article{carrillo2012search,
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702+@incollection{han2015microstructure,
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1253+}
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1264+}
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1297+}
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1308+}
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1622+}
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1628+ editor = {{Price Statistics Review Committee}},
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1633+}
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1638+ booktitle = {Household Production and Consumption},
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1643+}
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1654+}
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1658+ title = {{A Direct Test of the ``Lemons'' Model: The Market for Used Pickup Trucks}},
1659+ journal = {American Economic Review},
1660+ volume = {72},
1661+ number = {4},
1662+ pages = {836--840},
1663+ year = {1982},
1664+ url = {https://ideas.repec.org/a/aea/aecrev/v72y1982i4p836-40.html}
1665+}
1666+
1667+@article{lazear1986clearance,
1668+ author = {Lazear, Edward P.},
1669+ title = {{Retail Pricing and Clearance Sales}},
1670+ journal = {American Economic Review},
1671+ volume = {76},
1672+ number = {1},
1673+ pages = {14--32},
1674+ year = {1986},
1675+ url = {https://ideas.repec.org/a/aea/aecrev/v76y1986i1p14-32.html}
1676+}
1677+
1678+@article{varian1980sales,
1679+ author = {Varian, Hal R.},
1680+ title = {{A Model of Sales}},
1681+ journal = {American Economic Review},
1682+ volume = {70},
1683+ number = {4},
1684+ pages = {651--659},
1685+ year = {1980},
1686+ url = {https://ideas.repec.org/a/aea/aecrev/v70y1980i4p651-59.html}
1687+}
1688+
1689+@article{engel1996border,
1690+ author = {Engel, Charles and Rogers, John H.},
1691+ title = {{How Wide Is the Border?}},
1692+ journal = {American Economic Review},
1693+ volume = {86},
1694+ number = {5},
1695+ pages = {1112--1125},
1696+ year = {1996},
1697+ url = {https://ideas.repec.org/a/aea/aecrev/v86y1996i5p1112-25.html}
1698+}
1699+
1700+@article{caseshiller1989efficiency,
1701+ author = {Case, Karl E. and Shiller, Robert J.},
1702+ title = {{The Efficiency of the Market for Single-Family Homes}},
1703+ journal = {American Economic Review},
1704+ volume = {79},
1705+ number = {1},
1706+ pages = {125--137},
1707+ year = {1989},
1708+ url = {https://ideas.repec.org/a/aea/aecrev/v79y1989i1p125-37.html}
1709+}
1710+
1711+@article{kiefer1988duration,
1712+ author = {Kiefer, Nicholas M.},
1713+ title = {{Economic Duration Data and Hazard Functions}},
1714+ journal = {Journal of Economic Literature},
1715+ volume = {26},
1716+ number = {2},
1717+ pages = {646--679},
1718+ year = {1988},
1719+ url = {https://ideas.repec.org/a/aea/jeclit/v26y1988i2p646-79.html}
1720+}
1721+
1722+@article{genesove1997equity,
1723+ author = {Genesove, David and Mayer, Christopher J.},
1724+ title = {{Equity and Time to Sale in the Real Estate Market}},
1725+ journal = {American Economic Review},
1726+ volume = {87},
1727+ number = {3},
1728+ pages = {255--269},
1729+ year = {1997},
1730+ url = {https://ideas.repec.org/a/aea/aecrev/v87y1997i3p255-69.html}
1731+}
1732+
1733+@inproceedings{ke2017lightgbm,
1734+ author = {Ke, Guolin and Meng, Qi and Finley, Thomas and Wang, Taifeng and Chen, Wei and Ma, Weidong and Ye, Qiwei and Liu, Tie-Yan},
1735+ title = {{LightGBM: A Highly Efficient Gradient Boosting Decision Tree}},
1736+ booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
1737+ year = {2017},
1738+ url = {https://proceedings.neurips.cc/paper/2017/hash/6449f44a102fde848669bdd9eb6b76fa-Abstract.html}
1739+}
1740+
1741+@inproceedings{lundberg2017shap,
1742+ author = {Lundberg, Scott M. and Lee, Su-In},
1743+ title = {{A Unified Approach to Interpreting Model Predictions}},
1744+ booktitle = {Advances in Neural Information Processing Systems 30 (NIPS 2017)},
1745+ year = {2017},
1746+ url = {https://proceedings.neurips.cc/paper/2017/hash/8a20a8621978632d76c43dfd28b67767-Abstract.html}
1747+}
1748+
1749+@inproceedings{yanes2010depreciation,
1750+ author = {Yanes, Ana},
1751+ title = {{Depreciation of Light Duty Road Vehicles: Evidence from the Canadian Red Book}},
1752+ booktitle = {Proceedings of the 45th Annual Conference of the Canadian Transportation Research Forum},
1753+ address = {Toronto},
1754+ year = {2010},
1755+ url = {https://ctrf.ca/wp-content/uploads/2014/07/47YanesDepreciationofLightDutyRoad.pdf},
1756+ note = {Transport Canada}
1757+}
1758+
1759+@techreport{statcan2022usedvehicles,
1760+ author = {Taylor, Lance and Mitchell, Taylor and O'Donnell, Gerry},
1761+ title = {{Measuring Price Change for Used Vehicles in the Canadian Consumer Price Index}},
1762+ institution = {Statistics Canada},
1763+ type = {Prices Analytical Series},
1764+ number = {62F0014M2022008},
1765+ year = {2022},
1766+ url = {https://www150.statcan.gc.ca/n1/pub/62f0014m/62f0014m2022008-eng.htm}
1767+}
1768+
1769+@misc{bls2026usedcars,
1770+ author = {{U.S. Bureau of Labor Statistics}},
1771+ title = {{Consumer Price Index Factsheet: Used Cars and Trucks}},
1772+ year = {2026},
1773+ howpublished = {Factsheet, last modified 20 May 2026},
1774+ url = {https://www.bls.gov/cpi/factsheets/used-cars-and-trucks.htm}
1775+}
1776+
1777+@misc{manheim2023methodology,
1778+ author = {{Manheim Consulting (Cox Automotive)}},
1779+ title = {{Summary Methodology for Manheim Used Vehicle Value Index}},
1780+ year = {2023},
1781+ howpublished = {Technical note, January 2023 rebasing},
1782+ url = {https://site.manheim.com/wp-content/uploads/sites/2/2023/07/Used-Vehicle-Summary-Methodology.pdf}
1783+}
1784+
1785+@misc{cox2022dayssupply,
1786+ author = {{Cox Automotive}},
1787+ title = {{Used-Vehicle Days' Supply Breaks 50; Average Listing Price Surpasses \$28,000}},
1788+ year = {2022},
1789+ howpublished = {Market Insights, 20 January 2022 (revised 17 February 2022)},
1790+ url = {https://www.coxautoinc.com/market-insights/used-vehicle-days-supply/}
1791+}
1792+
1793+@misc{cbb2026index,
1794+ author = {{Canadian Black Book}},
1795+ title = {{Used Vehicle Retention Index}},
1796+ year = {2026},
1797+ howpublished = {Web page, accessed 1 November 2026},
1798+ url = {https://www.canadianblackbook.com/black-book-index/}
1799+}
1800+
1801+@techreport{cbb2026depreciation,
1802+ author = {{Canadian Black Book and Fitch Ratings}},
1803+ title = {{Vehicle Depreciation Report 2026}},
1804+ institution = {Canadian Black Book},
1805+ year = {2026},
1806+ url = {https://www.canadianblackbook.com/wp-content/uploads/2026/08/CBB-Fitch-2026-Updated-2.pdf}
1807+}
1808+
1809+@misc{autotrader2024priceindex,
1810+ author = {{AutoTrader.ca}},
1811+ title = {{AutoTrader Price Index: June 2024}},
1812+ year = {2024},
1813+ howpublished = {Editorial report, 22 July 2024},
1814+ url = {https://www.autotrader.ca/editorial/car-news/autotrader-price-index-june-2024/}
1815+}
1816+
1817+@misc{cargurus2026imv,
1818+ author = {{CarGurus}},
1819+ title = {{About CarGurus: Instant Market Value}},
1820+ year = {2026},
1821+ howpublished = {Web page, accessed 1 November 2026},
1822+ url = {https://www.cargurus.ca/Cars/aboutCargurus.html}
1823+}
1824+
1825+@misc{iseecars2013pricedrops,
1826+ author = {{iSeeCars.com}},
1827+ title = {{iSeeCars.com Reveals How Used Car Shoppers Can Get a Better Deal by Waiting}},
1828+ year = {2013},
1829+ howpublished = {Study of 30 million used-car listings; press release},
1830+ url = {https://www.iseecars.com/wait-for-used-car-deals-2013-study}
1831+}
added paper/sections/abstract.tex +1 −0
@@ -0,0 +1 @@
1+We study price discovery and liquidity in the Quebec used-car market with a daily panel of 273{,}265 dealer listings observed by the aggregator Auto-Ka between 12 August and 1 November 2026, with every asking-price change and listing disappearance recorded about seven times a day. Hedonic models with flexible age and mileage splines reject linear depreciation and improve dealer-held-out fit by three percent; profiles differ by powertrain. One listing in five receives a sustained markdown (median 2.8 percent of the entry price); the markdown hazard rises over the first three weeks on the lot, and cuts are larger for vehicles that entered above their quality-adjusted value. A competitor event study with market-by-week and model-year-by-week fixed effects finds a same-day co-movement of price cuts among close local competitors (0.9 percentage points against a daily base rate of 1.4 percent) but no lagged response, and a within-make--model placebo yields nothing: we interpret this as common shocks and synchronised repricing calendars, not strategic reaction. Quality-adjusted asking prices do not differ across Quebec's seventeen regions (joint $p = 0.53$), a precisely estimated null. Listing exit is strongly predicted by mileage, price and listing age; cross-source matching by vehicle identification number shows that 94 percent of exits coincide within a week on two independent sources while at least 10 percent are followed by a relisting, so exit overstates sales. A transparent five-component Automobile Market Liquidity Index captures 56 percent of the common variation in regional turnover and, built without the exit component, predicts next-week exit intensity within regions; deep markdowns load as a symptom of illiquidity. No external forecasting claim is possible with an eleven-week window, and the paper documents what listing data can and cannot say about transactions.
added paper/sections/background.tex +15 −0
@@ -0,0 +1,15 @@
1+\section{Institutional background and data-generating process}\label{sec:background}
2+
3+\subsection{The Quebec used-car market}
4+
5+Quebec is Canada's second-largest provincial vehicle market. New motor vehicle sales in the province were 41{,}657 units in August 2026 (Statistics Canada table 20-10-0085-01), and the used market is conventionally two to three times larger in units. Used vehicles are sold by franchised dealers, independent used-car dealers, dealer groups operating under a common banner, and private sellers. Almost every dealer publishes its inventory on its own website, typically through one of a handful of website platforms (D2C Media, SM360, AMVOQ's Theme Central, EvalAuto, SyncAuto) that expose structured listings, and most dealers simultaneously syndicate their inventory to portals such as Otogo (operated by LC Media), AutoUsagée (operated by the dealer association AMVOQ), AutoTrader.ca, CarGurus and Kijiji Autos. Prices are posted in Canadian dollars, usually before taxes and fees; Quebec's consumer-protection law requires the advertised price to be the all-in price excluding sales taxes, which limits the scope for hidden fees and makes posted prices comparable across dealers.
6+
7+Transactions are not public. The provincial registry (SAAQ) publishes annual vehicle-stock files but no ownership-transfer series, and the only Canadian transaction-based used-vehicle index (the Statistics Canada used-vehicle CPI component, built from J.D.~Power dealer transaction data since June 2022) is national and monthly \citep{statcan2022usedvehicles}. Consequently, the asking price and the listing's presence on the dealer's website are the only high-frequency, vehicle-level signals available to a researcher, as they are to a buyer.
8+
9+\subsection{Auto-Ka and the listing panel}
10+
11+Auto-Ka (\url{https://www.auto-ka.com}) is an independent aggregator of Quebec used-vehicle listings built and operated by the author. It maintains a registry of 143 sources: 131 dealer websites, 5 dealer groups, 2 full-inventory portals (Otogo and AutoUsagée), 2 sampled portals (AutoTrader and CarGurus), the private-seller section of Kijiji and two small portals. A dedicated connector per website platform reads each source directly, normalises each listing into a common schema (make, model, trim, model year, asking price, mileage, transmission, fuel, drivetrain, body type, colours, VIN when published, stock number, dealer name and location), and hashes the content. A synchronisation cycle re-reads every source; over the sample the cycle took 3.3 hours on average, giving 7.3 observations per listing per day, with no gap longer than 13.6 hours for any source. Each cycle classifies every listing as new, changed or unchanged, appends every asking-price change to a price log, and increments a miss counter for listings that are no longer returned by the source; a listing is declared inactive after two consecutive misses, unless a drift alert (a fall in the source's volume below a quarter of its recent median, or a jump in missing prices) suspends removals for that source. Listings carrying the same valid 17-character VIN on several sources are de-duplicated in favour of the dealer's own site.
12+
13+The research dataset is a read-only copy of the production database taken on 1~November 2026 after the last synchronisation of the morning, from which the user-account table was removed before transfer. No scraping was performed for this paper and no public API was used; the platform's data-governance rules, the field-level audit, and the restricted status of VINs, URLs, free text and exact coordinates are documented in the replication package.
14+
15+Three features of this data-generating process matter for the analysis. First, the panel starts on 12~August 2026 for dealer websites, on 18~August for Otogo and on 23~August for AutoUsagée; the stock present on each source's first crawl is \emph{left-censored} (its true listing date is unknown) and is excluded from every duration analysis. Second, a listing's disappearance is an observed event on the source website; it may reflect a retail sale, a wholesale transfer, a withdrawal, or a temporary feed failure. We therefore require seven days of continuous absence before counting an exit, and we validate exits across sources and against relistings in Section~\ref{sec:dynamics}. Third, two portals are deliberately sampled by the platform (AutoTrader returns a sliding window of at most 2{,}000 of more than 4{,}000 listings; CarGurus is capped at 500) and cannot distinguish a listing leaving the market from a listing leaving the sample; they are excluded from all dynamic analyses and from the baseline hedonic sample.
added paper/sections/conclusion.tex +5 −0
@@ -0,0 +1,5 @@
1+\section{Conclusion}\label{sec:conclusion}
2+
3+Using a daily panel of every listing published by 131 dealer websites, two full-inventory portals and five dealer groups in Quebec over eleven weeks of 2026, we have described how the price of a used car is discovered on the dealer's lot. Asking prices depreciate nonlinearly in age and mileage; one listing in five is cut, by 2.8~percent at the median, with a markdown hazard that rises over the first three weeks; cuts are larger for vehicles that entered above their quality-adjusted value; and listings disappear after nine days at the median, faster for cheap, high-mileage and older vehicles. Repricing co-moves across neighbouring dealers on the same day and at weekly harmonics but not with a lag, which we interpret as common shocks and shared routines rather than strategic reaction. Quality-adjusted asking prices do not differ across the province's regions in any detectable way. A transparent five-component liquidity index summarises more than half of the common variation in regional turnover, and its non-exit components anticipate next week's exits; the data also tell us that deep markdowns are a sign of slow, not liquid, markets.
4+
5+The limits are as important as the findings. Asking prices are not transaction prices; exits overstate retail sales by at least a tenth, as our relisting analysis shows; the competitor event study is descriptive; the regional null applies to one quarter; the thirty-day predictability of repricing is mostly dealer practice and does not transfer to unseen dealers; and the liquidity index has no external validation yet. A longer panel, matched to registration transfers, would allow the markdown and exit hazards to be modelled as competing risks with a verified sale outcome, the regional null to be tested across seasons, and the index to be tested as a leading indicator. The pipeline that produced this paper, with its immutable snapshot, exclusion log, preregistered plan and claims ledger, is designed to make that extension a matter of rerunning it.
added paper/sections/data.tex +47 −0
@@ -0,0 +1,47 @@
1+\section{Data, measurement and descriptive evidence}\label{sec:data}
2+
3+\subsection{Sources and coverage}
4+
5+Table~\ref{tab:sources} summarises the 143 sources. Over the 81-day window (12~August to 1~November 2026, 586 synchronisation cycles) the platform observed 273{,}265 distinct listings, of which 62{,}821 were active on the snapshot date. Dealer websites contribute 33{,}061 listings from 131 dealers; the two full-inventory portals contribute 107{,}903 listings from 929 named dealers, with exact dealer coordinates and VINs for 82~percent of Otogo listings; dealer groups add 17{,}162. The sampled portals (103{,}027 listings) and Kijiji private sellers (11{,}874) are observed but excluded from the analysis samples for the reasons given in Section~\ref{sec:background}. Figure~\ref{fig:coverage} shows the daily stock and flows of the reliable-source universe, the crawl intensity, and the regional distribution of the active stock. After the two portals come online, the reliable stock is stable at 44{,}000--49{,}000 listings, with roughly 1{,}000 entries and 1{,}000 confirmed exits per day and a pronounced weekly cycle in both.
6+
7+\begin{table}[tbp]\centering\caption{Data sources, coverage and sample membership}\label{tab:sources}
8+\tabsize\input{tables/tab01_sources}
9+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Dealers = distinct dealer identifiers (dealer websites and groups are one dealer each; portals identify the selling dealer by name). Active = active on 2026-11-01. Found/cycle = mean number of listings returned per synchronisation cycle. S1 = hedonic sample; S2 = dynamic sample (Table~\ref{tab:sample}). Sampled portals are capped sliding windows and cannot reveal exits.\end{minipage}
10+\end{table}
11+
12+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig01_coverage.pdf}
13+\caption{Coverage across time, region and source. (a) Active listings, entries and confirmed exits per day in the reliable-source universe (S2L); the two spikes are the connector start-ups of Otogo (day~6) and AutoUsagée (day~11), whose stock is treated as left-censored. (b) Synchronisation cycles per day. (c) Active canonical listings by administrative region on 1~November 2026.}\label{fig:coverage}\end{figure}
14+
15+\subsection{Variables}
16+
17+\emph{Asking price.} The platform logs the entry price and every subsequent change. Price logs are contaminated by two artefacts that any listing-based study must address: placeholder prices (2{,}164 listings at \$1 or less; 5 above \$1~million) and \emph{oscillations}, in which a connector alternately returns two variants of the same listing (3{,}451 listings exhibit at least one A$\to$B$\to$A reversion, some twice a day for weeks). We therefore work with a \emph{sustained price path}: a price level that lasts less than 48~hours and reverts to the previous level is deleted, listings with three or more such transients are flagged as oscillating and excluded from markdown analyses, and a \emph{markdown} is a sustained decrease of at least 1~percent and \$100. Under these rules the price log yields 76{,}261 cuts and 26{,}486 rises across all sources; among reliable sources cuts outnumber rises nine to one.
18+
19+\emph{Vehicle age} is measured in years from 1~October of the year preceding the model year to the listing's entry date; \emph{mileage} in units of 10{,}000~km. \emph{Powertrain} (combustion, hybrid, plug-in hybrid, battery-electric, diesel) is read from the fuel field and, when missing, recovered from the title or from the make--model(--trim--year) cell when at least 90~percent of known listings in the cell agree (84{,}753 recoveries; 11{,}099 listings remain unknown). \emph{Body type} is normalised to eight classes and recovered from the make--model modal class when missing. \emph{Region} is the administrative region reported by the source, recovered from the city or from the nearest known city when missing (13{,}073 recoveries; 12{,}434 listings, mostly on excluded sources, remain without region). \emph{Dealer} identifiers are the source for dealer websites and the normalised dealer name within portal for portals; coordinates are the listing's, or the dealer's median location when missing.
20+
21+\emph{Listing age} is days since entry; \emph{vehicle age} is distinct. \emph{Exit} is defined as the last observation of a listing that is inactive at the snapshot and has been absent for at least seven days; exits within the final seven days of the window are right-censored, as are listings still active. The exit time is placed at the last observation plus half the source's median crawl interval, which treats exit as interval-censored between two crawls.
22+
23+\subsection{Sample construction and quality controls}
24+
25+Table~\ref{tab:sample} reports the attrition from the raw snapshot to the analysis samples. The hedonic sample S1 (136{,}393 listings) keeps canonical automobile listings from reliable sources with model years 2005--2026, mileage between 1{,}000 and 400{,}000~km, an entry price between \$2{,}000 and \$250{,}000, and non-missing make and model. The dynamic sample S2 (80{,}856 listings) further excludes the sampled portals and Kijiji, six connectors with documented failures or flicker (up to 94~percent of their exit events were later reversed), 53 oscillating listings, and the 47{,}235 listings that were already present on the first crawl of their source. The intermediate sample S2L retains the left-censored stock and is used as the competitor universe and for sensitivity analysis. Every excluded listing is written to an auditable exclusion log with the rule that removed it.
26+
27+\begin{table}[tbp]\centering\caption{Sample construction and exclusions}\label{tab:sample}
28+\tabsize\input{tables/tab02_sample_construction}
29+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Sequential exclusions from the 2026-11-01 snapshot. S1 = hedonic sample (reliable sources); S2L = dynamic sample including the left-censored stock; S2 = dynamic sample used for hazards, competition and the liquidity index.\end{minipage}
30+\end{table}
31+
32+Three further quality controls are reported in the replication package: a per-source reactivation share (the fraction of exit events later reversed, which motivated the seven-day confirmation rule and the exclusion of flickering connectors), the stability of listing identifiers within sources (only 1.7~percent of Otogo VINs appear under more than one identifier, almost all genuine dealer-to-dealer transfers), and the distribution of crawl gaps (median 3.1~hours, maximum 13.6~hours).
33+
34+\subsection{Descriptive evidence}
35+
36+Table~\ref{tab:desc} reports descriptive statistics for S1 and S2 and by region. The median listing in S1 asks \$25{,}627 for a 5.7-year-old vehicle with 68{,}629~km; sport-utility vehicles make up 57~percent of listings, sedans 21~percent and pickups 11~percent; battery-electric vehicles are 6~percent and hybrids 4~percent. Completed listing spells in S2 are short: the median confirmed exit occurs 8.9~days after entry (interquartile range 1.0--20.6 days), 44~percent of completed spells end within a week and 87~percent within 30~days; 46~percent of S2 listings are right-censored. One listing in five (19.7~percent) receives at least one sustained markdown and 6.6~percent receive two or more; the median first cut is 2.8~percent of the entry price (mean 4.6; three quarters are 5~percent or less, 7.6~percent exceed 10~percent). Figure~\ref{fig:agecuts} displays these distributions and Figure~\ref{fig:paths} shows six representative price paths selected by a deterministic rule described in the caption.
37+
38+\begin{table}[tbp]\centering\caption{Descriptive statistics by sample and region}\label{tab:desc}
39+\scriptsize\input{tables/tab03_descriptives}
40+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Entry price in Canadian dollars; vehicle age in years at entry; duration = completed spells (confirmed exits). Exit within 30~days is computed on S2 entrants observed for at least 37 days. ``Region missing'' = listings whose region could not be recovered.\end{minipage}
41+\end{table}
42+
43+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig03_listing_age_and_cuts.pdf}
44+\caption{Listing durations and markdowns (S2). (a) Empirical distribution of completed spell lengths. (b) Size of the first sustained markdown as a percentage of the entry price. (c) Share of listings with at least one sustained markdown, by source class.}\label{fig:agecuts}\end{figure}
45+
46+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig04_price_paths.pdf}
47+\caption{Asking-price paths of six anonymised listings. Selection rule: S2 listings with at least three sustained cuts and a duration between 30 and 70 days, in the three largest body classes; within each class the two listings with the lowest SHA-256 digest of their anonymised identifier. Only make, body and model year are shown.}\label{fig:paths}\end{figure}
added paper/sections/introduction.tex +13 −0
@@ -0,0 +1,13 @@
1+\section{Introduction}\label{sec:intro}
2+
3+Used cars are the largest durable good that most households trade, and the market that reallocates them is organised almost entirely through posted asking prices. A dealer lists a vehicle at a price, waits, cuts the price if the vehicle does not move, and eventually the listing disappears, either because the car was sold, wholesaled to another dealer, or simply withdrawn. Economists have studied the resulting equilibrium with transaction data from auctions and registration records \citep{genesove1993adverse,gavazza2014usedcar,larsen2021bargaining}, but the price-discovery process itself, the sequence of asking prices and the decision to cut them in the face of local competition, has remained largely hidden, because no public source records the daily asking-price paths of a whole market. This paper opens that black box for the Canadian province of Quebec.
4+
5+We assemble a daily panel of 273{,}265 used-vehicle listings observed between 12 August and 1 November 2026 by Auto-Ka, an aggregator that re-reads the inventory pages of 131 dealer websites, two full-inventory portals and several dealer groups roughly seven times a day, logs every asking-price change, and records when a listing disappears from its source. The data are asking prices, not transaction prices, and listing disappearance is not a verified sale. We take these limits seriously throughout: every dynamic outcome is defined as an observed event on the dealer's website, and the paper's central methodological contribution is to show what can and cannot be learned about market liquidity from such listing data alone.
6+
7+The empirical programme was preregistered before any model was estimated (Section~\ref{sec:method}; the full plan is in the replication package) and has six parts. First, we estimate hedonic asking-price models with flexible age and mileage profiles and ask whether depreciation is nonlinear and powertrain-specific (H1). Second, we model the hazard of a first sustained markdown and the size of the cut as functions of listing age, local competition and the vehicle's relative price (H2). Third, we estimate an event study of a dealer's repricing around the price cuts of close local competitors, with market-by-week and model-year-by-week fixed effects, treating the estimates explicitly as associations (H3). Fourth, we test whether quality-adjusted asking prices differ across Quebec's seventeen administrative regions (H4). Fifth, we estimate the hazard of listing exit and validate exit as a sale proxy using vehicles observed simultaneously on a dealer's own site and on a portal, and by detecting relistings of the same vehicle identification number elsewhere (H5). Sixth, we construct an Automobile Market Liquidity Index (AMLI) from five transparent components and validate it internally with a held-out-component design (H6).
8+
9+Five findings stand out. (i) Depreciation of asking prices is strongly nonlinear in both age and mileage: the linear specification is rejected at any conventional level and splines improve dealer-grouped out-of-sample fit by about 3~percent, a gain that gradient boosting only modestly extends. Age profiles differ by powertrain, with battery-electric vehicles depreciating faster than comparable combustion vehicles at young ages. (ii) Markdowns are frequent but shallow: one listing in five receives at least one sustained cut, the median first cut is 2.8~percent of the entry price, and the daily markdown hazard rises steeply over the first three weeks of a listing before plateauing. Listings that enter above their hedonic value are cut more often and more deeply. (iii) Repricing is synchronised rather than sequential: a close competitor's cut is associated with a 0.5--0.9 percentage-point higher probability that the dealer cuts the same day, against a daily base rate of 1.4~percent, but the association vanishes on the following days and reappears only at seven-day harmonics; it survives dealer-by-weekday fixed effects at $k=0$ but not as a lagged response. A placebo that permutes competitor series within make--model cells produces no effect. We read this as common local shocks and shared repricing calendars rather than strategic reaction, and we do not claim causality. (iv) Quality-adjusted asking prices are remarkably integrated across Quebec: with make--model--trim fixed effects and dealer-clustered inference, the seventeen regional premia are jointly insignificant ($p = 0.53$) and all lie within $\pm 4$~percent of Montréal. This is a precisely estimated null. (v) Listing exit is strongly predicted by mileage, price and listing age, and exit is a reasonably consistent event across sources: for vehicles observed both on a dealer's site and on a portal, 80~percent of exits coincide within one day and 94~percent within a week; 10~percent of exits are followed by a relisting of the same vehicle within 30~days, 6~percent at a different location, which bounds the share of non-sale exits from below. The AMLI's first principal component explains 55~percent of component variance, and an index built without the exit component predicts next-week exit intensity within regions. We are explicit that the 81-day window does not allow any test of the index's forecasting value for external economic variables.
10+
11+The paper contributes to three literatures. To the economics of durable-goods markets \citep{akerlof1970lemons,hendel1999adverse,stolyarov2002turnover,gavazza2014usedcar}, it adds listing-level evidence on how dealers search for the price at which a used car clears, the counterpart on the sell side to the buyer-search evidence of \citet{murry2020colocation} and the intermediary margins of \citet{biglaiser2020intermediaries}. To the literature on asking prices and time on market, developed mostly for housing \citep{knight2002listing,han2016askingprice,anenberg2016frictions,guren2018momentum}, it brings a market with far shorter spells, near-complete price histories and many close substitutes. To the measurement of market liquidity, where industry days'-supply and days-to-turn statistics \citep{cox2022dayssupply,cbb2026index} exist but are opaque, it offers a reproducible, component-based index with documented validation limits.
12+
13+The remainder of the paper is organised as follows. Section~\ref{sec:background} describes the Quebec used-car market and the data-generating process of the aggregator. Section~\ref{sec:lit} reviews the related literature. Section~\ref{sec:data} documents the data, the quality controls and the sample construction. Section~\ref{sec:method} sets out the econometric framework and identification. Sections~\ref{sec:prices}--\ref{sec:amli} report the results on price formation, dealer dynamics and the liquidity index. Section~\ref{sec:robust} presents robustness checks and limitations, and Section~\ref{sec:conclusion} concludes.
added paper/sections/literature.tex +25 −0
@@ -0,0 +1,25 @@
1+\section{Related literature and mechanisms}\label{sec:lit}
2+
3+\paragraph{Hedonic prices and depreciation.}
4+Quality-adjusted pricing of automobiles is the founding application of hedonic methods \citep{court1939hedonic,griliches1961hedonic,ohta1976automobile}, formalised by \citet{lancaster1966newapproach} and \citet{rosen1974hedonic} and extended to flexible functional forms and identification by \citet{pakes2003hedonic}, \citet{ekeland2004hedonic} and \citet{bajari2005hedonic}; \citet{triplett2006handbook} and \citet{hill2013hedonic} survey measurement practice. For used vehicles, the price of a given model falls with age and with accumulated mileage, and \citet{engers2009annualmiles} show that annual miles, not only odometer readings, drive used-car prices. The Quebec-specific evidence on depreciation is thin \citep{yanes2010depreciation}. Our hedonic models follow this tradition with asking rather than transaction prices and ask how far the flexible-form conclusions carry over.
5+
6+\paragraph{Durable-goods markets, adverse selection and liquidity.}
7+Since \citet{akerlof1970lemons}, used-car markets have been the canonical setting for asymmetric information \citep{kim1985lemons,bond1982lemons,genesove1993adverse,hendel1999adverse,emons2009usedcars,peterson2014adverse}, with online evidence from eBay Motors \citep{lewis2011asymmetric,adams2011vettes}. \citet{stolyarov2002turnover}, \citet{gavazza2011frictions} and \citet{gavazza2014usedcar} model trading frictions and the resulting pattern of turnover and prices over the vehicle's life; \citet{larsen2021bargaining} measures bargaining efficiency in wholesale auctions and \citet{biglaiser2020intermediaries} the premium that dealers earn as intermediaries. Dealers in these models hold inventory precisely because buyers arrive stochastically; the markdown and exit hazards we estimate are the empirical shadows of that search process.
8+
9+\paragraph{Dealer pricing and inventory.}
10+Retail price cuts are the standard tool for clearing inventory \citep{lazear1986clearance,pashigian1991sale,pesendorfer2002retailsales,aguirregabiria1999markups}, and the dynamic-pricing literature characterises optimal markdown paths for perishable or seasonal goods \citep{gallego1994dynamicpricing,elmaghraby2003dynamicpricing,sweeting2012perishable}. For cars, \citet{zettelmeyer2006internet} and \citet{scottmorton2001internet} document how internet referrals compressed dealer margins, \citet{busse2006cashback} how promotions pass through, and \citet{israeli2022marketpower} how new-car dealers vary prices with inventory. \citet{murry2020colocation} show that used-car dealers co-locate to attract searching buyers. Industry sources report that a substantial share of used listings receive price drops \citep{autotrader2024priceindex,iseecars2013pricedrops}, but to our knowledge no peer-reviewed study estimates markdown hazards or cut sizes on listing-level paths.
11+
12+\paragraph{Search, dispersion and asking prices.}
13+Price dispersion for homogeneous goods is the signature of costly search \citep{stigler1961information,diamond1971paradox,varian1980sales,burdett1983equilibrium,sorensen2000dispersion,lach2002dispersion,baye2006dispersion,kaplan2015dispersion}. The role of the asking price as a signal and a commitment device has been studied mainly for housing: time on market and list-price cuts \citep{knight2002listing,anglin2003tradeoff,genesove2001lossaversion,carrillo2012search,han2016askingprice}, momentum and strategic complementarity in list prices \citep{guren2018momentum}, and listing-based price indexes \citep{anenberg2017timely,lyons2019listprices}. Liquidity itself is measured with price impact in financial markets \citep{amihud1986bidask,amihud2002illiquidity}, with search-and-matching models in over-the-counter and housing markets \citep{duffie2005otc,lagos2009liquidity,krainer2001housingliquidity,head2014searchliquidity,ngai2014hotcold}, and with days'-supply or days-to-turn statistics in the car trade \citep{cox2022dayssupply,manheim2023methodology,cbb2026index}. Our index formalises the latter as a transparent multi-component measure.
14+
15+\paragraph{Survival analysis.}
16+We rely on discrete-time hazard models \citep{allison1982discrete,singer1993discrete}, the Cox model and its diagnostics \citep{cox1972regression,grambsch1994proportional,lin1989robust,therneau2000modeling}, flexible parametric and competing-risk alternatives \citep{royston2002flexible,fine1999proportional,putter2007tutorial}, and the econometric treatment of duration data with censoring and unobserved heterogeneity \citep{kiefer1988duration,lancaster1990econometric,heckman1984method}.
17+
18+\paragraph{Spatial competition and regional integration.}
19+Localised competition follows \citet{hotelling1929stability} and \citet{salop1979monopolistic}; empirical work measures spatial differentiation in retail gasoline \citep{houde2012spatial,pinkse2002spatial,chandra2011gasoline,byrne2019coordinate} and hotels \citep{kalnins2003hamburger}, and regional price integration through border effects \citep{engel1996border,gorodnichenko2009border,ceglowski2003lawofoneprice}; \citet{haan2010internetregional} document regional differences in Dutch used-car listing prices. Spatial inference follows \citet{conley1999gmm} and \citet{anselin1988spatial}.
20+
21+\paragraph{Identification of strategic interactions.}
22+Whether a dealer's price cut \emph{causes} a neighbour's cut is a reflection problem \citep{manski1993reflection,brock2001discrete,bramoulle2009peer,angrist2014perils,blume2011identification}. We use event-study and distributed-lag designs with the diagnostics developed in the recent difference-in-differences literature \citep{sun2021estimating,callaway2021difference,dechaisemartin2020twoway,goodmanbacon2021difference,borusyak2024revisiting,roth2022pretest,rambachan2023credible,roth2023trending}, cluster-robust inference \citep{bertrand2004howmuch,cameron2008bootstrap,cameron2011multiway,abadie2023clustering}, and multiple-testing control \citep{benjamini1995fdr,romano2005stepwise}; predictive comparisons use \citet{diebold1995comparing}, \citet{giacomini2006tests}, \citet{brier1950verification} and gradient boosting \citep{friedman2001greedy,ke2017lightgbm,athey2019machine,mullainathan2017machine}.
23+
24+\paragraph{What this paper adds.}
25+Relative to this literature the paper offers (i) the first listing-level daily panel of a whole regional used-car market with complete asking-price histories, (ii) hazard-based evidence on markdowns and exits with dealer-clustered inference, (iii) a competitor event study that distinguishes same-day synchronisation from lagged response, (iv) a precisely estimated null on regional premia, and (v) a transparent liquidity index. It does not add transaction prices, and we acknowledge that days'-supply measures already capture part of what the index measures.
added paper/sections/methodology.tex +43 −0
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1+\section{Econometric framework and identification}\label{sec:method}
2+
3+The hypotheses, estimands, samples and tests were written down before estimation and are reproduced in the replication package together with every amendment (three amendments were made after the data audit: quality fixed effects at the make--model level rather than make--model--year, because model-year effects absorb vehicle age in an 81-day window; a linear-probability specification for the competition event study; and a per-source definition of left-censoring after the portal start-ups were discovered). We summarise the six models here.
4+
5+\subsection{Model A: hedonic asking prices (H1, H4)}
6+
7+For listing $i$ with entry price $P_i$,
8+\begin{equation}\label{eq:hedonic}
9+\log P_i = f(\mathrm{Age}_i,\mathrm{KM}_i) + X_i'\beta + \mu_{m(i)} + \tau_{w(i)} + \theta_{s(i)} + \rho_{r(i)} + \varepsilon_i ,
10+\end{equation}
11+where $f$ is a sum of cubic B-splines in age and mileage (five degrees of freedom each, knots at quintiles), optionally augmented by their tensor product (A2t) or interacted with powertrain (A3); $X_i$ contains transmission, drivetrain, powertrain and body class; $\mu_{m}$ are make--model (A1--A3) or make--model--trim (A4) fixed effects; $\tau_w$ are entry-week effects; $\theta_s$ source-class effects; and $\rho_r$ region effects with Montréal as reference. A5 adds dealer fixed effects, which absorb region. Standard errors are clustered by dealer. The nonlinear components of the spline bases are orthogonalised on the linear term so that H1 is a cluster-robust Wald test that the nonlinear coefficients are zero; H4 is a Wald test that the sixteen region effects are jointly zero in A4. Out-of-sample performance is assessed by five-fold cross-validation grouped by dealer, against a linear benchmark and a gradient-boosting model (A6) with the same inputs.
12+
13+\subsection{Models B and C: markdown and exit hazards (H2, H5)}
14+
15+Listings in S2 are expanded to a listing-day risk set (1.66~million listing-days). For the first markdown, the risk set runs from entry to the day of the first sustained cut (318{,}323 listing-days, 15{,}692 events); for exit it runs to the confirmed exit or censoring day (43{,}311 events). We estimate discrete-time complementary log-log hazards
16+\begin{equation}\label{eq:hazard}
17+\operatorname{cloglog}\, h_{it} = g(a_{it}) + \gamma \log(1+N_{it}) + \delta\, r_i + \lambda\, \pi_{it} + Z_i'\beta + \rho_{r(i)} + \tau_{t} + \theta_{s(i)},
18+\end{equation}
19+with listing-age bins $g(\cdot)$, the number $N_{it}$ of competing listings (same make and model, model year within one year, different dealer, within 25~km) active on day $t$, the entry hedonic residual $r_i$ from A2 (the listing's price relative to its quality-adjusted value), the price position $\pi_{it}$ relative to the mean log price of its competitors (zero with an indicator when there is no competitor), vehicle characteristics $Z_i$, region, calendar-week and source-class effects; the exit model adds the number of prior cuts. Standard errors are clustered by dealer. Cox proportional-hazards models with dealer-clustered sandwich errors, Schoenfeld tests and a log-logistic accelerated-failure-time model are estimated at the listing level with entry covariates; the conditional size of the first cut is modelled by least squares on log cut size with make--model, region and week fixed effects.
20+
21+\subsection{Model D: competitor price response (H3)}
22+
23+For listing-day $(i,t)$ in S2, let $C_{i,t-k}$ indicate that at least one competitor in the 25-km set cut its price on day $t-k$. We estimate
24+\begin{equation}\label{eq:es}
25+\mathbb{1}[\text{cut}_{it}] = \sum_{k=-7}^{14}\beta_k\, C_{i,t-k} + \gamma\log(1+N_{it}) + g(a_{it}) + \alpha_{r(i),w(t)} + \alpha_{my(i),w(t)} + u_{it},
26+\end{equation}
27+with region-by-week and make--model--year-by-week fixed effects, by linear probability (the daily base rate is 1.4~percent, so the linear approximation is local), with errors clustered by dealer and, alternatively, by region. The leads $k<0$ test for pretrends and reverse timing; a placebo permutes competitor series across listings within make--model cells, breaking geography while preserving each series' own dynamics; a robustness specification adds dealer-by-weekday fixed effects to absorb repricing calendars. We interpret all estimates as conditional associations: there is no exogenous source of variation in competitor cuts, and common local demand shocks are the leading alternative explanation.
28+
29+\subsection{Model E: regional segmentation (H4)}
30+
31+Region effects from A4, within-specification dispersion (the interquartile range of A4 residuals by region), and a comparison of the Outaouais region, which borders Ontario, with the Montréal-area regions.
32+
33+\subsection{Model F: prediction}
34+
35+Log entry price, a first markdown within 30~days, and an exit within 30~days are predicted from entry information only, with a regularised linear or logistic baseline and gradient boosting, under chronological, dealer-holdout and region-holdout splits; we report root-mean-squared error, log loss, Brier score, calibration and bootstrap intervals.
36+
37+\subsection{The liquidity index (H6)}
38+
39+For market $m$ (region, or price tier, or body class) and panel week $w$, we compute exit intensity (confirmed exits per listing-day), markdown frequency (cuts per listing-day), markdown depth (median cut size), quality-adjusted dispersion (interquartile range of hedonic residuals among active listings, sign reversed) and arrival rate (entries per listing-day); higher values of the index mean a \emph{more liquid} market. Components are standardised across market-weeks, markets with fewer than 30 listing-days are set to missing, and three weighting schemes are compared: equal weights, the first principal component (signed so that exit intensity loads positively), and supervised weights from a ridge regression of next-week exit intensity on the non-exit components fitted on weeks 2--5. Internal validation regresses next-week exit intensity on an index built \emph{without} the exit component, with market fixed effects, on weeks 6--10. Uncertainty bands come from a block bootstrap over dealers (200 replications). Weeks 0--1 are excluded because of the portal start-ups. No external forecasting test is possible with 81 days of data, and none is claimed.
40+
41+\subsection{Inference and multiple testing}
42+
43+The six primary tests (H1 joint nonlinearity, H2 listing-age profile, H3 lag sum, H4 region joint test, H5 relative-price coefficient in the exit hazard, H6 held-out validation slope) are reported with Holm-adjusted $p$-values in Section~\ref{sec:robust}; secondary tests are unadjusted and labelled exploratory.
added paper/sections/results_amli.tex +28 −0
@@ -0,0 +1,28 @@
1+\section{The Automobile Market Liquidity Index}\label{sec:amli}
2+
3+\subsection{Construction}
4+
5+Table~\ref{tab:amli} summarises the index. Five components are computed for each of the seventeen regions and each panel week from week~2 to week~10 (the two first weeks are contaminated by the portal start-ups): exit intensity, markdown frequency, markdown depth, quality-adjusted dispersion (sign reversed) and arrival rate. Each is standardised across the 153 region-weeks; higher values mean a more liquid market under the preregistered sign convention. Figure~\ref{fig:amli} plots the province-level components and index and the regional indices with dealer-bootstrap bands.
6+
7+\begin{table}[tbp]\centering\caption{AMLI construction, weights and internal validation (region $\times$ week, weeks 2--10)}\label{tab:amli}
8+\tabsize\input{tables/tab07_amli}
9+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: PCA loading = first principal component of the five standardised components, signed so that exit intensity loads positively. Supervised = ridge coefficients of next-week exit intensity on the four non-exit components, fitted on weeks 2--5. Leave-one-out corr. = correlation between the equal-weight index without the component and the full index. Held-out validation: coefficient of next-week exit intensity on an equal-weight index built without the exit component, region fixed effects, weeks 6--10, standard errors clustered by region; slope in percentage points of daily exit intensity per index unit.\end{minipage}
10+\end{table}
11+
12+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig08_amli_series.pdf}
13+\caption{AMLI time series. (a) Province-level standardised components (signed). (b) Province-level index under three weighting schemes. (c) Equal-weight index for the six largest regions with 95\% dealer-block-bootstrap bands (200 replications).}\label{fig:amli}\end{figure}
14+
15+\subsection{What the components say about liquidity}
16+
17+The component correlations (Figure~\ref{fig:amlival}(b)) carry the paper's most useful lesson about liquidity measurement. Exit intensity co-moves positively with the arrival rate ($\rho = 0.62$) and, weakly, with markdown frequency (0.19), but \emph{negatively} with markdown depth ($-0.35$) and positively with reversed dispersion (0.44): region-weeks in which vehicles leave quickly are region-weeks with many new arrivals, frequent but \emph{shallow} price cuts, and \emph{tight} quality-adjusted price distributions; region-weeks with deep cuts and dispersed prices are slow. Markdown depth and dispersion are themselves strongly correlated (0.85). The first principal component explains 56~percent of the variance of the five components, above the 40~percent threshold set in the analysis plan, and its loadings are positive on exit, markdown frequency, arrival and reversed dispersion, but \emph{negative} on markdown depth. The preregistered sign for depth (deeper cuts = more liquid) is therefore contradicted by the data, which treat deep cuts as a symptom of illiquidity, in the spirit of the list-price-cut literature in housing \citep{knight2002listing,anenberg2016frictions}. We keep the preregistered equal-weight index as the primary measure, report the PCA-weighted index (correlation 0.85 with the equal-weight index) and the supervised index (0.68) alongside it, and record this sign reversal as a substantive finding rather than a tuning choice. At the province level, with only nine weekly observations, the first component explains 44~percent of the variance and its loadings are unstable; the regional panel is the appropriate level for the index in a window this short.
18+
19+\subsection{Internal validation}
20+
21+The analysis plan called for a held-out-component test. An equal-weight index built \emph{without} exit intensity, from the markdown, dispersion and arrival components of week $w$, predicts the exit intensity of week $w+1$ within regions: the slope is 0.97 percentage points of daily exit intensity per index unit (95\% interval 0.69 to 1.24; the mean daily exit intensity is 2.3~percent), with an $R^2$ of 0.66 including region effects, and it is unchanged when the current exit intensity is added as a control (1.03, interval 0.70 to 1.35). Figure~\ref{fig:amlival}(a) shows the within-region relation. The index is robust to leaving any single component out (correlations with the full index between 0.91 and 0.95), and the same construction on price-tier and body-class markets gives first components explaining 44--46~percent of the variance and held-out slopes of 0.46 points, significant for body classes (interval 0.06 to 0.86) but not for the three price tiers (12 market-weeks only).
22+
23+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig09_amli_validation.pdf}
24+\caption{AMLI internal validation. (a) Within-region relation between the leave-exit-out index in week $w$ and exit intensity in week $w+1$, weeks 6--10. (b) Correlations among the five standardised components across region-weeks.}\label{fig:amlival}\end{figure}
25+
26+\subsection{What the index cannot do yet}
27+
28+The window contains eleven weeks. No weekly or daily independent benchmark of used-vehicle turnover exists for Quebec, and the monthly or quarterly series that do exist (Statistics Canada new-vehicle sales, the Canadian Black Book retention index, the Statistics Canada used-vehicle CPI, AutoTrader's quarterly price index) offer at most three overlapping observations, most of them national. We therefore make no claim that the AMLI forecasts any economic variable, and we regard its validation as internal and partial: it summarises coherent variation in observed turnover across regions and weeks, and its non-exit components anticipate next week's exits. Establishing external validity requires a panel of at least one to two years and, ideally, access to registration transfers, which is the natural next step for the platform.
added paper/sections/results_dynamics.tex +45 −0
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1+\section{Dealer repricing, competition and inventory dynamics}\label{sec:dynamics}
2+
3+\subsection{Markdown hazards and cut sizes (H2)}
4+
5+Figure~\ref{fig:hazards}(a) shows the empirical daily hazard of a first sustained markdown by listing age. It is low in the first days (0.7~percent per day), doubles by the end of the first week (1.1~percent) and reaches a plateau of 1.4--1.5~percent per day between the second and fourth weeks, after which it declines slightly among the surviving listings. In the discrete-time model of Table~\ref{tab:hazards}, column~(1), the listing-age bins are jointly significant ($\chi^2(8) = 170$) and the coefficients rise monotonically from 0.79 (days 1--3, relative to the entry day) to 1.68 (day 61 and beyond), so the first half of H2 is supported: the probability of a markdown increases with the time a vehicle has been on the lot. Cheaper and higher-mileage vehicles are cut less often, consistent with a thinner margin to give away. The competition and relative-price terms are less clear-cut. In the listing-level Cox model with dealer-clustered sandwich errors (column~2), a listing that enters 10~percent above its hedonic value has a 7.4~percent higher markdown hazard (coefficient 0.72 on the residual), each log unit of competitor density raises the hazard by 12~percent, and a 10~percent higher price than the competitor set adds 3.6~percent; all three are significant at the 1~percent level. In the discrete-time model with region, calendar-week and source-class effects (column~1), the point estimates have the same signs but the dealer-clustered standard errors are three to five times larger and none of the three is significant. The cut-size regression (column~5) is unambiguous: the first cut is 2.7~percent of the entry price at the median, it is larger for listings that entered above their hedonic value (a 10~percent overpricing raises the cut by 22~percent) and for expensive vehicles in absolute terms, and it declines with listing age at the time of the cut, that is, late cuts are smaller than early ones.
6+
7+\begin{table}[tbp]\centering\caption{Markdown and listing-exit models (S2)}\label{tab:hazards}
8+\scriptsize\input{tables/tab05_hazards}
9+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: cloglog = discrete-time complementary log-log hazard on listing-days with listing-age bins, powertrain, body, source class, region and calendar-week effects, standard errors clustered by dealer. Cox = proportional-hazards model at the listing level with entry covariates and dealer-clustered sandwich errors. Cut size = OLS on the log of the first cut (percent of entry price) with make--model, region and week fixed effects. The entry hedonic residual is from Model A2. $^{*}$, $^{**}$, $^{***}$: 10, 5, 1 percent.\end{minipage}
10+\end{table}
11+
12+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig05_hazard_curves.pdf}
13+\caption{Empirical daily hazards by listing age (S2) with 95\% Wilson intervals. (a) First sustained markdown. (b) Observed listing exit. (c) Implied survival by source class.}\label{fig:hazards}\end{figure}
14+
15+\subsection{Competitor price response (H3)}
16+
17+Figure~\ref{fig:es} and Table~\ref{tab:competition} report the event study of equation~\eqref{eq:es}. The daily probability that a dealer cuts the price of a listing is 1.44~percent on average. On the day a close competitor (same make and model, model year within one year, within 25~km) cuts its price, that probability is 0.89 percentage points higher (standard error 0.21), a 60~percent increase relative to the base rate. The association is, however, entirely contemporaneous: the coefficients for days $+1$ to $+6$ are zero or negative, and the only other positive coefficients appear at $k=+7$, $k=+14$ and $k=-7$, a weekly periodicity. The leads are jointly significant ($p < 10^{-4}$), so the pretrend test fails in the sense that a dealer's cut today also predicts a competitor's cut a week later; lagged and lead sums are of the same order. Adding dealer-by-weekday fixed effects, which absorb each dealer's repricing calendar, reduces the same-day coefficient to 0.55 points and the seven-day harmonics to 0.17 and 0.33 points, but does not change the picture. The placebo, in which competitor series are permuted across listings within make--model cells, yields coefficients indistinguishable from zero at every horizon (joint $p = 0.18$), which confirms that the same-day association is spatially structured and not an artefact of the fixed effects. Results are similar at 10~km (larger coefficients, fewer competitors) and 50~km (smaller), when errors are clustered by region, and with coarser fixed effects. For the first markdown only (panel~b), the same-day coefficient is 2.6 points against a base rate of 4.9~percent, with negative leads, consistent with the fact that a listing that has just been cut by a neighbour is often one that was about to be cut anyway.
18+
19+We draw two conclusions. First, there is no evidence of a lagged strategic response: dealers do not follow a competitor's cut within the next one to six days. Second, the strong same-day co-movement, its weekly harmonics and its robustness to dealer-by-weekday effects point to common local shocks and synchronised repricing routines (dealers and dealer groups that update their inventory feeds on fixed days, manufacturer incentive calendars, local demand shocks) rather than to observation and reaction. Because the design has no exogenous source of variation in competitor cuts, the estimates are associations and we make no causal claim; the reflection problem of \citet{manski1993reflection} applies with full force.
20+
21+\begin{table}[tbp]\centering\caption{Competitor price-response event study (dependent variable: own sustained cut on day $t$, percentage points)}\label{tab:competition}
22+\resizebox{\textwidth}{!}{\input{tables/tab06_competition}}
23+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Linear probability models on S2 listing-days with region-by-week and make--model--year-by-week fixed effects, log competitor density and listing-age effects; coefficients are percentage points, standard errors clustered by dealer (by region in column 2) in parentheses. Placebo: competitor-cut series permuted across listings within make--model cells. ``+ dealer $\times$ weekday FE'' adds dealer-by-weekday effects.\end{minipage}
24+\end{table}
25+
26+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig06_competitor_event_study.pdf}
27+\caption{Own repricing around a competitor's price cut (S2). (a) Any sustained own cut: main specification, placebo with permuted competitors, and the specification with dealer-by-weekday fixed effects. (b) First sustained markdown. Bars are 95\% confidence intervals (dealer-clustered).}\label{fig:es}\end{figure}
28+
29+\subsection{Listing exit and its validation (H5)}
30+
31+Figure~\ref{fig:hazards}(b) shows the exit hazard. It is very high on the first day after entry (13~percent), which reflects listings that are posted and withdrawn within a day (dealer-to-dealer transfers, duplicate postings, feed corrections), then falls to 2~percent, rises to about 3~percent per day in the second and third weeks, and declines thereafter to under 1~percent per day beyond two months; the implied survival curves by source class in panel~(c) are similar. The discrete-time exit model (Table~\ref{tab:hazards}, column~3) confirms strong negative duration dependence after the first days and shows that cheaper and, above all, higher-mileage vehicles disappear faster (a 10~percent higher mileage lowers the exit hazard by 5~percent; a 10~percent higher price by 3~percent), and that older vehicles exit faster. The preregistered relative-price test of H5 is not supported: the coefficient on the entry hedonic residual is positive (0.18 in the cloglog, 0.13 in the Cox model) but imprecise once errors are clustered by dealer ($p = 0.42$ and $0.54$), and the current price position relative to competitors has a negative but insignificant coefficient. Overpriced listings are cut sooner and more deeply (previous subsection), but they do not leave the market faster or more slowly within the horizon of the data. Competitor density raises the exit hazard mildly (Cox coefficient 0.05, $p = 0.09$). The Schoenfeld tests reject proportional hazards for most covariates in both models, which is expected given the shape of the first-day hazard; the discrete-time models with listing-age bins do not impose proportionality and the log-logistic accelerated-failure-time estimates (replication package) agree in sign.
32+
33+Because exit is not a sale, we validate it in three ways. First, for the 17{,}898 vehicles observed simultaneously on a dealer's own website and on the Otogo portal (matched by VIN, overlapping spells), exits coincide closely: among the 8{,}541 pairs where both listings exited, 80~percent exited within one day of each other and 94~percent within a week (median gap 0.4~days). Second, the exceptions are informative about feed failures: in 11~percent of the dealer-site exits the portal copy was still listed seven days later, against 3~percent in the other direction, so dealer websites generate more spurious exits than the portal. Third, relistings bound the share of non-sale exits from below: of the 47{,}398 confirmed exits observable for 30~days, 10.2~percent were followed by a new listing of the same VIN within 30~days on a reliable source and 5.8~percent by a listing at a location more than 5~km away; the median gap for these dealer-to-dealer transfers is 1.1~days (upper quartile 8.7~days). Over the whole window, 10{,}139 of 95{,}968 VINs (10.6~percent) were listed at two or more locations at least 5~km apart. Listing exit is therefore a consistent, cross-source event that overstates retail sales by at least one tenth, and we label it ``observed disappearance'' throughout.
34+
35+\subsection{Prediction (Model F)}
36+
37+Table~\ref{tab:prediction} and Figure~\ref{fig:pred} report the out-of-sample prediction of a first markdown and of an exit within 30~days of entry, from entry information only. On the chronological split (entries before 15~September used to predict entries from 15 to 25~September, the latest cohort whose 30-day outcomes are fully observable), gradient boosting attains a log loss of 0.405 for markdowns (area under the curve 0.785; base rate 0.19) and 0.585 for exits (0.727; base rate 0.59), both well calibrated (expected calibration error below 0.02), and improves on the logistic baseline. The gains disappear when dealers or regions are held out: areas under the curve fall to 0.58--0.61 and the logistic model does as well or better, because the single most informative feature is the dealer's own markdown or exit rate in the training data (a third of the gradient-boosting gain). Thirty-day repricing and exit are, to a first approximation, dealer practices rather than vehicle attributes, and they do not transfer to unseen dealers. A Cox model for time to exit with entry covariates attains a concordance of 0.66 on the later cohort.
38+
39+\begin{table}[tbp]\centering\caption{Out-of-sample prediction of 30-day markdown and exit (S2, entry information only)}\label{tab:prediction}
40+\resizebox{\textwidth}{!}{\input{tables/tab08_prediction}}
41+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Chronological = train on entries before 2026-09-15, test on 2026-09-15 to 2026-09-25 (latest entries with fully observable 30-day outcomes); dealer holdout = five-fold grouped cross-validation by dealer (pooled out-of-fold); region LORO = leave-one-region-out over the six largest regions (mean). 95\% intervals from 200 bootstrap resamples of test rows. Exit = observed disappearance, not a sale.\end{minipage}
42+\end{table}
43+
44+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig10_prediction.pdf}
45+\caption{Prediction performance. (a) Reliability diagrams on the chronological split. (b) Log loss by split and model with bootstrap intervals.}\label{fig:pred}\end{figure}
added paper/sections/results_prices.tex +33 −0
@@ -0,0 +1,33 @@
1+\section{Price formation and depreciation}\label{sec:prices}
2+
3+\subsection{Hedonic asking prices}
4+
5+Table~\ref{tab:hedonic} reports six specifications of equation~\eqref{eq:hedonic} on the 136{,}393 listings of S1. The linear benchmark (column~1) implies that asking prices fall by 8.5~percent per year of age and 3.2~percent per 10{,}000~km, conditional on make--model, entry week, source class and region; make--model effects alone explain most of the cross-sectional variance ($R^2 = 0.92$). Adding spline terms (column~2) raises the within-$R^2$ from 0.836 to 0.846 and lowers the in-sample root-mean-squared error from 0.167 to 0.162 log points; the tensor interaction (column~3) and the powertrain interactions (column~4) add little in sample. Trim fixed effects (column~5) reduce the error to 0.107 and dealer fixed effects (column~6) to 0.091, which shows that a substantial share of residual price variation is dealer-specific pricing rather than vehicle heterogeneity.
6+
7+\begin{table}[tbp]\centering\caption{Hedonic asking-price models (S1, dependent variable: log entry price)}\label{tab:hedonic}
8+\resizebox{\textwidth}{!}{\input{tables/tab04_hedonic}}
9+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Standard errors clustered by dealer in parentheses; $^{*}$, $^{**}$, $^{***}$: 10, 5, 1 percent. Columns (2)--(6) add orthogonalised cubic B-spline terms in age and mileage (not shown); in column (3) the linear age and mileage coefficients are not separately interpretable because of the tensor terms. Reference categories: combustion engine, automatic transmission, all-wheel drive, dealer website, Montréal. Grouped-dealer CV = root-mean-squared error in five-fold cross-validation with dealers held out; a gradient-boosting model with the same inputs attains 0.159.\end{minipage}
10+\end{table}
11+
12+\begin{table}[tbp]\centering\caption{Hypothesis tests on the hedonic models (cluster-robust Wald tests)}\label{tab:hedtests}
13+\tabsize\input{tables/tab04b_hedonic_tests}
14+\end{table}
15+
16+\subsection{Nonlinear depreciation (H1)}
17+
18+The preregistered test of H1 is decisive (Table~\ref{tab:hedtests}): the nonlinear age and mileage terms are jointly significant ($\chi^2(12) = 664$), as are the tensor interaction ($\chi^2(49) = 667$) and the interaction of the age profile with powertrain ($\chi^2(35) = 312$). The out-of-sample criterion is met as well: with dealers held out, the spline specification lowers the root-mean-squared error from 0.176 to 0.171 log points (2.8~percent), beyond the 0.5~percent threshold set in the analysis plan; the tensor model does not improve on the additive splines out of sample (0.172), and gradient boosting, which can exploit make, model and trim interactions freely, reaches 0.159. Figure~\ref{fig:surface} shows the estimated surface. Relative to a one-year-old vehicle with 10{,}000~km of the same make and model, the quality-adjusted asking price is 14~percent lower at one year and 50{,}000~km, 27~percent lower at three years, 37~percent lower at five years and 57~percent lower at ten years (all at 50{,}000~km); at five years, moving from 10{,}000 to 200{,}000~km lowers the price from 28 to 59~percent below the reference. Age profiles are concave early and close to linear in logs afterwards, and the mileage gradient is steeper for younger vehicles, which is the signature of a market that prices expected remaining service rather than the odometer alone \citep{engers2009annualmiles}.
19+
20+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig02_depreciation_surface.pdf}
21+\caption{Quality-adjusted depreciation surface (Model A2t). (a) Predicted log asking price relative to a one-year-old vehicle with 10{,}000~km of the same make and model, over age and mileage; cells without data support are blank. (b) Age profiles at fixed mileage.}\label{fig:surface}\end{figure}
22+
23+\subsection{Regional segmentation (H4)}
24+
25+Table~\ref{tab:regions} and Figure~\ref{fig:map} report the region effects. With make--model--trim fixed effects, mileage, age, powertrain, body, transmission, drivetrain, entry week and source class held fixed, the sixteen regional premia relative to Montréal range from $-1.0$~percent (Lanaudière) to $+3.7$~percent (Bas-Saint-Laurent); none is individually significant at the 5~percent level with dealer-clustered standard errors, and the joint test does not reject equality ($\chi^2(16) = 14.9$, $p = 0.53$). The point estimates have a mild geographic pattern, with the remote regions (Bas-Saint-Laurent, Abitibi-Témiscamingue, Gaspésie, Saguenay) 2--4~percent above Montréal, consistent with transport costs and thinner local supply, but the data cannot distinguish these from zero. The Outaouais, which borders Ontario, asks 2.1~percent more than Montréal ($p = 0.14$), so there is no evidence of a cross-border discount. Within-specification dispersion, measured by the interquartile range of A2 residuals, is larger in the remote regions, that is, prices for the same vehicle are more dispersed where there are fewer competing listings. We therefore read H4 as a precisely estimated null: the Quebec used-car market is integrated in quality-adjusted asking prices, at least at the resolution of administrative regions and within a single quarter.
26+
27+\begin{table}[tbp]\centering\caption{Quality-adjusted regional premia and dispersion}\label{tab:regions}
28+\tabsize\input{tables/tab06b_region_premia}
29+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: A4 = Model A4 (make--model--trim FE); A2 = Model A2 (make--model FE); 95\% confidence intervals from dealer-clustered standard errors. Residual IQR = interquartile range of A2 residuals among S1 listings in the region.\end{minipage}
30+\end{table}
31+
32+\begin{figure}[tbp]\centering\includegraphics[width=\textwidth]{figures/fig07_regional_premia_map.pdf}
33+\caption{Regional premia. (a) Listing locations (hexagonal bins, log scale) and region effects (marker size proportional to the number of listings, colour = premium versus Montréal). (b) Region effects from Model A4 with 95\% confidence intervals.}\label{fig:map}\end{figure}
added paper/sections/robustness.tex +33 −0
@@ -0,0 +1,33 @@
1+\section{Robustness, identification threats and limitations}\label{sec:robust}
2+
3+\subsection{Sensitivity analyses}
4+
5+Table~\ref{tab:robust} summarises the sensitivity of the headline estimates to the main measurement and sample choices, following the list fixed in the analysis plan: alternative markdown thresholds (0.5 and 2~percent), transient rules (24 and 72~hours), exit-confirmation windows (3 and 14~days), inclusion of the sampled portals and of private-seller listings in the hedonic sample, wider price filters, inclusion of the left-censored stock in the hazards, heterogeneity of the depreciation slopes by powertrain, model-year band, body class and source class, composition reweighting of the dealer-website sample to the portal's make--model--year distribution, alternative standardisation, aggregation and minimum-size rules for the liquidity index, and competitor radii of 10 and 50~km (Table~\ref{tab:competition}).
6+
7+\IfFileExists{tables/tab09_robustness.tex}{%
8+\begin{table}[tbp]\centering\caption{Identification threats and sensitivity summary}\label{tab:robust}
9+\scriptsize\input{tables/tab09_robustness}
10+\begin{minipage}{\textwidth}\scriptsize\smallskip Notes: Each row re-estimates the baseline model under the alternative rule and reports the quantity in the second column. Details and all variants are in the replication package (\texttt{results/robustness/}).\end{minipage}
11+\end{table}}{\emph{[Table 9 is generated by \texttt{scripts/12\_robustness.py}.]}}
12+
13+\subsection{Multiple testing}
14+
15+The six primary preregistered tests are: H1 (joint nonlinearity, $p < 10^{-100}$), H2 (listing-age profile of the markdown hazard, $p < 10^{-30}$), H3 (sum of lag coefficients $k = 0$ to $7$, $p < 10^{-12}$, an association), H4 (joint region test, $p = 0.53$), H5 (entry hedonic residual in the exit hazard, $p = 0.42$) and H6 (held-out validation slope, $p < 10^{-10}$). Holm's step-down adjustment leaves the conclusions unchanged: H1, H2, H3 and H6 remain significant at any conventional level and H4 and H5 are not rejected. The null results are therefore not artefacts of the number of tests, and the positive ones are not artefacts of selective reporting; every estimated specification, including failed and null ones, is retained in the experiment log.
16+
17+\subsection{Identification threats}
18+
19+\paragraph{Asking versus transaction prices.} Every price in this paper is a dealer's posted price. The hedonic coefficients measure how dealers \emph{price} quality, not what buyers pay; negotiated discounts, financing and trade-in terms are unobserved. Because Quebec law requires all-in advertised prices, posted prices are comparable across dealers, but the level of the asking--transaction gap and its variation with listing age are unknown here, as in the housing literature on list prices \citep{han2016askingprice,haurin2010listprices}.
20+
21+\paragraph{Exit versus sale.} Listing disappearance is an event on a website. Cross-source matching shows that it is consistent (94~percent of matched exits coincide within a week) and that at least a tenth of exits are relistings; the true share of retail sales among exits is unknown and may vary by dealer and vehicle, which would bias the exit hazards if the variation is correlated with covariates. The exit results should be read as describing \emph{inventory turnover on dealer websites}.
22+
23+\paragraph{Composition and selection.} The hedonic sample is the universe of listings on registry sources, not a random sample of Quebec dealers; the registry over-represents the Capitale-Nationale, Bas-Saint-Laurent, Chaudière-Appalaches and Côte-Nord regions relative to population, and under-represents Montréal. Region effects are identified from variation across dealers located in different regions and cannot be separated from dealer effects for single-location dealers; the reweighting exercise addresses composition but not unobserved dealer quality. Left-censored stock is excluded from durations, which removes the vehicles that had already been on the lot longest; the sensitivity analysis with the left-censored stock included (entry set at first observation, a lower bound on listing age) is reported in Table~\ref{tab:robust}.
24+
25+\paragraph{Reflection and common shocks.} The competitor event study cannot distinguish a dealer's response to a neighbour's cut from a common shock that moves both; the same-day concentration, the weekly harmonics, the significant leads and the robustness to dealer-by-weekday effects all point to the latter. An exogenous shifter of competitor repricing (for example, a platform-specific feed outage or a manufacturer programme that affects one brand's dealers only) would be needed for a causal estimate, and we have not found one in this window.
26+
27+\paragraph{Window length and seasonality.} Eleven weeks of late summer and autumn cannot identify seasonal patterns, the response to macroeconomic events (the Bank of Canada raised its policy rate on 28~October 2026, three days before the end of the panel), or the long-run dynamics of vehicle cohorts. Listing durations longer than about 70~days are right-censored by construction, and the plateau of the markdown hazard beyond three weeks is estimated on a shrinking risk set.
28+
29+\paragraph{Measurement of the index.} The liquidity index is validated internally only; its components are mechanically related to one another through the stock of listings (exits and arrivals both enter per listing-day), and the held-out test uses next-week exits, which are themselves observed disappearances. The index should be read as a descriptive summary of turnover on dealer websites until external benchmarks become available.
30+
31+\subsection{Economic and policy implications}
32+
33+Three implications follow with appropriate caution. For dealers, the evidence that cuts are frequent, shallow and increasing in listing age, that overpriced vehicles are cut sooner and more deeply, and that deep cuts characterise slow markets, suggests that entry prices close to hedonic value shorten time on the lot without larger total concessions, a hypothesis that transaction data could test. For consumers and regulators, the regional null means that a Quebec buyer gains little, on average, from searching across regions for the same vehicle once quality is held fixed, although dispersion within remote regions is larger. For statistical agencies, the paper shows that listing panels can deliver a timely, reproducible turnover index at the regional level, provided that connector artefacts (sampled portals, start-up stock, oscillating feeds, flickering connectors) are diagnosed and excluded; the audit trail documented here is a template for that work.
added paper/sections/titlepage.tex +77 −0
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1+% ============================================================================
2+% Title Page
3+% ============================================================================
4+\thispagestyle{empty}
5+
6+\begin{center}
7+
8+% --- Logo ---
9+\includegraphics[width=3.5cm]{uq_logo.jpg}
10+
11+\vspace{0.6cm}
12+
13+{\footnotesize\textsc{Université du Québec en Outaouais}}\\[0.15cm]
14+{\footnotesize\textsc{Département des sciences administratives}}
15+
16+\vspace{0.8cm}
17+
18+{\footnotesize\textsc{Working Paper No.~\WPnumber}}
19+
20+\vspace{1.2cm}
21+
22+% --- Title ---
23+{\LARGE\bfseries \WPtitle\par}
24+
25+\ifx\WPsubtitle\empty\else
26+ \vspace{0.3cm}
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35+{\normalsize \href{mailto:\WPemail}{\WPemail}}\\[0.15cm]
36+{\small \WPaddress}
37+
38+\vspace{0.8cm}
39+
40+% --- Date & Version ---
41+{\normalsize \WPdate}\\[0.1cm]
42+{\small Version~\WPversion}
43+
44+\end{center}
45+
46+\vfill
47+
48+\newpage
49+
50+% ============================================================================
51+% Abstract Page
52+% ============================================================================
53+\thispagestyle{empty}
54+
55+\vspace*{1cm}
56+
57+\noindent\rule{\textwidth}{0.4pt}
58+\vspace{0.3cm}
59+
60+\noindent\textbf{Abstract}
61+
62+\vspace{0.15cm}
63+
64+\noindent\WPabstract
65+
66+\vspace{0.4cm}
67+
68+\noindent\textbf{Keywords:} \WPkeywords
69+
70+\vspace{0.15cm}
71+
72+\noindent\textbf{JEL Classification:} \WPjel
73+
74+\vspace{0.3cm}
75+\noindent\rule{\textwidth}{0.4pt}
76+
77+\newpage
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1+% ============================================================================
2+% ANONYMOUS TITLE PAGE — for double-anonymized journal submission
3+% Used by paper/main_anon.tex (same macros as main.tex, no author block,
4+% no institution, no logo). Abstract/keywords/JEL page identical to titlepage.tex.
5+% ============================================================================
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20+{\normalsize [Author identity withheld for review]}
21+
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24+{\normalsize \WPdate}
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26+\end{center}
27+
28+\vfill
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30+\newpage
31+\thispagestyle{empty}
32+\vspace*{1cm}
33+
34+\noindent\rule{\textwidth}{0.4pt}
35+\vspace{0.3cm}
36+
37+\noindent\textbf{Abstract}
38+\vspace{0.15cm}
39+
40+\noindent\WPabstract
41+
42+\vspace{0.4cm}
43+
44+\noindent\textbf{Keywords:} \WPkeywords
45+
46+\vspace{0.15cm}
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48+\noindent\textbf{JEL Classification:} \WPjel
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53+\newpage
added paper/tables/tab01_sources.tex +14 −0
@@ -0,0 +1,14 @@
1+\begin{tabular}{lrrrrrcc}
2+\toprule
3+Source class & Sources & Dealers & Listings & Active & Found/cycle & S1 & S2 \\
4+\midrule
5+Dealer websites & 131 & 131 & 33\,061 & 12\,222 & 12\,523 & Yes & Yes (113 of 131) \\
6+Full-inventory portals & 2 & 929 & 107\,903 & 39\,125 & 36\,693 & Yes & Yes \\
7+Dealer groups & 5 & 5 & 17\,162 & 5\,579 & 5\,131 & Yes & Yes (4 of 5) \\
8+Sampled portals & 2 & 1\,313 & 103\,027 & 2\,654 & 2\,359 & No & No \\
9+Private sellers (Kijiji) & 1 & 1 & 11\,874 & 3\,158 & 3\,041 & No & No \\
10+Small portals & 2 & 2 & 238 & 83 & 194 & Yes & No \\
11+\midrule
12+All sources & 143 & 2\,381 & 273\,265 & 62\,821 & 59\,941 & & \\
13+\bottomrule
14+\end{tabular}
added paper/tables/tab02_sample_construction.tex +18 −0
@@ -0,0 +1,18 @@
1+\begin{tabular}{lrr}
2+\toprule
3+Step & Remaining & Dropped \\
4+\midrule
5+S0 all listings in snapshot & 273\,265 & -- \\
6+$-$ kind != auto & 271\,992 & 1\,273 \\
7+$-$ VIN duplicate (non-canonical) & 259\,133 & 12\,859 \\
8+$-$ model year outside [2005,2026] or missing & 256\,651 & 2\,482 \\
9+$-$ mileage outside [1000,400000] km or missing & 249\,958 & 6\,693 \\
10+$-$ entry price outside [2000,250000] \$ or missing/placeholder & 241\,321 & 8\,637 \\
11+$-$ make or model missing & 241\,298 & 23 \\
12+$-$ uid identity collision \hfill $\Rightarrow$ S1 (all sources) & 241\,297 & 1 \\
13+$-$ sampled portal (autotrader/cargurus) or private marketplace (kijiji) \hfill $\Rightarrow$ S1 hedonic & 136\,393 & 104\,904 \\
14+$-$ flickering/failed connector or small portal & 128\,144 & 8\,249 \\
15+$-$ oscillating price path \hfill $\Rightarrow$ S2L & 128\,091 & 53 \\
16+$-$ left-censored stock (day 1 or source start-up day) & 80\,856 & 47\,235 \\
17+\bottomrule
18+\end{tabular}
added paper/tables/tab03_descriptives.tex +59 −0
@@ -0,0 +1,59 @@
1+\begin{tabular}{lrr}
2+\toprule
3+ & S1 hedonic & S2 dynamic \\
4+\midrule
5+Listings (N) & 136\,393 & 80\,856 \\
6+Dealers & 1\,046 & 982 \\
7+Entry asking price, mean (\$) & 29\,644 & 29\,942 \\
8+Entry asking price, median (\$) & 25\,627 & 25\,995 \\
9+Mileage, mean (km) & 77\,817 & 74\,387 \\
10+Vehicle age at entry, mean (years) & 5.7 & 5.5 \\
11+\addlinespace
12+\multicolumn{3}{l}{\textit{Powertrain (\% of listings)}} \\
13+\quad ICE & 81.3 & 80.4 \\
14+\quad HEV & 3.6 & 3.7 \\
15+\quad PHEV & 1.7 & 1.9 \\
16+\quad BEV & 6.1 & 7.1 \\
17+\quad Diesel & 1.7 & 1.4 \\
18+\quad Unknown & 5.6 & 5.5 \\
19+\addlinespace
20+\multicolumn{3}{l}{\textit{Body type, top 5 (\% of listings)}} \\
21+\quad SUV & 56.7 & 57.1 \\
22+\quad Sedan & 20.8 & 20.7 \\
23+\quad Pickup & 10.6 & 10.3 \\
24+\quad Hatchback & 4.2 & 4.4 \\
25+\quad Van & 3.1 & 3.0 \\
26+\addlinespace
27+\multicolumn{3}{l}{\textit{Listing dynamics}} \\
28+\quad Completed spells (confirmed exits) & 47\,934 & 43\,734 \\
29+\quad Duration of completed spells, median (days) & 8.9 & 8.9 \\
30+\quad Listings with $\geq 1$ sustained markdown (\%) & 27.2 & 19.7 \\
31+\quad First markdown, median (\% of entry price) & 2.9 & 2.8 \\
32+\bottomrule
33+\end{tabular}
34+
35+\begin{tabular}{lrrrrr}
36+\toprule
37+Region & N (S1) & Median price (\$) & Median km & Median age (y) & Exit $\leq$ 30 d (\%) \\
38+\midrule
39+Montréal & 12\,120 & 28\,995 & 56\,032 & 4.0 & 65.1 \\
40+Laval & 8\,968 & 26\,495 & 68\,084 & 4.9 & 58.1 \\
41+Montérégie & 26\,490 & 25\,488 & 73\,319 & 4.9 & 61.3 \\
42+Laurentides & 12\,654 & 25\,696 & 71\,500 & 4.9 & 53.8 \\
43+Lanaudière & 8\,549 & 26\,495 & 44\,422 & 3.9 & 56.3 \\
44+Capitale-Nationale & 17\,015 & 26\,555 & 55\,315 & 4.0 & 63.6 \\
45+Chaudière-Appalaches & 9\,176 & 25\,322 & 76\,918 & 5.0 & 53.2 \\
46+Estrie & 4\,627 & 23\,995 & 78\,690 & 5.0 & 56.8 \\
47+Mauricie & 7\,146 & 23\,995 & 68\,997 & 4.9 & 61.1 \\
48+Centre-du-Québec & 3\,839 & 25\,688 & 72\,640 & 4.9 & 52.0 \\
49+Outaouais & 4\,348 & 24\,988 & 78\,546 & 5.1 & 55.9 \\
50+Bas-Saint-Laurent & 4\,423 & 23\,995 & 76\,619 & 5.9 & 60.3 \\
51+Saguenay–Lac-Saint-Jean & 4\,009 & 25\,995 & 72\,220 & 5.9 & 69.2 \\
52+Abitibi-Témiscamingue & 3\,338 & 26\,970 & 73\,252 & 5.0 & 53.2 \\
53+Côte-Nord & 2\,909 & 25\,490 & 65\,486 & 4.9 & 57.6 \\
54+Gaspésie–Îles-de-la-Madeleine & 741 & 23\,995 & 79\,782 & 5.0 & 50.8 \\
55+Region missing & 6\,041 & 21\,995 & 85\,479 & 5.9 & 43.2 \\
56+\midrule
57+All regions & 136\,393 & 25\,627 & 68\,629 & 4.9 & 59.3 \\
58+\bottomrule
59+\end{tabular}
added paper/tables/tab04_hedonic.tex +41 −0
@@ -0,0 +1,41 @@
1+\begin{tabular}{lcccccc}
2+\toprule
3+& (1) & (2) & (3) & (4) & (5) & (6) \\
4+& A1 linear & A2 splines & A2t tensor & A3 splines $\times$ powertrain & A4 splines, trim FE & A5 + dealer FE \\
5+\midrule
6+Vehicle age (years) & -0.085$^{***}$ & -0.085$^{***}$ & 0.010 & -0.085$^{***}$ & -0.083$^{***}$ & -0.080$^{***}$ \\
7+ & (0.001) & (0.001) & (0.015) & (0.001) & (0.001) & (0.001) \\
8+Mileage (10\,000 km) & -0.031$^{***}$ & -0.032$^{***}$ & 0.076 & -0.032$^{***}$ & -0.030$^{***}$ & -0.028$^{***}$ \\
9+ & (0.000) & (0.000) & (0.442) & (0.000) & (0.001) & (0.000) \\
10+Battery electric & 0.056$^{***}$ & 0.060$^{***}$ & 0.059$^{***}$ & 0.099$^{***}$ & 0.026 & 0.039$^{***}$ \\
11+ & (0.011) & (0.011) & (0.011) & (0.023) & (0.019) & (0.014) \\
12+Hybrid & 0.072$^{***}$ & 0.075$^{***}$ & 0.074$^{***}$ & 0.047$^{***}$ & 0.044$^{***}$ & 0.031$^{***}$ \\
13+ & (0.008) & (0.008) & (0.008) & (0.013) & (0.011) & (0.010) \\
14+Plug-in hybrid & 0.106$^{***}$ & 0.118$^{***}$ & 0.117$^{***}$ & 0.134 & 0.031$^{***}$ & 0.033$^{***}$ \\
15+ & (0.017) & (0.019) & (0.019) & (0.719) & (0.012) & (0.011) \\
16+Diesel & 0.079$^{***}$ & 0.091$^{***}$ & 0.090$^{***}$ & -0.011 & 0.057$^{***}$ & 0.047$^{***}$ \\
17+ & (0.010) & (0.009) & (0.009) & (0.014) & (0.015) & (0.013) \\
18+Manual transmission & -0.034$^{***}$ & -0.023$^{**}$ & -0.023$^{**}$ & -0.023$^{**}$ & -0.060$^{***}$ & -0.055$^{***}$ \\
19+ & (0.011) & (0.009) & (0.009) & (0.009) & (0.011) & (0.008) \\
20+Front-wheel drive & -0.029$^{***}$ & -0.024$^{***}$ & -0.024$^{***}$ & -0.024$^{***}$ & -0.018$^{***}$ & -0.024$^{***}$ \\
21+ & (0.006) & (0.005) & (0.005) & (0.005) & (0.006) & (0.004) \\
22+Rear-wheel drive & -0.043$^{**}$ & -0.039$^{*}$ & -0.043$^{**}$ & -0.039$^{*}$ & -0.051$^{**}$ & -0.060$^{***}$ \\
23+ & (0.021) & (0.020) & (0.019) & (0.020) & (0.020) & (0.019) \\
24+Full-inventory portal & -0.041$^{***}$ & -0.032$^{***}$ & -0.033$^{***}$ & -0.032$^{***}$ & -0.030$^{***}$ & absorbed \\
25+ & (0.012) & (0.010) & (0.010) & (0.010) & (0.012) & \\
26+Dealer group & -0.011 & -0.021$^{**}$ & -0.021$^{**}$ & -0.021$^{**}$ & -0.020 & absorbed \\
27+ & (0.011) & (0.010) & (0.010) & (0.010) & (0.013) & \\
28+\midrule
29+Nonlinear age/km terms & no & yes & yes & yes & yes & yes \\
30+Tensor age $\times$ km & no & no & yes & no & no & no \\
31+Quality FE & make-model & make-model & make-model & make-model & make-model-trim & make-model-trim \\
32+Entry-week FE & yes & yes & yes & yes & yes & yes \\
33+Region dummies & yes & yes & yes & yes & yes & absorbed \\
34+Dealer FE & no & no & no & no & no & yes \\
35+Observations & 136\,393 & 136\,393 & 136\,393 & 136\,393 & 136\,393 & 136\,393 \\
36+$R^2$ & 0.923 & 0.928 & 0.929 & 0.928 & 0.968 & 0.977 \\
37+Within $R^2$ & 0.836 & 0.846 & 0.848 & 0.847 & 0.801 & 0.777 \\
38+In-sample RMSE (log) & 0.167 & 0.162 & 0.161 & 0.161 & 0.107 & 0.091 \\
39+Grouped-dealer CV RMSE & 0.176 & 0.171 & 0.172 & -- & 0.161 & -- \\
40+\bottomrule
41+\end{tabular}
added paper/tables/tab04b_hedonic_tests.tex +12 −0
@@ -0,0 +1,12 @@
1+\begin{tabular}{lccc}
2+\toprule
3+Test & $\chi^2$ & df & $p$ \\
4+\midrule
5+H1: nonlinear age terms = 0 (A2) & 340.0 & 6 & 2.12e-70 \\
6+H1: nonlinear mileage terms = 0 (A2) & 412.9 & 6 & 4.77e-86 \\
7+H1: all nonlinear terms = 0 (A2) & 663.8 & 12 & 2.45e-134 \\
8+H1: age $\times$ mileage tensor = 0 (A2t) & 662.6 & 49 & 1.88e-108 \\
9+H1: age profile $\times$ powertrain = 0 (A3) & 312.2 & 35 & 3.27e-46 \\
10+H4: region effects = 0 (A4) & 14.9 & 16 & 0.534 \\
11+\bottomrule
12+\end{tabular}
added paper/tables/tab05_hazards.tex +32 −0
@@ -0,0 +1,32 @@
1+\begin{tabular}{lccccc}
2+\toprule
3+& \multicolumn{2}{c}{First markdown} & \multicolumn{2}{c}{Listing exit} & Cut size \\
4+\cmidrule(lr){2-3}\cmidrule(lr){4-5}\cmidrule(lr){6-6}
5+& cloglog & Cox & cloglog & Cox & log(\%) \\
6+\midrule
7+log(1 + competitors within 25 km) & 0.019 & 0.114$^{***}$ & 0.033 & 0.050$^{*}$ & -0.027$^{**}$ \\
8+ & (0.016) & (0.025) & (0.030) & (0.029) & (0.011) \\
9+Entry hedonic residual (relative price) & 0.256 & 0.716$^{***}$ & 0.180 & 0.129 & 2.224$^{***}$ \\
10+ & (0.186) & (0.206) & (0.225) & (0.209) & (0.221) \\
11+Price position vs competitors at entry & 0.032 & 0.356$^{***}$ & -- & -0.155 & -- \\
12+ & (0.083) & (0.116) & & (0.125) & \\
13+Current price position vs competitors & -- & -- & -0.159 & -- & -- \\
14+ & & & (0.139) & & \\
15+Prior sustained cuts (count) & -- & -- & 0.033 & -- & -- \\
16+ & & & (0.036) & & \\
17+log entry price & -0.134$^{***}$ & 0.091 & -0.307$^{***}$ & -0.256$^{***}$ & -1.526$^{***}$ \\
18+ & (0.049) & (0.079) & (0.111) & (0.095) & (0.117) \\
19+log mileage & -0.079$^{***}$ & 0.106$^{***}$ & -0.512$^{***}$ & -0.490$^{***}$ & -0.194$^{***}$ \\
20+ & (0.024) & (0.039) & (0.070) & (0.070) & (0.018) \\
21+Vehicle age at entry (years) & 0.013$^{**}$ & -0.010 & 0.034$^{**}$ & 0.038$^{***}$ & -0.069$^{***}$ \\
22+ & (0.006) & (0.012) & (0.015) & (0.014) & (0.010) \\
23+Own-dealer same-model stock & 0.000 & -- & 0.009 & -- & -- \\
24+ & (0.002) & & (0.006) & & \\
25+\midrule
26+Listing-age bins (joint $\chi^2$, df 8) & 169.8 & -- & 103.5 & -- & -- \\
27+Listing-days / listings & 318\,323 & 79\,699 & 1\,663\,764 & 79\,699 & 15\,692 \\
28+Events & 15\,692 & 15\,692 & 43\,311 & 43\,311 & -- \\
29+Concordance / within $R^2$ & -- & 0.575 & -- & 0.648 & 0.233 \\
30+PH test: covariates with $p<0.01$ & -- & 5 & -- & 9 & -- \\
31+\bottomrule
32+\end{tabular}
added paper/tables/tab06_competition.tex +31 −0
@@ -0,0 +1,31 @@
1+\begin{tabular}{lcccccccc}
2+\toprule
3+& Main, 25 km & Cluster: region & 10 km & 50 km & Coarse FE & Placebo & + dealer $\times$ weekday FE & First cut, + dealer $\times$ weekday \\
4+\midrule
5+$k=-7$ & 0.42 & 0.42 & 0.59 & 0.40 & 0.35 & -0.04 & 0.12 & -0.98 \\
6+ & (0.15) & (0.11) & (0.15) & (0.12) & (0.14) & (0.04) & (0.14) & (0.34) \\
7+$k=-3$ & -0.04 & -0.04 & 0.09 & -0.07 & 0.04 & 0.07 & 0.00 & -0.84 \\
8+ & (0.06) & (0.09) & (0.12) & (0.05) & (0.06) & (0.04) & (0.05) & (0.20) \\
9+$k=-1$ & -0.07 & -0.07 & -0.00 & -0.08 & 0.10 & -0.00 & -0.03 & -0.43 \\
10+ & (0.12) & (0.08) & (0.20) & (0.08) & (0.11) & (0.03) & (0.10) & (0.29) \\
11+$k=0$ & 0.89 & 0.89 & 1.80 & 0.63 & 1.10 & 0.01 & 0.55 & 1.77 \\
12+ & (0.21) & (0.21) & (0.55) & (0.13) & (0.20) & (0.04) & (0.20) & (0.50) \\
13+$k=+1$ & -0.11 & -0.11 & 0.11 & -0.20 & 0.07 & 0.04 & -0.01 & 0.31 \\
14+ & (0.20) & (0.12) & (0.44) & (0.17) & (0.20) & (0.04) & (0.18) & (0.45) \\
15+$k=+2$ & -0.24 & -0.24 & -0.15 & -0.28 & -0.11 & -0.04 & -0.16 & -0.52 \\
16+ & (0.07) & (0.07) & (0.11) & (0.06) & (0.06) & (0.04) & (0.07) & (0.20) \\
17+$k=+3$ & -0.08 & -0.08 & 0.12 & -0.10 & 0.04 & -0.04 & -0.03 & 0.07 \\
18+ & (0.08) & (0.08) & (0.10) & (0.07) & (0.08) & (0.04) & (0.06) & (0.23) \\
19+$k=+7$ & 0.45 & 0.45 & 0.66 & 0.54 & 0.42 & 0.10 & 0.17 & 0.65 \\
20+ & (0.08) & (0.08) & (0.17) & (0.06) & (0.08) & (0.06) & (0.06) & (0.20) \\
21+$k=+14$ & 0.59 & 0.59 & 0.81 & 0.55 & 0.57 & 0.09 & 0.33 & 1.04 \\
22+ & (0.11) & (0.09) & (0.22) & (0.09) & (0.11) & (0.08) & (0.09) & (0.33) \\
23+\midrule
24+Sum of leads $k=-7..-1$ & 0.37 & 0.37 & 0.90 & 0.24 & 0.69 & 0.05 & 0.50 & -5.14 \\
25+\quad joint $p$ & 3.2e-05 & 1.5e-13 & 3.7e-06 & 3.8e-07 & 0.00087 & 0.22 & 2.5e-05 & 6.7e-08 \\
26+Sum of lags $k=0..7$ & 0.95 & 0.95 & 2.92 & 0.73 & 1.70 & -0.03 & 0.71 & 3.29 \\
27+\quad joint $p$ & 4.3e-13 & 1.3e-147 & 2.9e-08 & 3e-21 & 6.5e-18 & 0.18 & 0.00021 & 3.5e-09 \\
28+Daily base rate (\%) & 1.44 & 1.44 & 1.44 & 1.44 & 1.44 & 1.44 & 1.44 & 4.93 \\
29+Listing-days & 1\,663\,764 & 1\,663\,764 & 1\,663\,764 & 1\,663\,764 & 1\,663\,764 & 1\,663\,764 & 1\,663\,764 & 318\,323 \\
30+\bottomrule
31+\end{tabular}
added paper/tables/tab06b_region_premia.tex +22 −0
@@ -0,0 +1,22 @@
1+\begin{tabular}{lcccc}
2+\toprule
3+Region & A4 premium (\%) & 95\% CI & A2 premium (\%) & Residual IQR \\
4+\midrule
5+Bas-Saint-Laurent & 3.7 & [-2.5, 9.9] & 2.7 & 0.168 \\
6+Abitibi-Témiscamingue & 3.0 & [-0.1, 6.2] & 2.8 & 0.158 \\
7+Gaspésie–Îles-de-la-Madeleine & 2.7 & [-1.1, 6.5] & 2.1 & 0.154 \\
8+Mauricie & 2.4 & [-0.6, 5.4] & 2.0 & 0.131 \\
9+Saguenay–Lac-Saint-Jean & 2.3 & [-1.5, 6.1] & 2.5 & 0.156 \\
10+Outaouais & 2.1 & [-0.7, 4.9] & 1.4 & 0.137 \\
11+Estrie & 1.5 & [-1.9, 4.9] & 0.7 & 0.148 \\
12+Montérégie & 1.3 & [-1.4, 4.0] & 0.4 & 0.159 \\
13+Centre-du-Québec & 1.2 & [-2.4, 4.9] & 1.0 & 0.154 \\
14+Chaudière-Appalaches & 1.1 & [-3.3, 5.6] & -0.1 & 0.166 \\
15+Laval & 1.1 & [-3.2, 5.3] & 0.7 & 0.131 \\
16+Côte-Nord & 0.9 & [-2.0, 3.9] & 0.6 & 0.129 \\
17+Capitale-Nationale & 0.0 & [-3.2, 3.2] & 0.9 & 0.143 \\
18+Laurentides & -0.5 & [-4.0, 2.9] & -1.3 & 0.149 \\
19+Lanaudière & -1.0 & [-6.2, 4.1] & -0.8 & 0.147 \\
20+Montréal (reference) & 0 & -- & 0 & 0.166 \\
21+\bottomrule
22+\end{tabular}
added paper/tables/tab07_amli.tex +19 −0
@@ -0,0 +1,19 @@
1+\begin{tabular}{lcccc}
2+\toprule
3+Component & Equal & PCA loading & Supervised & Leave-one-out corr. \\
4+\midrule
5+Exit intensity & 0.20 & +0.41 & -- & 0.94 \\
6+Markdown frequency & 0.20 & +0.35 & +0.0012 & 0.91 \\
7+Markdown depth & 0.20 & -0.51 & -0.0039 & 0.95 \\
8+Residual dispersion (sign reversed) & 0.20 & +0.52 & -0.0016 & 0.91 \\
9+Arrival rate & 0.20 & +0.44 & +0.0015 & 0.94 \\
10+\midrule
11+PCA first-component share & \multicolumn{4}{c}{0.56} \\
12+Corr(equal, PCA) / Corr(equal, supervised) & \multicolumn{4}{c}{0.85 / 0.68} \\
13+Leave-exit-out AMLI $\to$ next-week exit (region FE) & \multicolumn{4}{c}{0.96 pp [0.69, 1.24], $n$ = 68} \\
14+\quad + lagged exit & \multicolumn{4}{c}{1.03 pp [0.70, 1.35], $n$ = 68} \\
15+\quad pooled OLS & \multicolumn{4}{c}{0.98 pp [0.59, 1.36], $n$ = 68} \\
16+Price-tier markets: PCA share / held-out slope & \multicolumn{4}{c}{0.44 / 0.46 pp [-0.64, 1.55]} \\
17+Body-type markets: PCA share / held-out slope & \multicolumn{4}{c}{0.46 / 0.46 pp [0.06, 0.86]} \\
18+\bottomrule
19+\end{tabular}
added paper/tables/tab08_prediction.tex +19 −0
@@ -0,0 +1,19 @@
1+\begin{tabular}{llllrrrr}
2+\toprule
3+Target & Split & Model & Log loss [95\% CI] & Brier & AUC & $n_{\text{test}}$ & Base rate \\
4+\midrule
5+Markdown $\le$ 30 d & Chronological & Logistic & 0.428 [0.417, 0.438] & 0.135 & 0.748 & 12\,953 & 0.193 \\
6+ & & LightGBM & 0.405 [0.395, 0.415] & 0.128 & 0.785 & 12\,953 & 0.193 \\
7+Markdown $\le$ 30 d & Dealer holdout & Logistic & 0.476 [0.469, 0.483] & 0.150 & 0.580 & 44\,003 & 0.184 \\
8+ & & LightGBM & 0.494 [0.486, 0.502] & 0.153 & 0.582 & 44\,003 & 0.184 \\
9+Markdown $\le$ 30 d & Region LORO & Logistic & 0.508 [0.500, 0.516] & 0.163 & 0.605 & 28\,947 & 0.198 \\
10+ & & LightGBM & 0.526 [0.516, 0.537] & 0.166 & 0.583 & 28\,947 & 0.198 \\
11+\addlinespace
12+Exit $\le$ 30 d & Chronological & Logistic & 0.609 [0.603, 0.616] & 0.212 & 0.705 & 12\,953 & 0.592 \\
13+ & & LightGBM & 0.585 [0.580, 0.591] & 0.204 & 0.727 & 12\,953 & 0.592 \\
14+Exit $\le$ 30 d & Dealer holdout & Logistic & 0.695 [0.694, 0.697] & 0.251 & 0.567 & 44\,003 & 0.593 \\
15+ & & LightGBM & 0.681 [0.678, 0.683] & 0.244 & 0.608 & 44\,003 & 0.593 \\
16+Exit $\le$ 30 d & Region LORO & Logistic & 0.690 [0.687, 0.693] & 0.248 & 0.584 & 28\,947 & 0.601 \\
17+ & & LightGBM & 0.689 [0.684, 0.692] & 0.247 & 0.607 & 28\,947 & 0.601 \\
18+\bottomrule
19+\end{tabular}
added paper/uq_logo.jpg +0 −0

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added pyproject.toml +42 −0
@@ -0,0 +1,42 @@
1+[build-system]
2+requires = ["setuptools>=69", "wheel"]
3+build-backend = "setuptools.build_meta"
4+
5+[project]
6+name = "ucml"
7+version = "0.1.0"
8+description = "The Hidden Dynamics of Used-Car Markets (UQO WP 36) — reproducible research code"
9+authors = [{ name = "Simon-Pierre Boucher", email = "simon-pierre.boucher@uqo.ca" }]
10+requires-python = ">=3.12,<3.13"
11+readme = "README.md"
12+dependencies = [
13+ "polars>=1.30",
14+ "pandas>=2.2",
15+ "numpy>=2.0",
16+ "pyarrow>=17",
17+ "duckdb>=1.1",
18+ "scipy>=1.14",
19+ "scikit-learn>=1.5",
20+ "lightgbm>=4.5",
21+ "statsmodels>=0.14",
22+ "linearmodels>=6.0",
23+ "lifelines>=0.30",
24+ "pyfixest>=0.25",
25+ "matplotlib>=3.9",
26+ "geopandas>=1.0",
27+ "shapely>=2.0",
28+ "pyyaml>=6.0",
29+ "typer>=0.12",
30+ "rich>=13",
31+ "requests>=2.32",
32+]
33+
34+[project.optional-dependencies]
35+dev = ["pytest>=8", "ruff>=0.6"]
36+
37+[tool.setuptools.packages.find]
38+where = ["src"]
39+include = ["ucml*"]
40+
41+[tool.pytest.ini_options]
42+testpaths = ["tests"]
added reports/data_access_audit.md +114 −0
@@ -0,0 +1,114 @@
1+# WP36 — Data access and schema audit (Phase 0)
2+
3+**Date of audit:** 2026-11-01 (snapshot taken 2026-11-01 ≈ 02:50 America/Toronto, last sync in snapshot 2026-11-01 06:49:42 UTC).
4+**Auditor:** lead research agent (Claude Code), on behalf of S.-P. Boucher (UQO).
5+**Status of the gate:** PASSED for H1, H2, H5 and the cross-sectional/dispersion parts of H4; CONDITIONAL for H3 (association only) and H6 (construction + internal validation only; no external forecasting test possible — see §7).
6+
7+## 1. Source inventory (evidence-backed)
8+
9+| Item | Finding | Evidence |
10+|---|---|---|
11+| Platform | Auto-Ka (www.auto-ka.com), independent Quebec used-vehicle aggregator built and operated by the author (Groupe KA). | `M4M64b:~/auto-ka/README.md`, `mld status` (node M4M64b, port 8095, PM2 `auto-ka-web` + `auto-ka-sync`). |
12+| Authorized access path | **Owner-approved, read-only copy of the production SQLite database** (`sqlite3 .backup`), taken on the production node, personal-account table dropped before transfer. No scraping of Auto-Ka, no use of the public HTTP API, no bypass of `robots.txt` (`Disallow: /api/`). | `data/raw/autoka-snapshot-20261101.db` (1.35 GB, mode 0444), SHA-256 in `data/raw/autoka-snapshot-20261101.db.sha256` (verified after transfer). |
13+| Documented API | FastAPI endpoints `/api/vehicles`, `/api/vehicles/{uid}` (includes price history), `/api/stats/*`, `/api/sources`, `/api/dealers` — documented in README but disallowed to crawlers; **not used** for this paper (the DB copy is strictly richer). | README §"API principale". |
14+| Upstream data-generation process | 143 registered sources (131 dealer websites "direct", 11 portals, 1 marketplace = Kijiji private sellers) crawled by 31 connector modules; each cycle re-reads every source, normalises each listing to one schema, hashes content, logs price changes, and marks a listing inactive after `MISS_GRACE = 2` consecutive absences; removals are suspended when a drift alert fires (volume ≤ 25 % of the 5-sync median, or ≥ 80 % null prices). VIN-based cross-source de-duplication elects a canonical listing. | `autoka/db.py` lines 150–310, `autoka/dedup.py`, `data/sources.json`. |
15+| Crawl cadence | Full cycle every ≈ 3.3 h on average (585 cycles between 2026-08-12 12:30 UTC and 2026-11-01 06:49 UTC; min 0.64 h, max 13.4 h; **no gap > 24 h** for any source). ≈ 7 observations per listing-day. | `sync_log` (83 531 rows). |
16+| Model APIs | **None required.** The paper uses no LLM/external model API; no credential verification needed. | — |
17+
18+## 2. Schema (tables kept in the research snapshot)
19+
20+| Table | Rows | Role / key fields |
21+|---|---|---|
22+| `vehicles` | 273 265 | One row per listing `uid = source:external_id`. Fields: make, model, trim, year, price, price_label, mileage_km, transmission, fuel, drivetrain, body_type, colours, engine, doors, seats, vin, stock_number, dealer_name, city, region, lat/lng, description, features (JSON), details (JSON), images (JSON), carfax_url, content_hash, `first_seen`, `last_seen`, `updated_at`, `miss_count`, `active`, `kind` (auto/moto/scooter), `dup_of`, `dup_sources`. |
23+| `price_log` | 428 484 | `(uid, ts, price)` — one row at first observation and **one row per price change** (not per observation). `price = NULL` = price withdrawn from display. |
24+| `sync_log` | 83 531 | One row per (source, cycle): found, added, updated, removed, ok, message, stats JSON (null-price rate, null-km rate, missed, alert). |
25+| `dealers` | 138 | Geocoded registry of direct dealer sources (address, phone, website, city, lat/lng). |
26+| `recalls` | 109 186 | Transport Canada recalls keyed by make/model/year (public data). |
27+| `vin_decode` | 4 655 | Decoded VIN attributes (partial coverage). |
28+| `detail_cache` | 77 039 | Opaque per-connector payload cache; not used. |
29+| `users` | — | **Dropped** from the research snapshot (personal accounts). |
30+
31+Timestamps are Unix epoch seconds (UTC). `first_seen` = first observation; `last_seen` = last observation; for inactive rows `updated_at` = time the listing was declared inactive (two consecutive misses after `last_seen`).
32+
33+## 3. Temporal coverage and panel structure
34+
35+| Quantity | Value |
36+|---|---|
37+| Observation window | 2026-08-12 12:32 UTC → 2026-11-01 06:49 UTC (**81 days**, 585 full crawl cycles) |
38+| Listings ever observed | 273 265 (auto 271 992; moto 1 218; scooter 55) |
39+| Active at snapshot | 62 821 (auto 62 052) ; inactive (exited) 210 444 |
40+| New listings per month (first_seen) | Aug 99 859 (incl. 46 k stock on day 1, **left-censored**) ; Sep 85 452 ; Oct 87 355 |
41+| Price observations | 428 484 rows for 270 909 uids; 61 962 uids with ≥ 2 price points, 26 576 with ≥ 3 |
42+| Monthly price-log rows | Aug 124 379 ; Sep 147 476 ; Oct 155 766 |
43+| Listing duration (inactive, all sources) | p10 0.74 d, p25 0.93 d, **p50 2.6 d**, p75 13.9 d, p90 35.1 d — the low quantiles are driven by *sampled* portals (see §5) |
44+
45+**A longitudinal listing panel therefore exists, but it is short (11.5 weeks) and starts with a left-censored stock.** Listing-level dynamics (H2, H5) are feasible; market-level time series (H6) have at most 81 daily / 11 weekly points.
46+
47+## 4. Field coverage (null rates, all 273 265 listings)
48+
49+| Field | Null/empty | Field | Null/empty |
50+|---|---|---|---|
51+| price | 1.7 % | make / model | 0.0 % / 0.0 % |
52+| mileage_km | 0.4 % | year | 0.1 % |
53+| trim | 5.6 % | VIN | 33.3 % (AutoTrader 100 %, Kijiji 79 %, Otogo 18 %, direct dealers ≈ 0 %) |
54+| body_type | 56.3 % | fuel | 35.1 % |
55+| transmission | 32.2 % | drivetrain | 37.7 % |
56+| region | 9.4 % | city | 1.7 % |
57+| lat/lng | 4.8 % | dealer_name | 0.0 % |
58+| stock_number | 57.8 % | exterior colour / engine | 56 % / 57 % |
59+
60+Price integrity: 2 164 listings at price ≤ 1 $ (placeholders), 29 between 1 and 500 $, 1 632 between 500 and 2 000 $, 506 above 250 000 $, 5 above 1 M$ (max 26.5 M$ — data error). `price_label` empty for 6 739. Mileage ≤ 10 km: 1 588 (new/demo or placeholder); > 500 000 km: 48. Year < 1990: 131; year = 2027: 784 (new-model-year stock). Hedonic-eligible core (auto, canonical, 2005 ≤ year ≤ 2026, 1 000 ≤ km ≤ 400 000, 2 000 ≤ price ≤ 250 000, make & model present): **242 508 of 259 133** canonical auto listings.
61+
62+## 5. Source-level quality (decisive for the dynamic analyses)
63+
64+Measured from `sync_log` and `vehicles`; "reactivation share" = (sum of `removed` events − inactive uids) / removed events, i.e. the share of exit events that were later reversed (flicker).
65+
66+| Source (type) | Listings | Mean found / cycle | Exits with duration < 1 d | Reactivation share | Verdict |
67+|---|---|---|---|---|---|
68+| otogo (portal, full inventory, VIN ≈ 82 %, exact GPS, 538 dealers) | 79 045 | 24 719 | 30 % | 0.44 | **Keep for levels and dynamics with flicker-robust exit rule**; uid stable per dealer listing (only 1.7 % of VINs have > 1 otogo uid, mostly genuine dealer-to-dealer transfers) |
69+| autousagee (AMVOQ portal, full inventory, 391 dealers) | 28 858 | 11 973 | 5 % | 0.04 | Keep |
70+| direct dealer websites (136 sources, 50 223 listings, 17 801 active) | 50 223 | — | 4.8 % | mostly ≤ 0.1 | **Core dynamic sample** (exceptions below) |
71+| hgregoire, automobileendirect, megacentre, clubautozone (banners) | 11 038 | — | low | 0.10–0.21 | Keep |
72+| autotrader (portal, **sliding window capped at 2 000 of > 4 000**) | 65 526 | 1 853 | 11 % (mean duration 2.2 d) | 0.12 | **Exclude from dynamics** (sampled, no VIN); eligible for cross-sectional hedonics only with a flag |
73+| cargurus (portal, **capped at 500**) | 37 501 | 507 | 78 % | 0.17 | **Exclude from dynamics**; cross-section only |
74+| kijiji (private sellers, no VIN 79 %, trim missing 55 %) | 11 874 | 3 041 | 15 % | 0.04 | Separate "private-seller" segment; excluded from dealer analyses |
75+| leprixdugros (banner) | 6 124 | 1 019 | 11 % | **0.94** | Connector flicker: exclude from exit/markdown hazards |
76+| albioccasion (rate-limited 429) | 2 057 | 853 | — | 0.52 | Exclude from dynamics |
77+| baiecomeaugm, septilesgm, stjeromeautodepot, excelmoto (404 / connector failures 214–271 cycles) | < 500 each | — | — | 0.85 / 0.39 / — | Exclude |
78+| classeauto:-98 | 1 uid | — | — | — | Identity collision (36 194 price rows, 48 distinct prices): exclude uid |
79+
80+Other anomalies: 3 451 uids show A→B→A price oscillation (e.g. `otogo:1694844` alternating 37 840 / 40 794 $ twice a day for weeks — two feed variants of the same listing); 11 479 of 121 239 price changes occur < 24 h after the previous one. A "sustained price path" rule is therefore required before any markdown analysis (see `research/analysis_plan.md`).
81+
82+Among non-portal sources, price changes are **100 532 cuts vs 11 447 rises** (mean cut 3.4 %, mean rise 15.0 % — rises are dominated by placeholder/oscillation artefacts).
83+
84+## 6. Geographic and dealer coverage
85+
86+Active canonical listings by administrative region (2026-11-01): Montérégie 8 744, Capitale-Nationale 5 442, Laurentides 4 999, Montréal 4 511, Chaudière-Appalaches 3 605, Laval 3 036, Lanaudière 2 965, Mauricie 2 060, Estrie 1 965, Centre-du-Québec 1 652, Bas-Saint-Laurent 1 504, Outaouais 1 342, Abitibi-Témiscamingue 1 265, Saguenay–Lac-Saint-Jean 1 027, Côte-Nord 799, Gaspésie–Îles-de-la-Madeleine 291; missing region 4 754 (to be recovered from lat/lng). 138 geocoded direct dealers; otogo adds 538 dealer names with exact GPS; 16 308 direct-dealer VINs are also observed on otogo (cross-source consistency checks for exits).
87+
88+Representativeness: the direct-dealer registry over-weights Capitale-Nationale, Bas-Saint-Laurent, Chaudière-Appalaches and Côte-Nord relative to population (registry built by the operator, not sampled). External benchmarks: see `reports/external_benchmarks_inventory.md`.
89+
90+## 7. Feasibility matrix (H1–H6)
91+
92+| Hypothesis | Feasible? | Sample | Limits |
93+|---|---|---|---|
94+| H1 nonlinear depreciation (hedonic asking prices) | **Yes** | 242 k canonical listings; first-observed price per listing (or listing-month panel) | Asking prices, not transactions; composition; dealer effects collinear with region for single-site dealers |
95+| H2 markdown hazard | **Yes** | Dynamic sample (direct dealers + otogo + autousagee + banners), listing-day risk set, flicker-robust price paths | Left-censoring of the day-1 stock (46 k); 81-day window → right-censoring heavy for long spells |
96+| H3 competitor response | **Association only** | Local competitor sets (same make/model/year ± 1, radius 25/50 km) from GPS | No exogenous variation; reflection problem; common shocks → report as conditional association with event-study diagnostics |
97+| H4 regional segmentation | **Yes (cross-section + within-spec dispersion)** | Hedonic residuals by region with model-trim-year cohorts | Dealer FE absorb region for single-location dealers → use dealer-group/chain FE variants |
98+| H5 listing exit & relative price | **Yes, exit ≠ sale** | Same as H2; exit = final absence ≥ 7 d | No transaction data; validation limited to cross-source consistency (direct site vs otogo) and relisting detection |
99+| H6 AMLI | **Construction + internal validation only** | Daily/weekly market-level panel, 81 days | **No out-of-sample forecasting test of external economic variables is possible** with ≤ 3 monthly points; stated as a documented limitation |
100+
101+## 8. Recommended extraction method (adopted)
102+
103+1. Immutable raw snapshot: `sqlite3 .backup` on M4M64b → drop `users` → `VACUUM` → SHA-256 → zstd → scp → verify (done 2026-11-01; `data/raw/`, read-only, **never committed**).
104+2. `src/ucml/ingest`: read-only DuckDB/Polars extraction to Parquet in `data/interim/` (vehicles, price_log, sync_log, dealers), with a manifest (row counts, min/max timestamps, SHA-256 of the source).
105+3. All downstream transformations versioned in `src/ucml/*` with tests; exclusion log in `data/processed/exclusions.csv`.
106+
107+## 9. Data governance
108+
109+- **Public vs restricted fields.** Make/model/trim/year/price/mileage/attributes/region/city/dealer name are public asking-price information published by dealers. VIN, stock number, listing URL, images, free-text descriptions and exact coordinates are treated as *restricted*: kept only in `data/raw` and `data/interim` (not committed, not released), used solely for linkage (VIN de-duplication, relisting detection, dealer geography). Released/processed tables carry anonymised dealer IDs (`d####`) and listing IDs (`l#######`), region/city and 1 km-rounded coordinates.
110+- **Personal information.** The `users` table was dropped before the data left the production node. Kijiji private-seller listings may contain phone numbers in free text: descriptions are never exported from `data/interim`; the private-seller segment is analysed only through structured fields.
111+- **Retention.** Raw snapshot retained for the life of the paper on the author's encrypted laptop and the production node; processed Parquet retained with the repository; nothing is pushed to any third-party service.
112+- **Attribution.** Data source cited as "Auto-Ka (www.auto-ka.com), operator's database snapshot of 2026-11-01"; dealer sites are the ultimate sources (listed in `data/sources.json`, mirrored in `data/processed/sources_registry.csv`).
113+- **Permitted use.** Owner-authorised academic research use by the platform operator; no redistribution of listing-level raw data.
114+- **Access control.** Snapshot mode 0444, directory excluded by `.gitignore`.
added reports/external_benchmarks_inventory.md +68 −0
@@ -0,0 +1,68 @@
1+# External benchmarks inventory — WP 36 (Quebec used-car listing panel, 2026-08-12 → 2026-11-01)
2+
3+Compiled 2026-11-01; every URL, table ID and latest period was checked by HTTP request that day (StatCan WDS, Données Québec CKAN API, BoC Valet, page fetches). **could not verify** = not confirmed. Raw copies: `data/raw/external/` (README there).
4+
5+## 1. Statistics Canada (all tables: Statistics Canada Open Licence; CSV = `https://www150.statcan.gc.ca/n1/tbl/csv/<pid8>-eng.zip`; API = `POST https://www150.statcan.gc.ca/t1/wds/rest/getDataFromVectorsAndLatestNPeriods` or `getDataFromCubePidCoordAndLatestNPeriods`; full-table link resolver `GET .../getFullTableDownloadCSV/<pid8>/en`)
6+
7+- **New motor vehicle sales.** Table **20-10-0001-01 is ARCHIVED** (last period Dec 2024; released 2025-04-14). The live successor is **20-10-0085-01 "New motor vehicle sales, monthly"** (dims: Geography incl. Quebec, Vehicle type, Fuel type, Origin, Units/Dollars, Unadjusted/SA). Latest period **Aug 2026, released 2026-10-15** (lag ≈ 6–7 weeks). Quebec, total, unadjusted: v1617815620 (units; Aug 2026 = 41,657; Jul 41,755; Jun 42,053) and v1617815621 ($ thousands; Aug 2026 = 2,258,218). SA Quebec vector (v1617815717) is null for 2026. Sep 2026 is not on the major-release calendar; expect ~mid-November by analogy (**not verified**). Annual: 20-10-0002-01 archived, 20-10-0086-01 live (2025).
8+- **New motor vehicle registrations.** **20-10-0024-01 is ARCHIVED** (last 2024-Q4). Live: **20-10-0025-01 "New motor vehicle registrations, quarterly, by geographic level"** (5,345 geographies incl. municipalities; fuel type; vehicle type). Latest **2026-Q2, released 2026-09-08** (lag ≈ 10 weeks); Q3 ≈ Dec 2026. New vehicles only.
9+- **CPI "Purchase of used passenger vehicles (202204=100)".** Table **18-10-0004-01**, product member 372; latest **Sep 2026, released 2026-10-19**; Oct 2026 due **2026-11-16** (verified in StatCan 2026 release calendar). **Published for Canada only**: v1374568107 (Sep 2026 = 104.5; Aug 105.4; Jul 105.0; Jun 105.0; May 105.2; Apr 103.8). Coordinates for Quebec (geo 11) and Montréal (geo 13) with member 372 return FAILED — there is **no provincial used-vehicle index**. Quebec has only the aggregate "Purchase of passenger vehicles" v41691856 (Sep 2026 = 131.4), All-items v41691783 (167.7) and Gasoline v41691858 (270.2). Methodology (StatCan 62F0014M2022008): **J.D. Power** dealer transaction prices (incl. trade-in value) aggregated by make/model with mean price, vintage age, odometer, count; hedonic **time-dummy WLS** (log price on log odometer, log age, model FE, rolling 5-month window); introduced with the June 2022 basket update (series start April 2022, not 2023); **one-month lag in the price data** (Sep index reflects Aug transactions).
10+- **Retail trade, NAICS 441.** **20-10-0008-01 ARCHIVED** (last Dec 2022). Live: **20-10-0056-01 "Monthly retail trade sales by province and territory"**, NAICS member 2 "Motor vehicle and parts dealers" (441); geographies include Quebec (6), Montréal CMA (7), Québec CMA (8), Gatineau (9). Latest **Aug 2026, released 2026-10-23**; Sep 2026 due **2026-11-20** (verified). Quebec 441 unadjusted v1446859668 (Aug 2026 = $4,755,964k; Jul 5,317,100; Jun 4,973,621); Montréal 441 unadjusted v1446859699 (Aug = $2,211,165k). SA exists for total retail (v1446859667); SA for Quebec 441 returned FAILED (**could not verify**). 441 mixes new/used dealers and parts.
11+- **Vehicle registrations (stock).** **23-10-0067-01 ARCHIVED** (last 2019). Live: **23-10-0308-01 "Vehicle registrations, by type of vehicle and fuel type"**, annual, Quebec; latest **2025, released 2026-09-22**. Stock only, no flows.
12+- **LFS Quebec.** **14-10-0287-01**, Quebec employment SA v2063754 (Sep 2026 = 7,577.2k; Aug 7,575.4; Jul 7,578.3); released 2026-10-09; Oct 2026 due **2026-11-06** (verified). Unemployment rate = member 7.
13+- **Gasoline, monthly.** **18-10-0001-01**, Montréal regular self-service v735096 (Sep 2026 = 198.4 ¢/L; Aug 184.9; Jul 187.4); Québec City is geo member 6. Released with the CPI (Sep 2026 on 2026-10-19).
14+
15+## 2. SAAQ / Données Québec
16+
17+- Organisation `saaq` on donneesquebec.ca has **8 datasets, all annual, all last updated 2023-12-18/19**: Véhicules en circulation, Permis de conduire, Rapports d'accident, Avis de non-conformité CRQ, Constats d'infraction CRQ, Interventions de contrôle, Dossiers indemnisation, Blessures indemnisation.
18+- **"Véhicules en circulation"** (`https://www.donneesquebec.ca/recherche/dataset/vehicules-en-circulation`), **CC-BY 4.0**, one CSV per year **2011–2022** (e.g. `https://dq-prd-bucket1.s3.ca-central-1.amazonaws.com/saaq/Vehicule_En_Circulation_2022.csv`, 905 MB, Last-Modified 2023-12-18). Vehicle-level stock snapshot (class, make, model, model year, mass, cylinders, colour, fuel, **région administrative, MRC**): useful for fleet *composition* benchmarking, not flows. No 2023–2025 vintage; revised for Loi 25.
19+- **Ownership transfers (transferts de propriété)**: **no dataset found** on Données Québec (queries "transfert propriété véhicule", "immatriculation", organisation SAAQ). Only procedures are published; the annual "SAAQ en bref" PDF may hold aggregate counts — **could not verify** any monthly/weekly series. This is the main missing official flow measure.
20+
21+## 3. Industry indices (all Canada-wide, none public at Quebec level except AutoTrader/AutoHebdo regional averages)
22+
23+- **Canadian Black Book — Wholesale Used Vehicle Retention Index** (`https://www.canadianblackbook.com/black-book-index/`; the `/used-vehicle-retention-index/` URL is 404). Monthly, Canada; % of original MSRP for 2–6-year-old vehicles at wholesale, volume-weighted, seasonally/age/mileage/inflation adjusted. PDF "Updated October 2026" contains Jan 2005 → **Sep 2026**: 2026 = 133.5, 132.2, 132.5, 130.8, 129.7, 128.0, 127.9, 127.5, **127.5** (Sep −7.6 % YoY, 0.0 % MoM). Lag ≈ 2–4 weeks; © Hearst, no reuse licence, no regional split, PDF only. Aug and Sep 2026 fall inside the panel window.
24+- **AutoTrader.ca Price Index** (`https://www.autotrader.ca/consumer/price-index/`). **Quarterly** since 2021 (monthly only in 2020); latest **Q2 2026 (published 2026-07-14)**: used average $36,690 (−2.6 % YoY), used sales −2.5 % YoY (DesRosiers/Dealertrack), used **days' supply 44 (vs 41)**, used inventory by segment; p. 11 "Average price by region (used)" gives **Quebec June 2025 $35,746 → June 2026 ≈ $35.7–35.9k (≈ 0.1 % YoY; column pairing ambiguous in text extraction, read the PDF)**. Make/age-adjusted listing prices. **Q3 2026 not yet published** (not verified). PDF only, © TRADER. French/Quebec edition **AutoHebdo Indice des prix**: latest found **Q4 2025** (`https://go.trader.ca/wp-content/uploads/2026/02/Price-Index-Q4-2025FR.pdf`); no 2026 French edition found.
25+- **Cox Automotive / Manheim**: Manheim Used Vehicle Value Index is **US only** (Sep 2026 = 205.9). **No Canadian Manheim/Cox index found.** Canadian wholesale alternatives: CBB (above) and **OPENLANE Canada "Vehicle Value Index"** (`https://www.openlane.ca/en/vehicle-value-index/`, narrative commentary, latest text references March 2026, "mid-150s"; monthly PDF commentaries e.g. `.../Commentary-January-2026.docx.pdf`; no data file; **no 2026-Q3 commentary found**).
26+- **CARFAX Canada Used Vehicle Market Insights** (`https://www.carfax.ca/media/...`, site returns 403 to automated fetch — **content could not be verified directly**; secondary coverage: autosphere.ca 2026-06-15, repairerdrivennews.com 2026-08-21). Monthly-ish press releases: national average **listing** price (~$31.0–31.5k, Jun 2026 $31,487), **used transactions** (Jun 2026 = 277,361, −2.9 % YoY), inventory (+15.5 % MoM, +2.5 % YoY), damage share. No Quebec split found; **no Sep/Oct 2026 edition found**.
27+- **Clutch used-car pricing report** (`https://clutch.ca/blog/posts/clutch-used-car-pricing-report-aug-2026`, published 2026-09-28). Monthly; Clutch retail sales (MY 2016+, <200k km): national $33,752 (−0.5 % YoY), **Quebec $30,097 (+1.9 % YoY; same model/age −1.7 %)** for **Aug 2026**. No Sep/Oct 2026 posts linked yet; no download; one retailer's own sales, not a market index.
28+- **Kijiji Autos / CarGurus Canada**: no 2026 index or report found (**could not verify** any current series). **DesRosiers** (desrosiers.ca): paid (DAR twice-monthly, AutoWatch monthly, CUVAFY used-vehicle yearbook with CBB); no public used-vehicle series. **J.D. Power Canada / PIN**: paid; it is the CPI vendor (above).
29+
30+## 4. Macro / event timing
31+
32+- **Bank of Canada**: 2026 fixed announcement dates verified: Jan 28, Mar 18, Apr 29, Jun 10, Jul 15, **Sep 2**, **Oct 28**, Dec 9. Valet V39079: 2.25 % throughout Aug–Oct 28 (Sep 2 = hold), **raised to 2.50 % effective 2026-10-29** (Oct 28 decision). `https://www.bankofcanada.ca/valet/observations/V39079/json` (daily, open).
33+- **Gasoline, weekly (inside window)**: (i) **NRCan "Weekly Average Retail Prices for Regular Gasoline"** `https://www2.nrcan.gc.ca/eneene/sources/pripri/prices_bycity_e.cfm?productID=1&locationID=28&frequency=W&priceYear=2026` — Montréal (28), Québec (29), Gatineau (98), Sherbrooke (30), Trois-Rivières (79); week ending Tue; latest **2026-11-03 = 187.0 ¢/L** (Oct 27 185.0; Oct 20 192.3; Oct 13 198.6; Oct 6 198.0; Sep 29 196.6); XLS export; published within days. (ii) **Régie de l'énergie du Québec**: the `/fr/consommateurs/prix-de-lessence` URL is 404; live pages are `https://www.regie-energie.qc.ca/fr/prix-produits-petroliers` (dynamic weekly table, daily + weekly averages by region/MRC/Montréal sectors, back to Jan 2023, "Exporter les données" button, format unverified) and daily/weekly PDFs: `.../releve-quotidien/rqe.pdf` (daily posted prices by MRC, overwritten daily) and `.../Bulletin/bulletin.pdf` (weekly, 17 regions + Quebec weighted average; week of 2026-10-26: Montréal 188.9, Québec weighted 184.5 ¢/L). Historical tables page lists only rack-price PDFs. No open licence stated (© Gouvernement du Québec).
34+
35+## 5. Google Trends
36+`https://trends.google.com/trends/explore?geo=CA-QC&q=...` returns HTTP 429 to scripts; no official API (pytrends is unofficial). Weekly values exist for windows ≥ 90 days but are **sampled, normalised 0–100 per query, re-sampled on each pull, relative** — a qualitative demand proxy only (average repeated draws; bilingual terms such as "auto usagée", "used car", "Kijiji autos").
37+
38+## 6. Open transaction-price datasets
39+**None found** for Canada (Kaggle/Zenodo/Dataverse/academic searches): only commercial scrapers (Apify actors), a 233-row Reddit sample (June 2026, unverified licence) and UK/German/US sets. Transaction prices exist only behind J.D. Power PIN / CBB (paid).
40+
41+## Within-sample (weekly) feasibility, honestly
42+
43+Panel = 2026-08-12 → 2026-11-01 (≈ 11.6 weeks). Series that are **weekly or finer AND already published for Aug–Oct 2026**: only **gasoline prices** (NRCan weekly, Régie daily/weekly) and the **BoC policy rate** (daily; two events, Sep 2 hold, Oct 28 hike). Google Trends is the only weekly *demand* proxy, with the caveats above. Everything about the *used-car market itself* is monthly or quarterly and lagged: CBB (monthly, through Sep → 2 of 3 panel months, no Quebec), CPI used-vehicle index (monthly, Canada only, through Sep, itself lagged one month in its inputs), Clutch (monthly, Quebec figure, through Aug only), CARFAX (national, through Jun), AutoTrader (quarterly, Q2 only; Q3 pending). Official Quebec volume measures (NMVS units, retail 441, LFS) are monthly, cover only Aug (Sep arrives Nov 16–20; Oct in December). **A contemporaneous weekly validation of the liquidity index is therefore not possible with external data; the feasible design is (a) composition benchmarking against the SAAQ 2022 fleet file, AutoTrader Q2 2026 regional prices and Clutch Aug 2026 Quebec prices, and (b) monthly aggregation of the index compared with CBB Sep/Aug and CPI-used Sep/Aug (2–3 points, descriptive only), with Sep/Oct official Quebec series added after 2026-11-20.**
44+
45+## Summary table
46+
47+| Source | Publisher | ID / URL | Geo | Freq | Latest (2026-11-01) | Lag | Licence | Machine-readable | Variable | Weekly & in-window? |
48+|---|---|---|---|---|---|---|---|---|---|---|
49+| New motor vehicle sales | StatCan | 20-10-0085-01 (v1617815620) | QC | M | Aug 2026 (rel. Oct 15) | ~6–7 wk | Open Licence | CSV zip / WDS | new units, $ | No |
50+| New vehicle registrations | StatCan | 20-10-0025-01 | QC → municipality | Q | 2026-Q2 (rel. Sep 8) | ~10 wk | Open | CSV/WDS | new regs | No |
51+| CPI used passenger vehicles | StatCan | 18-10-0004-01 m.372, v1374568107 | **Canada only** | M | Sep 2026 (rel. Oct 19) | ~3 wk (+1 mo data lag) | Open | CSV/WDS | hedonic used price index (J.D. Power) | No |
52+| Retail sales NAICS 441 | StatCan | 20-10-0056-01, v1446859668 | QC, Mtl CMA | M | Aug 2026 (rel. Oct 23) | ~7 wk | Open | CSV/WDS | dealer sales $ | No |
53+| Vehicle registrations stock | StatCan | 23-10-0308-01 | QC | A | 2025 | ~9 mo | Open | CSV/WDS | fleet stock | No |
54+| LFS employment | StatCan | 14-10-0287-01, v2063754 | QC | M | Sep 2026 (Oct 2026 on Nov 6) | ~1 wk | Open | CSV/WDS | employment, unemp. rate | No |
55+| Gasoline monthly | StatCan | 18-10-0001-01, v735096 | Mtl, Qc City | M | Sep 2026 | ~3 wk | Open | CSV/WDS | ¢/L | No |
56+| Gasoline weekly | NRCan | prices_bycity_e.cfm locationID 28/29 | Mtl, Qc, Gat., Sherb., T-R | W | week ending 2026-11-03 | days | GoC terms | XLS | ¢/L | **Yes** |
57+| Gasoline daily/weekly | Régie de l'énergie | rqe.pdf, bulletin.pdf, prix-produits-petroliers | 17 regions, MRC | D/W | week of 2026-10-26 | days | © Gouv. QC | PDF; web export (unverified) | ¢/L posted | **Yes** |
58+| Véhicules en circulation | SAAQ via DQ | vehicules-en-circulation | QC, région, MRC | A | 2022 | >1 yr | CC-BY 4.0 | CSV (905 MB) | fleet composition | No (composition only) |
59+| Ownership transfers | SAAQ | none found | — | — | — | — | — | — | — | No |
60+| Used Vehicle Retention Index | Canadian Black Book | black-book-index/ PDF | Canada | M | Sep 2026 (127.5) | ~2–4 wk | © Hearst | PDF only | wholesale retention % | No (monthly, Aug–Sep in window) |
61+| Price Index | AutoTrader.ca / AutoHebdo | consumer/price-index/ PDF | Canada + regions (QC) | Q | Q2 2026 (Jul 14) | ~2 wk | © TRADER | PDF only | listing prices, days' supply | No |
62+| Vehicle Value Index | OPENLANE Canada | openlane.ca/en/vehicle-value-index/ | Canada | M (narrative) | ~Mar 2026 text | ? | © | none | wholesale index | No |
63+| Used Vehicle Market Insights | CARFAX Canada | carfax.ca/media (403) | Canada | ~M | Jun 2026 | ~2 mo | © | none | listing price, transactions, inventory | No |
64+| Pricing report | Clutch | clutch.ca/blog | Canada + QC | M | Aug 2026 (Sep 28) | ~4 wk | © | none | retail sold prices | No |
65+| Manheim UVVI | Cox Automotive | US only | US | M | Sep 2026 | — | © | — | — | n/a |
66+| DesRosiers, J.D. Power PIN, Kijiji, CarGurus | various | paid / none found | — | — | — | — | — | — | — | No |
67+| Policy rate | Bank of Canada | Valet V39079 | Canada | D | 2026-10-29 (2.50 %) | 0 | BoC terms | JSON API | rate; events Sep 2, Oct 28 | **Yes** |
68+| Google Trends | Google | trends.google.com (429 scripted) | CA-QC | W | current | 0 | Google ToS | unofficial only | relative search interest | Yes, with caveats |
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added research/analysis_plan.md +51 −0
@@ -0,0 +1,51 @@
1+# WP36 — Preregistered analysis plan (v1.0, 2026-11-01)
2+
3+This plan was written after the Phase 0 audit and **before** estimating any main model. Deviations are recorded in `DECISIONS.md` with a dated amendment. Exploratory analyses are labelled as such in the paper.
4+
5+## 1. Samples
6+
7+| Sample | Definition | Used for |
8+|---|---|---|
9+| S0 full | all 273 265 listings in the 2026-11-01 snapshot | coverage tables only |
10+| S1 hedonic | `kind = auto`, canonical (`dup_of IS NULL`), 2005 ≤ model year ≤ 2026, 1 000 ≤ km ≤ 400 000, 2 000 ≤ price ≤ 250 000 (first sustained price), make & model non-missing; all sources except uid collisions; sampled portals (autotrader, cargurus) and Kijiji **flagged** and dropped in the primary specification, kept in a robustness variant | H1, H4, Model A/E, Model F price |
11+| S2 dynamic | S1 ∩ sources in the dynamic set (direct dealer sites, otogo, autousagee, hgregoire, automobileendirect, megacentredeliquidation, clubautozone), **entered after 2026-08-13 00:00 UTC and more than 24 h after the source's first non-empty crawl** (not left-censored; amendment D-014), not an oscillating uid, source not in the excluded connector list (D-003) | H2, H3, H5, AMLI, Model B/C/D |
12+| S2L | S2 plus the left-censored day-1 stock (entry = first_seen) | sensitivity |
13+| S3 market panel | S2 aggregated to market (region × segment) × day and × ISO week | H6 |
14+
15+Exclusions are logged row by row in `data/processed/exclusions.csv` (uid, rule, value).
16+
17+## 2. Variable definitions
18+- **Listing time** t: UTC days since 2026-08-12. Listing age a = t − entry day (entry = first_seen).
19+- **Vehicle age** (years) = (observation date − 1 October of (model year − 1)) / 365.25, floored at 0. Mileage in 10 000 km.
20+- **Sustained price path** (D-005): from `price_log`, drop NULL rows; remove any level of duration < 48 h that reverts to the preceding level; the first remaining level is the *entry price*; a **markdown** is a sustained decrease ≥ 1 % and ≥ 100 $; a rise is a sustained increase ≥ 1 %. Uids with ≥ 3 removed transients are *oscillating*.
21+- **Exit** (D-004): listing inactive at snapshot and last_seen ≤ snapshot − 7 days → exit at last_seen (interval-censored between last_seen and the next crawl of that source; we use last_seen + half the source's median crawl interval). Otherwise right-censored at min(last_seen, snapshot − 7 d).
22+- **Segments**: body type (recovered from body_type, then from a make-model lookup built from non-missing rows, else "unknown"); powertrain = EV / PHEV-hybrid / ICE from `fuel` + engine strings; price tier.
23+- **Markets**: administrative region (17, recovered from lat/lng when missing via nearest city file); local market for H3 = 25 km radius around the listing's dealer (alternatives 10, 50 km).
24+- **Competitor set** (H2/H3): listings of the same make, model and model year ± 1 within the radius, active on day t, from other dealers. Density = count; price position = log(price) − median log price of the set.
25+- **Hedonic residual at entry** r_i: residual of Model A (estimated on S1 with cohort FE) at the entry price.
26+
27+## 3. Models (primary specifications)
28+
29+**Model A (H1, H4).** log P_i = f(age_i, km_i) + X_i'β + μ_{make-model-cohort} + τ_{entry week} + θ_{source type} + ε_i, f = cubic regression splines (5 knots each) + tensor interaction; variants: (A1) linear age/km, (A2) splines, (A3) splines × powertrain, (A4) model-trim-year FE, (A5) dealer FE (region absorbed → report region effects from A4 with dealer-chain FE instead), (A6) LightGBM. SEs clustered by dealer. Preregistered test: Wald A2 vs A1; 5-fold grouped CV RMSE.
30+
31+**Model B (H2).** Discrete-time hazard of first markdown on the listing-day risk set (S2): cloglog(h_{it}) = g(a_{it}) + γ·density_{it} + δ·pricepos_{i,entry} + X_i'β + μ_{segment} + τ_{week} + region FE; g = listing-age dummies (weekly bins). Cox PH with Schoenfeld test as a check; AFT (log-logistic) as alternative. Cut size: OLS of log cut on the same covariates for markdown events. Repeated markdowns: PWP gap-time model (exploratory). SEs clustered by dealer.
32+
33+**Model C (H5).** Same risk set; outcome = exit. Covariates add r_i (entry residual), current price position, number of prior cuts, listing age. Competing-risk description: exit vs. still listed at snapshot; relisting at another dealer (VIN) coded separately (exploratory Fine–Gray if ≥ 200 events). Validation: for VINs observed both on a direct site and on otogo, share of exits within ± 3 days on both; relisting rate within 30 days as the lower bound of "not-a-sale".
34+
35+**Model D (H3).** For each listing-day in S2 with a defined competitor set: event = any sustained competitor markdown in the set on day t−k (k = −7..+14). cloglog(h_{it}) = Σ_k β_k·CompCut_{i,t−k} + g(a_{it}) + listing-age × segment + μ_{market × week} + μ_{make-model-year × week} + X_i'β. Cluster by market (region) with wild-cluster bootstrap if < 40 clusters. Diagnostics: pretrends (leads), placebo with pseudo-competitors drawn from other regions, selection check (do listings that later get a competitor cut differ at entry?). Interpretation: association only.
36+
37+**Model E (H4).** Region effects from A4 (cohort FE, no dealer FE) with 95 % CIs; within make-model-year-trim price dispersion (IQR of log price) by region; border comparison (Outaouais vs Montérégie/Laurentides as a within-Quebec check; Ontario unavailable). Conley spatial HAC SEs (50 km) as a robustness.
38+
39+**Model F (prediction).** Targets: log entry price; markdown within 30 days; exit within 30 days. Models: ridge/OLS baseline, LightGBM. Splits: chronological (entry before/after 2026-10-01), dealer holdout (20 % dealers), region holdout (leave-one-region-out). Metrics: MAE/RMSE; log loss, Brier, calibration (reliability bins); C-index for survival. 200-bootstrap CIs.
40+
41+## 4. AMLI (H6)
42+Higher AMLI = **more liquid** market. Components (market m, week w, computed with information available at w): (1) exit intensity = exits / listing-days; (2) staleness = share of active listings with listing age > 30 d (sign reversed); (3) markdown frequency = sustained cuts / listing-days and depth = median cut %; (4) quality-adjusted dispersion = IQR of hedonic residuals among active listings (sign reversed); (5) arrival rate = entries / active stock. Each component standardised across market-weeks (z-score within the panel; robustness: rank). Weights: equal; PCA first component (signed); supervised (ridge on next-week exit intensity using only components 2–5, fitted on weeks 1–6, evaluated weeks 7–11). Missingness: component dropped for a market-week with < 30 listing-days; index requires ≥ 3 components. Sensitivity: leave-one-component-out; geographic aggregation (region vs province); segment (price tier, body). Uncertainty: block bootstrap over dealers (200 reps).
43+
44+## 5. Inference and multiple testing
45+Cluster-robust SEs by dealer (A, B, C) and by region with wild-cluster bootstrap (D). Six confirmatory families (H1–H6): primary tests reported with Holm-adjusted p-values across the six primary tests; secondary tests unadjusted and labelled.
46+
47+## 6. Robustness (mandatory list)
48+Alternative price filters (1 %, 2 % cut thresholds; 24 h / 72 h transient rule); include sampled portals; include Kijiji; left-censored stock (S2L); exit confirmation 3 / 14 days; radius 10/50 km; cohort vs trim FE; dealer FE; heterogeneity by model year, mileage, brand, EV/ICE, region; placebo events; composition reweighting (entropy balancing to the otogo make-model distribution).
49+
50+## 7. What will NOT be claimed
51+Causal competitor contagion; sale prices; sale events; forecasting value of AMLI for external economic variables; representativeness beyond the Auto-Ka registry.
added research/questions.md +30 −0
@@ -0,0 +1,30 @@
1+# WP36 — Research questions (preregistered, 2026-11-01, before any main-model estimation)
2+
3+**Central question.** How do dealer competition, vehicle characteristics, inventory age and local market conditions jointly determine asking-price dynamics and the liquidity of a durable good (used cars) in Quebec?
4+
5+**Data regime (from the Phase 0 audit).** Listing-level panel of asking prices from Auto-Ka, 2026-08-12 → 2026-11-01 (81 days, ≈ 7 crawls/day), 273 265 listings, 138 direct dealers + 2 full-inventory portals (≈ 900 dealer names), no transaction prices. All outcomes are *asking prices* and *observed listing disappearance*.
6+
7+## Confirmatory hypotheses (tested exactly as specified in `analysis_plan.md`)
8+
9+| ID | Hypothesis | Primary outcome | Unit | Primary test | Direction / falsifier |
10+|---|---|---|---|---|---|
11+| H1 | Conditional asking-price depreciation is nonlinear in vehicle age and mileage and differs by powertrain and segment. | log first sustained asking price | listing | Hedonic model A with tensor-product splines in (age, km); Wald test of nonlinearity (spline vs linear) and of interaction with powertrain (EV/hybrid vs ICE) and body segment | Falsified if linear-in-age-and-km specification is not rejected at α = 0.01 **and** the out-of-sample RMSE gain of the spline is < 0.5 % |
12+| H2 | The hazard of a first asking-price cut and the conditional cut size depend on listing age, local competition and vehicle attributes. | first sustained markdown (event), cut size (%) | listing-day | Discrete-time complementary log-log hazard with listing-age dummies/splines; Cox with PH check; cut-size OLS on markdown events | Falsified if the listing-age profile is flat (joint test p > 0.05) and the competition density coefficient has a 95 % CI covering 0 |
13+| H3 | A price cut by a close local competitor is associated with a higher subsequent repricing hazard of nearby comparable listings, beyond market×time and product×time shocks. **Association, not causal.** | repricing (cut) indicator in the window after a competitor cut | listing-day | Event-study / distributed-lag hazard with leads and lags, market×week and model-year-cohort×week FE; clustered by local market | Falsified if lead coefficients are as large as lags (no lead–lag asymmetry) or if placebo (random pseudo-competitors) gives similar estimates |
14+| H4 | Quality-adjusted asking-price differences persist across Quebec administrative regions net of model-trim-year cohort, mileage, condition proxies, dealer type and time. | hedonic residual / region premium | listing | Model E: region effects in the hedonic model with cohort FE; within-specification dispersion | Falsified if region effects are jointly insignificant (p > 0.05) and all |premia| < 1 % |
15+| H5 | Relative asking price (hedonic residual at entry) predicts listing disappearance; disappearance is an imperfect sale proxy. | observed disappearance (exit) | listing-day | Discrete-time and Cox exit hazards with the entry residual; concordance; cross-source consistency of exit timing (direct site vs otogo) and relisting detection | Falsified if the residual coefficient's 95 % CI covers 0 in the dynamic sample |
16+| H6 | A transparent market-level liquidity index (AMLI) built from inventory exit intensity, listing-age distribution, markdown frequency/depth, quality-adjusted dispersion and arrival rates captures common variation in observed turnover across regions and segments. | AMLI (daily/weekly, by region & segment) | market-day / market-week | PCA/equal/supervised weights; bootstrap CIs; leave-one-component-out prediction of next-week exit intensity (held-out component) | Falsified if the first component explains < 40 % of component variance **or** if the leave-exit-out index has no predictive association with next-week exit intensity (CI covers 0). **External forecasting value: NOT testable in this window (declared).** |
17+
18+## Exploratory analyses (reported as such; no confirmatory claims)
19+- Depreciation surfaces by make and by model-year cohort; EV vs ICE vs hybrid residual depreciation.
20+- Repeated markdowns and markdown sequences (size of 2nd/3rd cuts); price increases.
21+- Dealer-level repricing styles (frequency, size); banner/chain vs independent dealers.
22+- Private-seller (Kijiji) vs dealer asking prices for the same make-model-year (descriptive only; no VIN).
23+- Dealer-to-dealer vehicle transfers detected by VIN relisting at another dealer (wholesale channel proxy).
24+- Prediction models (Model F) with LightGBM; chronological, dealer and region holdouts.
25+
26+## Interpretation constraints (apply to every result)
27+1. Asking price ≠ transaction price; listing disappearance ≠ sale (may be withdrawal, wholesale, relisting, feed error).
28+2. 81-day window: seasonality, macro shocks and long-run dynamics are unidentified; the day-1 stock is left-censored.
29+3. Dealer coverage is an operator-built registry, not a random sample of Quebec dealers.
30+4. H3 is conditional association; H6 forecasting value is untested.
added results/amli/bootstrap_region_week.csv +154 −0
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added results/amli/index_body_filled_week.csv +73 −0
@@ -0,0 +1,73 @@
1+body_filled,week,ld,exits,cuts,entries,stale,exit,mdfreq,arrival,mddepth,disp,z_exit,z_mdfreq,z_mddepth,z_disp,z_arrival,amli_equal,amli_pca,exit_next,amli_supervised
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24+Hatchback,6,12207.0,347.0,281.0,314.0,0.46334070615220774,0.028426312771360696,0.02301957893012206,0.025722945850741377,2.708443972384493,0.13288592406667307,1.2793141461949935,2.0311853722377715,-0.3954442111466087,1.0875645643697753,0.7817720201687774,0.9568783783649419,0.6379176540019882,0.02758506772381896,0.6123682511944669
25+Hatchback,7,12108.0,334.0,183.0,308.0,0.48265609514370666,0.02758506772381896,0.015113974231912785,0.025437727122563595,3.0321406913280775,0.1325874904635641,1.1216957402825276,-0.4057158661106675,0.12119075349463433,1.0932985868666352,0.7251356134555363,0.5311209655977331,0.8348693221554151,0.024398312794640643,0.708791224732157
26+Hatchback,8,12091.0,295.0,230.0,294.0,0.4858986022661484,0.024398312794640643,0.019022413365313042,0.024315606649574065,2.9630340350477353,0.1321862831497429,0.5246149807124569,0.799059799734351,0.010893327433631878,1.101007275631538,0.5023140901442203,0.5875778947312396,0.4902620326757131,0.029273704873261072,0.6760875318516673
27+Hatchback,9,11717.0,343.0,228.0,286.0,0.5101988563625501,0.029273704873261072,0.01945890586327558,0.02440897840744218,2.73126903631066,0.13161103419598907,1.4380842841882573,0.9336085343878374,-0.359014351329325,1.112059953372105,0.5208550915992598,0.7291187024436269,0.7081968199130005,0.02010450801523337,0.574327718601107
28+Hatchback,10,11291.0,227.0,236.0,302.0,0.5058896466212027,0.02010450801523337,0.020901603046674343,0.026746966610574793,2.9689889739624,0.13034286047038196,-0.279886236390905,1.378319683881892,0.020397685913566766,1.136426300180252,0.9851137032597176,0.6480742273689046,0.36257620410204205,,0.7788922970940347
29+Pickup,2,37452.0,765.0,475.0,823.0,0.0,0.02042614546619673,0.012682900779664637,0.02197479440350315,2.381009071644563,0.1862798700748132,-0.21962319834897392,-1.1550938242118933,-0.918045487174882,0.06166773365994591,0.03749470309060689,-0.4387200145970393,-0.03794232513613939,0.016235306520478627,-0.27043121740621456
30+Pickup,3,37942.0,616.0,554.0,701.0,0.0,0.016235306520478627,0.014601233461599283,0.018475567972168046,2.3984052189984744,0.18526478223837797,-1.004832402242915,-0.563768117118327,-0.8902804298013832,0.08117135667616263,-0.6573531163810754,-0.6070125417735076,-0.5048423269973322,0.023011022305803687,-0.3349752704869392
31+Pickup,4,38286.0,881.0,701.0,869.0,0.09282766546518309,0.023011022305803687,0.018309564853993626,0.02269759180901635,2.4393549261417538,0.186902298955395,0.26468772642789784,0.5793243732485552,-0.8249227994519555,0.04970855357174272,0.1810219114596328,0.04996395305117463,0.004553073185066653,0.0205703233226169,-0.1875658006695477
32+Pickup,5,38259.0,787.0,602.0,779.0,0.36153584777437986,0.0205703233226169,0.01573485977155702,0.02036122219608458,2.5460184623605855,0.18346107993129881,-0.19260956663342302,-0.2143280092431974,-0.6546828524955262,0.11582720473980664,-0.28291529377179137,-0.24574170348082625,-0.16083708244338665,0.022150402017972567,-0.18382677631223923
33+Pickup,6,38058.0,843.0,573.0,823.0,0.5548636291975406,0.022150402017972567,0.015055967207945766,0.021624888328340953,2.3893283808639443,0.18454268054050096,0.10343911253084086,-0.42359652090360866,-0.9047674841156549,0.09504562330536573,-0.03198678892406727,-0.23237321162142482,-0.01759031673762546,0.01850250281730744,-0.2412709978229031
34+Pickup,7,38157.0,706.0,681.0,770.0,0.5635663181067694,0.01850250281730744,0.017847315040490603,0.02017978352595854,2.22246916324036,0.18420194598164571,-0.5800431363315757,0.43683595067323855,-1.1710825282667516,0.10159240491970119,-0.3189439037456876,-0.30632824255021507,-0.39354397767914623,0.01897359399125906,-0.33943613011354024
35+Pickup,8,38211.0,725.0,658.0,669.0,0.5629269058648033,0.01897359399125906,0.017220172201722016,0.01750804742089974,2.230376500630731,0.18613747413328477,-0.4917779630688435,0.24351928246660529,-1.158462051338802,0.06440369074949472,-0.849475449283756,-0.43835849809506033,-0.5138224713399762,0.017698878804057662,-0.4340472030033335
36+Pickup,9,37460.0,663.0,541.0,571.0,0.5914308595835558,0.017698878804057662,0.014442071542979178,0.015242925787506674,2.532286654849329,0.187609165166121,-0.7306127430015328,-0.6128297501482587,-0.6765994536621718,0.036127017470082304,-1.2992648123575805,-0.6566359483398923,-0.5971365499654373,0.0148927449105069,-0.384344022922605
37+Pickup,10,36595.0,545.0,773.0,741.0,0.6103566060937287,0.0148927449105069,0.021123104249214375,0.02024866785079929,2.439083880094637,0.18989727805372048,-1.2563791084272613,1.44659739006264,-0.8253554015043502,-0.007836164410813924,-0.3052654168427661,-0.18964774022451028,-0.6554630067966689,,-0.2659621065904306
38+SUV,2,186133.0,5016.0,2427.0,4865.0,0.0,0.02694847232892609,0.013039063465371535,0.02613722445778019,2.550153009180551,0.14640063562571362,1.0024211583430085,-1.0453067398603013,-0.6480839241618946,0.8278965534259228,0.8640360840707106,0.20019262636348917,0.7504880546966678,0.019805568583976463,0.3367897668196072
39+SUV,3,187624.0,3716.0,2695.0,4302.0,0.0,0.019805568583976463,0.014363834051080885,0.02292883639619665,2.523501447617686,0.14572365401575677,-0.33589649969858315,-0.6369464430731417,-0.6906210515402674,0.8409038948948919,0.22694057386112657,-0.11912390511119479,0.14909456779826288,0.024983452037385157,0.25105644550007217
40+SUV,4,188845.0,4718.0,3350.0,4591.0,0.09084169557044136,0.024983452037385157,0.017739415923111547,0.024310942836717944,2.6319252533228057,0.14512459645992393,0.634248570767728,0.4035760669837234,-0.5175716329068454,0.8524140246734251,0.5013879882593922,0.3748110035554847,0.43956677158615703,0.026744951535814257,0.37294322419429066
41+SUV,5,186839.0,4997.0,3094.0,4583.0,0.3483426907658465,0.026744951535814257,0.016559711837464342,0.024529140061764407,2.564760194921775,0.14438634035868636,0.9642888411613895,0.039932492969761965,-0.6247701787722152,0.866598677637571,0.5447158119979836,0.3581531289988982,0.5683863633827154,0.02508628991246578,0.34516567028979106
42+SUV,6,184842.0,4637.0,3361.0,4563.0,0.5269960290410188,0.02508628991246578,0.018183096915203256,0.024685947998831434,2.5006251562890722,0.1442305099587034,0.653516609236423,0.5403406536705404,-0.7271326726659081,0.8695927607766294,0.5758534520692791,0.3824341606173927,0.43727001746599203,0.02244813818244944,0.3314491060992878
43+SUV,7,184336.0,4138.0,2925.0,4381.0,0.5349850273413767,0.02244813818244944,0.0158677632149987,0.023766383126464716,2.503764010483466,0.14387924129267315,0.15922392786434006,-0.1733605465041154,-0.7221229159064728,0.8763419418757967,0.39325376640590215,0.10666723474709014,0.30491447703004887,0.022064936887909906,0.2943116336269751
44+SUV,8,185226.0,4087.0,3405.0,3751.0,0.5341420750866509,0.022064936887909906,0.018382948398173043,0.02025093669355274,2.452803705523195,0.14412576222708615,0.08742608802514827,0.601944837654741,-0.8034579242969817,0.8716053553481901,-0.30481488857004796,0.09054069363220994,0.0077172431801511105,0.023003344221063586,0.18733442458135915
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46+SUV,10,173422.0,3190.0,3854.0,4218.0,0.5746964053003656,0.01839443669199986,0.022223247338861274,0.024322173657321446,2.5643327003181193,0.14479388300889875,-0.6002907567531064,1.7857163012483048,-0.6254524799712039,0.8587682634335293,0.503618112934189,0.38447188817834255,-0.036036194882775846,,0.37590376118039054
47+Sedan,2,57344.0,1774.0,742.0,1783.0,0.0,0.030936104910714284,0.012939453125,0.031093052455357144,3.2260145815859085,0.17088615282627717,1.7495569575286605,-1.076011609515457,0.4306224671218319,0.3574384514808152,1.8481239518795969,0.6619460436990895,1.2540253388535505,0.023509842924754783,0.5549176601971971
48+Sedan,3,58316.0,1371.0,716.0,1583.0,0.0,0.023509842924754783,0.012277934014678648,0.02714520886206187,3.2263268269075653,0.17125540567389852,0.35814837861847215,-1.2799247572976136,0.4311208251257068,0.3503437271829038,1.0641933985858956,0.18477631444307296,0.6455362362907635,0.028586626400590263,0.4368414651610014
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56+Van,2,10644.0,245.0,148.0,242.0,0.0,0.023017662532882375,0.013904547162720781,0.022735813603908306,3.5949275456544,0.16727089527669514,0.2659318609521937,-0.7785215445015575,1.0194246544570666,0.4269010317844053,0.18861168347299642,0.2244695372330209,0.403585271993154,0.017381758302076663,0.5469374882530929
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65+Wagon,2,1811.0,38.0,34.0,66.0,0.0,0.020982882385422418,0.018774157923799006,0.036443953616786304,3.7750713319166955,0.17516397031343012,-0.11531115992759276,0.7225351041611802,1.3069424858076557,0.275245623819113,2.9106622234623662,1.0200148554645445,0.9550014518396267,0.02598808879263671,0.9706634961259869
66+Wagon,3,1847.0,48.0,19.0,49.0,0.0,0.02598808879263671,0.01028695181375203,0.026529507309149974,5.002501250625312,0.16612583044094836,0.8224805780128772,-1.893644665664241,3.2659775193451743,0.44890199769486266,0.9419324103700147,0.7171295679517375,1.115522409187329,0.03114571746384872,1.3264063376669646
67+Wagon,4,1798.0,56.0,25.0,45.0,0.13793103448275862,0.03114571746384872,0.013904338153503892,0.02502780867630701,5.0006250781347665,0.1860373527190454,1.788830646664587,-0.778585971555389,3.2629830611020467,0.06632739651079395,0.6437373546884025,0.9966584974820885,1.120757707538934,0.027313266443701228,1.1070093300613757
68+Wagon,5,1794.0,49.0,19.0,49.0,0.33166109253065773,0.027313266443701228,0.010590858416945374,0.027313266443701228,5.672149744753262,0.18825539056681873,1.07077016916349,-1.7999655094281746,4.334767549762971,0.02371061748348242,1.0975649012420519,0.9453695456447642,1.2358269800649972,0.03305322128851541,1.4524802408796729
69+Wagon,6,1785.0,59.0,36.0,56.0,0.5484593837535015,0.03305322128851541,0.020168067226890758,0.03137254901960784,3.5902010926289973,0.18981894055973847,2.1462267620942934,1.152207398251362,1.0118810163886096,-0.006331008998819501,1.9036240979919838,1.2415216531454858,1.1733003435686062,0.02962085308056872,0.6000825986735665
70+Wagon,7,1688.0,50.0,15.0,38.0,0.5983412322274881,0.02962085308056872,0.008886255924170616,0.022511848341232227,3.125976867771178,0.1820677759765295,1.5031271004414932,-2.325408923829551,0.2709576397855018,0.14259776991203393,0.14413848990818348,-0.052917584756467705,0.7253483427816964,0.01837109614206981,0.12440171425432274
71+Wagon,8,1633.0,30.0,24.0,24.0,0.6203306797305572,0.01837109614206981,0.014696876913655848,0.014696876913655848,3.256473335007578,0.1801116144151622,-0.6046639180380912,-0.5342860400788112,0.4792360491450808,0.18018292883355574,-1.4076947401617805,-0.3774451440600093,-0.44332078401253067,0.016219588271990017,0.03292089265777088
72+Wagon,9,1603.0,26.0,15.0,28.0,0.6406737367436057,0.016219588271990017,0.009357454772301934,0.017467248908296942,2.0834201425059375,0.17212349856865589,-1.0077774243595388,-2.1801619629260207,-1.393011209363603,0.33366442520066164,-0.8575768848655987,-1.0209726112628197,-0.4228589511047026,0.0189873417721519,-0.42100912605708507
73+Wagon,10,1580.0,30.0,25.0,29.0,0.6316455696202532,0.0189873417721519,0.015822784810126583,0.018354430379746836,4.167013917826486,0.17696944037234646,-0.4892021341644178,-0.18722513178455885,1.9325010168726338,0.24055581065000597,-0.6814076306006116,0.1630443861946103,-0.06089577899336372,,0.6200528613646569
added results/amli/index_province_week.csv +10 −0
@@ -0,0 +1,10 @@
1+market,week,ld,exits,cuts,entries,stale,exit,mdfreq,arrival,mddepth,disp,z_exit,z_mdfreq,z_mddepth,z_disp,z_arrival,amli_equal,amli_pca,exit_next,amli_supervised
2+Quebec,2,320250.0,8605.0,4275.0,8454.0,0.0,0.026869633099141296,0.013348946135831381,0.026398126463700235,2.679088373072267,0.1572800715430433,1.2491837138666295,-1.3791172055457157,0.7393416738080363,-2.2840143028365336,1.32750704655098,-0.06941981483132072,1.3997151164879655,0.01998474352758055,-1.5385253178530185
3+Quebec,3,323797.0,6471.0,4484.0,7542.0,0.0,0.01998474352758055,0.013848182657652789,0.023292371454954802,2.632271650434325,0.1562913594887413,-1.1062596342328173,-1.1669376866359207,-0.17841577632679595,-0.769009469117128,-0.054262430783467014,-0.6549769994192258,0.12165797867079571,0.025271711591082886,-0.3395682172300569
4+Quebec,4,325621.0,8229.0,5649.0,7984.0,0.08878420003623845,0.025271711591082886,0.017348389692311,0.024519303116199508,2.67022696929239,0.15556897979648654,0.7025063056350848,0.3206783274908188,0.5656298272841155,0.3378939217991366,0.4916070004850383,0.4836630765388388,0.21564623180650624,0.02615837154404222,0.09726795918580959
5+Quebec,5,323644.0,8466.0,5334.0,8001.0,0.3308419127189134,0.02615837154404222,0.016481071794935177,0.024721607692402763,2.678619261058233,0.15521567282504406,1.0058484677012793,-0.0479387230720188,0.7301455784479047,0.8792666775910071,0.5816135488881136,0.6297871099112572,0.2947606413468745,0.025278582850289825,0.48509961190771844
6+Quebec,6,320192.0,8094.0,5720.0,7860.0,0.5271587047771337,0.025278582850289825,0.017864281431141316,0.024547771337197682,2.6194631225264584,0.15550142350502857,0.7048570857926789,0.539936446979345,-0.4295038758422786,0.441410519641395,0.5042726868442401,0.3521945726830761,-0.04065091309979616,0.022268495143956523,0.10791044051520438
7+Quebec,7,318881.0,7101.0,5067.0,7438.0,0.5389784904086478,0.022268495143956523,0.015889940134407508,0.023325315713385242,2.627975563513798,0.15542561929452992,-0.3249475307353151,-0.2991744116901241,-0.2626328157857895,0.5575654151158176,-0.039605327881139664,-0.07375893419531017,-0.17587244392563836,0.02190472628442483,0.4032073799166777
8+Quebec,8,320844.0,7028.0,5678.0,6635.0,0.5372610988517784,0.02190472628442483,0.017697073967410954,0.02067983194324968,2.5293725309117026,0.15538276101416693,-0.4493993366101638,0.4688719361478901,-2.1955675168389814,0.623237216761514,-1.2165973044097698,-0.5538910009899022,-0.975585240038213,0.022987732184747915,0.5209556064048424
9+Quebec,9,312036.0,7173.0,4882.0,5940.0,0.5605923675473343,0.022987732184747915,0.015645630632362933,0.019036265046340806,2.7030679821597516,0.15543246156061966,-0.07888372886452168,-0.40300790612235476,1.209418681580559,0.547081001423836,-1.9478303107837505,-0.13464445255324636,-0.1841887666585369,0.018240766191660512,0.8134317952602429
10+Quebec,10,301961.0,5508.0,6408.0,7310.0,0.5777931587191724,0.018240766191660512,0.021221283543239027,0.024208424266709938,2.632271650434325,0.1560070952456019,-1.7029053425528475,1.9666892224480943,-0.17841577632679595,-0.33343098037929986,0.3532950910897579,0.021046442855781767,-0.6554826045899121,,-0.5497792581075893
added results/amli/index_region_week.csv +154 −0
@@ -0,0 +1,154 @@
1+region,week,ld,exits,cuts,entries,stale,exit,mdfreq,arrival,mddepth,disp,z_exit,z_mdfreq,z_mddepth,z_disp,z_arrival,amli_equal,amli_pca,exit_next,amli_supervised
2+(missing),2,746.0,2.0,2.0,5.0,0.0,0.002680965147453083,0.002680965147453083,0.006702412868632708,12.158080827608725,0.37907720463186767,-2.766524140628517,-1.6030598385938295,2.323463873826365,-4.436964246189289,-2.046844256737203,-1.705985721664495,-2.729717603678916,0.018387553041018388,-0.8575858914772412
3+(missing),3,707.0,13.0,2.0,5.0,0.0,0.018387553041018388,0.002828854314002829,0.007072135785007072,15.660123059491706,0.32553509990205287,-0.45837569785442545,-1.5817144263050473,3.296729500127968,-3.326651815994245,-1.9949393644715205,-0.8129903608994541,-2.252950399119264,0.010014306151645207,-1.5260489075616186
4+(missing),4,699.0,7.0,1.0,11.0,0.0,0.010014306151645207,0.001430615164520744,0.015736766809728183,12.507817385866165,0.3109663727678235,-1.6888591675701807,-1.7835276613128446,2.4206604659502626,-3.0245374536455567,-0.7785237192896577,-0.9709575071735955,-2.0020113819782837,0.00423728813559322,-0.9702523870976217
5+(missing),5,708.0,3.0,1.0,2.0,0.1271186440677966,0.00423728813559322,0.0014124293785310734,0.002824858757062147,13.337779259753251,0.326041963260562,-2.537816001988116,-1.7861524857594313,2.6513182677440583,-3.3371627342744095,-2.5912087510701296,-1.5202043410696058,-2.6424519119973264,0.019662921348314606,-1.354384294932544
6+(missing),6,712.0,14.0,3.0,6.0,0.7949438202247191,0.019662921348314606,0.004213483146067416,0.008426966292134831,30.03003003003003,0.3417550010575242,-0.2709550185574795,-1.3818656207645026,7.29032397487446,-3.663006876377712,-1.804736570984082,0.03395197763813682,-3.139693683977291,0.017830609212481426,-3.307825733819839
7+(missing),7,673.0,12.0,2.0,9.0,0.7860326894502229,0.017830609212481426,0.0029717682020802376,0.01337295690936107,15.96491170147378,0.3675379101435875,-0.5402209107842737,-1.5610871150964267,3.381434449093931,-4.197671783357057,-1.1103757435522705,-0.8055842207392194,-2.3123867386241375,0.003125,-1.231604168959422
8+(missing),8,640.0,2.0,4.0,4.0,0.7484375,0.003125,0.00625,0.00625,9.73187704622224,0.350792042612528,-2.70127137151095,-1.0879273094303712,1.6491885263366837,-3.850409661930191,-2.1103578777923073,-1.620155538865427,-2.359005306357383,0.0029985007496251873,-0.586707817964847
9+(missing),9,667.0,2.0,3.0,9.0,0.7451274362818591,0.0029985007496251873,0.004497751124437781,0.013493253373313344,17.657445556209534,0.3522524653196879,-2.7198609626903516,-1.3408361299615168,3.8518128492082977,-3.880694715035244,-1.0934874882539078,-1.0366132893465445,-2.7115671974099924,0.010014306151645207,-1.4834962254487025
10+(missing),10,699.0,7.0,7.0,10.0,0.7238912732474965,0.010014306151645207,0.010014306151645207,0.01430615164520744,22.234574763757642,0.3993745071815271,-1.6888591675701807,-0.5446105225240236,5.123859909146929,-4.857873125921227,-0.9793658167075878,-0.5893697447152181,-2.8913358310012764,,-1.7674202702722688
11+Abitibi-Témiscamingue,2,10073.0,261.0,61.0,214.0,0.0,0.02591085078923856,0.006055792713193686,0.021244912141368012,2.9832356345443003,0.15548251871200433,0.6472042091089635,-1.1159579941862563,-0.22635159434600013,0.19975969948216354,-0.005242763848420283,-0.10011768875790994,0.042031365485469215,0.018694421988682295,-0.09127626684634807
12+Abitibi-Témiscamingue,3,9896.0,185.0,67.0,165.0,0.0,0.018694421988682295,0.006770412287793048,0.01667340339531124,3.8465237042023275,0.15873573258954599,-0.41328002917794926,-1.012814202079439,0.013568014888025988,0.1322972075882628,-0.6470305958892159,-0.3854519209340631,-0.33511872537232923,0.022222222222222223,-0.2964749895674227
13+Abitibi-Témiscamingue,4,9765.0,217.0,83.0,208.0,0.10701484895033282,0.022222222222222223,0.008499743983614952,0.021300563236047107,4.167534903104814,0.15969342858429167,0.10514489357686298,-0.7632131062550156,0.10278143519040471,0.11243729037581858,0.0025700168742680044,-0.08805589404753227,-0.09694219094891232,0.019979188345473464,-0.179839082196727
14+Abitibi-Témiscamingue,5,9610.0,192.0,67.0,181.0,0.4559833506763788,0.019979188345473464,0.006971904266389178,0.018834547346514047,3.6874451273046533,0.16118611755438042,-0.22447826740440951,-0.9837320897357325,-0.03064210609964573,0.08148312586794322,-0.34363056578604606,-0.30019998063157816,-0.23750878378107482,0.023098532931016543,-0.20538474879228438
15+Abitibi-Témiscamingue,6,9611.0,222.0,63.0,195.0,0.6052439912600146,0.023098532931016543,0.0065549890750182084,0.020289251898865882,4.763038818766373,0.15931043032189757,0.23392239837906859,-1.0439070630410041,0.26828015081547957,0.12037959547617218,-0.13940659230703614,-0.11214630213546399,-0.1810442905688519,0.018477690288713912,-0.32693301560786453
16+Abitibi-Témiscamingue,7,9525.0,176.0,79.0,170.0,0.6127034120734908,0.018477690288713912,0.008293963254593176,0.01784776902887139,3.593246552390772,0.16085480130576096,-0.4451296542634045,-0.7929142304450587,-0.05682118360351196,0.08835369158485143,-0.4821630180603655,-0.3377348789574978,-0.2681867005811967,0.014966859097712209,-0.19239003625524706
17+Abitibi-Témiscamingue,8,9354.0,140.0,88.0,129.0,0.611075475732307,0.014966859097712209,0.009407740004276246,0.013790891597177678,4.65447978048459,0.16180839416277545,-0.9610609056126012,-0.6321585478329194,0.23811010334371926,0.0685788618908889,-1.0517024669321984,-0.46764659102862216,-0.522756196297735,0.018739168110918546,-0.41159480622243566
18+Abitibi-Témiscamingue,9,9232.0,173.0,72.0,150.0,0.6290077989601387,0.018739168110918546,0.00779896013864818,0.01624783362218371,4.0377393687938685,0.1593118298253477,-0.40670440033611194,-0.8643599342824902,0.06670947385247115,0.12035057371685548,-0.7067757516462071,-0.3581560077390965,-0.33733829926721154,0.0166189111747851,-0.30932828341919233
19+Abitibi-Témiscamingue,10,8725.0,145.0,81.0,110.0,0.6514613180515759,0.0166189111747851,0.009283667621776504,0.012607449856733524,3.3338889814969157,0.16045604561809573,-0.718284979044891,-0.6500663920644818,-0.1289002140543663,0.09662276129848818,-1.2178442269367864,-0.5236946101604074,-0.4230669623006836,,-0.2746247550414798
20+Bas-Saint-Laurent,2,12786.0,299.0,109.0,276.0,0.0,0.023384952291568905,0.008524949163147192,0.021586109807602064,3.5778175313059033,0.16717557298039754,0.2760129151430645,-0.7595751457371472,-0.06110912043439113,-0.04272130806613827,0.04265750774104409,-0.10894703027071362,-0.05551649972938962,0.019613947696139476,-0.06341255918520519
21+Bas-Saint-Laurent,3,12848.0,252.0,76.0,263.0,0.0,0.019613947696139476,0.005915317559153176,0.02047011207970112,3.391415932968525,0.16714145568182598,-0.27815190051369976,-1.1362333135303289,-0.11291268428002277,-0.042013811398238676,-0.11401588000425986,-0.33666551794530997,-0.23599421963296788,0.024665981500513873,-0.12158965228913726
22+Bas-Saint-Laurent,4,12649.0,312.0,146.0,272.0,0.14364771918728753,0.024665981500513873,0.011542414420112262,0.021503676179935174,3.7396457319920486,0.16482605419795376,0.46426548749111746,-0.32405277800962506,-0.01613484123805574,0.0060010931597402435,0.031084764008851577,0.032232745082405694,0.046347611737757796,0.02651915724395998,-0.034132984418487305
23+Bas-Saint-Laurent,5,12293.0,326.0,52.0,259.0,0.4164158464166599,0.02651915724395998,0.004230049621735947,0.02106890100056943,2.825035419305825,0.16382952937025685,0.736597373665722,-1.3794745176055168,-0.27031760656691384,0.026666210991272307,-0.029952726082570165,-0.1832962531196013,-0.017821947620847656,0.022860985039502436,-0.07895543463714626
24+Bas-Saint-Laurent,6,11898.0,272.0,77.0,253.0,0.5731215330307615,0.022860985039502436,0.00647167591191797,0.021264077996301898,3.3338889814969157,0.16076368970361188,0.19901374733416652,-1.055931972349415,-0.1289002140543663,0.09024308956260375,-0.0025520958063134473,-0.1796254890626649,-0.07870506556839178,0.020659378497030215,-0.10755828641709389
25+Bas-Saint-Laurent,7,11617.0,240.0,103.0,216.0,0.579151243866747,0.020659378497030215,0.008866316604975466,0.018593440647327195,3.182541486701523,0.15774468121304164,-0.12452149450472845,-0.7103042697305513,-0.17096176116255615,0.15284882146013853,-0.3774792040272208,-0.2460835815929836,-0.1341945229076459,0.016583174987044393,-0.11916891058176085
26+Bas-Saint-Laurent,8,11578.0,192.0,97.0,176.0,0.5863707030575229,0.016583174987044393,0.00837795819657972,0.015201243738124029,2.8575510787255323,0.15563139768560513,-0.7235365605241623,-0.7807909173674098,-0.2612810583089805,0.19667236894449042,-0.8537050675544106,-0.48452824696209457,-0.319195069568152,0.020937723693629205,-0.18231830907070187
27+Bas-Saint-Laurent,9,11176.0,234.0,133.0,165.0,0.6142627057981389,0.020937723693629205,0.011900501073729421,0.014763779527559055,3.3910949845705174,0.1587404840068306,-0.08361750972858166,-0.2723687538522674,-0.11300188024709407,0.13219867657797135,-0.9151200670414192,-0.2503819068582782,-0.18208223991658173,0.01677248175874159,-0.17924460815281165
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74+Gaspésie–Îles-de-la-Madeleine,2,2585.0,61.0,29.0,62.0,0.0,0.023597678916827854,0.011218568665377175,0.023984526112185687,4.8792388387411565,0.15582749990887804,0.30727397783569327,-0.3707946815179325,0.3005737320257234,0.19260576123103712,0.3793678775457054,0.16180533342404532,0.049752155105711085,0.014584154513204573,-0.16437797110289679
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76+Gaspésie–Îles-de-la-Madeleine,4,2481.0,39.0,20.0,37.0,0.12011285771866183,0.015719467956469165,0.008061265618702136,0.014913341394598953,4.654231186285223,0.15763094178067794,-0.8504619023708133,-0.826500375343971,0.23804101560201837,0.15520745689656779,-0.8941232820114651,-0.43556741744553273,-0.48149260180657244,0.018307567127746133,-0.4271673114953857
77+Gaspésie–Îles-de-la-Madeleine,5,2458.0,45.0,7.0,31.0,0.4890154597233523,0.018307567127746133,0.0028478437754271765,0.012611879576891782,6.263322884012538,0.1609397535200385,-0.47012996061437545,-1.5789736042915008,0.6852296944556246,0.08659202196127345,-1.2172223446097825,-0.4989008386197522,-0.7108329907999655,0.01552013422818792,-0.7951358831348843
78+Gaspésie–Îles-de-la-Madeleine,6,2384.0,37.0,11.0,25.0,0.6220637583892618,0.01552013422818792,0.004614093959731544,0.010486577181208054,4.167534903104814,0.16159827152957135,-0.8797548231275362,-1.324043921011202,0.10278143519040471,0.0729362134124461,-1.5155906279765163,-0.7087343447024808,-0.675702047388221,0.012449977767896843,-0.5322866132366972
79+Gaspésie–Îles-de-la-Madeleine,7,2249.0,28.0,17.0,19.0,0.6545131169408626,0.012449977767896843,0.00755891507336594,0.008448199199644287,3.7043897017966287,0.1560504903145834,-1.3309270892437703,-0.8990065619285627,-0.025932976165344682,0.18798156835482707,-1.8017557122708243,-0.7739281542507349,-0.6928725002848141,0.02427637721755369,-0.4848809879543683
80+Gaspésie–Îles-de-la-Madeleine,8,2142.0,52.0,19.0,21.0,0.6956115779645191,0.02427637721755369,0.008870214752567693,0.00980392156862745,8.33368057002375,0.15798309372484307,0.40701151704849325,-0.709741635093682,1.2606105255601678,0.14790481754578097,-1.6114277115228388,-0.10112849729241576,-0.6071917068986958,0.014335145823035097,-1.030460668530134
81+Gaspésie–Îles-de-la-Madeleine,9,2023.0,29.0,4.0,25.0,0.7261492832427089,0.014335145823035097,0.001977261492832427,0.012357884330202669,3.996976098989338,0.15708307485608547,-1.0538937997527122,-1.7046280939801903,0.055380801412368744,0.16656867363500902,-1.2528803883271618,-0.7578905614025373,-0.6831402971058609,0.008525576730190571,-0.5338751290921854
82+Gaspésie–Îles-de-la-Madeleine,10,1994.0,17.0,3.0,39.0,0.7407221664994985,0.008525576730190571,0.0015045135406218655,0.019558676028084254,3.1254883575558683,0.15331415562968928,-1.9076341503435077,-1.7728616244275848,-0.18681760920598645,0.24472544178197633,-0.24197113506432444,-0.7729118154518855,-0.5789431205189361,,-0.25663584294130426
83+Lanaudière,2,15352.0,338.0,199.0,347.0,0.0,0.0220166753517457,0.012962480458572173,0.022602918186555496,2.34796900680911,0.16642000412363642,0.07493892572296407,-0.11908918415484329,-0.40290091367809683,-0.027052938326780986,0.18540583906433597,-0.0577396542744842,0.11748991376096274,0.01692658805629517,0.21512513151744364
84+Lanaudière,3,15774.0,267.0,346.0,340.0,0.0,0.01692658805629517,0.02193482946621022,0.021554456700900216,2.8795205200091463,0.1648920892461866,-0.6730705811280782,1.1759244617734224,-0.25517544889178734,0.004631712272466146,0.03821377157952896,0.05810478312111041,0.12659788074204428,0.021512751761551412,0.29666877276228104
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86+Lanaudière,5,15918.0,406.0,301.0,368.0,0.32428697072496543,0.025505716798592787,0.018909410729991204,0.023118482221384597,3.545486869507797,0.15888811480342696,0.587668083506089,0.739254137790777,-0.07009425531289332,0.12913722971540056,0.25778516557805686,0.328750072255486,0.3214836958858558,0.019387641077215332,0.16187646917456755
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88+Lanaudière,7,18414.0,394.0,367.0,450.0,0.4649179971760617,0.021396763332247205,0.019930487672423156,0.024437927663734114,3.2263268269075653,0.15671831294722316,-0.016159724866104355,0.8866301006368971,-0.15879321385878784,0.17413280813280682,0.4430202984369908,0.2657660536963605,0.30009496713604233,0.020280136282515817,0.2508302705578415
89+Lanaudière,8,18491.0,375.0,432.0,361.0,0.4631983126926613,0.020280136282515817,0.023362716997458222,0.019523011194635228,2.435863397197481,0.1553597700689095,-0.18025271587485292,1.3820169617690024,-0.37847385562296315,0.20230516057125436,-0.246978072032145,0.1557234957620591,0.2679868316471033,0.02262318600673178,0.29426903337883564
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91+Lanaudière,10,18377.0,208.0,478.0,453.0,0.509386733416771,0.011318495946019481,0.026010774337487077,0.024650378190128965,2.5213867627194957,0.1556754211378549,-1.497203050826958,1.7642212638551762,-0.3547057379371152,0.19575944655379107,0.47284593595075175,0.11618357151912914,0.219192364073398,,0.4715688521604399
92+Laurentides,2,32513.0,836.0,597.0,735.0,0.0,0.02571279180635438,0.01836188601482484,0.022606342078553193,2.19115980355119,0.15334748723251784,0.6180986174922878,0.6602277895045231,-0.4464803445682308,0.24403423823057263,0.18588651455779076,0.2523533630433887,0.4132358547877792,0.015062332095746929,0.29509379144741926
93+Laurentides,3,33129.0,499.0,781.0,712.0,0.0,0.015062332095746929,0.023574511757070843,0.021491744393129886,2.5006251562890722,0.1507722829639434,-0.9470307513082986,1.4125861141282066,-0.36047567346261244,0.2974367207089529,0.02940967685239725,0.08638521738372913,0.20341662356450754,0.02514442079659471,0.32251344032640034
94+Laurentides,4,32890.0,827.0,718.0,685.0,0.05782912739434479,0.02514442079659471,0.021830343569474005,0.020826999087868652,2.0374176060496842,0.1495307367075279,0.5345741327356605,1.1608436103748354,-0.4892074124425189,0.32318289277515194,-0.06391300325641734,0.2930960440373423,0.4551273463670629,0.025872702602689035,0.3258516064700287
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96+Laurentides,6,32435.0,815.0,731.0,900.0,0.5043934021889934,0.02512717743178665,0.022537382457222137,0.027747803298905502,2.0002400288034563,0.15003042543593045,0.5320401485310949,1.2628932527880221,-0.4995395716819211,0.3128207561132965,0.9076891914044584,0.5031807554309902,0.6622292114086548,0.021700209902457093,0.5264779161996032
97+Laurentides,7,32396.0,703.0,737.0,692.0,0.5153414001728609,0.021700209902457093,0.022749722187924436,0.02136066181010001,2.1279312252627998,0.15149335834403316,0.028433011203709647,1.293541062920923,-0.4640524347297255,0.2824836485116351,0.011007173115831806,0.23028249220447483,0.3818207932952689,0.019691421024194033,0.35452739276046363
98+Laurentides,8,33314.0,656.0,707.0,785.0,0.49717836345080146,0.019691421024194033,0.02122230893918473,0.023563666926817554,2.016535591853196,0.15020728349394663,-0.2667668726058299,1.073083633277283,-0.4950108109396457,0.3091532181786854,0.32028403473235617,0.1881486405285698,0.3669217934262387,0.018691588785046728,0.3895827995756116
99+Laurentides,9,32635.0,610.0,610.0,503.0,0.5275930749195649,0.018691588785046728,0.018691588785046728,0.015412900260456565,2.102962613762656,0.14998659238222678,-0.41369638024056143,0.7078150585880398,-0.4709915547347126,0.31372973017448047,-0.823990899180072,-0.13742680907856514,0.05238719594146228,0.01150100479208533,0.11400589443854786
100+Laurentides,10,32345.0,372.0,1020.0,678.0,0.5511516463131859,0.01150100479208533,0.031535013139588806,0.02096150873396197,2.273502359718159,0.15044156030014622,-1.4703826165165903,2.561555900080102,-0.42359621748103987,0.30429497714870685,-0.04502937884483367,0.18536853287726895,0.2879371483381027,,0.502240851871326
101+Laval,2,22504.0,729.0,500.0,719.0,0.0,0.032394241023817986,0.022218272307145397,0.03194987557767508,2.500093753906426,0.13107651938288234,1.5999653801060065,1.216834857316357,-0.36062335748197005,0.705871376697692,1.4976123453733645,0.9319321204022899,1.0287069584961472,0.0204278873610684,0.48586998446193086
102+Laval,3,22763.0,465.0,432.0,544.0,0.0,0.0204278873610684,0.018978166322540967,0.02389843166542196,2.3742481707083476,0.1326576035140625,-0.158540080120333,0.7491778969434837,-0.3955975731648895,0.673084145547701,0.36728119645012497,0.24708111713121744,0.40742994233448154,0.027031778822908883,0.23367156910277384
103+Laval,4,22751.0,615.0,644.0,555.0,0.09546833106237089,0.027031778822908883,0.02830644806821678,0.024394532108478747,2.5442313509148775,0.13063872747979755,0.8119292476030299,2.0955646841081332,-0.34835691317562,0.7149499475586375,0.43692805605605434,0.742203004430047,0.8106823436644077,0.025277084154729047,0.40863578031539516
104+Laval,5,22827.0,577.0,648.0,663.0,0.31112279318351077,0.025277084154729047,0.028387435931134183,0.02904455250361414,2.3941316956670082,0.12882481554981828,0.5540695651978821,2.1072539738964955,-0.39007166853821423,0.7525653714068069,1.0897380306434608,0.8227110545212861,0.9118287268840394,0.026588613817141837,0.5432752074850686
105+Laval,6,22378.0,595.0,696.0,492.0,0.47957815711859864,0.026588613817141837,0.031101975154169275,0.021985878988292074,2.6147362616993353,0.12982566882563873,0.7468043060890148,2.499053861890751,-0.3287626302948341,0.7318104937539291,0.09878055377329155,0.7495373170424305,0.7946107380850487,0.01906052393857272,0.391468077531721
106+Laval,7,22140.0,422.0,428.0,470.0,0.5138211382113821,0.01906052393857272,0.019331526648599818,0.02122854561878952,2.5013240261064933,0.1302022375707265,-0.3594798243682001,0.8001797520637335,-0.36028144794346734,0.7240015187313042,-0.007540437467169639,0.15937591220324016,0.3080912395691617,0.018161229911589215,0.14512551047802225
107+Laval,8,22961.0,417.0,680.0,480.0,0.4983232437611602,0.018161229911589215,0.029615434867819347,0.02090501284787248,2.2117370990132676,0.13043833987748937,-0.491634823407068,2.284495784133581,-0.44076163159179593,0.7191054219618664,-0.05296075867296434,0.40364879848472385,0.524505104639785,0.02881612989561959,0.38882151451812147
108+Laval,9,22418.0,646.0,536.0,502.0,0.512623784458917,0.02881612989561959,0.02390935855116424,0.022392720135605317,2.5002343994168097,0.1312236975107051,1.0741470620480627,1.460915839964455,-0.36058427016328953,0.7028193169103703,0.15589642348356542,0.6066388744486328,0.7043007067752844,0.03147311671280781,0.2741426308883721
109+Laval,10,20589.0,648.0,598.0,497.0,0.5264947301957356,0.03147311671280781,0.0290446354849677,0.02413910340473068,2.3050638127043332,0.13220089072524793,1.4646023299981903,2.2021101135303702,-0.41482485629415283,0.6825550822807407,0.4010687712716027,0.8671022881573502,0.9487270097326551,,0.4553755206914071
110+Mauricie,2,17070.0,445.0,82.0,438.0,0.0,0.02606912712360867,0.004803749267721149,0.025659050966608084,3.4511930869370486,0.13796391842880998,0.6704635749992488,-1.2966702408678878,-0.09629979233199484,0.5630461214897353,0.6144521212202187,0.09099835690186404,0.1950616032436828,0.02257388875959972,-0.1356690016444705
111+Mauricie,3,17188.0,388.0,178.0,442.0,0.0,0.02257388875959972,0.010356062369094718,0.02571561554572958,2.5005938914400274,0.13670228280416935,0.1568237544551168,-0.49528353303652356,-0.3604843623934911,0.5892088904635022,0.622393144768735,0.10253157885146788,0.2935695711150607,0.028742726150590724,0.10190541156330218
112+Mauricie,4,17013.0,489.0,136.0,445.0,0.12555104919767238,0.028742726150590724,0.007993887027567155,0.02615646858284841,2.601482868918962,0.14114436468413324,1.0633600762984983,-0.8362253865856137,-0.3324459301441553,0.49709262510268887,0.6842838969597143,0.2152130563262265,0.3922757192454489,0.023965014577259476,0.06893343484739625
113+Mauricie,5,17150.0,411.0,191.0,416.0,0.33527696793002915,0.023965014577259476,0.011137026239067056,0.024256559766763847,2.7789099262662567,0.14120904437134496,0.36125548154268217,-0.38256401357398717,-0.2831365144781094,0.4957513505850977,0.41755830856357135,0.12177292252785095,0.26925662357999586,0.027452153110047846,0.06142913186559429
114+Mauricie,6,16720.0,459.0,147.0,394.0,0.5000598086124401,0.027452153110047846,0.008791866028708134,0.023564593301435406,3.031221582297666,0.14371719371004077,0.8737049981872529,-0.7210500077899316,-0.2130156405220669,0.44373939846643723,0.3204140871922059,0.14075856710677953,0.26377366898356003,0.023544520547945206,-0.028414867814024747
115+Mauricie,7,16352.0,385.0,186.0,387.0,0.5108243639921722,0.023544520547945206,0.011374755381604696,0.02366682974559687,2.5787351998252825,0.14427049030379466,0.29946213434757574,-0.34825165196623087,-0.3387678197563938,0.4322655856843366,0.3347669211248855,0.07589503388683463,0.24480338143055644,0.02191028379615502,0.09002103915132208
116+Mauricie,8,16385.0,359.0,109.0,345.0,0.5107110161733293,0.02191028379615502,0.006652425999389686,0.02105584375953616,3.3338889814969157,0.14555150525348015,0.05930424395598716,-1.0298436169388823,-0.1289002140543663,0.405700944098451,-0.03178581421182395,-0.14510489143012686,-0.032845189334726854,0.02470610423084177,-0.17041644389554364
117+Mauricie,9,15907.0,393.0,194.0,236.0,0.5422141195700007,0.02470610423084177,0.012195888602502043,0.014836235619538568,2.7785495971103082,0.1477217058027128,0.4701616896550589,-0.2297343355664951,-0.2832366548985892,0.3606970979106986,-0.9049480558361929,-0.11741205174710394,0.02111978774264332,0.01730952705465449,-0.13415011495036555
118+Mauricie,10,14674.0,254.0,226.0,246.0,0.5794602698650675,0.01730952705465449,0.015401390213983917,0.016764345100177182,2.632029166985682,0.14915446386256126,-0.6167961019904007,0.23292804252203392,-0.3239566944494971,0.330985731662781,-0.6342634148883369,-0.20222048742868398,-0.051753442966968884,,0.007252086272926126
119+Montréal,2,26491.0,887.0,538.0,874.0,0.0,0.033483069721792304,0.020308784115359933,0.03299233702012004,2.630657632013027,0.16830660597700406,1.7599732876609482,0.9412310814271233,-0.3243378628523229,-0.06617574642693887,1.6439620750353636,0.7909305669688347,0.8560918606921748,0.024935064935064935,0.6058014226845092
120+Montréal,3,26950.0,672.0,280.0,793.0,0.0,0.024935064935064935,0.01038961038961039,0.02942486085343228,2.9555940652785813,0.1670139993636809,0.5038084177980738,-0.4904414181873695,-0.2340335660191099,-0.039370726389580314,1.1431289973669365,0.1766183409137901,0.2861283297938466,0.02619090744430357,0.2600266168293265
121+Montréal,4,27605.0,723.0,537.0,788.0,0.06248867958703133,0.02619090744430357,0.019452997645354103,0.02854555334178591,2.750764101139205,0.16661090156177835,0.6883596998277113,0.8177121282839847,-0.2909586257795274,-0.03101161345789425,1.0196842238917723,0.44075716255320935,0.5158614027767305,0.029906339276027916,0.4502686664771414
122+Montréal,5,27653.0,827.0,493.0,751.0,0.2869851372364662,0.029906339276027916,0.017828083752214948,0.027157993707735147,2.6638509132673858,0.16992560536815465,1.2343578666314112,0.5831820555814763,-0.31511299418143357,-0.09974923326582523,0.8248866332423198,0.4455128656015897,0.5312540250759071,0.026880554953392585,0.4056695778666866
123+Montréal,6,27678.0,744.0,557.0,865.0,0.5292651203121613,0.026880554953392585,0.020124286436881276,0.031252258111135196,2.514754939697203,0.17066381542713338,0.7897062707668379,0.9146018217908172,-0.35654881262323695,-0.11505763043689649,1.399674787705863,0.5264752874406768,0.6201698627115446,0.02382442960489705,0.5818634144795144
124+Montréal,7,28752.0,685.0,563.0,880.0,0.5337367835281024,0.02382442960489705,0.01958124652198108,0.03060656649972176,2.2201403111246676,0.16957772266278737,0.3405959349094691,0.8362227819785211,-0.43842626355308745,-0.09253512590175497,1.309027029498378,0.39097687138630516,0.5308378633683493,0.025570335653732165,0.5887351045834492
125+Montréal,8,29018.0,742.0,556.0,646.0,0.5395271900199876,0.025570335653732165,0.01916052105589634,0.022262044248397547,2.7232563640845706,0.16968467550756214,0.5971640931343103,0.7754978570669456,-0.2986034036433922,-0.09475302662862019,0.13755101521470411,0.2233713070287895,0.3053018072248052,0.02818159088473765,0.2976803018722812
126+Montréal,9,27997.0,789.0,532.0,679.0,0.5586670000357181,0.02818159088473765,0.01900203593242133,0.024252598492695645,2.5866636158657874,0.16883800586254089,0.9808989142153568,0.7526230895560025,-0.33656440406568905,-0.07719548316110596,0.41700219073955014,0.3473528614568229,0.44059946527861393,0.01771979009417008,0.3606589702180416
127+Montréal,10,27822.0,493.0,513.0,779.0,0.5677161958162605,0.01771979009417008,0.018438645676083675,0.02799942491553447,2.564760194921775,0.16711446031073868,-0.556506241315211,0.6713068066830123,-0.34265166552391535,-0.04145400344471853,0.9430140044588353,0.13474178017160057,0.25680679563556336,,0.44192678636359556
128+Montérégie,2,63548.0,1949.0,1030.0,1930.0,0.0,0.030669729967898282,0.016208220557688675,0.030370743375086547,2.8203740942018003,0.15879639007901378,1.3465413051101587,0.34938096963813053,-0.2716130528226618,0.13103934212181595,1.2759201450293791,0.5662537418153645,0.6480955022903473,0.023173891065335524,0.38970876182776054
129+Montérégie,3,63304.0,1467.0,739.0,1566.0,0.0,0.023173891065335524,0.011673827878175155,0.024737773284468596,2.8987607797666497,0.15731910696866136,0.24499658976661845,-0.3050853681033458,-0.2498283164978346,0.16167403249528045,0.485115213172547,0.0673744301666531,0.18787776937187525,0.02623415479096134,0.13386111532368328
130+Montérégie,4,63505.0,1666.0,1322.0,1646.0,0.08363121014093379,0.02623415479096134,0.02081725848358397,0.025919218959137076,2.7575930263312167,0.15642896053835054,0.6947150773622006,1.014621142925436,-0.28906077369383276,0.18013316202063695,0.6509767482452927,0.4502770713719467,0.5238611931030915,0.026342578816306715,0.36864914574617996
131+Montérégie,5,63851.0,1682.0,1301.0,1670.0,0.30405162017822746,0.026342578816306715,0.02037556185494354,0.026154641274216534,2.8577144000228616,0.15380986317934225,0.7106484390163096,0.9508693694530792,-0.26123566907772383,0.23444586355286834,0.6840273636114333,0.46375107331119325,0.5296202102154858,0.024807230438629757,0.34175731885989075
132+Montérégie,6,63288.0,1570.0,1276.0,1566.0,0.5253760586525091,0.024807230438629757,0.020161800025281253,0.024744027303754267,2.85832723195197,0.1542639857146179,0.4850226066544232,0.9200163023069396,-0.2610653546692135,0.22502864138178177,0.4859932063476348,0.3709990804043132,0.4417442252067527,0.024874992088106843,0.3025883029220347
133+Montérégie,7,63196.0,1572.0,1079.0,1577.0,0.5400816507373885,0.024874992088106843,0.017073865434521173,0.02495411101968479,2.8169807600214094,0.15450523689929774,0.49498046298707815,0.47432282456058905,-0.2725561069886742,0.22002577138813864,0.5154865705920813,0.2864519045078426,0.38102528494048327,0.02364658533911709,0.2506515514837738
134+Montérégie,8,63857.0,1510.0,1045.0,1515.0,0.5290727719748813,0.02364658533911709,0.016364689853892292,0.02372488529057112,2.564760194921775,0.15441071111150467,0.3144609800804519,0.37196478504477365,-0.34265166552391535,0.22198596995878198,0.34291725932102485,0.1817354657762234,0.3140075838537346,0.02381791389031266,0.23734400475789486
135+Montérégie,9,62432.0,1487.0,1142.0,1335.0,0.5457617888262429,0.02381791389031266,0.018291901588928756,0.021383264992311635,2.7536077599632893,0.15404383019300347,0.3396384235407725,0.6501266681080269,-0.29016833394317737,0.22959404674748599,0.014180402811899622,0.18867424145300155,0.28720107064008377,0.025510033164718098,0.19001850996004663
136+Montérégie,10,59702.0,1523.0,1511.0,1705.0,0.5508860674684265,0.025510033164718098,0.025309034873203577,0.028558507252688353,2.7028488026379804,0.1557227430135043,0.5883023914676967,1.662936507615767,-0.3042749452061348,0.19477812415305962,1.021502805645925,0.6326489767352627,0.6857195021489331,,0.5342340708762194
137+Outaouais,2,12099.0,315.0,104.0,298.0,0.0,0.026035209521448055,0.00859575171501777,0.02463013472187784,2.9719386459556505,0.15064028652692973,0.6654792422377249,-0.7493559410716927,-0.22949118260508933,0.3001739549939571,0.47000398325557585,0.09136201136209518,0.22067105974541776,0.01691713886835838,0.030836638895855518
138+Outaouais,3,12177.0,206.0,112.0,251.0,0.0,0.01691713886835838,0.0091976677342531,0.02061263036872793,1.9617074701820465,0.14989731382020208,-0.6744591786114765,-0.6624790866438272,-0.5102482988520972,0.31558111606351463,-0.09400793321435681,-0.32512267625164865,-0.0572173798527951,0.020268087855297157,0.070677474420587
139+Outaouais,4,12384.0,251.0,256.0,276.0,0.12330426356589147,0.020268087855297157,0.020671834625323,0.022286821705426358,2.644692938675803,0.1471809079421469,-0.1820232823856112,0.9936315580651974,-0.3204372583978367,0.37191172217396273,0.14102948836511003,0.20082244556416445,0.3095333184567864,0.022765263074106503,0.2494668669918979
140+Outaouais,5,12563.0,286.0,209.0,260.0,0.31736050306455466,0.022765263074106503,0.016636153784923984,0.020695693703733184,2.2728305832083278,0.1405200705789101,0.18494700624023266,0.41114621844243815,-0.4237829133979603,0.5100387262809033,-0.08234678573408942,0.12000045036630488,0.29784777075799357,0.021777849740932644,0.14751173292611647
141+Outaouais,6,12352.0,269.0,223.0,288.0,0.529630829015544,0.021777849740932644,0.01805375647668394,0.023316062176165803,2.500125006250313,0.14251328527049767,0.03984250851075939,0.6157542679923361,-0.3606146720264465,0.46870506823206887,0.2855231439737288,0.2098420633364894,0.3515711001084659,0.024693417869358473,0.2224109251738672
142+Outaouais,7,11987.0,296.0,140.0,239.0,0.5462584466505381,0.024693417869358473,0.011679319262534413,0.019938266455326602,2.395127242544655,0.14132094040164725,0.4682973760498019,-0.3042927754752367,-0.38979499239006155,0.49343094211477234,-0.18868095650490657,0.01579191875887388,0.20557752261909681,0.020176113533384627,0.012398237974135518
143+Outaouais,8,11697.0,236.0,144.0,159.0,0.5568949303240147,0.020176113533384627,0.01231084893562452,0.013593229033085406,2.2184687906443457,0.14100437809233926,-0.19553929167650114,-0.2131416684725902,-0.43889080204789643,0.49999555269049717,-1.0794520427355536,-0.2854056504484089,-0.06593432517635237,0.020415440848711778,-0.11648820189321654
144+Outaouais,9,11217.0,229.0,177.0,180.0,0.5898190246946599,0.020415440848711778,0.015779620219309978,0.016047071409467772,2.00160128102482,0.14175680067574387,-0.16036914689846335,0.28751943411634195,-0.4991612609968819,0.48439242779786673,-0.7349604828612166,-0.12451580576847063,0.0971046688203781,0.03221125943122461,0.0505750511383051
145+Outaouais,10,10338.0,333.0,213.0,270.0,0.6008899206809828,0.03221125943122461,0.02060359837492745,0.0261172373766686,2.564431337350942,0.14594426975203978,1.5730754737264196,0.983782757563346,-0.34274305955872353,0.39755611477037933,0.6787762818240759,0.6580895136650995,0.7498985088087174,,0.35285299447300467
146+Saguenay–Lac-Saint-Jean,2,10342.0,267.0,110.0,291.0,0.0,0.025817056662154322,0.01063624057242313,0.02813769096886482,2.8065009661103013,0.15392008069232957,0.6334207718762639,-0.4548443351973533,-0.2754685856251532,0.23216026280346683,0.9624249857214798,0.2195386199157408,0.35268513993936146,0.019521531100478468,0.1989636931821384
147+Saguenay–Lac-Saint-Jean,3,10450.0,204.0,72.0,206.0,0.0,0.019521531100478468,0.006889952153110048,0.01971291866028708,2.6091251322257483,0.15303676894710916,-0.29173290377213684,-0.9955605534879546,-0.3303220402276017,0.2504776602125636,-0.2203172238440726,-0.3174910122238404,-0.11973311677569982,0.036881559220389805,-0.0737629467398107
148+Saguenay–Lac-Saint-Jean,4,10005.0,369.0,61.0,254.0,0.06946526736631684,0.036881559220389805,0.006096951524237881,0.025387306346826587,2.826148394530316,0.15727814952381008,2.259395459881151,-1.1100173846639294,-0.2700082954686465,0.16252337452912238,0.5763022674003498,0.32363908433560945,0.4573101573862617,0.02257836911601653,0.044858861553306796
149+Saguenay–Lac-Saint-Jean,5,9921.0,224.0,111.0,443.0,0.3090414272754763,0.02257836911601653,0.011188388267311764,0.04465275677855055,3.047560412498076,0.16126767289992902,0.1574821614723895,-0.3751507344647119,-0.2084748552444755,0.07979189773321807,3.280952384613968,0.5869201708220776,0.6836390189711854,0.04521249874409726,0.6351673065251734
150+Saguenay–Lac-Saint-Jean,6,9953.0,450.0,95.0,210.0,0.4975384306239325,0.04521249874409726,0.009544860845976088,0.02109916608057872,3.2609404552272876,0.16785789299325038,3.483661686180262,-0.612367369291904,-0.14917361123422102,-0.0568707031163387,-0.02570385308140681,0.5279092298912783,0.5644737524937501,0.020534099372273646,-0.010045519370409728
151+Saguenay–Lac-Saint-Jean,7,9399.0,193.0,97.0,196.0,0.5360144696244281,0.020534099372273646,0.010320246834769656,0.020853282264070645,2.914798206278027,0.15903139523585264,-0.14293178314784233,-0.5004529268761616,-0.2453712954443024,0.1261659971512582,-0.060223144260003324,-0.16456263051541029,-0.031360369414506525,0.023785817015156543,0.010134572891969942
152+Saguenay–Lac-Saint-Jean,8,9039.0,215.0,166.0,131.0,0.5514990596304901,0.023785817015156543,0.018364863369841798,0.014492753623188406,3.210046839460745,0.16177582928640677,0.33492165418822234,0.6606575226070196,-0.16331764592712766,0.06925416569529427,-0.9531690213922056,-0.010330664965759406,0.03091439366401253,0.026567233745047776,-0.01632504601578273
153+Saguenay–Lac-Saint-Jean,9,8582.0,228.0,59.0,145.0,0.5791190864600326,0.026567233745047776,0.006874854346306222,0.016895828478210206,3.027950310559006,0.1630961325235527,0.7436624155368012,-0.9977396780202146,-0.21392477185936024,0.04187479569802972,-0.6158046440919338,-0.20838637654733555,-0.08158651931825445,0.014311043566362716,-0.1621738064261221
154+Saguenay–Lac-Saint-Jean,10,7896.0,113.0,86.0,143.0,0.6152482269503546,0.014311043566362716,0.010891590678824722,0.018110435663627154,3.687478172761127,0.16744038168880865,-1.0574357266551484,-0.4179886720930992,-0.03063292231351127,-0.04821269474315525,-0.4452876100859717,-0.3999115251781772,-0.35322618766870295,,-0.11792257038322057
added results/amli/index_region_week_5comp.csv +86 −0
@@ -0,0 +1,86 @@
1+region,week,ld,exits,cuts,entries,stale,exit,mdfreq,arrival,mddepth,disp,z_exit,z_mdfreq,z_mddepth,z_disp,z_arrival,z_stale,amli_equal,amli_pca,exit_next,amli_supervised
2+(missing),6,712.0,14.0,3.0,6.0,0.7949438202247191,0.019662921348314606,0.004213483146067416,0.008426966292134831,30.03003003003003,0.3417550010575242,-0.10853825870682066,-1.4726318494123463,6.151389069651688,-3.4568510032768436,-1.9222854671562417,-2.708689707520129,-0.5862678694034489,-2.837149648669716,0.017830609212481426,
3+(missing),7,673.0,12.0,2.0,9.0,0.7860326894502229,0.017830609212481426,0.0029717682020802376,0.01337295690936107,15.96491170147378,0.3675379101435875,-0.38638355523248136,-1.648674573778559,2.838122091609335,-3.96358127828012,-1.0762338564621572,-2.6013214836289937,-1.1396787759621627,-2.229507694799538,0.003125,
4+(missing),8,640.0,2.0,4.0,4.0,0.7484375,0.003125,0.00625,0.00625,9.73187704622224,0.350792042612528,-2.616289842664494,-1.183906987945508,1.3698295814560717,-3.634462557391908,-2.294673120192332,-2.1483454449792467,-1.751308061952903,-2.225740398998594,0.0029985007496251873,
5+(missing),9,667.0,2.0,3.0,9.0,0.7451274362818591,0.0029985007496251873,0.004497751124437781,0.013493253373313344,17.657445556209534,0.3522524653196879,-2.6354717391565354,-1.4323300799887582,3.2368259204089282,-3.6631653076343227,-1.055656175474824,-2.108463226731432,-1.2763767680961573,-2.4109838172144027,0.010014306151645207,
6+(missing),10,699.0,7.0,7.0,10.0,0.7238912732474965,0.010014306151645207,0.010014306151645207,0.01430615164520744,22.234574763757642,0.3993745071815271,-1.5716199563901652,-0.6502267632747486,4.3150430177235295,-4.589289087990544,-0.9166033657361479,-1.8525934426421078,-0.8775482663850306,-2.457675258581891,,
7+Abitibi-Témiscamingue,6,9611.0,222.0,63.0,195.0,0.6052439912600146,0.023098532931016543,0.0065549890750182084,0.020289251898865882,4.763038818766373,0.15931043032189757,0.41242566456532265,-1.1406675086043532,0.19933906486801,0.1288649139270798,0.10685421805335373,-0.4230388486319313,-0.11937041597041974,-0.19101009116867804,0.018477690288713912,
8+Abitibi-Témiscamingue,7,9525.0,176.0,79.0,170.0,0.6127034120734908,0.018477690288713912,0.008293963254593176,0.01784776902887139,3.593246552390772,0.16085480130576096,-0.28826248187059683,-0.8941264214914979,-0.07622449576571323,0.09851226711178324,-0.31078113035320604,-0.5129157435251921,-0.3306330009824038,-0.2845839192125745,0.014966859097712209,
9+Abitibi-Témiscamingue,8,9354.0,140.0,88.0,129.0,0.611075475732307,0.014966859097712209,0.009407740004276246,0.013790891597177678,4.65447978048459,0.16180839416277545,-0.8206324390623317,-0.7362219891582927,0.17376622087872043,0.07977061274442135,-1.0047427552537058,-0.4933011022429681,-0.46689357534902604,-0.4863488558298687,0.018739168110918546,
10+Abitibi-Témiscamingue,9,9232.0,173.0,72.0,150.0,0.6290077989601387,0.018739168110918546,0.00779896013864818,0.01624783362218371,4.0377393687938685,0.1593118298253477,-0.24861291473377536,-0.9643049254409689,0.02848300374926551,0.128837408467328,-0.5844629936224364,-0.7093636586049997,-0.3915706800309311,-0.3843807557837304,0.0166189111747851,
11+Abitibi-Témiscamingue,10,8725.0,145.0,81.0,110.0,0.6514613180515759,0.0166189111747851,0.009283667621776504,0.012607449856733524,3.3338889814969157,0.16045604561809573,-0.5701211368373809,-0.7538122101181258,-0.13732038241767427,0.10634930282389944,-1.207180015684034,-0.9799010938599085,-0.590330922682204,-0.5195725264859195,,
12+Bas-Saint-Laurent,6,11898.0,272.0,77.0,253.0,0.5731215330307615,0.022860985039502436,0.00647167591191797,0.021264077996301898,3.3338889814969157,0.16076368970361188,0.3764047470229245,-1.1524791375766499,-0.13732038241767427,0.10030294973254117,0.27360608915676066,-0.036002525816093714,-0.09591470998303193,-0.039864466790859227,0.020659378497030215,
13+Bas-Saint-Laurent,7,11617.0,240.0,103.0,216.0,0.579151243866747,0.020659378497030215,0.008866316604975466,0.018593440647327195,3.182541486701523,0.15774468121304164,0.04256095989553263,-0.8129816760011285,-0.17297274203536148,0.15963772059014064,-0.1832279822242677,-0.1086531645980712,-0.17927281406219261,-0.10204679301836989,0.016583174987044393,
14+Bas-Saint-Laurent,8,11578.0,192.0,97.0,176.0,0.5863707030575229,0.016583174987044393,0.00837795819657972,0.015201243738124029,2.8575510787255323,0.15563139768560513,-0.5755400452675203,-0.8822181340927711,-0.2495295090510494,0.2011716196332335,-0.7634906395829556,-0.195638815508571,-0.4108742539782723,-0.2795611938417589,0.020937723693629205,
15+Bas-Saint-Laurent,9,11176.0,234.0,133.0,165.0,0.6142627057981389,0.020937723693629205,0.011900501073729421,0.014763779527559055,3.3910949845705174,0.1587404840068306,0.08476823570466947,-0.3828136227811682,-0.12384457961943293,0.14006648361834081,-0.8383224232665805,-0.5317033252560334,-0.2753082052667008,-0.23090791724701842,0.01677248175874159,
16+Bas-Saint-Laurent,10,10553.0,177.0,81.0,205.0,0.634701032881645,0.01677248175874159,0.0076755424997631,0.019425755709276984,3.4485731528580055,0.1555115175038222,-0.5468342395874167,-0.9818023208692704,-0.11030466388629287,0.20352771208500214,-0.040853773847285765,-0.7779601622157832,-0.37570457472017454,-0.3138412553139468,,
17+Capitale-Nationale,6,37469.0,1042.0,579.0,924.0,0.558942058768582,0.027809655982278685,0.015452774293415891,0.024660385919026397,2.488924286923192,0.16424868353564984,1.126803611176258,0.12080587404048077,-0.3363655347402664,0.0318098298522913,0.8545719685553761,0.13484279177227199,0.32207809010940197,0.3940086347531075,0.027490210802982352,
18+Capitale-Nationale,7,37286.0,1025.0,577.0,1107.0,0.5582256074666094,0.027490210802982352,0.015474977203239822,0.029689427667220943,2.527038626609442,0.16230846178181996,1.078364080143559,0.12395366633628035,-0.32738708321768306,0.06994241991820425,1.714830145448955,0.14347515344936115,0.4671963970131128,0.5310473916013865,0.03013669142180605,
19+Capitale-Nationale,8,37164.0,1120.0,613.0,970.0,0.5569099128188569,0.03013669142180605,0.016494457001399204,0.026100527392099882,2.3679848448969927,0.1627943175203618,1.4796669902245023,0.26848925558191294,-0.36485478383493825,0.060393543521773514,1.1009197898952565,0.1593276643244744,0.45065707661883025,0.5160975895805466,0.028080908289241622,
20+Capitale-Nationale,9,36288.0,1019.0,446.0,929.0,0.5833608906525574,0.028080908289241622,0.012290564373897707,0.025600749559082894,3.109269663757673,0.1619708208128543,1.1679353464234936,-0.3275128418979111,-0.19023310954239717,0.07657832358932348,1.01542875820172,-0.15937425825490126,0.2638037030865546,0.28361371252577255,0.018235474261532894,
21+Capitale-Nationale,10,35919.0,655.0,719.0,1139.0,0.5987082045713967,0.018235474261532894,0.020017261059606335,0.03171023692196331,3.0307622367025306,0.16221702366056606,-0.32499125890550723,0.7679307955409728,-0.208726808601065,0.07173951985654345,2.060505877005258,-0.3442906149625749,0.3370279183222711,0.3811178335606764,,
22+Centre-du-Québec,6,10279.0,184.0,88.0,175.0,0.5426597918085417,0.017900573985796284,0.00856114408016344,0.017025002432143204,2.4398972185260015,0.15252271943955542,-0.37577434580366414,-0.8562471637282211,-0.3479146566854999,0.2622687354497325,-0.45152199669507437,0.33102418879813533,-0.23969420644409864,-0.06929724135705585,0.01750861079219288,
23+Centre-du-Québec,7,10452.0,183.0,192.0,223.0,0.5588404133180253,0.01750861079219288,0.018369690011481057,0.021335629544584768,2.475252351938991,0.15106252419699723,-0.4352102506498445,0.5343482834649437,-0.3395861809979751,0.29096701516479956,0.2858455589328532,0.13606749509770538,0.07873865350208037,0.21002690353883136,0.01845748187211602,
24+Centre-du-Québec,8,10619.0,196.0,194.0,157.0,0.561917318014879,0.01845748187211602,0.018269140220359734,0.014784819662868444,2.9423880828936504,0.15319319998646408,-0.29132681430181895,0.5200929511528328,-0.22954477533888587,0.24909129333932992,-0.8347233383217504,0.0989945578079761,-0.08123602094371939,0.014647542821179575,0.011357183418512209,
25+Centre-du-Québec,9,10566.0,120.0,188.0,164.0,0.5822449365890593,0.011357183418512209,0.01779292068900246,0.01552148400530002,2.5347059463663264,0.15692794776161473,-1.367990822950752,0.4525774685515368,-0.3255809215653491,0.17568957746418296,-0.7087109574481567,-0.14592837705598505,-0.31999067216742055,-0.15953152187844893,0.008299537866641516,
26+Centre-du-Québec,10,10603.0,88.0,227.0,202.0,0.6019994341224182,0.008299537866641516,0.021409035178723,0.01905121192115439,2.437298721577047,0.15966469292337898,-1.8316413213651914,0.9652479888453758,-0.34852677483000455,0.1219023332162364,-0.10492251148559029,-0.3839459045384557,-0.26364769835960494,-0.10125084311515728,,
27+Chaudière-Appalaches,6,25260.0,537.0,525.0,511.0,0.5688044338875693,0.021258907363420426,0.020783847980997625,0.02022961203483769,3.0562147123803287,0.16710562523512196,0.1334713852538937,0.8766127847195003,-0.202731064718736,-0.024339724420635013,0.09665233799986377,0.016013237289200052,0.14927982602051446,0.19749735784458897,0.01730090231463319,
28+Chaudière-Appalaches,7,25490.0,441.0,313.0,516.0,0.5744213417026285,0.01730090231463319,0.012279325225578659,0.020243232640251078,3.2444444444444445,0.16641557695958892,-0.46670642556996733,-0.32910625937204396,-0.158390495306846,-0.010777703499752826,0.09898225247816671,-0.051663630116803555,-0.1529437102312078,-0.0766263489223546,0.017665542611894545,
29+Chaudière-Appalaches,8,26096.0,461.0,646.0,472.0,0.569435928877989,0.017665542611894545,0.02475475168608216,0.018087063151440833,2.9724973616162593,0.16713459542407305,-0.4114136675641284,1.439583142629752,-0.22245204589332182,-0.02490909662627258,-0.2698479395128557,0.008404495230160357,0.08656081471055566,0.15319226845030448,0.02036445917006748,
30+Chaudière-Appalaches,9,24749.0,504.0,241.0,305.0,0.5836599458563982,0.02036445917006748,0.009737767182512425,0.012323730251727342,3.2270556344391377,0.16822475594344788,-0.002159558103901639,-0.6894327612014851,-0.1624867118215029,-0.0463348146450017,-1.255712543822065,-0.16297750759489446,-0.38651731619814184,-0.30996839349621763,0.010147259002598689,
31+Chaudière-Appalaches,10,24243.0,246.0,446.0,536.0,0.5991007713566803,0.010147259002598689,0.018397063069752095,0.022109474899971126,3.1254883575558683,0.1664526670217818,-1.5514594588555546,0.5382290676784245,-0.18641253289787374,-0.011506661483204183,0.4182180521342119,-0.34902056445621027,-0.1903253496467011,-0.09294344490407573,,
32+Côte-Nord,6,5851.0,139.0,37.0,163.0,0.33037087677320115,0.02375662279952145,0.006323705349512904,0.027858485728935223,3.7043897017966287,0.12712984392453386,0.5122160720398306,-1.173457495820567,-0.05004292244653217,0.7613333968461519,1.4016327506583606,2.8888458991869697,0.7234212834107022,0.8242360202852371,0.01602136181575434,
33+Côte-Nord,7,5992.0,96.0,124.0,104.0,0.3256008010680908,0.01602136181575434,0.02069425901201602,0.017356475300400534,2.778009287126522,0.127389711597186,-0.660731396738937,0.863911410503902,-0.26826686947156275,0.7562260283646539,-0.39482088749487587,2.9463194756885893,0.5404396268086282,0.7371325401016781,0.02494370344708124,
34+Côte-Nord,8,5773.0,144.0,60.0,87.0,0.4129568681794561,0.02494370344708124,0.010393209769617183,0.015070154165944916,2.5321466591291566,0.12841292329614618,0.6922207601445387,-0.5965081336464254,-0.32618380322564944,0.73611610423352,-0.7859145688360877,1.8937857390380584,0.2689193496179924,0.44178877056599036,0.020211053478805224,
35+Côte-Nord,9,5591.0,113.0,66.0,113.0,0.5331783223037023,0.020211053478805224,0.011804686102664998,0.020211053478805224,3.186624787968173,0.12914531244443128,-0.02542145161207497,-0.3963976813631555,-0.17201085412712241,0.7217219273281478,0.09347774721743544,0.44526429663339656,0.11110566401277115,0.20582467320031175,0.023655093475772607,
36+Côte-Nord,10,5242.0,124.0,49.0,82.0,0.5650515070583747,0.023655093475772607,0.009347577260587561,0.01564288439526898,4.255500234052513,0.13340327842184857,0.4968205264276089,-0.7447514937403134,0.07978011104103595,0.63803702336758,-0.6879444410066575,0.06123141422329299,-0.026137809947908857,-0.036462174464280565,,
37+Estrie,6,14026.0,398.0,226.0,205.0,0.5666619135890489,0.028375873378012263,0.01611293312419792,0.014615713674604307,2.5006251562890722,0.15131804387456246,1.2126628035807483,0.2143991416805631,-0.3336092049904023,0.28594510099486714,-0.8636502823500276,0.04182798571161214,0.09292925743789344,0.17700453832302146,0.019288635136177765,
38+Estrie,7,12961.0,250.0,63.0,183.0,0.6179307152225908,0.019288635136177765,0.004860736054316797,0.014119280919682124,3.125976867771178,0.15459334041087588,-0.16529368894324123,-1.380868304265424,-0.18629745638539702,0.22157331466837737,-0.948569111439671,-0.5758983513515411,-0.5058922662861495,-0.40951855808218235,0.01659326832337213,
39+Estrie,8,12716.0,211.0,118.0,197.0,0.6383296634161686,0.01659326832337213,0.009279647687952187,0.015492293173954074,2.364166063115535,0.15379346761734808,-0.5740095276389626,-0.7543821316654437,-0.36575435988380556,0.23729379676289306,-0.7137042847285846,-0.8216807208640721,-0.4987062046696626,-0.34517293145955164,0.015040617710930588,
40+Estrie,9,12433.0,187.0,135.0,160.0,0.6683825303627443,0.015040617710930588,0.010858199951741334,0.012868977720582321,2.779553603691247,0.15731814699992067,-0.8094479452415432,-0.5305846792649966,-0.26790308063182305,0.16802070787802303,-1.162443563964935,-1.1837810006765226,-0.6310232603169662,-0.5146842619350162,0.00857843137254902,
41+Estrie,10,12240.0,105.0,150.0,216.0,0.6901143790849673,0.00857843137254902,0.012254901960784314,0.01764705882352941,2.6328261412100047,0.1594542602154277,-1.7893509018823144,-0.33256883996925257,-0.3024671166288775,0.12603812036166295,-0.345114230768094,-1.4456231908791253,-0.6815143599610001,-0.5367621918105834,,
42+Gaspésie–Îles-de-la-Madeleine,6,2384.0,37.0,11.0,25.0,0.6220637583892618,0.01552013422818792,0.004614093959731544,0.010486577181208054,4.167534903104814,0.16159827152957135,-0.7367357657384952,-1.4158357067956342,0.05905844916565411,0.08390030576519394,-1.5699724029068987,-0.6256964651151603,-0.7008802642708899,-0.668101911747889,0.012449977767896843,
43+Gaspésie–Îles-de-la-Madeleine,7,2249.0,28.0,17.0,19.0,0.6545131169408626,0.012449977767896843,0.00755891507336594,0.008448199199644287,3.7043897017966287,0.1560504903145834,-1.202283373816669,-0.9983370416425014,-0.05004292244653217,0.19293488721389618,-1.918653406970861,-1.01667153688308,-0.8321755657576245,-0.7574840585536824,0.02427637721755369,
44+Gaspésie–Îles-de-la-Madeleine,8,2142.0,52.0,19.0,21.0,0.6956115779645191,0.02427637721755369,0.008870214752567693,0.00980392156862745,8.33368057002375,0.15798309372484307,0.5910297837293094,-0.8124290205583956,1.0404617009172978,0.15495202599818853,-1.686746154672946,-1.5118577120650385,-0.3707648961085974,-0.7623900952248857,0.014335145823035097,
45+Gaspésie–Îles-de-la-Madeleine,9,2023.0,29.0,4.0,25.0,0.7261492832427089,0.014335145823035097,0.001977261492832427,0.012357884330202669,3.996976098989338,0.15708307485608547,-0.9164231936446819,-1.789669631737908,0.01888055373032955,0.1726407517874827,-1.2498702130652737,-1.879799700618733,-0.9407069055914641,-0.9137260558181703,0.008525576730190571,
46+Gaspésie–Îles-de-la-Madeleine,10,1994.0,17.0,3.0,39.0,0.7407221664994985,0.008525576730190571,0.0015045135406218655,0.019558676028084254,3.1254883575558683,0.15331415562968928,-1.7973655918969955,-1.8566929351528296,-0.18641253289787374,0.24671406406120183,-0.018116680608736592,-2.0553851152927547,-0.9445431319646648,-0.8265985808996619,,
47+Lanaudière,6,17898.0,347.0,386.0,688.0,0.47441054866465526,0.019387641077215332,0.021566655492233768,0.03844004916750475,2.8577552478509056,0.1561703479190984,-0.15028078011213955,0.9875944295806324,-0.24948141369875776,0.19057923848960434,3.211694573194543,1.1533440720665473,0.8572416865867383,0.9363430993787779,0.021396763332247205,
48+Lanaudière,7,18414.0,394.0,367.0,450.0,0.4649179971760617,0.021396763332247205,0.019930487672423156,0.024437927663734114,3.2263268269075653,0.15671831294722316,0.15437537414718983,0.7556285972335576,-0.1626583942681983,0.17980968302683728,0.8165186886832477,1.2677177042482675,0.5018986088451503,0.5681273637580575,0.020280136282515817,
49+Lanaudière,8,18491.0,375.0,432.0,361.0,0.4631983126926613,0.020280136282515817,0.023362716997458222,0.019523011194635228,2.435863397197481,0.1553597700689095,-0.014945980917740636,1.2422290073797209,-0.34886488879097566,0.20651011485296278,-0.024217438196021908,1.2884377983988642,0.3915247687878016,0.5281429786993872,0.02262318600673178,
50+Lanaudière,9,18123.0,410.0,344.0,340.0,0.4742592286045357,0.02262318600673178,0.018981404844672516,0.01876069083485074,3.4111623938253883,0.1556797920814642,0.340345747313448,0.6210734584269678,-0.11911737556285318,0.2002204893538523,-0.15461848788953325,1.1551672936649373,0.34051185421780317,0.3935251051703508,0.011318495946019481,
51+Lanaudière,10,18377.0,208.0,478.0,453.0,0.509386733416771,0.011318495946019481,0.026010774337487077,0.024650378190128965,2.5213867627194957,0.1556754211378549,-1.3738572537058389,1.6176543259473937,-0.3287184715184078,0.20030639468942846,0.8528600657621173,0.7319238359197883,0.2833614828490802,0.43181445995476225,,
52+Laurentides,6,32435.0,815.0,731.0,900.0,0.5043934021889934,0.02512717743178665,0.022537382457222137,0.027747803298905502,2.0002400288034563,0.15003042543593045,0.7200421027467577,1.125218141334932,-0.4514830458265344,0.31125160319480616,1.3826996277615977,0.7920873683371346,0.6466359662581156,0.7720249604777046,0.021700209902457093,
53+Laurentides,7,32396.0,703.0,737.0,692.0,0.5153414001728609,0.021700209902457093,0.022749722187924436,0.02136066181010001,2.1279312252627998,0.15149335834403316,0.20038893298951208,1.1553223652270839,-0.4214033113562956,0.28249951814653923,0.2901275299670745,0.6601773864910847,0.3611854035774998,0.4996751626185578,0.019691421024194033,
54+Laurentides,8,33314.0,656.0,707.0,785.0,0.49717836345080146,0.019691421024194033,0.02122230893918473,0.023563666926817554,2.016535591853196,0.15020728349394663,-0.10421666920209152,0.9387750885407186,-0.44764436138179065,0.3077756830774938,0.6669693340891082,0.879019758202011,0.37344647222090827,0.5391925725634159,0.018691588785046728,
55+Laurentides,9,32635.0,610.0,610.0,503.0,0.5275930749195649,0.018691588785046728,0.018691588785046728,0.015412900260456565,2.102962613762656,0.14998659238222678,-0.2558276745155934,0.5799851160915055,-0.4272850731757489,0.312113086097428,-0.7272850833067942,0.5125596945803924,-0.000956655704801764,0.17299933772222204,0.01150100479208533,
56+Laurentides,10,32345.0,372.0,1020.0,678.0,0.5511516463131859,0.01150100479208533,0.031535013139588806,0.02096150873396197,2.273502359718159,0.15044156030014622,-1.346182261294863,2.4008470011106677,-0.38711166719942675,0.3031712705173698,0.2218491750094432,0.2287077310131107,0.23688020819271693,0.38405530111957764,,
57+Laval,6,22378.0,595.0,696.0,492.0,0.47957815711859864,0.026588613817141837,0.031101975154169275,0.021985878988292074,2.6147362616993353,0.12982566882563873,0.9416491193142507,2.3394535330877053,-0.3067284814478124,0.7083503882594303,0.3970759739856874,1.0910807122859163,0.8618135409141963,0.9293275614995588,0.01906052393857272,
58+Laval,7,22140.0,422.0,428.0,470.0,0.5138211382113821,0.01906052393857272,0.019331526648599818,0.02122854561878952,2.5013240261064933,0.1302022375707265,-0.19988365977841122,0.6707115790937443,-0.33344457485833223,0.7009494086756678,0.2675279892263981,0.6784946832274755,0.29739257093109045,0.43817019103338484,0.018161229911589215,
59+Laval,8,22961.0,417.0,680.0,480.0,0.4983232437611602,0.018161229911589215,0.029615434867819347,0.02090501284787248,2.2117370990132676,0.13043833987748937,-0.3362494081160272,2.1287009741719984,-0.4016614771550391,0.6963091182245204,0.21218509775520683,0.8652253511170023,0.5274182759996103,0.6736529690635462,0.02881612989561959,
60+Laval,9,22418.0,646.0,536.0,502.0,0.512623784458917,0.02881612989561959,0.02390935855116424,0.022392720135605317,2.5002343994168097,0.1312236975107051,1.2794217363465026,1.3197284921303696,-0.33370125411583923,0.6808739131385885,0.46666943556286167,0.692921331093479,0.6843189423593271,0.7576691704471655,0.03147311671280781,
61+Laval,10,20589.0,648.0,598.0,497.0,0.5264947301957356,0.03147311671280781,0.0290446354849677,0.02413910340473068,2.3050638127043332,0.13220089072524793,1.682317768987113,2.0477765406614403,-0.37967685452914746,0.6616684237702731,0.76540238757993,0.5257934047796929,0.8838802785415503,0.9372501343683587,,
62+Mauricie,6,16720.0,459.0,147.0,394.0,0.5000598086124401,0.027452153110047846,0.008791866028708134,0.023564593301435406,3.031221582297666,0.14371719371004077,1.0725931469211678,-0.823536821801863,-0.20861860228837573,0.43533014056779545,0.667127797943611,0.8443018691850205,0.331199588421226,0.40724324657815164,0.023544520547945206,
63+Mauricie,7,16352.0,385.0,186.0,387.0,0.5108243639921722,0.023544520547945206,0.011374755381604696,0.02366682974559687,2.5787351998252825,0.14427049030379466,0.4800536408522181,-0.4573506205145163,-0.3152091160713821,0.42445579996583305,0.6846161667959867,0.7146021462937994,0.25519466955365644,0.37891796001667505,0.02191028379615502,
64+Mauricie,8,16385.0,359.0,109.0,345.0,0.5107110161733293,0.02191028379615502,0.006652425999389686,0.02105584375953616,3.3338889814969157,0.14555150525348015,0.2322437912013525,-1.1268534994364032,-0.13732038241767427,0.3992790809396499,0.23798594244566415,0.7159678488403196,0.0535504635954848,0.14033132655934097,0.02470610423084177,
65+Mauricie,9,15907.0,393.0,194.0,236.0,0.5422141195700007,0.02470610423084177,0.012195888602502043,0.014836235619538568,2.7785495971103082,0.1477217058027128,0.6561920599032983,-0.3409353917421187,-0.2681395906826207,0.3566265502475507,-0.8259282239912401,0.33639399403393017,-0.014298433705200056,0.0864285591424436,0.01730952705465449,
66+Mauricie,10,14674.0,254.0,226.0,246.0,0.5794602698650675,0.01730952705465449,0.015401390213983917,0.016764345100177182,2.632029166985682,0.14915446386256126,-0.46539860066294886,0.11352095460220096,-0.302654856846778,0.3284675134508605,-0.49610953691017295,-0.11237654981320207,-0.15575851269667343,-0.02686660803640801,,
67+Montréal,6,27678.0,744.0,557.0,865.0,0.5292651203121613,0.026880554953392585,0.020124286436881276,0.031252258111135196,2.514754939697203,0.17066381542713338,0.9859180350646467,0.7831041967173356,-0.33028070510298924,-0.0942714256298879,1.9821649053876886,0.49241359319197614,0.6365080999381283,0.6950807553598352,0.02382442960489705,
68+Montréal,7,28752.0,685.0,563.0,880.0,0.5337367835281024,0.02382442960489705,0.01958124652198108,0.03060656649972176,2.2201403111246676,0.16957772266278737,0.5224980548841316,0.7061153302133548,-0.3996819641109391,-0.07292565416092615,1.8717141443317846,0.43853552210485836,0.5110425722103774,0.6120925316894436,0.025570335653732165,
69+Montréal,8,29018.0,742.0,556.0,646.0,0.5395271900199876,0.025570335653732165,0.01916052105589634,0.022262044248397547,2.7232563640845706,0.16968467550756214,0.7872410396398148,0.6464674556198706,-0.2811648094411025,-0.07502767625099348,0.4443162704626387,0.3687682079026314,0.31510008132214323,0.3770179591669481,0.02818159088473765,
70+Montréal,9,27997.0,789.0,532.0,679.0,0.5586670000357181,0.02818159088473765,0.01900203593242133,0.024252598492695645,2.5866636158657874,0.16883800586254089,1.1832024971744473,0.6239984074352141,-0.3133414489503844,-0.058387461579079064,0.7848166383155035,0.1381569129944483,0.39307425756502495,0.4438685927117408,0.01771979009417008,
71+Montréal,10,27822.0,493.0,513.0,779.0,0.5677161958162605,0.01771979009417008,0.018438645676083675,0.02799942491553447,2.564760194921775,0.16711446031073868,-0.4031877722573972,0.5441243944120736,-0.31850115536910795,-0.02451336659119036,1.4257415359473788,0.029125174690179393,0.20879813513865608,0.31196721788228143,,
72+Montérégie,6,63288.0,1570.0,1276.0,1566.0,0.5253760586525091,0.024807230438629757,0.020161800025281253,0.024744027303754267,2.85832723195197,0.1542639857146179,0.6715264784447074,0.7884226430601646,-0.24934667355724224,0.22804636219188726,0.8688795024239672,0.5392720282847515,0.4744667234747059,0.5335107302333979,0.024874992088106843,
73+Montérégie,7,63196.0,1572.0,1079.0,1577.0,0.5400816507373885,0.024874992088106843,0.017073865434521173,0.02495411101968479,2.8169807600214094,0.15450523689929774,0.6818016140091333,0.3506342274061413,-0.25908650630560587,0.2233048770960974,0.9048160174720481,0.3620876345891847,0.3772596440444998,0.44291763254052946,0.02364658533911709,
74+Montérégie,8,63857.0,1510.0,1045.0,1515.0,0.5290727719748813,0.02364658533911709,0.016364689853892292,0.02372488529057112,2.564760194921775,0.15441071111150467,0.49553038284033285,0.25009166578807945,-0.31850115536910795,0.22516266119188713,0.6945470362893732,0.4947311553358594,0.306926957679404,0.40743543128366855,0.02381791389031266,
75+Montérégie,9,62432.0,1487.0,1142.0,1335.0,0.5457617888262429,0.02381791389031266,0.018291901588928756,0.021383264992311635,2.7536077599632893,0.15404383019300347,0.5215100350946139,0.5233199183022558,-0.2740150453684975,0.23237323886972033,0.29399398669878596,0.29364891974032004,0.2651385088895331,0.34219498796060344,0.025510033164718098,
76+Montérégie,10,59702.0,1523.0,1511.0,1705.0,0.5508860674684265,0.025510033164718098,0.025309034873203577,0.028558507252688353,2.7028488026379804,0.1557227430135043,0.7780969846430223,1.5181660112694078,-0.2859721418690195,0.19937634343022223,1.5213770816785446,0.231907631181391,0.6604919850555947,0.6998733612608948,,
77+Outaouais,6,12352.0,269.0,223.0,288.0,0.529630829015544,0.021777849740932644,0.01805375647668394,0.023316062176165803,2.500125006250313,0.14251328527049767,0.21216196200655485,0.4895571657823764,-0.3337270234522722,0.4589914292382117,0.6246145432460348,0.4880072507268752,0.3232675545912968,0.4398323654149425,0.024693417869358473,
78+Outaouais,7,11987.0,296.0,140.0,239.0,0.5462584466505381,0.024693417869358473,0.011679319262534413,0.019938266455326602,2.395127242544655,0.14132094040164725,0.6542683451610197,-0.414171422581816,-0.35846095139569667,0.48242545063876463,0.046815325378883,0.2876648005723208,0.11642359129557926,0.24395766687181084,0.020176113533384627,
79+Outaouais,8,11697.0,236.0,144.0,159.0,0.5568949303240147,0.020176113533384627,0.01231084893562452,0.013593229033085406,2.2184687906443457,0.14100437809233926,-0.030719620688252538,-0.32463702122243754,-0.4000757178982306,0.48864708007227253,-1.0385545320374587,0.1595081850576311,-0.19097193778607932,-0.019666639410958645,0.020415440848711778,
80+Outaouais,9,11217.0,229.0,177.0,180.0,0.5898190246946599,0.020415440848711778,0.015779620219309978,0.016047071409467772,2.00160128102482,0.14175680067574387,0.005571122353475867,0.16714408356354052,-0.45116238076503445,0.4738591714877681,-0.618804990318355,-0.2371868725345541,-0.11009664436885984,0.05382887519887638,0.03221125943122461,
81+Outaouais,10,10338.0,333.0,213.0,270.0,0.6008899206809828,0.03221125943122461,0.02060359837492745,0.0261172373766686,2.564431337350942,0.14594426975203978,1.7942471059521363,0.8510581017609149,-0.3185786231091203,0.39155979442693345,1.1037781676102887,-0.37057762495589136,0.575247820280877,0.5947460092076455,,
82+Saguenay–Lac-Saint-Jean,6,9953.0,450.0,95.0,210.0,0.4975384306239325,0.04521249874409726,0.009544860845976088,0.02109916608057872,3.2609404552272876,0.16785789299325038,3.7657088025934646,-0.7167818377313339,-0.15450459214417883,-0.03912459011131714,0.24539657508402907,0.874681389274554,0.662562624494203,0.6158146203247269,0.020534099372273646,
83+Saguenay–Lac-Saint-Jean,7,9399.0,193.0,97.0,196.0,0.5360144696244281,0.020534099372273646,0.010320246834769656,0.020853282264070645,2.914798206278027,0.15903139523585264,0.023564078913441543,-0.6068523707763006,-0.2360440187141239,0.13434899353249335,0.20333616391468248,0.4110921911817017,-0.011759160324684251,0.09383201931665336,0.023785817015156543,
84+Saguenay–Lac-Saint-Jean,8,9039.0,215.0,166.0,131.0,0.5514990596304901,0.023785817015156543,0.018364863369841798,0.014492753623188406,3.210046839460745,0.16177582928640677,0.5166429790833066,0.5336639918343815,-0.16649340962679504,0.08041063395780224,-0.8846835914689584,0.22452182556754932,0.050677071557881016,0.09546548302781166,0.026567233745047776,
85+Saguenay–Lac-Saint-Jean,9,8582.0,228.0,59.0,145.0,0.5791190864600326,0.026567233745047776,0.006874854346306222,0.016895828478210206,3.027950310559006,0.1630961325235527,0.9384071212100284,-1.0953189734511344,-0.20938920345750342,0.054461753627292185,-0.47361824398759883,-0.10826570718876603,-0.14895387554128034,-0.08507438151647204,0.014311043566362716,
86+Saguenay–Lac-Saint-Jean,10,7896.0,113.0,86.0,143.0,0.6152482269503546,0.014311043566362716,0.010891590678824722,0.018110435663627154,3.687478172761127,0.16744038168880865,-0.9200779741381977,-0.5258507468976747,-0.05402670767980459,-0.030918936613857653,-0.26584988268025006,-0.5435776494465601,-0.3900503162427242,-0.3402626411859801,,
added results/amli/index_tier_week.csv +28 −0
@@ -0,0 +1,28 @@
1+tier,week,ld,exits,cuts,entries,stale,exit,mdfreq,arrival,mddepth,disp,z_exit,z_mdfreq,z_mddepth,z_disp,z_arrival,amli_equal,amli_pca,exit_next,amli_supervised
2+high,2,102925.0,2595.0,1439.0,2725.0,0.0,0.025212533398105415,0.013981054165654603,0.026475589021131892,2.174054829662804,0.16390587388937802,0.6583263323270194,-0.9723489104642813,-0.7872743594987778,-0.28552969478098034,1.2354836416259196,-0.030268598158220028,0.3141550986607731,0.019522797342065454,-0.7402455066672095
3+high,3,104442.0,2039.0,1679.0,2400.0,0.0,0.019522797342065454,0.01607590815955267,0.02297926121675188,2.138653161784834,0.16419316694569688,-1.2190465119388885,-0.19185650369922824,-0.8334743755906384,-0.29802340970207375,-0.17534317529199595,-0.543548795244565,0.014820127108284007,0.024120317820658342,-0.28273148042617596
4+high,4,105720.0,2550.0,2099.0,2592.0,0.08146046159667045,0.024120317820658342,0.019854332198259553,0.024517593643586834,2.22246916324036,0.16336141990695763,0.29794127316304797,1.2158937645004964,-0.7240925060880888,-0.26185264204892694,0.4453997191226261,0.19465792172983093,0.4300404358315859,0.02467302800185355,-0.16401263692746768
5+high,5,105743.0,2609.0,1856.0,2552.0,0.39758660147716635,0.02467302800185355,0.017551989256972093,0.024133985228336628,2.247380398998061,0.16150603626453527,0.48031231370080046,0.35809594724057947,-0.691582752682089,-0.18116627045783226,0.29060729984421,0.05125330752913373,0.29603316425410303,0.023666959902380883,-0.2307725600695616
6+high,6,105717.0,2502.0,1830.0,2599.0,0.5673732701457666,0.023666959902380883,0.01731036635545844,0.024584503911386058,2.196129593389867,0.16187292584246737,0.14835228764174027,0.268073043266718,-0.7584662693565343,-0.19712145521912522,0.47239913262728844,-0.013352652207982551,0.3157198167625436,0.02156752712308268,-0.31049982709702106
7+high,7,106920.0,2306.0,1907.0,2644.0,0.5739711934156378,0.02156752712308268,0.01783576505798728,0.024728769173213618,2.22246916324036,0.16209298437873393,-0.5443719528846936,0.4638240116122114,-0.7240925060880888,-0.2066912953443804,0.5306125815725022,-0.09614383222648985,0.29992103107648793,0.02197013607132014,-0.288378223549319
8+high,8,108693.0,2388.0,2101.0,2393.0,0.5687946785901576,0.02197013607132014,0.019329671643988112,0.022016137193747527,2.1695092465529635,0.16221722564383273,-0.4115279863395157,1.02041781251015,-0.7932064532505586,-0.21209426250087732,-0.5639798465932904,-0.1920781472348184,0.1730320087759171,0.021338877028778118,0.0528014696376553
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added results/amli/summary.json +290 −0
@@ -0,0 +1,290 @@
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\ No newline at end of file
added results/competition/eventstudy_coarse_fe.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
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7+cc_m2,-0.0006537692986789424,0.0005727033581686504,-1.1415496161390057,0.2539415755714891,-0.0017777421839568776,0.0004702035865989928,-2
8+cc_m1,0.000956676534850631,0.0011472514948385017,0.8338856293975037,0.40456426648297605,-0.0012948897505583807,0.0032082428202596424,-1
9+cc_p0,0.010988529504295106,0.002032877104965589,5.405407674401012,8.263077666548213e-08,0.00699885716783437,0.014978201840755842,0
10+cc_p1,0.0006843385516677944,0.002027096635392321,0.33759542575302465,0.7357459462945166,-0.0032939891838370417,0.0046626662871726305,1
11+cc_p2,-0.0011310746288265637,0.0006342800998128884,-1.7832415507915649,0.07488021392404409,-0.002375896443981922,0.00011374718632879504,2
12+cc_p3,0.00036561254898562993,0.0008026442100616738,0.45551010572609363,0.6488509648161707,-0.0012096363399321513,0.0019408614379034111,3
13+cc_p4,0.001442975288744538,0.0010068539487989832,1.4331525346509078,0.15215771050848725,-0.0005330503827372381,0.0034190009602263144,4
14+cc_p5,2.4382506241423596e-05,0.0008367033499641519,0.029141160056868724,0.9767583982926913,-0.0016177099749135099,0.0016664749873963572,5
15+cc_p6,0.00044183204678274686,0.000737706745680456,0.5989264018118795,0.5493710258290099,-0.0010059722442255928,0.0018896363377910864,6
16+cc_p7,0.0041531183847718185,0.0007801333722075137,5.323600467212288,1.2820824690784605e-07,0.0026220486874234243,0.005684188082120213,7
17+cc_p8,-0.0007712127651020634,0.0010009808712659384,-0.7704570459240767,0.4412287981869334,-0.0027357120856272357,0.0011932865554231092,8
18+cc_p9,-0.0012219504283445912,0.0007450277699095532,-1.6401407809173834,0.10132149334894369,-0.0026841227732653104,0.00024022191657612803,9
19+cc_p10,-0.0008699457654341853,0.0008469092353585801,-1.0272007071287297,0.30459892158416446,-0.002532068054848447,0.0007921765239800766,10
20+cc_p11,0.0004086034154183972,0.0007210629170361566,0.5666681863184879,0.5710793724400784,-0.001006536125442108,0.0018237429562789024,11
21+cc_p12,0.0002742861982021247,0.0008078932606724245,0.3395079666512272,0.7343054344917368,-0.0013112643424796713,0.0018598367388839208,12
22+cc_p13,0.00012169415096981409,0.0009709281423023279,0.12533795825635974,0.9002836861504744,-0.0017838244564131842,0.0020272127583528124,13
23+cc_p14,0.005677947695755378,0.0010725238197063743,5.294006148329492,1.5007195952598806e-07,0.003573040024147676,0.007782855367363079,14
added results/competition/eventstudy_dealer_dow_fe.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.0011855023305778903,0.0013676839207729073,0.8667955457924355,0.3862825329918831,-0.0014986789661671435,0.003869683627322924,-7
3+cc_m6,0.0023306015868701686,0.0008082785795514225,2.8834137707368206,0.004026596902474644,0.0007442948292638474,0.00391690834447649,-6
4+cc_m5,0.0010904010787013412,0.00048584763825995354,2.2443272187276135,0.025050994557220374,0.00013688899606448626,0.002043913161338196,-5
5+cc_m4,0.0014305326582674217,0.000614039834577875,2.329706604866847,0.02003961501620677,0.00022543386724053214,0.002635631449294311,-4
6+cc_m3,4.8706684418271505e-06,0.0005455736264493854,0.008927609777484392,0.9928788493474459,-0.0010658581029289935,0.0010755994398126479,-3
7+cc_m2,-0.0007757634127739608,0.0005445707822632338,-1.4245410110874688,0.15463275836038548,-0.0018445240279303717,0.00029299720238245,-2
8+cc_m1,-0.00031346897203399373,0.0010283698775360776,-0.3048212310390203,0.7605720101097027,-0.0023317212520333067,0.0017047833079653193,-1
9+cc_p0,0.005520239166829862,0.001970313222256877,2.8017064010293526,0.0051911712917427355,0.0016533530979181174,0.009387125235741606,0
10+cc_p1,-5.8614291999277003e-05,0.0018090615554905306,-0.03240038561506196,0.9741598598551533,-0.0036090319858506576,0.0034918034018521036,1
11+cc_p2,-0.0016387004278449148,0.0007122228597375767,-2.3008253743058806,0.02162681975527425,-0.0030364906995485573,-0.00024091015614127243,2
12+cc_p3,-0.00030560154682379324,0.000582867796328023,-0.5243067960677117,0.6001928530499474,-0.001449522897083236,0.0008383198034356495,3
13+cc_p4,0.0008985096362607102,0.0008412299220686411,1.0680904384038246,0.28576297303807086,-0.0007524665789183623,0.0025494858514397826,4
14+cc_p5,0.0004578402282583448,0.0007443395407677656,0.6150959383215021,0.5386451442998834,-0.0010029814158445884,0.0019186618723612777,5
15+cc_p6,0.0005391364637315637,0.0005846136063576002,0.9222099141527358,0.35666352626830533,-0.0006082111684033089,0.0016864840958664363,6
16+cc_p7,0.0016547820977557673,0.0006242245869572921,2.650940274271804,0.008166260776332201,0.0004296949735814342,0.0028798692219301003,7
17+cc_p8,0.0004458514702669446,0.0006546930499718083,0.6810084058264303,0.49603941996645906,-0.0008390322760084327,0.001730735216542322,8
18+cc_p9,-0.0004956217600056062,0.0006667461823281329,-0.7433439787761553,0.45746531666314905,-0.0018041606739309444,0.0008129171539197321,9
19+cc_p10,-0.0004499992275540965,0.0006680655002158724,-0.6735854903578885,0.50074592127317,-0.0017611274008433458,0.0008611289457351527,10
20+cc_p11,0.0009268912048264381,0.0006947514162834221,1.3341335952718882,0.1824938443228985,-0.0004366100611833462,0.0022903924708362225,11
21+cc_p12,0.0011891425849576288,0.0007694675903441552,1.5454095791425966,0.1225948968911783,-0.00032099472314646503,0.0026992798930617224,12
22+cc_p13,0.0003394491762001744,0.0007641555456100174,0.4442147651093674,0.6569929330510678,-0.0011602628494930625,0.0018391612018934112,13
23+cc_p14,0.0032606251620528074,0.0009005841392544512,3.620566940865866,0.000310188264211142,0.0014931618863524266,0.005028088437753188,14
added results/competition/eventstudy_dealer_dow_fe_firstcut.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,-0.009785568550553679,0.0034481826447644503,-2.8378915964360534,0.004666941726250151,-0.016555075429330185,-0.0030160616717771734,-7
3+cc_m6,-0.006183528587324767,0.001895871069660092,-3.261576531379532,0.0011592931419559527,-0.009905521755762071,-0.0024615354188874615,-6
4+cc_m5,-0.005285370166112276,0.0017275654738044567,-3.0594326213713896,0.002298497758622897,-0.008676944094544638,-0.0018937962376799132,-5
5+cc_m4,-0.007165101397180121,0.0017068632298196623,-4.197818121570963,3.0265903449677722e-05,-0.010516032471825733,-0.00381417032253451,-4
6+cc_m3,-0.008388951744227355,0.0020073850002575544,-4.179044748840419,3.2811670715560126e-05,-0.012329870183750612,-0.0044480333047041,-3
7+cc_m2,-0.010352708140806589,0.0018508303948812663,-5.593547722923975,3.143716642561856e-08,-0.01398627700312341,-0.006719139278489769,-2
8+cc_m1,-0.004286846890541203,0.002940291678448538,-1.4579665418783154,0.14527865597909706,-0.010059257111643741,0.0014855633305613349,-1
9+cc_p0,0.017698412713532136,0.004980708128496648,3.5533928624069238,0.00040469292738620766,0.007920236378171758,0.027476589048892514,0
10+cc_p1,0.003135152962347562,0.004474764559304733,0.7006297025903608,0.48375677245940274,-0.00564974986497584,0.011920055789670963,1
11+cc_p2,-0.00517440975636979,0.002026898766909834,-2.552870345990976,0.010885715521178119,-0.009153637819016989,-0.0011951816937225915,2
12+cc_p3,0.0007140923452447535,0.0023133100089980873,0.3086885642076275,0.7576462744476322,-0.0038274211509827186,0.005255605841472225,3
13+cc_p4,0.0049304521816563385,0.0020638226240322316,2.388990276704776,0.017147042080524866,0.0008787348310183912,0.008982169532294287,4
14+cc_p5,0.001998403330498302,0.0019113832645393728,1.0455272720931248,0.2961244689302076,-0.0017540435350770173,0.005750850196073621,5
15+cc_p6,0.003070468194755744,0.0018772723032920184,1.6356008605524706,0.10235303770729542,-0.0006150116883262744,0.006755948077837762,6
16+cc_p7,0.006545205515335979,0.002030942426015645,3.2227430140284827,0.0013259504968750058,0.002558038900427409,0.01053237213024455,7
17+cc_p8,0.0031664447777507826,0.0024923679745299464,1.270456373260038,0.20432575689164167,-0.0017265971188669912,0.008059486674368557,8
18+cc_p9,-0.0004588164014969321,0.002424140360412435,-0.18926973412499537,0.8499338815660593,-0.005217913159276924,0.00430028035628306,9
19+cc_p10,-0.0012642691987351374,0.002567689990671874,-0.49237610588820446,0.6226011275873304,-0.006305184035659682,0.0037766456381894066,10
20+cc_p11,0.0038907549129783827,0.002182732046610382,1.7825160532280777,0.07507948532136632,-0.0003944066130475302,0.008175916439004297,11
21+cc_p12,-0.0014703966081780438,0.0027881955996208636,-0.5273649411031232,0.5980999800072391,-0.006944210276275424,0.0040034170599193356,12
22+cc_p13,-0.0014761037248287856,0.002863799600638399,-0.5154354112277033,0.606404418517851,-0.0070983439287792835,0.004146136479121712,13
23+cc_p14,0.010415547497978716,0.0033131107396886113,3.1437366017405264,0.0017354092584449532,0.003911215142926664,0.01691987985303077,14
added results/competition/eventstudy_dow_fe.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.0012230162530917044,0.0014631284781722138,0.8358912230452481,0.4034355228339961,-0.0016484820779155641,0.004094514584098973,-7
3+cc_m6,0.00297337158573866,0.0009554358655843718,3.1120577454145093,0.001915870558645727,0.0010982577223017896,0.004848485449175531,-6
4+cc_m5,0.0012638431061395422,0.000551312690862724,2.29242520095413,0.022108573927240638,0.00018185099451508086,0.0023458352177640036,-5
5+cc_m4,0.0011040124167593023,0.000671169363009403,1.6449088376279701,0.10033396888991186,-0.000213207317841386,0.0024212321513599907,-4
6+cc_m3,-0.000374075625344806,0.0006240240262932189,-0.599457087520912,0.5490173337592981,-0.0015987691343166853,0.0008506178836270731,-3
7+cc_m2,-0.0014427145672953937,0.0005914193036708658,-2.439410682641984,0.014901334553590662,-0.0026034188859878245,-0.00028201024860296316,-2
8+cc_m1,-0.00037319313149012295,0.001069509477105585,-0.3489385923910614,0.7272161314920533,-0.0024721849318148983,0.0017257986688346523,-1
9+cc_p0,0.005449759619718737,0.0020072516726825167,2.7150355353474973,0.006752347295678529,0.0015103790977704932,0.009389140141666981,0
10+cc_p1,0.0005087375882558505,0.0018372418968146127,0.27690288858418344,0.7819175581764253,-0.0030969861188995737,0.004114461295411274,1
11+cc_p2,-0.0017545343972236362,0.0007349359000081249,-2.387329829995023,0.017174523041477263,-0.00319690069776,-0.0003121680966872728,2
12+cc_p3,-0.0006844544808482739,0.0007173678680769895,-0.9541192340870455,0.34027666315269545,-0.0020923422136293595,0.0007234332519328115,3
13+cc_p4,0.0006105576609314272,0.0009487234612208711,0.6435570383657709,0.5200248624420762,-0.0012513826102923272,0.0024724979321551813,4
14+cc_p5,3.0301818956919844e-05,0.0008260250017042484,0.036683900480495586,0.9707450909319641,-0.001590833610466964,0.0016514372483808035,5
15+cc_p6,0.0007143676495553629,0.0006593484819255202,1.0834447475622726,0.27889804293988574,-0.0005796527277709674,0.002008388026881693,6
16+cc_p7,0.001737845148493905,0.0006981677572594438,2.489151254013167,0.012982547781897447,0.0003676390595262263,0.0031080512374615835,7
17+cc_p8,0.0010671002215603346,0.0007819569905320052,1.364653343445823,0.1726993678332891,-0.0004675484622224655,0.0026017489053431347,8
18+cc_p9,-0.00045362401153946596,0.000718535752423832,-0.6313172448403006,0.5279916529263375,-0.001863803804110787,0.0009565557810318553,9
19+cc_p10,-0.0004827125825773821,0.0007715370042274632,-0.625650590875703,0.5317010219154561,-0.0019969112691596703,0.0010314861040049061,10
20+cc_p11,0.0007521801790357521,0.0007191434348121635,1.0459390194283253,0.2958669287727589,-0.0006591922353660569,0.0021635525934375613,11
21+cc_p12,0.0009241977571252911,0.0008200608401714168,1.1269868183586313,0.2600451365394332,-0.0006852325622259774,0.00253362807647656,12
22+cc_p13,0.0006122673954056753,0.0008700687391074602,0.7037000272343489,0.4817996159783666,-0.001095307140584323,0.0023198419313956737,13
23+cc_p14,0.0035465474498485475,0.0010119001326928825,3.5048393959692707,0.000479169017918446,0.0015606182676050988,0.0055324766320919965,14
added results/competition/eventstudy_firstcut.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,-0.002253311623153832,0.0032075704302996218,-0.7024979410797687,0.48259201396536877,-0.008550446180408903,0.004043822934101238,-7
3+cc_m6,-0.00867267898613712,0.00245752646240401,-3.529027710917628,0.0004431513252509678,-0.013497319675422788,-0.003848038296851452,-6
4+cc_m5,-0.010289003249053701,0.0020559401496703066,-5.004524694312556,7.02023004395258e-07,-0.014325245646676659,-0.006252760851430743,-5
5+cc_m4,-0.00530744830343835,0.002085298775288127,-2.545174037569277,0.011126411526531488,-0.009401327850408087,-0.0012135687564686134,-4
6+cc_m3,-0.005760757053488481,0.002130276979986837,-2.7042291249488493,0.007005323388521001,-0.009942938264688575,-0.001578575842288387,-3
7+cc_m2,-0.013010682600534628,0.002019988978369408,-6.440967124007388,2.1497004176751489e-10,-0.01697634529676299,-0.009045019904306267,-2
8+cc_m1,-0.006623691503957036,0.003419026461533928,-1.9373033752378013,0.053092649549126625,-0.013335958670319361,8.85756624052882e-05,-1
9+cc_p0,0.026162117223121114,0.004693955400184186,5.573576012693802,3.509927481992747e-08,0.016946896730533893,0.035377337715708335,0
10+cc_p1,-0.0002412302759736918,0.0063860226453249175,-0.037774729181439995,0.9698775924244989,-0.012778334217143453,0.012295873665196069,1
11+cc_p2,-0.01155498833477535,0.0021016729821107184,-5.497995374699364,5.3095872143416045e-08,-0.015681013889407514,-0.007428962780143188,2
12+cc_p3,0.0016935474026374354,0.002650174029238453,0.6390325253938469,0.5230016791598338,-0.0035093009291132984,0.00689639573438817,3
13+cc_p4,0.007757456875089256,0.0026130880896097497,2.968693212423539,0.003088337463158064,0.0026274160333372275,0.012887497716841284,4
14+cc_p5,0.0003186396449352228,0.002256411933662606,0.14121519221803053,0.887738797295146,-0.004111170977038325,0.0047484502669087715,5
15+cc_p6,0.000716941232520684,0.0022669084885021625,0.31626386162345527,0.7518923999991016,-0.00373347633157576,0.005167358796617128,6
16+cc_p7,0.012044059170710678,0.002581850242271626,4.664894568057433,3.670909135999878e-06,0.00697534478530361,0.017112773556117747,7
17+cc_p8,-0.0026366535173581356,0.003416143476658753,-0.771821656605882,0.44046915629469785,-0.009343260778792412,0.004069953744076141,8
18+cc_p9,-0.0073299807665459125,0.0030081619516677087,-2.436697519720376,0.015059372684890215,-0.013235634591237494,-0.0014243269418543307,9
19+cc_p10,-0.001219092030632005,0.0028990344188345413,-0.4205165770753766,0.6742315411008217,-0.006910505584350789,0.004472321523086778,10
20+cc_p11,0.004113745409527339,0.0026978353371197444,1.5248319098374792,0.12773294100431376,-0.001182672081630821,0.0094101629006855,11
21+cc_p12,-0.0037831710900687074,0.003128017785295974,-1.209446796579113,0.22688170585806477,-0.009924127093848734,0.0023577849137113196,12
22+cc_p13,-0.00513858213436243,0.003192518676607059,-1.6095699524062317,0.10792291265824483,-0.011406166937252209,0.0011290026685273477,13
23+cc_p14,0.014907721347740521,0.003974106570501226,3.7512132810923378,0.00018989164639227063,0.00710571532382913,0.022709727371651913,14
added results/competition/eventstudy_main.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.004215101408807976,0.0014775131216297702,2.852835177638565,0.004431309638764924,0.0013153721464117512,0.007114830671204201,-7
3+cc_m6,0.0010227046741625755,0.0011174174077426854,0.9152396115150553,0.36030830882734266,-0.0011703099990122312,0.0032157193473373825,-6
4+cc_m5,0.00028534845977386096,0.0006249287751846971,0.45660957072992275,0.6480606627853527,-0.000941120686109348,0.00151181760565707,-5
5+cc_m4,0.000980475815802045,0.0006893409192910907,1.4223380454628367,0.15527080920023573,-0.0003724069479263759,0.002333358579530466,-4
6+cc_m3,-0.00036610756836122376,0.0006081926614645356,-0.6019598583771664,0.5473508024359699,-0.0015597308477795636,0.000827515711057116,-3
7+cc_m2,-0.0018006764920446422,0.0005893715584719233,-3.0552483677924602,0.002314299027061484,-0.0029573619586616643,-0.0006439910254276201,-2
8+cc_m1,-0.0006617304194695431,0.0011967411281396782,-0.5529436600028915,0.5804377926056974,-0.003010423786708045,0.001686962947768959,-1
9+cc_p0,0.008859857232535049,0.0020538370264469486,4.313807336438095,1.780164325082545e-05,0.0048290494931007115,0.012890664971969387,0
10+cc_p1,-0.0011443238750779607,0.002048587393557327,-0.55859168062675,0.5765778364522813,-0.005164828819982744,0.0028761810698268225,1
11+cc_p2,-0.002429550039956297,0.0006711172367992905,-3.6201574132462593,0.00031067260075867686,-0.003746667472997298,-0.0011124326069152958,2
12+cc_p3,-0.0007572458900792266,0.000836050796435241,-0.9057414852159423,0.3653124445554943,-0.0023980576864569073,0.0008835659062984538,3
13+cc_p4,0.0006705568621060159,0.0010520099523562063,0.6374054357605241,0.5240211203227876,-0.0013940908208969266,0.0027352045451089586,4
14+cc_p5,-0.00043311041138595336,0.0008388835255243693,-0.5162938574997342,0.6057746163005977,-0.0020794816490382574,0.0012132608262663508,5
15+cc_p6,0.00030700482908803585,0.0007334589153298548,0.418571269189588,0.6756282565063798,-0.0011324627792952814,0.0017464724374713532,6
16+cc_p7,0.0044655975201398825,0.000771004017304119,5.791925100149573,9.579276705196094e-09,0.002952444859989083,0.005978750180290682,7
17+cc_p8,-0.0005859737835928647,0.0010176667015045502,-0.5758012743529318,0.5648918739921045,-0.0025832202855149158,0.0014112727183291864,8
18+cc_p9,-0.0010953909609043386,0.0007646260408998784,-1.4325839067881958,0.15232020335128382,-0.0025960263685559874,0.00040524444674731007,9
19+cc_p10,-0.0007322588652560102,0.0008855170314921977,-0.8269280422783852,0.4084946228660109,-0.0024701518226706957,0.001005634092158675,10
20+cc_p11,0.0005923864022707967,0.0007228583947793405,0.8195054613035634,0.41271266471535295,-0.0008262768970427964,0.00201104970158439,11
21+cc_p12,0.0004822650718576879,0.0008257669381775434,0.5840208048557145,0.5593510509267317,-0.0011383638887240658,0.0021028940324394414,12
22+cc_p13,0.0003803826413688817,0.0009743428130862273,0.3903991862617851,0.6963327570034039,-0.0015318375110958957,0.002292602793833659,13
23+cc_p14,0.005858402459977514,0.001077127776530718,5.438911322894886,6.890866011133312e-08,0.0037444591810842187,0.007972345738870809,14
added results/competition/eventstudy_main_10km.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.0059230003631768535,0.0014501598501644153,4.084377568793757,4.8088651988020814e-05,0.00307695392804572,0.008769046798307987,-7
3+cc_m6,0.002246064680045967,0.0017014308647038468,1.3201034062802897,0.18713245129725786,-0.0010931197874894235,0.005585249147581357,-6
4+cc_m5,-0.0007426062836961843,0.0009210054045383184,-0.8062995939404264,0.4202807824809762,-0.0025501478095670055,0.0010649352421746366,-5
5+cc_m4,0.0017824588553019445,0.0012775580129360737,1.3952077614115634,0.1632935928131254,-0.0007248436520579772,0.004289761362661866,-4
6+cc_m3,0.0009258531071489162,0.0011889845944078466,0.7786922652349599,0.4363634458031782,-0.0014076174864250475,0.0032593237007228796,-3
7+cc_m2,-0.001122705965497383,0.0009648283345023457,-1.1636328716200794,0.24487783426979925,-0.00301625324693162,0.0007708413159368541,-2
8+cc_m1,-2.489703061698866e-05,0.0019959209125728,-0.01247395648803322,0.9900502149240655,-0.003942040094112683,0.0038922460328787056,-1
9+cc_p0,0.01802860780468989,0.005456857282500027,3.303844478855454,0.0009909025914196512,0.007319120009866608,0.028738095599513173,0
10+cc_p1,0.0011323914385027512,0.004411806751990172,0.25667294651832334,0.7974892750548825,-0.007526107055691185,0.009790889932696688,1
11+cc_p2,-0.0014775262833336994,0.001059357396126011,-1.3947382523942344,0.16343513776617669,-0.00355659387056031,0.0006015413038929108,2
12+cc_p3,0.0012381922398061254,0.0010150583554508644,1.2198237009301307,0.2228477560334312,-0.0007539351892172797,0.0032303196688295305,3
13+cc_p4,0.003193013570647791,0.0016155022097704057,1.9764835673617434,0.048401626732359926,2.2470471869924773e-05,0.006363556669425657,4
14+cc_p5,-0.0004455005558433049,0.0015466838118344812,-0.28803595953778577,0.773384800222499,-0.003480982436304606,0.0025899813246179964,5
15+cc_p6,0.0009200855929347508,0.0010655778952492435,0.8634615986657068,0.3881112410754519,-0.0011711901859283915,0.0030113613717978934,6
16+cc_p7,0.006624514273546138,0.0016977944349705137,3.9018353088553908,0.00010250008547285638,0.003292466569504775,0.0099565619775875,7
17+cc_p8,-0.001227878702190878,0.0013837017047779745,-0.8873868536484174,0.3751051270431094,-0.003943496089971775,0.0014877386855900187,8
18+cc_p9,-0.003845919197050464,0.0009847654302426025,-3.905416537726147,0.000101018606369907,-0.005778594509957916,-0.001913243884143012,9
19+cc_p10,-0.0010406601877600446,0.0016504240892302247,-0.6305410800477467,0.528498945451505,-0.004279740069247367,0.0021984196937272784,10
20+cc_p11,-0.00023462921052500707,0.001145307933767133,-0.2048612461395142,0.8377263459923285,-0.0024823811129485837,0.0020131226918985693,11
21+cc_p12,0.0001021710671190257,0.0012195535445495463,0.08377743443545446,0.9332518151402716,-0.002291293362008047,0.002495635496246099,12
22+cc_p13,-0.0013372648318918288,0.001631022358820662,-0.8198936235667306,0.4124914445057253,-0.004538267376138536,0.0018637377123548785,13
23+cc_p14,0.008119771440051705,0.0021851876485362055,3.715823419325524,0.0002148974811047033,0.0038311783470185877,0.012408364533084821,14
added results/competition/eventstudy_main_50km.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.004026767254569008,0.0011666972838340526,3.45142421291671,0.0005832971308137314,0.0017370371636713632,0.006316497345466653,-7
3+cc_m6,0.0010442977390723715,0.001004425528758753,1.0396965321688825,0.2987570712125982,-0.0009269619776784729,0.003015557455823216,-6
4+cc_m5,-0.0002244025228656547,0.000606332331372998,-0.37009823038384015,0.7113954189684155,-0.001414374766279524,0.0009655697205482145,-5
5+cc_m4,0.0013031334757010074,0.0005432861043060605,2.3986136685117323,0.016657667724068403,0.00023689413647144707,0.0023693728149305677,-4
6+cc_m3,-0.0006649290087108227,0.0005125796158513156,-1.2972209353399244,0.1948839560001714,-0.0016709045832305038,0.00034104656580885833,-3
7+cc_m2,-0.002193031330420316,0.0005313803841367693,-4.127046078268224,4.011761267097569e-05,-0.003235904809367895,-0.0011501578514727365,-2
8+cc_m1,-0.0008448168284253167,0.0007703137449999803,-1.0967178424491677,0.2730548881562447,-0.002356614777900434,0.0006669811210498008,-1
9+cc_p0,0.006327999439092483,0.0012835046990798528,4.930250308883979,9.758227128564556e-07,0.0038090261184091697,0.008846972759775796,0
10+cc_p1,-0.0019753788727409007,0.001695574704674355,-1.1650202537789591,0.24431609389953235,-0.005303070191177174,0.0013523124456953726,1
11+cc_p2,-0.0027656653943448563,0.0006032599602154897,-4.584533330136707,5.185538859198147e-06,-0.003949607881122424,-0.001581722907567288,2
12+cc_p3,-0.001044748141578079,0.0007403986690173751,-1.4110616149062278,0.15856822241094504,-0.002497835532112182,0.00040833924895602417,3
13+cc_p4,0.0006091896331953549,0.0010426138276962696,0.5842907671207501,0.5591695171665756,-0.0014370174571450417,0.0026553967235357512,4
14+cc_p5,-0.0004596260950852927,0.000697603749806881,-0.6588641405848692,0.510149687296821,-0.0018287252775284445,0.0009094730873578592,5
15+cc_p6,0.001255815998630382,0.0007912667805313888,1.5870955656536183,0.11283838682105363,-0.0002971038396300167,0.0028087358368907803,6
16+cc_p7,0.005365115344934985,0.000646930568812436,8.29318570427685,4.440892098500626e-16,0.004095466044592516,0.006634764645277454,7
17+cc_p8,-0.0015078233034196598,0.0009421571434969405,-1.6003947046701517,0.10985813361041963,-0.003356876688312909,0.00034123008147358925,8
18+cc_p9,-0.0016698019002221135,0.0006910322786179253,-2.416387702701445,0.01587125929692168,-0.003026004082276809,-0.0003135997181674178,9
19+cc_p10,-0.0011029189575430063,0.0008679195591660826,-1.270761726585257,0.20413818894962343,-0.0028062755682402036,0.000600437653154191,10
20+cc_p11,8.030803388821721e-05,0.0007665279664787575,0.10476856344476605,0.9165825424425988,-0.0014240600440018469,0.0015846761117782812,11
21+cc_p12,-0.00012070893808373365,0.0007051211633294018,-0.17118893086937984,0.8641132678656289,-0.0015045616030169376,0.0012631437268494701,12
22+cc_p13,0.00040847721687671697,0.0008401118944705289,0.4862176331096409,0.6269299344729715,-0.001240304786085251,0.0020572592198386847,13
23+cc_p14,0.00546288610094891,0.0008642617760165714,6.320869732464212,4.0683700852639504e-10,0.0037667081614112877,0.007159064040486532,14
added results/competition/eventstudy_main_cluster_region.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,0.004215101408807976,0.0011105004389981054,3.795677390826468,0.0015871415438923897,0.0018609456433883627,0.006569257174227589,-7
3+cc_m6,0.0010227046741625755,0.0007381790322520435,1.385442595196044,0.18492851425402645,-0.000542164968082549,0.0025875743164077,-6
4+cc_m5,0.00028534845977386096,0.0005466195557297426,0.5220238770874523,0.6088056293219593,-0.0008734332330755883,0.0014441301526233103,-5
5+cc_m4,0.000980475815802045,0.0004760300519204318,2.0596931051864162,0.05608280535404808,-2.8662813852647212e-05,0.0019896144454567373,-4
6+cc_m3,-0.00036610756836122376,0.0008800962735752625,-0.41598581809006563,0.6829464794475899,-0.0022318283225383016,0.0014996131858158541,-3
7+cc_m2,-0.0018006764920446422,0.0003650199303822953,-4.9330908867325265,0.00014981296145677447,-0.0025744841767834435,-0.001026868807305841,-2
8+cc_m1,-0.0006617304194695431,0.0007978544113173636,-0.8293874296902592,0.419087591584439,-0.0023531062140282766,0.0010296453750891906,-1
9+cc_p0,0.008859857232535049,0.0021226047170601163,4.174049535142026,0.0007163448906464254,0.004360136244687277,0.01335957822038282,0
10+cc_p1,-0.0011443238750779607,0.001216765500013902,-0.9404637747083445,0.36096269040581674,-0.003723751506467031,0.0014351037563111097,1
11+cc_p2,-0.002429550039956297,0.0007152872422805438,-3.3966075393854145,0.0036864609401623394,-0.003945891255332179,-0.0009132088245804146,2
12+cc_p3,-0.0007572458900792266,0.0008065240222125758,-0.9389006021194978,0.3617404816411456,-0.002467000438716907,0.0009525086585584538,3
13+cc_p4,0.0006705568621060159,0.0011077376570983572,0.6053390510010228,0.553443756996711,-0.0016777420673237285,0.0030188557915357605,4
14+cc_p5,-0.00043311041138595336,0.0011201595983114428,-0.386650627320281,0.7041087019972272,-0.002807742679819933,0.0019415218570480264,5
15+cc_p6,0.00030700482908803585,0.0004448229159388001,0.6901731410129832,0.4999749702751406,-0.0006359776276256772,0.001249987285801749,6
16+cc_p7,0.0044655975201398825,0.000778274616204633,5.737817252626078,3.054214228237484e-05,0.0028157290369982928,0.006115466003281472,7
17+cc_p8,-0.0005859737835928647,0.0006850889658765901,-0.8553250932060994,0.4049978379260153,-0.0020382975127926574,0.000866349945606928,8
18+cc_p9,-0.0010953909609043386,0.000967063047584934,-1.1326986008201643,0.2740302884972716,-0.0031454730401606966,0.0009546911183520193,9
19+cc_p10,-0.0007322588652560102,0.00043426359025173654,-1.6862082884534022,0.11115369429773048,-0.001652856551489514,0.0001883388209774936,10
20+cc_p11,0.0005923864022707967,0.001085471404224616,0.5457411406373766,0.592771955602363,-0.0017087101796981032,0.002893482984239697,11
21+cc_p12,0.0004822650718576879,0.0006778027325088086,0.7115124338207363,0.48701168924246074,-0.0009546125326143819,0.0019191426763297578,12
22+cc_p13,0.0003803826413688817,0.0009058654327564374,0.4199107589428862,0.6801351759024481,-0.001539966289912845,0.0023007315726506087,13
23+cc_p14,0.005858402459977514,0.0008968688181404485,6.532061703431968,6.907873155936883e-06,0.003957125499695274,0.007759679420259754,14
added results/competition/eventstudy_placebo_permuted.csv +23 −0
@@ -0,0 +1,23 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%,k
2+cc_m7,-0.00043388091512943234,0.0003994369522400346,-1.0862312880574436,0.2776643253552753,-0.0012178056071925217,0.0003500437769336571,-7
3+cc_m6,0.00043346290644795176,0.00038224281076675053,1.1339988463836836,0.25709367342049894,-0.00031671700565889197,0.0011836428185547955,-6
4+cc_m5,0.0001395454207849452,0.0003876009352722815,0.36002343670021714,0.7189131460129701,-0.0006211502087072792,0.0009002410502771697,-5
5+cc_m4,0.00036955860595780143,0.0004060079159697215,0.9102251247371287,0.3629448230181944,-0.00042726209056723137,0.0011663793024828343,-4
6+cc_m3,0.0006914418697872922,0.0003910413394990216,1.7682065805961218,0.07736138504912615,-7.60058085799675e-05,0.0014588895481545518,-3
7+cc_m2,-0.0007141658480046691,0.00038262251770676764,-1.8665024010740214,0.062292553536627215,-0.0014650909631888892,3.6759267179550977e-05,-2
8+cc_m1,-1.8992587638240582e-05,0.0003450784297940834,-0.055038466616339696,0.956119899239892,-0.0006962346412261614,0.0006582494659496802,-1
9+cc_p0,0.00010863729289439367,0.00035649270432439365,0.3047391757996209,0.760634489230978,-0.000591006122400593,0.0008082807081893804,0
10+cc_p1,0.00039106585140691677,0.0004089556353041864,0.956254951997512,0.3391974059333491,-0.0004115399632915848,0.0011936716661054183,1
11+cc_p2,-0.000405229592442749,0.00043894575663027264,-0.9231883127283014,0.3561537888863606,-0.0012666932481438,0.0004562340632583019,2
12+cc_p3,-0.0004165490353688006,0.0004136983225856391,-1.0068908009231083,0.3142549070196816,-0.0012284627261809524,0.0003953646554433513,3
13+cc_p4,6.429215678624721e-05,0.0004992382620121989,0.12878050758192133,0.8975597855625579,-0.000915500019724355,0.0010440843332968496,4
14+cc_p5,-0.0009302031948889564,0.0004505307192120445,-2.064683173027213,0.0392358287718757,-0.0018144032002781062,-4.600318949980655e-05,5
15+cc_p6,-0.00015780386058557454,0.0005412686740240564,-0.2915444180657701,0.7707014211508754,-0.0012200838430162467,0.0009044761218450976,6
16+cc_p7,0.0010124176806929237,0.0005928375398147001,1.7077489408133117,0.08802400944160049,-0.0001510700317894232,0.0021759053931752707,7
17+cc_p8,0.00012560941057137033,0.0006111894595470374,0.2055163233090792,0.8372147101710321,-0.001073895307709413,0.0013251141288521536,8
18+cc_p9,0.0009044490893160293,0.0006934232097302164,1.3043247999557366,0.19245265616263119,-0.0004564454726635474,0.002265343651295606,9
19+cc_p10,-0.0004898603597373944,0.000676027197213349,-0.7246163493963664,0.46887368880017677,-0.0018166139548411375,0.0008368932353663487,10
20+cc_p11,0.0012856276615492309,0.0007258467519026187,1.7712108763720331,0.07686030325450632,-0.00013890051061726913,0.0027101558337157307,11
21+cc_p12,-0.0006290408228413749,0.0007458046994185404,-0.8434390710219453,0.39920457437919143,-0.002092737949639865,0.0008346563039571149,12
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added results/competition/summary.json +1133 −0
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1133+}
\ No newline at end of file
added results/competition/summary_dow.json +479 −0
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\ No newline at end of file
added results/descriptives/fig01_daily_coverage.csv +83 −0
@@ -0,0 +1,83 @@
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added results/descriptives/fig01_region_stock.csv +18 −0
@@ -0,0 +1,18 @@
1+region,active_canonical,share
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3+Montréal,5535,0.11251600837517534
4+Capitale-Nationale,5496,0.11172321265220661
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11+Centre-du-Québec,1657,0.03368365417844002
12+Region missing,1585,0.03222003130526701
13+Bas-Saint-Laurent,1513,0.030756408432094
14+Outaouais,1342,0.027280304108308093
15+Abitibi-Témiscamingue,1266,0.025735368853292136
16+Saguenay–Lac-Saint-Jean,1034,0.021019250706401316
17+Côte-Nord,801,0.016282804464049762
18+Gaspésie–Îles-de-la-Madeleine,293,0.005956131969995731
added results/descriptives/fig03_cut_share_by_class.csv +4 −0
@@ -0,0 +1,4 @@
1+source_class,n,n_with_cut,n_exit_confirmed,share_with_cut
2+direct_dealer,15376,3167,7769,0.2059703433922997
3+full_portal,58853,11157,32914,0.18957402341426946
4+banner,6627,1604,3051,0.2420401388260148
added results/descriptives/fig03_duration_ecdf.csv +1751 −0
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added results/descriptives/fig04_price_paths.csv +41 −0
@@ -0,0 +1,41 @@
1+label,days_since_entry,price_cad,kind
2+Listing A,0.0,20495.0,entry
3+Listing A,14.82,18995.0,cut
4+Listing A,30.888,17995.0,cut
5+Listing A,48.096,17495.0,cut
6+Listing A,63.484,16995.0,cut
7+Listing A,67.295,16995.0,censored
8+Listing B,0.0,42990.0,entry
9+Listing B,18.061,42490.0,cut
10+Listing B,32.069,41990.0,cut
11+Listing B,46.099,40990.0,cut
12+Listing B,60.003,40490.0,cut
13+Listing B,65.998,40490.0,censored
14+Listing C,0.0,19888.0,entry
15+Listing C,6.017,19688.0,cut
16+Listing C,12.142,19497.0,minor
17+Listing C,19.092,19288.0,cut
18+Listing C,24.396,19125.0,minor
19+Listing C,32.946,18777.0,cut
20+Listing C,39.98,18667.0,minor
21+Listing C,47.089,18515.0,minor
22+Listing C,55.245,18515.0,censored
23+Listing D,0.0,18245.0,entry
24+Listing D,15.444,17970.0,cut
25+Listing D,21.426,17590.0,cut
26+Listing D,35.53,17249.0,cut
27+Listing D,43.381,17249.0,censored
28+Listing E,0.0,26961.0,entry
29+Listing E,12.878,26461.0,cut
30+Listing E,19.786,24991.0,cut
31+Listing E,47.956,24961.0,minor
32+Listing E,56.086,24891.0,minor
33+Listing E,61.994,23971.0,cut
34+Listing E,66.368,23971.0,censored
35+Listing F,0.0,39995.0,entry
36+Listing F,16.473,37995.0,cut
37+Listing F,18.387,37636.0,minor
38+Listing F,26.98,35401.0,cut
39+Listing F,46.924,34895.0,cut
40+Listing F,53.365,34660.0,minor
41+Listing F,56.623,34660.0,censored
added results/descriptives/fig04_selection.json +95 −0
@@ -0,0 +1,95 @@
1+{
2+ "rule": "S2 listings with n_cuts >= 3 and 30 <= duration_days <= 70, body in {SUV, Sedan, Pickup}; within each body type the two listings with the lowest SHA-256(listing_id) hex digest; labelled A-F in body order.",
3+ "n_candidates": 1342,
4+ "n_candidates_by_body": {
5+ "SUV": 897,
6+ "Sedan": 283,
7+ "Pickup": 162
8+ },
9+ "selected": [
10+ {
11+ "label": "Listing A",
12+ "listing_id": "lb0c0057ac8",
13+ "sha256_prefix": "00e2f4280878",
14+ "make": "Ford",
15+ "body": "SUV",
16+ "year": 2020,
17+ "n_cuts": 4,
18+ "duration_days": 67.3,
19+ "exit_confirmed": false,
20+ "entry_price": 20495.0,
21+ "last_price": 16995.0,
22+ "total_change_pct": -17.1
23+ },
24+ {
25+ "label": "Listing B",
26+ "listing_id": "l4898ebdf75",
27+ "sha256_prefix": "011aec727988",
28+ "make": "Hyundai",
29+ "body": "SUV",
30+ "year": 2025,
31+ "n_cuts": 4,
32+ "duration_days": 66.0,
33+ "exit_confirmed": false,
34+ "entry_price": 42990.0,
35+ "last_price": 40490.0,
36+ "total_change_pct": -5.8
37+ },
38+ {
39+ "label": "Listing C",
40+ "listing_id": "l677e15cbc3",
41+ "sha256_prefix": "00eda16b4332",
42+ "make": "Honda",
43+ "body": "Sedan",
44+ "year": 2018,
45+ "n_cuts": 3,
46+ "duration_days": 55.2,
47+ "exit_confirmed": false,
48+ "entry_price": 19888.0,
49+ "last_price": 18515.0,
50+ "total_change_pct": -6.9
51+ },
52+ {
53+ "label": "Listing D",
54+ "listing_id": "lce48e5369e",
55+ "sha256_prefix": "017095ce09ee",
56+ "make": "Kia",
57+ "body": "Sedan",
58+ "year": 2022,
59+ "n_cuts": 3,
60+ "duration_days": 43.4,
61+ "exit_confirmed": false,
62+ "entry_price": 18245.0,
63+ "last_price": 17249.0,
64+ "total_change_pct": -5.5
65+ },
66+ {
67+ "label": "Listing E",
68+ "listing_id": "l5fbc700db4",
69+ "sha256_prefix": "02796de2fcbe",
70+ "make": "RAM",
71+ "body": "Pickup",
72+ "year": 2018,
73+ "n_cuts": 3,
74+ "duration_days": 66.4,
75+ "exit_confirmed": false,
76+ "entry_price": 26961.0,
77+ "last_price": 23971.0,
78+ "total_change_pct": -11.1
79+ },
80+ {
81+ "label": "Listing F",
82+ "listing_id": "ldfb3fd378d",
83+ "sha256_prefix": "037f1a321503",
84+ "make": "GMC",
85+ "body": "Pickup",
86+ "year": 2021,
87+ "n_cuts": 3,
88+ "duration_days": 56.6,
89+ "exit_confirmed": false,
90+ "entry_price": 39995.0,
91+ "last_price": 34660.0,
92+ "total_change_pct": -13.3
93+ }
94+ ]
95+}
\ No newline at end of file
added results/descriptives/summary.json +550 −0
@@ -0,0 +1,550 @@
1+{
2+ "snapshot_date": "2026-11-01",
3+ "window_start_utc": "2026-08-12 12:30 UTC",
4+ "window_end_utc": "2026-11-01 06:49 UTC",
5+ "window_days": 80.8,
6+ "S0_n": 273265,
7+ "S1_n": 136393,
8+ "S2_n": 80856,
9+ "S2L_n": 128091,
10+ "active_canonical_auto_at_snapshot": 49193,
11+ "active_canonical_region_missing": 1585,
12+ "active_canonical_top_regions": [
13+ {
14+ "region": "Montérégie",
15+ "active_canonical": 9716,
16+ "share": 0.19750777549651374
17+ },
18+ {
19+ "region": "Montréal",
20+ "active_canonical": 5535,
21+ "share": 0.11251600837517534
22+ },
23+ {
24+ "region": "Capitale-Nationale",
25+ "active_canonical": 5496,
26+ "share": 0.11172321265220661
27+ },
28+ {
29+ "region": "Laurentides",
30+ "active_canonical": 5059,
31+ "share": 0.10283983493586486
32+ },
33+ {
34+ "region": "Chaudière-Appalaches",
35+ "active_canonical": 3682,
36+ "share": 0.074848047486431
37+ }
38+ ],
39+ "sources_total": 143,
40+ "dealers_total": 2381,
41+ "coverage": {
42+ "T_days": 82,
43+ "day0_initial_stock_s2l": 13324,
44+ "connector_startup_entries": {
45+ "6": 22583,
46+ "11": 11271
47+ },
48+ "connector_startup_source": {
49+ "6": "otogo",
50+ "11": "autousagee"
51+ },
52+ "connector_startup_listings_s2l": {
53+ "otogo": 22150,
54+ "autousagee": 11074
55+ },
56+ "connector_startup_listings_s2_total": 0,
57+ "entries_per_full_day_mean_excl_startup": 1034.6025641025642,
58+ "entries_day0_left_censored": 47235,
59+ "active_s2l_min": 13436,
60+ "active_s2l_max": 48797,
61+ "active_s2l_mean_full_days": 43918.4125,
62+ "active_s2l_at_last_full_day": 41708,
63+ "entries_per_full_day_mean": 1431.9125,
64+ "entries_per_full_day_median": 1050.5,
65+ "exits_per_full_day_mean_confirmable": 1041.2702702702702,
66+ "crawl_cycles_per_full_day_mean": 7.277843915627136,
67+ "crawl_cycles_per_full_day_min": 1.972027972027972,
68+ "crawl_cycles_per_full_day_max": 10.0,
69+ "crawl_cycles_total": 586,
70+ "days_with_fewer_than_4_cycles": [
71+ 45,
72+ 46,
73+ 50
74+ ],
75+ "max_gap_hours_same_source": 13.570681512819396,
76+ "median_gap_hours_same_source": 3.089500305851301,
77+ "sources_logged_min": 138,
78+ "sources_logged_max": 143,
79+ "failed_source_cycles_share": 0.01261806993810681
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81+ "dynamics": {
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83+ "S2_exit_confirmed": 43734,
84+ "S2_right_censored_share": 0.4591124962897002,
85+ "S2L_n": 128091,
86+ "S2L_left_censored_n": 47235,
87+ "S2L_left_censored_share": 0.36876127128369673,
88+ "S2_completed_duration_median_days": 8.937921426483879,
89+ "S2_completed_duration_p25_days": 1.046458214259258,
90+ "S2_completed_duration_p75_days": 20.64263841892834,
91+ "S2_completed_duration_mean_days": 13.261397278354009,
92+ "S2_completed_share_within_7d": 0.44361366442584715,
93+ "S2_completed_share_within_30d": 0.8737366808432798,
94+ "S2_share_with_cut": 0.19699218363510437,
95+ "S2_n_with_cut": 15928,
96+ "S2_share_with_2plus_cuts": 0.0658083506480657,
97+ "S2_first_cut_pct_median": 2.77808645405045,
98+ "S2_first_cut_pct_mean": 4.592908169524914,
99+ "S2_first_cut_pct_p25": 1.755001755001755,
100+ "S2_first_cut_pct_p75": 4.76417341591234,
101+ "S2_first_cut_share_le_5pct": 0.7628704168759417,
102+ "S2_first_cut_share_gt_10pct": 0.07602963335007534,
103+ "S2_share_with_cut_by_class": {
104+ "direct_dealer": 0.2059703433922997,
105+ "full_portal": 0.18957402341426946,
106+ "banner": 0.2420401388260148
107+ }
108+ },
109+ "fig04": {
110+ "rule": "S2 listings with n_cuts >= 3 and 30 <= duration_days <= 70, body in {SUV, Sedan, Pickup}; within each body type the two listings with the lowest SHA-256(listing_id) hex digest; labelled A-F in body order.",
111+ "n_candidates": 1342,
112+ "n_candidates_by_body": {
113+ "SUV": 897,
114+ "Sedan": 283,
115+ "Pickup": 162
116+ },
117+ "selected": [
118+ {
119+ "label": "Listing A",
120+ "listing_id": "lb0c0057ac8",
121+ "sha256_prefix": "00e2f4280878",
122+ "make": "Ford",
123+ "body": "SUV",
124+ "year": 2020,
125+ "n_cuts": 4,
126+ "duration_days": 67.3,
127+ "exit_confirmed": false,
128+ "entry_price": 20495.0,
129+ "last_price": 16995.0,
130+ "total_change_pct": -17.1
131+ },
132+ {
133+ "label": "Listing B",
134+ "listing_id": "l4898ebdf75",
135+ "sha256_prefix": "011aec727988",
136+ "make": "Hyundai",
137+ "body": "SUV",
138+ "year": 2025,
139+ "n_cuts": 4,
140+ "duration_days": 66.0,
141+ "exit_confirmed": false,
142+ "entry_price": 42990.0,
143+ "last_price": 40490.0,
144+ "total_change_pct": -5.8
145+ },
146+ {
147+ "label": "Listing C",
148+ "listing_id": "l677e15cbc3",
149+ "sha256_prefix": "00eda16b4332",
150+ "make": "Honda",
151+ "body": "Sedan",
152+ "year": 2018,
153+ "n_cuts": 3,
154+ "duration_days": 55.2,
155+ "exit_confirmed": false,
156+ "entry_price": 19888.0,
157+ "last_price": 18515.0,
158+ "total_change_pct": -6.9
159+ },
160+ {
161+ "label": "Listing D",
162+ "listing_id": "lce48e5369e",
163+ "sha256_prefix": "017095ce09ee",
164+ "make": "Kia",
165+ "body": "Sedan",
166+ "year": 2022,
167+ "n_cuts": 3,
168+ "duration_days": 43.4,
169+ "exit_confirmed": false,
170+ "entry_price": 18245.0,
171+ "last_price": 17249.0,
172+ "total_change_pct": -5.5
173+ },
174+ {
175+ "label": "Listing E",
176+ "listing_id": "l5fbc700db4",
177+ "sha256_prefix": "02796de2fcbe",
178+ "make": "RAM",
179+ "body": "Pickup",
180+ "year": 2018,
181+ "n_cuts": 3,
182+ "duration_days": 66.4,
183+ "exit_confirmed": false,
184+ "entry_price": 26961.0,
185+ "last_price": 23971.0,
186+ "total_change_pct": -11.1
187+ },
188+ {
189+ "label": "Listing F",
190+ "listing_id": "ldfb3fd378d",
191+ "sha256_prefix": "037f1a321503",
192+ "make": "GMC",
193+ "body": "Pickup",
194+ "year": 2021,
195+ "n_cuts": 3,
196+ "duration_days": 56.6,
197+ "exit_confirmed": false,
198+ "entry_price": 39995.0,
199+ "last_price": 34660.0,
200+ "total_change_pct": -13.3
201+ }
202+ ]
203+ },
204+ "tab01": [
205+ {
206+ "source_class": "direct_dealer",
207+ "n_sources_with_listings": 131,
208+ "n_dealers": 131,
209+ "listings": 33061,
210+ "active_at_snapshot": 12222,
211+ "in_s1": true,
212+ "in_s2": true,
213+ "n_sources_in_s2": 113,
214+ "n_s1": 28380,
215+ "n_s2": 15376,
216+ "n_sources": 131,
217+ "found_per_cycle": 12522.994457833945,
218+ "mean_found_per_source": 95.59537754071714,
219+ "label": "Dealer websites"
220+ },
221+ {
222+ "source_class": "full_portal",
223+ "n_sources_with_listings": 2,
224+ "n_dealers": 929,
225+ "listings": 107903,
226+ "active_at_snapshot": 39125,
227+ "in_s1": true,
228+ "in_s2": true,
229+ "n_sources_in_s2": 2,
230+ "n_s1": 92475,
231+ "n_s2": 58853,
232+ "n_sources": 2,
233+ "found_per_cycle": 36692.775528034676,
234+ "mean_found_per_source": 18346.387764017338,
235+ "label": "Full-inventory portals"
236+ },
237+ {
238+ "source_class": "banner",
239+ "n_sources_with_listings": 5,
240+ "n_dealers": 5,
241+ "listings": 17162,
242+ "active_at_snapshot": 5579,
243+ "in_s1": true,
244+ "in_s2": true,
245+ "n_sources_in_s2": 4,
246+ "n_s1": 15309,
247+ "n_s2": 6627,
248+ "n_sources": 5,
249+ "found_per_cycle": 5131.368507237205,
250+ "mean_found_per_source": 1026.273701447441,
251+ "label": "Dealer groups"
252+ },
253+ {
254+ "source_class": "sampled_portal",
255+ "n_sources_with_listings": 2,
256+ "n_dealers": 1313,
257+ "listings": 103027,
258+ "active_at_snapshot": 2654,
259+ "in_s1": false,
260+ "in_s2": false,
261+ "n_sources_in_s2": 0,
262+ "n_s1": 0,
263+ "n_s2": 0,
264+ "n_sources": 2,
265+ "found_per_cycle": 2359.140591777665,
266+ "mean_found_per_source": 1179.5702958888326,
267+ "label": "Sampled portals (excluded)"
268+ },
269+ {
270+ "source_class": "private_marketplace",
271+ "n_sources_with_listings": 1,
272+ "n_dealers": 1,
273+ "listings": 11874,
274+ "active_at_snapshot": 3158,
275+ "in_s1": false,
276+ "in_s2": false,
277+ "n_sources_in_s2": 0,
278+ "n_s1": 0,
279+ "n_s2": 0,
280+ "n_sources": 1,
281+ "found_per_cycle": 3040.91061452514,
282+ "mean_found_per_source": 3040.91061452514,
283+ "label": "Private sellers (Kijiji)"
284+ },
285+ {
286+ "source_class": "small_portal",
287+ "n_sources_with_listings": 2,
288+ "n_dealers": 2,
289+ "listings": 238,
290+ "active_at_snapshot": 83,
291+ "in_s1": true,
292+ "in_s2": false,
293+ "n_sources_in_s2": 0,
294+ "n_s1": 229,
295+ "n_s2": 0,
296+ "n_sources": 2,
297+ "found_per_cycle": 194.13841906625663,
298+ "mean_found_per_source": 97.06920953312832,
299+ "label": "Small portals"
300+ }
301+ ],
302+ "tab03": {
303+ "S1": {
304+ "N": 136393,
305+ "entry_price_mean": 29644.45475207672,
306+ "entry_price_median": 25627.0,
307+ "mileage_mean_km": 77817.36238663274,
308+ "mileage_median_km": 68629.0,
309+ "age_mean_years": 5.671836242804624,
310+ "age_median_years": 4.879980315332635,
311+ "dealers": 1046,
312+ "active_share": 0.3021049467348031,
313+ "powertrain_share": {
314+ "ICE": 0.8129156188367438,
315+ "HEV": 0.035742303490648344,
316+ "PHEV": 0.016804381456526362,
317+ "BEV": 0.06119082357598997,
318+ "Diesel": 0.017442244103436394,
319+ "unknown": 0.05590462853665511
320+ },
321+ "body_share": {
322+ "SUV": 0.566641983092974,
323+ "Sedan": 0.20755463990087467,
324+ "Pickup": 0.10573856429582164,
325+ "Hatchback": 0.04202561715044027,
326+ "Van": 0.03079336916117396,
327+ "Coupe": 0.013717712785846782,
328+ "Convertible": 0.011063617634336073,
329+ "Wagon": 0.005154223457215546
330+ },
331+ "body_missing_share": 0.017310272521317078,
332+ "duration_median_completed": 8.9356921752832,
333+ "n_completed": 47934,
334+ "share_with_cut": 0.2723233597032106,
335+ "first_cut_pct_median": 2.910421185535304
336+ },
337+ "S2": {
338+ "N": 80856,
339+ "entry_price_mean": 29942.407860888492,
340+ "entry_price_median": 25995.0,
341+ "mileage_mean_km": 74386.86401751262,
342+ "mileage_median_km": 65037.5,
343+ "age_mean_years": 5.5225845579461055,
344+ "age_median_years": 4.900723511527273,
345+ "dealers": 982,
346+ "active_share": 0.3589096665677253,
347+ "powertrain_share": {
348+ "ICE": 0.8035767289997032,
349+ "HEV": 0.03749876323340259,
350+ "PHEV": 0.019070940932027308,
351+ "BEV": 0.07063174037795587,
352+ "Diesel": 0.014111506876422281,
353+ "unknown": 0.05511031958048877
354+ },
355+ "body_share": {
356+ "SUV": 0.5713490650044524,
357+ "Sedan": 0.20734392005540714,
358+ "Pickup": 0.10331948154744236,
359+ "Hatchback": 0.043892846542000596,
360+ "Van": 0.029731869001682003,
361+ "Coupe": 0.01238003364005145,
362+ "Convertible": 0.009894132779261898,
363+ "Wagon": 0.005342831700801425
364+ },
365+ "body_missing_share": 0.01674581972890076,
366+ "duration_median_completed": 8.937921426483879,
367+ "n_completed": 43734,
368+ "share_with_cut": 0.19699218363510437,
369+ "first_cut_pct_median": 2.77808645405045
370+ },
371+ "top_bodies_S1": [
372+ "SUV",
373+ "Sedan",
374+ "Pickup",
375+ "Hatchback",
376+ "Van"
377+ ],
378+ "region_total": {
379+ "N": 136393,
380+ "median_price": 25627.0,
381+ "median_km": 68629.0,
382+ "median_age": 4.879980315332635,
383+ "n_s2_entrants_37d": 44003,
384+ "share_exit_30d": 0.5930504738313297
385+ },
386+ "exit30_definition": "S2 listings with entry_ts <= snapshot - 37 days (so that an exit at day 30 is confirmable); exit within 30 d = exit_confirmed and duration_days <= 30",
387+ "S1_duration_note": "S1 completed spells exclude left-censored day-1 stock (duration unknown) but include all S1 sources"
388+ },
389+ "tab03_by_region": [
390+ {
391+ "region": "Montréal",
392+ "N": 12120,
393+ "median_price": 28995.0,
394+ "median_km": 56032.0,
395+ "median_age": 4.004882281678036,
396+ "n_s2_entrants_37d": 4290,
397+ "share_exit_30d": 0.6508158508158508
398+ },
399+ {
400+ "region": "Laval",
401+ "N": 8968,
402+ "median_price": 26495.0,
403+ "median_km": 68084.0,
404+ "median_age": 4.864039387345642,
405+ "n_s2_entrants_37d": 3309,
406+ "share_exit_30d": 0.5811423390752494
407+ },
408+ {
409+ "region": "Montérégie",
410+ "N": 26490,
411+ "median_price": 25488.5,
412+ "median_km": 73319.0,
413+ "median_age": 4.894800661584851,
414+ "n_s2_entrants_37d": 9137,
415+ "share_exit_30d": 0.6132209696837037
416+ },
417+ {
418+ "region": "Laurentides",
419+ "N": 12654,
420+ "median_price": 25696.0,
421+ "median_km": 71500.0,
422+ "median_age": 4.879980315332635,
423+ "n_s2_entrants_37d": 4033,
424+ "share_exit_30d": 0.5383089511529878
425+ },
426+ {
427+ "region": "Lanaudière",
428+ "N": 8549,
429+ "median_price": 26495.0,
430+ "median_km": 44422.0,
431+ "median_age": 3.880664778029418,
432+ "n_s2_entrants_37d": 2144,
433+ "share_exit_30d": 0.5634328358208955
434+ },
435+ {
436+ "region": "Capitale-Nationale",
437+ "N": 17015,
438+ "median_price": 26555.0,
439+ "median_km": 55315.0,
440+ "median_age": 3.9663401192143217,
441+ "n_s2_entrants_37d": 5358,
442+ "share_exit_30d": 0.6362448674878686
443+ },
444+ {
445+ "region": "Chaudière-Appalaches",
446+ "N": 9176,
447+ "median_price": 25321.5,
448+ "median_km": 76917.5,
449+ "median_age": 5.018263869246222,
450+ "n_s2_entrants_37d": 2820,
451+ "share_exit_30d": 0.5315602836879433
452+ },
453+ {
454+ "region": "Estrie",
455+ "N": 4627,
456+ "median_price": 23995.0,
457+ "median_km": 78690.0,
458+ "median_age": 5.01842924399327,
459+ "n_s2_entrants_37d": 1769,
460+ "share_exit_30d": 0.5681175805539853
461+ },
462+ {
463+ "region": "Mauricie",
464+ "N": 7146,
465+ "median_price": 23995.0,
466+ "median_km": 68997.0,
467+ "median_age": 4.929613817183437,
468+ "n_s2_entrants_37d": 2303,
469+ "share_exit_30d": 0.611376465479809
470+ },
471+ {
472+ "region": "Centre-du-Québec",
473+ "N": 3839,
474+ "median_price": 25688.0,
475+ "median_km": 72640.0,
476+ "median_age": 4.897451169230362,
477+ "n_s2_entrants_37d": 1333,
478+ "share_exit_30d": 0.5198799699924981
479+ },
480+ {
481+ "region": "Outaouais",
482+ "N": 4348,
483+ "median_price": 24988.0,
484+ "median_km": 78546.0,
485+ "median_age": 5.063261864948048,
486+ "n_s2_entrants_37d": 1471,
487+ "share_exit_30d": 0.5588035350101972
488+ },
489+ {
490+ "region": "Bas-Saint-Laurent",
491+ "N": 4423,
492+ "median_price": 23995.0,
493+ "median_km": 76619.0,
494+ "median_age": 5.863293371344908,
495+ "n_s2_entrants_37d": 1516,
496+ "share_exit_30d": 0.6029023746701847
497+ },
498+ {
499+ "region": "Saguenay–Lac-Saint-Jean",
500+ "N": 4009,
501+ "median_price": 25995.0,
502+ "median_km": 72220.0,
503+ "median_age": 5.863213607185498,
504+ "n_s2_entrants_37d": 1622,
505+ "share_exit_30d": 0.6923551171393342
506+ },
507+ {
508+ "region": "Abitibi-Témiscamingue",
509+ "N": 3338,
510+ "median_price": 26970.0,
511+ "median_km": 73252.0,
512+ "median_age": 4.987991108879079,
513+ "n_s2_entrants_37d": 1070,
514+ "share_exit_30d": 0.5317757009345795
515+ },
516+ {
517+ "region": "Côte-Nord",
518+ "N": 2909,
519+ "median_price": 25490.0,
520+ "median_km": 65486.0,
521+ "median_age": 4.926119384576455,
522+ "n_s2_entrants_37d": 1031,
523+ "share_exit_30d": 0.5761396702230844
524+ },
525+ {
526+ "region": "Gaspésie–Îles-de-la-Madeleine",
527+ "N": 741,
528+ "median_price": 23995.0,
529+ "median_km": 79782.0,
530+ "median_age": 4.981709989566159,
531+ "n_s2_entrants_37d": 244,
532+ "share_exit_30d": 0.5081967213114754
533+ },
534+ {
535+ "region": "Region missing",
536+ "N": 6041,
537+ "median_price": 21995.0,
538+ "median_km": 85479.0,
539+ "median_age": 5.894116198888067,
540+ "n_s2_entrants_37d": 553,
541+ "share_exit_30d": 0.4321880650994575
542+ }
543+ ],
544+ "notes": [
545+ "exit = observed disappearance from the source confirmed by >= 7 days of absence before the snapshot; it is NOT a sale",
546+ "day index 0 = 2026-08-12 UTC (partial day, crawl started 12:30 UTC); day 81 = 2026-11-01 (partial, snapshot 06:49 UTC)",
547+ "crawl cycles per day = sync_log rows / distinct sources logged that day (one row per source per cycle)",
548+ "S2 contains no left-censored listing by construction; the left-censored share refers to S2L"
549+ ]
550+}
\ No newline at end of file
added results/descriptives/tab01_sources.csv +7 −0
@@ -0,0 +1,7 @@
1+source_class,n_sources_with_listings,n_dealers,listings,active_at_snapshot,in_s1,in_s2,n_sources_in_s2,n_s1,n_s2,n_sources,found_per_cycle,mean_found_per_source,label
2+direct_dealer,131,131,33061,12222,True,True,113,28380,15376,131,12522.994457833945,95.59537754071714,Dealer websites
3+full_portal,2,929,107903,39125,True,True,2,92475,58853,2,36692.775528034676,18346.387764017338,Full-inventory portals
4+banner,5,5,17162,5579,True,True,4,15309,6627,5,5131.368507237205,1026.273701447441,Dealer groups
5+sampled_portal,2,1313,103027,2654,False,False,0,0,0,2,2359.140591777665,1179.5702958888326,Sampled portals (excluded)
6+private_marketplace,1,1,11874,3158,False,False,0,0,0,1,3040.91061452514,3040.91061452514,Private sellers (Kijiji)
7+small_portal,2,2,238,83,True,False,0,229,0,2,194.13841906625663,97.06920953312832,Small portals
added results/descriptives/tab02_sample_construction.csv +13 −0
@@ -0,0 +1,13 @@
1+step,listings_remaining,dropped
2+S0 all listings in snapshot,273265,0
3+− kind != auto,271992,1273
4+− VIN duplicate (non-canonical),259133,12859
5+"− model year outside [2005,2026] or missing",256651,2482
6+"− mileage outside [1000,400000] km or missing",249958,6693
7+"− entry price outside [2000,250000] $ or missing/placeholder",241321,8637
8+− make or model missing,241298,23
9+− uid identity collision,241297,1
10+− sampled portal (autotrader/cargurus) or private marketplace (kijiji),136393,104904
11+− flickering/failed connector or small portal,128144,8249
12+− oscillating price path,128091,53
13+− left-censored stock (day 1 or source start-up day),80856,47235
added results/descriptives/tab03_by_region.csv +18 −0
@@ -0,0 +1,18 @@
1+region,N,median_price,median_km,median_age,n_s2_entrants_37d,share_exit_30d
2+Montréal,12120,28995.0,56032.0,4.004882281678036,4290,0.6508158508158508
3+Laval,8968,26495.0,68084.0,4.864039387345642,3309,0.5811423390752494
4+Montérégie,26490,25488.5,73319.0,4.894800661584851,9137,0.6132209696837037
5+Laurentides,12654,25696.0,71500.0,4.879980315332635,4033,0.5383089511529878
6+Lanaudière,8549,26495.0,44422.0,3.880664778029418,2144,0.5634328358208955
7+Capitale-Nationale,17015,26555.0,55315.0,3.9663401192143217,5358,0.6362448674878686
8+Chaudière-Appalaches,9176,25321.5,76917.5,5.018263869246222,2820,0.5315602836879433
9+Estrie,4627,23995.0,78690.0,5.01842924399327,1769,0.5681175805539853
10+Mauricie,7146,23995.0,68997.0,4.929613817183437,2303,0.611376465479809
11+Centre-du-Québec,3839,25688.0,72640.0,4.897451169230362,1333,0.5198799699924981
12+Outaouais,4348,24988.0,78546.0,5.063261864948048,1471,0.5588035350101972
13+Bas-Saint-Laurent,4423,23995.0,76619.0,5.863293371344908,1516,0.6029023746701847
14+Saguenay–Lac-Saint-Jean,4009,25995.0,72220.0,5.863213607185498,1622,0.6923551171393342
15+Abitibi-Témiscamingue,3338,26970.0,73252.0,4.987991108879079,1070,0.5317757009345795
16+Côte-Nord,2909,25490.0,65486.0,4.926119384576455,1031,0.5761396702230844
17+Gaspésie–Îles-de-la-Madeleine,741,23995.0,79782.0,4.981709989566159,244,0.5081967213114754
18+Region missing,6041,21995.0,85479.0,5.894116198888067,553,0.4321880650994575
added results/hazards/aft_loglogistic_exit.csv +25 −0
@@ -0,0 +1,25 @@
1+param,covariate,coef,exp(coef),se(coef),coef lower 95%,coef upper 95%,exp(coef) lower 95%,exp(coef) upper 95%,cmp to,z,p,-log2(p)
2+alpha_,age_years_entry,-0.06942807552443821,0.9329272312733835,0.004006882490012925,-0.07728142089514771,-0.061574730153728705,0.925629325515829,0.9402826756449144,0.0,-17.327205301749252,2.9323577695954804e-67,221.0171212236498
3+alpha_,body_Coupe,0.15286430531737466,1.1651668612676713,0.09091574736150469,-0.025327285138716987,0.3310558957734663,0.9749907598182189,1.3924376215108702,0.0,1.6813842458946784,0.09268830356908335,3.431468894586621
4+alpha_,body_Hatchback,-0.3504160445212509,0.7043949690796413,0.07277805189360001,-0.49305840509769405,-0.2077736839448078,0.6107555967838756,0.8123908730062549,0.0,-4.8148588125655225,1.4730400036272403e-06,19.372771958875077
5+alpha_,body_Pickup,-0.6095031956093105,0.5436208752365109,0.06800259286045351,-0.74278582847114,-0.47622056274748104,0.47578660767561026,0.6211264697773865,0.0,-8.962940528753917,3.1613308992637917e-19,61.45610175156502
6+alpha_,body_SUV,-0.42272854150996503,0.655256483906867,0.06562854258920295,-0.5513581213426559,-0.2940989616772742,0.5761667743786795,0.7452027412809424,0.0,-6.441230062901188,1.1850907267959726e-10,32.97428343735499
7+alpha_,body_Sedan,-0.5128477679076362,0.5987879394086412,0.06701186411777081,-0.6441886081153589,-0.38150692769991346,0.5250884217897276,0.6828316555889079,0.0,-7.6530891157769565,1.9620783770772347e-14,45.53461065589641
8+alpha_,body_Van,-0.6360510733414131,0.5293787798750828,0.07600128055482126,-0.7850108460077871,-0.4870913006750391,0.45611475473462976,0.6144109342506968,0.0,-8.36895205841455,5.813289398583319e-17,53.93342288213304
9+alpha_,body_Wagon,-0.5346826048388744,0.5858552082466978,0.10833393301147687,-0.747013211844944,-0.3223519978328049,0.47377952063305556,0.7244431430281514,0.0,-4.93550441653614,7.994387140456673e-07,20.25450922504174
10+alpha_,body_unknown,-0.5935882762545903,0.5523417697200244,0.08688541009073049,-0.7638805508144151,-0.42329600169476556,0.46585514034363945,0.6548847574213824,0.0,-6.831852156014836,8.38252175983806e-12,36.79575281559264
11+alpha_,ldens_entry,-0.05242568610265227,0.9489248367385293,0.007315626885865469,-0.0667640513232815,-0.03808732088202303,0.9354158854410428,0.9626288796182715,0.0,-7.166260242706472,7.707419652580653e-13,40.238817288765716
12+alpha_,lkm,0.9729002177463169,2.6456061776442525,0.009261181376496794,0.9547486357940901,0.9910517996985436,2.5980174522423005,2.694066601110984,0.0,105.05141603374294,0.0,inf
13+alpha_,lprice,0.4293686274132291,1.5362872476241476,0.025595757779018506,0.3792018640093419,0.4795353908171163,1.4611179531039649,1.6153237335826796,0.0,16.7749918216992,3.719102505036121e-63,207.3865154661816
14+alpha_,nocomp_entry,0.14611799030578962,1.157332734038276,0.022969302178959098,0.1010989852850124,0.19113699532656683,1.1063861522919871,1.2106252907285338,0.0,6.36144664593425,1.9986233464677297e-10,32.22027433641249
15+alpha_,pricepos_entry,0.4221590796561357,1.525251141812203,0.04712062562013183,0.32980435051168194,0.5145138088005894,1.3906960128816848,1.6728249912638165,0.0,8.959114487558338,3.2729617463524467e-19,61.40603706227682
16+alpha_,pt_Diesel,0.7108941882571675,2.035810843149721,0.07051568615376877,0.572685983050651,0.8491023934636841,1.7730229710633683,2.337547711860892,0.0,10.08136241781678,6.679346567553765e-24,76.98656730481594
17+alpha_,pt_HEV,0.20972585437324234,1.2333398988665891,0.042533071138629225,0.12636256678964902,0.29308914195683566,1.1346934958896946,1.3405622854509776,0.0,4.930889041369174,8.185622168567515e-07,20.220404587720544
18+alpha_,pt_ICE,0.35260310435471964,1.422766341592091,0.02701755719215138,0.2996496653078518,0.4055565434015875,1.349385988033588,1.5001371592106352,0.0,13.05088768192378,6.281125288889519e-39,126.90417265370868
19+alpha_,pt_PHEV,-0.3669781247051834,0.6928248006253946,0.059424523502199254,-0.48344805056794793,-0.2505081988424188,0.6166534695306722,0.7784050979668452,0.0,-6.1755333165036115,6.594049880244354e-10,30.498116148654983
20+alpha_,pt_unknown,0.3805217586467163,1.4630477481377349,0.03874185982130627,0.3045891087028566,0.45645440859057596,1.3560676921039423,1.5784674509941996,0.0,9.82197964686885,9.054764090401412e-23,73.225669128207
21+alpha_,resid_A2,-0.5147563635259741,0.5976461852912176,0.054758175845065725,-0.6220804160414141,-0.4074323110105341,0.5368264526974024,0.6653564871820498,0.0,-9.400538925592404,5.428430590679558e-21,67.31995483075777
22+alpha_,src_direct_dealer,-0.14499096138383466,0.865030111750558,0.028662985211048153,-0.20116938008689322,-0.08881254268077608,0.8177739052072652,0.9150170841481321,0.0,-5.058473858052575,4.226251728987942e-07,21.17411796287654
23+alpha_,src_full_portal,-0.6460110279210675,0.5241323616861505,0.0253764741510791,-0.6957480033117941,-0.5962740525303408,0.4987012782287174,0.5508602936459898,0.0,-25.45708375698823,5.8931404804368305e-143,472.4766809113892
24+alpha_,Intercept,-10.787136783815628,2.0663601567931155e-05,0.3228214645167261,-11.419855227704886,-10.15441833992637,1.0975388206862882e-05,3.890381111906499e-05,0.0,-33.415178262586444,8.253054999384882e-245,810.8274549926973
25+beta_,Intercept,0.002094867717164992,1.0020970634855548,0.0039950818387560925,-0.005735348802087004,0.009925084236416989,0.9942810669125768,1.0099745012389614,0.0,0.5243616530812419,0.6000270226857836,0.7369006198047275
added results/hazards/aft_loglogistic_first_markdown.csv +25 −0
@@ -0,0 +1,25 @@
1+param,covariate,coef,exp(coef),se(coef),coef lower 95%,coef upper 95%,exp(coef) lower 95%,exp(coef) upper 95%,cmp to,z,p,-log2(p)
2+alpha_,age_years_entry,0.012898069061916989,1.012981607931676,0.0036726862915335313,0.005699736203997294,0.020096401919836682,1.005716010605635,1.0202996941352407,0.0,3.5118896736838896,0.00044493266120742674,11.13412537311832
3+alpha_,body_Coupe,-0.07373910305815956,0.9289140130549806,0.07635182028519508,-0.22338592097121662,0.0759077148548975,0.7998061277697165,1.0788630065339446,0.0,-0.965780550911867,0.3341540025984396,1.581414939556087
4+alpha_,body_Hatchback,-0.02873266350961947,0.9716761942509908,0.06426813806655493,-0.1546958994735148,0.09723057245427585,0.8566756533933996,1.1021144615633403,0.0,-0.44707477723820843,0.6548210697792725,0.6108273516076317
5+alpha_,body_Pickup,0.03993521572927451,1.040743348209514,0.059318498490780004,-0.07632690492964785,0.15619733638819688,0.9265132752901408,1.1690568777907242,0.0,0.6732337592038296,0.5007985870307203,0.997697603077391
6+alpha_,body_SUV,0.0347271140105147,1.0353371412467736,0.057137523288206635,-0.07726037380018891,0.14671460182121832,0.9256488075291588,1.1580234180891278,0.0,0.6077812269766772,0.5433325818406731,0.8800925316798044
7+alpha_,body_Sedan,0.01342943845975083,1.0135200183934523,0.05861149293055895,-0.1014469767642687,0.12830585368377037,0.9035290861078462,1.1369006747854196,0.0,0.2291263673433742,0.8187707003583113,0.288468618324863
8+alpha_,body_Van,-0.02282874541457103,0.9774298587862511,0.0671402217418967,-0.15442116194272165,0.1087636711135796,0.8569110466813081,1.1148988364042178,0.0,-0.34001593712827266,0.7338445261183659,0.4464536519235501
9+alpha_,body_Wagon,0.33432675052262273,1.3969995393362498,0.11859175151365585,0.10189118869233382,0.5667623123529116,1.1072629824397042,1.7625512130872383,0.0,2.8191400013526655,0.004815251086682564,7.698173256672846
10+alpha_,body_unknown,-0.18290253036331422,0.8328493293025888,0.07825780790686321,-0.33628501536982,-0.029520045356808466,0.7144194562702751,0.9709114151803822,0.0,-2.3371793212121608,0.019429861931511767,5.685580540597788
11+alpha_,ldens_entry,-0.08634721332363651,0.9172756855822244,0.0066546799756107685,-0.09939014640447351,-0.07330428024279952,0.9053894046871296,0.9293180138893892,0.0,-12.975411836496546,1.6868720546911062e-38,125.47891705299689
12+alpha_,lkm,-0.032782907526291774,0.9677486277296224,0.010779859369138523,-0.05391104364820995,-0.011654771404373602,0.9475163903542979,0.9884128823590849,0.0,-3.0411257145102844,0.002356953993070327,8.72886068670917
13+alpha_,lprice,0.049119424429122184,1.0503457801953893,0.023287899265341826,0.0034759805934554203,0.09476286826478894,1.0034820288198072,1.0993981220289144,0.0,2.109225219048593,0.03492514607767778,4.839590039920929
14+alpha_,nocomp_entry,-0.10249760533800677,0.9025803111251046,0.0210583098274362,-0.1437711341750676,-0.06122407650094593,0.8660859428521057,0.9406124470141664,0.0,-4.867323454632903,1.131198058701645e-06,19.75371701993402
15+alpha_,pricepos_entry,-0.23722134317658478,0.7888166695056431,0.04449108568810251,-0.3244222687583511,-0.1500204175948184,0.7229449008706426,0.8606904030177421,0.0,-5.331884792373606,9.71985997331455e-08,23.29448922893655
16+alpha_,pt_Diesel,-0.09859276868484126,0.9061116299309107,0.05699668419373714,-0.21030421694276943,0.013118679573086905,0.810337690014172,1.01320510697422,0.0,-1.7297983221219515,0.08366631434154376,3.5792093067166686
17+alpha_,pt_HEV,0.0636798107184735,1.065751102044996,0.04222215280405186,-0.01907408812721499,0.14643370956416202,0.9811066711942004,1.1576981839575093,0.0,1.5082085229998614,0.13150116662360964,2.926852496323166
18+alpha_,pt_ICE,-0.005097753209788975,0.9949152182829216,0.02702835291659507,-0.058072351487753436,0.04787684506817549,0.9435816754290034,1.0490414527400727,0.0,-0.18860761606598006,0.8504003631949295,0.23378588186193386
19+alpha_,pt_PHEV,-0.043187669958596346,0.9577316357438886,0.05813662547672207,-0.15713336207566536,0.07075802215847266,0.854590081310629,1.0733214744289314,0.0,-0.7428650975947806,0.4575633585264671,1.1279565685330837
20+alpha_,pt_unknown,-0.11021497185810394,0.895641576865974,0.03680667296908817,-0.1823547252682607,-0.03807521844794719,0.8333056933964681,0.9626405298413246,0.0,-2.9944290794951023,0.002749589692682426,8.506567936023638
21+alpha_,resid_A2,-0.7719898012985914,0.4620926803049377,0.05287938007432373,-0.8756314817690709,-0.6683481208281119,0.41659886201124297,0.5125545570636695,0.0,-14.599070568027349,2.8470017190012424e-48,157.943105190423
22+alpha_,src_direct_dealer,0.08446712964258576,1.088137076189543,0.024497124371999524,0.03645364814866831,0.13248061113650322,1.037126230182769,1.1416568804451779,0.0,3.448042650227657,0.0005646648649761213,10.790317513949546
23+alpha_,src_full_portal,-0.12152657923058256,0.8855675153328711,0.021547533574957898,-0.16375896899316764,-0.07929418946799749,0.8489466196804978,0.9237681216140313,0.0,-5.6399299162396135,1.7011939145376613e-08,25.808877159780895
24+alpha_,Intercept,3.9182808502663926,50.31387330283753,0.30897584687441676,3.312699318299773,4.523862382233012,27.4591465004445,92.19098804447574,0.0,12.681511807163936,7.487363846599228e-37,120.00688164727116
25+beta_,Intercept,0.4351776666857286,1.5452375718027676,0.006174797851148756,0.42307528528566174,0.44728004808579547,1.5266492258632116,1.5640522477983148,0.0,70.47642322489446,0.0,inf
added results/hazards/cloglog_B_first_markdown.csv +60 −0
@@ -0,0 +1,60 @@
1+term,coef,se,p,ci_lo,ci_hi
2+const,-2.7739861038513602,0.6456908870495763,1.737885537649959e-05,-4.03951698761425,-1.5084552200884707
3+ldens,0.018919584034498692,0.01627908403935399,0.24515342232317672,-0.012986834383935955,0.05082600245293334
4+resid_A2,0.2558872219208771,0.18605527145137654,0.16902945056234064,-0.10877440925764426,0.6205488530993984
5+pricepos_entry,0.031623014121177656,0.08322866856972193,0.7039800201939956,-0.1315021787566981,0.1947482069990534
6+nocomp_entry,-0.06536151830239936,0.036501767365311646,0.07335111198577178,-0.1369036677104697,0.006180631105670964
7+lprice,-0.13401256064719333,0.049295119471057545,0.00655634634372644,-0.23062921942406528,-0.03739590187032138
8+lkm,-0.07920955253798637,0.023550516594834613,0.0007699174282199426,-0.1253677168811751,-0.03305138819479765
9+age_years_entry,0.012640976939940925,0.0063594394733528315,0.04683932646026594,0.00017670461030700607,0.025105249269574845
10+own_stock,0.000296453843173415,0.0015878028052484928,0.8518903946616194,-0.0028155824696652963,0.0034084901560121265
11+age_bin_1-3,0.7897410295056355,0.14540734121574594,5.596626411455131e-08,0.5047478776350469,1.0747341813762241
12+age_bin_15-21,1.41044927573657,0.1434109593409295,7.955388080192602e-23,1.1293689604400101,1.69152959103313
13+age_bin_22-30,1.3898528982229017,0.14626903226199756,2.0583562541578317e-21,1.1031708629358592,1.6765349335099442
14+age_bin_31-45,1.4431253203521872,0.14643675244440124,6.525605211637942e-23,1.156114559548153,1.7301360811562214
15+age_bin_4-7,1.035511581345353,0.15830396363263768,6.099185656459979e-11,0.7252415140154447,1.3457816486752614
16+age_bin_46-60,1.5082279402313585,0.1475002832943984,1.5282594463103336e-24,1.2191326972648826,1.7973231831978345
17+age_bin_61+,1.681629159480226,0.17585274072775775,1.1471032313333146e-21,1.3369641210711607,2.026294197889291
18+age_bin_8-14,1.2129349691254652,0.149087114004152,4.0940762282246037e-16,0.9207295951183101,1.5051403431326202
19+powertrain_Diesel,0.08799325692313117,0.06754141067097529,0.1926414980743838,-0.04438547545700966,0.220371989303272
20+powertrain_HEV,-0.035542720761100995,0.06367694872281773,0.576726607073463,-0.16034724690322755,0.08926180538102554
21+powertrain_ICE,-0.008773143583287443,0.044859822537293766,0.8449483471069279,-0.09669678010924145,0.07915049294266656
22+powertrain_PHEV,0.026391571110399824,0.07298043897295567,0.7176320765573359,-0.11664746085251662,0.16943060307331628
23+powertrain_unknown,-0.03441088128418502,0.0634766971128486,0.5877478413866447,-0.15882292148292593,0.09000115891455587
24+body_filled_Coupe,0.05724256368577487,0.09394160834889659,0.5422978005511733,-0.1268796053278297,0.24136473269937944
25+body_filled_Hatchback,0.027889839856285,0.09769346951861611,0.7752740083981092,-0.16358584192496417,0.21936552163753414
26+body_filled_Pickup,0.09968355300917792,0.10133310776387,0.32525248386535976,-0.09892568864952342,0.2982927946678793
27+body_filled_SUV,-0.010316182136954996,0.09379330177195103,0.9124185158375008,-0.19414767560107582,0.17351531132716586
28+body_filled_Sedan,0.01606520387726386,0.09172300126607907,0.8609624546837975,-0.1637085751581729,0.19583898291270063
29+body_filled_Van,0.061094131739464916,0.10603733534859351,0.5645092024775027,-0.14673522656037435,0.26892349003930416
30+body_filled_Wagon,0.24285156001647112,0.15113269861617887,0.1080814940462362,-0.05336308615758595,0.5390662061905283
31+body_filled_unknown,-0.10086653775721904,0.111172420823014,0.3642491236378729,-0.3187604786444572,0.11702740313001914
32+source_class_direct_dealer,-0.046044938397762185,0.06780199498377905,0.497068862138627,-0.17893440664593455,0.08684452985041016
33+source_class_full_portal,0.007693324408564267,0.05714346727204029,0.8929030454521423,-0.104305813396378,0.11969246221350653
34+region_Abitibi-Témiscamingue,-0.3223084160224777,0.11888808425239922,0.006707582837309168,-0.5553247793481437,-0.08929205269681165
35+region_Bas-Saint-Laurent,-0.2080446273217538,0.09372000658017679,0.026428755395229254,-0.3917324648497572,-0.024356789793750416
36+region_Capitale-Nationale,-0.18373297671719066,0.08837882059319958,0.03762420587759096,-0.3569522820759887,-0.010513671358392623
37+region_Centre-du-Québec,-0.05926462172657194,0.11448511526486725,0.6046939449709899,-0.28365132441162855,0.16512208095848466
38+region_Chaudière-Appalaches,-0.21280800593322324,0.08224495407218424,0.009667796864652264,-0.3740051538248552,-0.05161085804159127
39+region_Côte-Nord,0.17847498955363156,0.11000760252952461,0.10472034497550692,-0.03713594942983403,0.3940859285370971
40+region_Estrie,-0.2812275464752341,0.1490271518081089,0.05914831302274568,-0.5733153967377107,0.010860303787242509
41+region_Gaspésie–Îles-de-la-Madeleine,-0.1971209506142064,0.14093920098253487,0.1619257621024497,-0.4733567085498269,0.07911480732141415
42+region_Lanaudière,0.08689407227882373,0.1386320134635421,0.5307925839120153,-0.18481968121399067,0.35860782577163813
43+region_Laurentides,-0.054533351601944925,0.09297984798677922,0.5575347151056128,-0.23677050494404123,0.1277038017401514
44+region_Laval,0.2562480202064292,0.12474573641045472,0.03995996156072555,0.011750869617011023,0.5007451707958473
45+region_Mauricie,-0.27523565217620843,0.16815021301679375,0.10166308818859206,-0.6048040136818624,0.054332709329445505
46+region_Montréal,-0.055202727952900786,0.12705130027075612,0.6639314635323172,-0.3042187006725668,0.1938132447667652
47+region_Montérégie,-0.06237072942222001,0.08595938682316571,0.4680940202804095,-0.23084803172877172,0.10610657288433169
48+region_Outaouais,-0.10622984107885196,0.10207220703192327,0.2979998158060029,-0.30628769068393763,0.0938280085262337
49+region_Saguenay–Lac-Saint-Jean,-0.03575029594933475,0.13083816279196253,0.7846682853385912,-0.2921883828249699,0.22068779092630036
50+cal_week_1,0.2293052023628206,0.3617141068675337,0.5261196883886154,-0.47964141979761765,0.9382518245232588
51+cal_week_10,1.7840238788267269,0.30826919424561083,7.155774669005539e-09,1.1798273605621477,2.388220397091306
52+cal_week_11,3.1238266889403454,0.31375177941754484,2.3655817768194025e-23,2.508884501196602,3.7387688766840887
53+cal_week_2,0.20764740660486886,0.30808804342332036,0.5003194331900103,-0.39619406257225126,0.8114888757819889
54+cal_week_3,0.18451781065512995,0.30464405366588443,0.5447250867121873,-0.4125735626342909,0.7816091839445508
55+cal_week_4,0.48858484345101516,0.29637221377342365,0.09923956153846185,-0.09229402156330091,1.0694637084653311
56+cal_week_5,0.5288346137882731,0.31135460929897135,0.08941415350098555,-0.08140920685825048,1.1390784344347966
57+cal_week_6,0.6159354876622096,0.30549962570228434,0.04378346583876706,0.01716722399526527,1.214703751329154
58+cal_week_7,0.6099492649409489,0.30253690457401217,0.04378792645937902,0.01698782798165388,1.2029107019002439
59+cal_week_8,0.8883147249016058,0.29358053504942033,0.0024797978333355583,0.312907449642743,1.4637220001604687
60+cal_week_9,0.99380787258802,0.31456965265721837,0.001581693621548834,0.3772626827505974,1.6103530624254425
added results/hazards/cloglog_C_exit.csv +61 −0
@@ -0,0 +1,61 @@
1+term,coef,se,p,ci_lo,ci_hi
2+const,2.1445124786744145,1.6504759320115934,0.19383078916173024,-1.0903609054184873,5.379385862767316
3+ldens,0.032635174880585587,0.0298694350387374,0.27457196040694076,-0.025907842033898468,0.09117819179506964
4+resid_A2,0.1803884357069441,0.2249175737951905,0.42254127819565435,-0.2604419084217592,0.6212187798356473
5+pricepos_25,-0.15918471235178258,0.13879966014732806,0.25143670485482195,-0.43122704730694505,0.11285762260337986
6+nocomp,-0.0306219273476532,0.09214433701001835,0.7396433681783956,-0.21122150926661035,0.14997765457130394
7+prior_cuts,0.03266692882887669,0.03642374515110683,0.36979467058532933,-0.03872229984935813,0.1040561575071115
8+lprice,-0.30650093117934923,0.11057049714499967,0.005571379024700772,-0.5232151233362374,-0.08978673902246101
9+lkm,-0.5117116922783111,0.07033764741721567,3.462268744139904e-13,-0.6495709479733306,-0.3738524365832917
10+age_years_entry,0.03397140514015179,0.015445266121916455,0.027844493981032385,0.003699239809558906,0.06424357047074467
11+own_stock,0.008960177371658795,0.005944657062758632,0.13174257941921957,-0.0026911363717897884,0.020611491115107378
12+age_bin_1-3,1.5683985476050826,0.18557248168439142,2.871497777938201e-17,1.2046831669819564,1.9321139282282087
13+age_bin_15-21,0.5717105520415987,0.11883373435890153,1.5016780991087885e-06,0.33880071254975175,0.8046203915334457
14+age_bin_22-30,0.47598142488540923,0.12320319281242532,0.00011182633554504327,0.23450760419271155,0.7174552455781069
15+age_bin_31-45,0.404545106455343,0.14262706708875442,0.0045627574475680735,0.12500119174080626,0.6840890211698797
16+age_bin_4-7,0.6225932555654913,0.1264723028350821,8.533048574949394e-07,0.3747120969668874,0.8704744141640952
17+age_bin_46-60,0.23028996623227885,0.1538748613042772,0.13449593396150483,-0.07129922005020042,0.5318791525147581
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20+powertrain_Diesel,-0.6011818620325686,0.13350192522437687,6.694729468763081e-06,-0.8628408273391066,-0.3395228967260306
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22+powertrain_ICE,-0.3287133761006915,0.10326273922618352,0.0014562079837358184,-0.5311046259289627,-0.12632212627242032
23+powertrain_PHEV,0.16343977305338614,0.21810007594898265,0.4536283728839622,-0.2640285208320703,0.5909080669388426
24+powertrain_unknown,-0.3513681328411926,0.1683480049361962,0.03687425852000913,-0.6813241593853083,-0.02141210629707685
25+body_filled_Coupe,0.01701032280271533,0.16676334058761141,0.9187544758014313,-0.3098398186905897,0.3438604642960203
26+body_filled_Hatchback,0.3639974307594968,0.17377152413059077,0.036198580830795614,0.023411501924905986,0.7045833595940876
27+body_filled_Pickup,0.5302974623356157,0.1782500104909776,0.0029296882412190015,0.18093386152941282,0.8796610631418186
28+body_filled_SUV,0.38306304616876946,0.17229492314633885,0.026195677831181957,0.04537120208284878,0.7207548902546901
29+body_filled_Sedan,0.48512048206215813,0.1845852962059403,0.008584704627134267,0.12333994942285725,0.8469010147014591
30+body_filled_Van,0.5411982071797572,0.20631860293457466,0.008712888428533686,0.136821176087371,0.9455752382721434
31+body_filled_Wagon,0.5816563936850994,0.2850204066550933,0.041275334227715246,0.023026661782156177,1.1402861255880425
32+body_filled_unknown,0.5220331816979212,0.2772091408226549,0.05967672529533229,-0.021286750499774465,1.065353113895617
33+source_class_direct_dealer,0.1879076691793818,0.14464321342325143,0.19390564991801584,-0.09558781973833153,0.4714031580970951
34+source_class_full_portal,0.5714892925082226,0.1632543655767907,0.0004642007251218767,0.2515166156587772,0.891461969357668
35+region_Abitibi-Témiscamingue,0.8773849828978448,0.2087676745248049,2.6376635648484176e-05,0.46820785969304707,1.2865621061026427
36+region_Bas-Saint-Laurent,0.9657546278042388,0.15824136563583113,1.0406466446682021e-09,0.6556072502935757,1.275902005314902
37+region_Capitale-Nationale,1.0519172451217853,0.19884631999481095,1.2224840009144096e-07,0.6621856194736291,1.4416488707699415
38+region_Centre-du-Québec,0.5645445494940753,0.1783724952922541,0.0015509609374199365,0.21494088288871688,0.9141482160994336
39+region_Chaudière-Appalaches,0.6623826248356204,0.14714065153502487,6.741305093092569e-06,0.3739922471652134,0.9507730025060274
40+region_Côte-Nord,1.056955785624188,0.1447109442547878,2.795223411365472e-13,0.7733275467160204,1.3405840245323555
41+region_Estrie,0.8230608229103544,0.16271486174803348,4.2300734709000253e-07,0.5041455541347947,1.141976091685914
42+region_Gaspésie–Îles-de-la-Madeleine,0.7802283061689462,0.16158831809999089,1.3756912892872597e-06,0.46352102237056236,1.09693558996733
43+region_Lanaudière,0.3945626723792274,0.2428188790110107,0.10417836027966941,-0.08135358524874248,0.8704789300071972
44+region_Laurentides,0.6365816865087697,0.1610489915762843,7.726759961147561e-05,0.3209314632727579,0.9522319097447814
45+region_Laval,0.8921445035580304,0.21678002564478807,3.864468123018626e-05,0.4672634607265765,1.3170255463894844
46+region_Mauricie,0.9745469916236909,0.20687002763980417,2.466078105976224e-06,0.5690891879688691,1.3800047952785126
47+region_Montréal,0.9687083961481775,0.2131687201534086,5.51110164607968e-06,0.5509053820169991,1.3865114102793559
48+region_Montérégie,0.9958087538499526,0.1799701397036052,3.144607405355825e-08,0.6430737617382444,1.3485437459616607
49+region_Outaouais,0.9571219407416862,0.18377473108789572,1.9075745900061385e-07,0.5969300865408772,1.3173137949424953
50+region_Saguenay–Lac-Saint-Jean,1.1673527584849401,0.18768931433034966,4.984188185814645e-10,0.7994884621144374,1.535217054855443
51+cal_week_1,0.5100789447683056,0.19256890036317556,0.008077508952921862,0.13265083551399937,0.8875070540226119
52+cal_week_10,0.3608360717775011,0.2826035846041178,0.20166310029364598,-0.1930567759484878,0.91472891950349
53+cal_week_11,-28.284352257526635,0.23359244758710368,0.0,-28.74218504185792,-27.82651947319535
54+cal_week_2,0.575702093326724,0.19919398796357318,0.003850482734347155,0.1852890509812155,0.9661151356722324
55+cal_week_3,0.36897414733306627,0.21516055160579606,0.08636755355624334,-0.052732784708065694,0.7906810793741983
56+cal_week_4,0.6327075816294747,0.2146173774518114,0.003197620964067644,0.21206525136748572,1.0533499118914638
57+cal_week_5,0.6398282871796855,0.21578588551777705,0.003025774525904901,0.2168957231927593,1.0627608511666118
58+cal_week_6,0.6339334369030561,0.21795406047720653,0.003630923003977696,0.20675132808346652,1.0611155457226458
59+cal_week_7,0.46535191552189215,0.22408368910064233,0.03783036182702588,0.026155955361762517,0.9045478756820218
60+cal_week_8,0.4784994640094191,0.2203371402261137,0.029880594877889636,0.04664660470968468,0.9103523233091535
61+cal_week_9,0.5625097445520414,0.20934636724750078,0.007210101954664984,0.15219840445264432,0.9728210846514385
added results/hazards/cox_exit.csv +23 −0
@@ -0,0 +1,23 @@
1+term,coef,exp(coef),se(coef),coef lower 95%,coef upper 95%,exp(coef) lower 95%,exp(coef) upper 95%,cmp to,z,p,-log2(p)
2+age_years_entry,0.03809898972455363,1.0388340616865392,0.01398298442931999,0.010692843846702091,0.06550513560240517,1.0107502166117925,1.0676982205729675,0.0,2.7246679646346736,0.006436618149990749,7.279481400629618
3+lkm,-0.49029209218766934,0.6124474769321526,0.06966581712234614,-0.6268345847010216,-0.3537495996743171,0.534280346305561,0.7020507390815388,0.0,-7.037771355306508,1.9533887280930966e-12,38.89715806178067
4+lprice,-0.2555280674681466,0.774507397793729,0.09454065040434634,-0.4408243373356575,-0.07023179760063569,0.6435057365772965,0.9321777183025306,0.0,-2.702838052999043,0.006875023403565098,7.18441965998836
5+resid_A2,0.12911437696627437,1.1378202571526603,0.20868389841586996,-0.27989854808224596,0.5381273020147948,0.7558604210552841,1.7127963067300966,0.0,0.6187079019818402,0.536108803417326,0.8994022691652018
6+ldens_entry,0.05010847578595092,1.051385140019826,0.029435946081315393,-0.007584918384290186,0.10780186995619204,0.9924437745188845,1.1138270409226845,0.0,1.7022886116019051,0.08870127861586458,3.4949012888620072
7+pricepos_entry,-0.15547473982430213,0.8560086995859824,0.1253146977358194,-0.4010870341200312,0.09013755447142696,0.6695917811703486,1.094324802622492,0.0,-1.2406744191496535,0.21472604795666056,2.2194308828314413
8+nocomp_entry,-0.08707157500603291,0.916611486812506,0.08425524490277064,-0.25220882052406535,0.07806567051199953,0.7770824503605075,1.081193658879897,0.0,-1.033426169569767,0.3014045049452238,1.7302271145356898
9+pt_Diesel,-0.5764854609219943,0.5618696132370707,0.12004046511774913,-0.8117604492402193,-0.3412104726037693,0.44407560512186145,0.71090926553496,0.0,-4.802426084874902,1.5675469095159003e-06,19.28305995238086
10+pt_HEV,-0.22271029966717842,0.8003466764112653,0.13187030251223952,-0.4811713432215697,0.03575074388721283,0.6180590084081442,1.0363974858846472,0.0,-1.6888586393172762,0.09124652313858862,3.454086602360798
11+pt_ICE,-0.28620801410302005,0.7511063589800872,0.09502857016474715,-0.47246058912826194,-0.09995543907777812,0.6234662849830968,0.9048777393241384,0.0,-3.0118101704238303,0.0025969494182890305,8.58896637025849
12+pt_PHEV,0.1639436573342396,1.178147933227456,0.20633963165455638,-0.24047458929195176,0.568361903960431,0.7862546244809893,1.76537283133231,0.0,0.7945330522286963,0.4268851751526769,1.2280800332741388
13+pt_unknown,-0.3355004757337221,0.7149801665714867,0.16159338832350137,-0.6522176969875801,-0.01878325447986412,0.5208893202235757,0.9813920515229135,0.0,-2.0762017506685857,0.037875295440769535,4.722599047611031
14+body_Coupe,-0.045372378366886255,0.9556415553258462,0.14432413691982085,-0.32824248882956264,0.23749773209579014,0.7201883608970536,1.2680721209213568,0.0,-0.31437831076095635,0.7532337272041163,0.40883049458905113
15+body_Hatchback,0.35661203273475084,1.4284815582498382,0.1465145024556168,0.06944888470893656,0.6437751807605652,1.0719172684412366,1.9036539687687206,0.0,2.4339708817751897,0.014934196419731172,6.065236578671629
16+body_Pickup,0.44786767706201147,1.56497160031207,0.1517605227066031,0.15042251828209635,0.7453128358419265,1.1623252426575201,2.1071005084460346,0.0,2.951147433301011,0.0031659575738059724,8.303142362247588
17+body_SUV,0.3347153114711986,1.397542464275298,0.14678163137017858,0.047028600393614006,0.6224020225487832,1.0481519862103477,1.8633985959558266,0.0,2.2803623883090474,0.02258620440463892,5.468414342535905
18+body_Sedan,0.43405506294304125,1.543503855641521,0.15896364333119756,0.1224920471626233,0.7456180787234592,1.1303101308656103,2.107743784049566,0.0,2.7305304146728457,0.006323249818341333,7.305118066168284
19+body_Van,0.44826693252539973,1.565596548522234,0.1785698288196523,0.0982764993133986,0.7982573657374008,1.1032677957269648,2.2216660018881993,0.0,2.510317311095872,0.012062271822838807,6.3733545380197025
20+body_Wagon,0.5355314534008135,1.708355912600153,0.25357364328259485,0.0385362451383206,1.0325266616633064,1.0392883968270283,2.808152128924068,0.0,2.111936581689569,0.03469188917236167,4.849257783990825
21+body_unknown,0.4788449602215688,1.614208849574603,0.25135584622168805,-0.01380344567652797,0.9714933661196656,0.98629138504805,2.6418868191959524,0.0,1.9050480321800132,0.056773827724359044,4.138630177547346
22+src_direct_dealer,0.09935632073783637,1.1044597713742816,0.1088557758788141,-0.11399707949380322,0.31270972096947597,0.8922605617311028,1.3671246258127754,0.0,0.9127335682072287,0.36138268463963374,1.468400712600149
23+src_full_portal,0.4177887995275358,1.518599911512,0.12143609548074936,0.1797784259620998,0.6557991730929718,1.1969521202229745,1.9266816544129213,0.0,3.4404004663816425,0.0005808540381246883,10.749536702889383
added results/hazards/cox_first_markdown.csv +23 −0
@@ -0,0 +1,23 @@
1+term,coef,exp(coef),se(coef),coef lower 95%,coef upper 95%,exp(coef) lower 95%,exp(coef) upper 95%,cmp to,z,p,-log2(p)
2+age_years_entry,-0.010175733152778012,0.9898758644570098,0.01178772184855405,-0.033279243435719856,0.012927777130163833,0.9672684185167769,1.013011702105435,0.0,-0.8632484956392339,0.3880008553008294,1.3658682622252043
3+lkm,0.10637957469587853,1.1122439760612641,0.03857562664725059,0.030772735786203778,0.1819864136055533,1.0312511107800313,1.1995978955589643,0.0,2.757688829494635,0.005821158142019405,7.42447807319066
4+lprice,0.09147871146773405,1.0957934486100442,0.07851806831349967,-0.062413874562380914,0.24537129749784903,0.9394939736588066,1.2780957788801863,0.0,1.1650657413334002,0.24399238287505615,2.0350919854595064
5+resid_A2,0.7162706536096468,2.046785786375974,0.20644247927244724,0.3116508293564936,1.1208904778628002,1.3656777551873778,3.067584603613842,0.0,3.469589476613322,0.0005212544125793452,10.90572468808204
6+ldens_entry,0.11387962838879358,1.1206172262642626,0.024880761941899622,0.06511423107475546,0.16264502570283168,1.0672809340692875,1.1766189460653147,0.0,4.57701531226093,4.7165694867962384e-06,17.693830646983102
7+pricepos_entry,0.35586478170457925,1.42741452265523,0.11608250478389512,0.12834725309294628,0.5833823103162122,1.1369477427759342,1.7920895946478022,0.0,3.065619426175155,0.0021721960857121365,8.846629942516278
8+nocomp_entry,0.13202654294773827,1.1411386080476922,0.07872919632258532,-0.022279846376312223,0.2863329322717888,0.9779665163676454,1.3315356926673054,0.0,1.6769705409765927,0.09354824260499381,3.418145638487981
9+pt_Diesel,0.08075691714778112,1.084107336631108,0.10591619753200197,-0.12683501539437292,0.2883488496899351,0.8808789920822022,1.3342226661113499,0.0,0.7624605020717523,0.4457852082930547,1.1655793479090217
10+pt_HEV,-0.0661293170828502,0.9360098144061604,0.08369289641925128,-0.23016437982642396,0.09790574566072353,0.7944030079415164,1.1028588309790917,0.0,-0.7901425319489714,0.42944453327818066,1.2194562888601594
11+pt_ICE,0.043772276338079195,1.0447444147732714,0.06943662520320647,-0.09232100826821169,0.1798655609443701,0.911812403240114,1.1970564211688128,0.0,0.6303917595358287,0.5284383009392,0.9201930587104329
12+pt_PHEV,0.06984500803682998,1.072341963987191,0.09859184598347823,-0.12339145926010732,0.2630814753337673,0.8839175771053327,1.300932708560535,0.0,0.7084258068211283,0.47868087108299495,1.0628639402713151
13+pt_unknown,0.13357778766499587,1.1429101669905597,0.10633885257957243,-0.07484253354828033,0.3419981088782721,0.9278895863060718,1.4077576352705334,0.0,1.2561522381016927,0.2090607795876583,2.258005661375135
14+body_Coupe,0.07547927196299113,1.0784008743531475,0.10761285758353568,-0.13543805317417684,0.2863965971001591,0.8733332614762794,1.3316204673572023,0.0,0.7013964098518571,0.48305566187082527,1.04973865638901
15+body_Hatchback,0.13194122342857545,1.141041250803651,0.12942007224069862,-0.12171745703976589,0.3855999038969168,0.885398496277167,1.470496213298274,0.0,1.0194803722809544,0.3079749667258691,1.6991150064613345
16+body_Pickup,-0.06679345420863633,0.9353883819193476,0.10240719352059119,-0.26750786526681863,0.13392095684954597,0.7652843114338529,1.1433024458457377,0.0,-0.652234007322992,0.5142502247969738,0.9594575754839525
17+body_SUV,0.016599696240591065,1.0167382367132352,0.08963577951288271,-0.15908320333083226,0.1922825958120144,0.8529253897833546,1.2120129783653357,0.0,0.18519051578287787,0.8530796077394054,0.2292477175502279
18+body_Sedan,0.07378509768752607,1.0765754220869364,0.08949700516524167,-0.1016258091605428,0.24919600453559493,0.9033675202833067,1.2829934809679504,0.0,0.8244420866518816,0.4096884094635054,1.2874010169240573
19+body_Van,0.032177377184543214,1.032700666597038,0.12146962846113395,-0.2058987198147408,0.2702534741838272,0.8139155056218156,1.3102965349888547,0.0,0.26490059772298435,0.7910860378628145,0.3380934853374314
20+body_Wagon,-0.40015365965594907,0.6702170528011449,0.16205331521627,-0.717772321055155,-0.08253499825674315,0.48783779237631947,0.9207792116256247,0.0,-2.469271666068168,0.013538839110261207,6.206752149655798
21+body_unknown,0.28857681757345305,1.334526860700666,0.1524248519844293,-0.010170402664876954,0.587324037811783,0.9898811409923921,1.799167463829137,0.0,1.893239939658476,0.058325968238482184,4.09971783795081
22+src_direct_dealer,-0.04553181418780024,0.9554892039754274,0.5201188976190433,-1.0649461211998008,0.9738824928242003,0.34474642879079886,2.648206167401965,0.0,-0.0875411649071625,0.9302413661414447,0.10432299957418109
23+src_full_portal,0.18344581023289688,1.2013498627784867,0.5070342416240269,-0.8103230422787754,1.1772146627445692,0.4447143814688716,3.2453222853529655,0.0,0.3618016204296604,0.7175002811419773,0.4789486978000028
added results/hazards/cox_ph_test_exit.csv +23 −0
@@ -0,0 +1,23 @@
1+index,test_statistic,p,-log2(p)
2+age_years_entry,1.2866582345381203,0.25666470998780566,1.9620431481190943
3+body_Coupe,4.212394070525067,0.040129661702279655,4.639187195692717
4+body_Hatchback,0.17229835034002747,0.6780772169117142,0.5604785234248181
5+body_Pickup,3.060132616414404,0.0802351205623527,3.639622317555238
6+body_SUV,2.741143370478921,0.09779477151237734,3.3540988546054264
7+body_Sedan,8.381607237094274,0.0037903708916112526,8.04344526019496
8+body_Van,22.716854667377707,1.8771413497952265e-06,19.02303127932023
9+body_Wagon,4.6781682753659375,0.03054830509493763,5.032763852249905
10+body_unknown,1.7524507404357343,0.18556893656583054,2.429972865096141
11+ldens_entry,1.0925548862026524,0.29590582122858805,1.7567900165542178
12+lkm,1140.3211664920264,5.694444240661252e-250,827.9724686754062
13+lprice,3.7044670176900985,0.05426700041577165,4.203781023215083
14+nocomp_entry,21.236452970983454,4.0596951989338115e-06,17.910197155089545
15+pricepos_entry,14.701944599257889,0.00012591649553860446,12.955245085588174
16+pt_Diesel,3.730219375449052,0.053436375067152854,4.2260340459227015
17+pt_HEV,10.29168822976813,0.00133630772455706,9.54753201519454
18+pt_ICE,4.253932154456059,0.03915956029719916,4.674491624031576
19+pt_PHEV,30.74934901740916,2.9360232226944873e-08,25.021561379768702
20+pt_unknown,0.5950565730834716,0.440470407781862,1.182882997377169
21+resid_A2,116.65326273302635,3.419094610826946e-27,87.91844421737339
22+src_direct_dealer,0.24082727281887106,0.6236092522521286,0.6812857619779611
23+src_full_portal,157.1332171295938,4.786744965040178e-36,117.33036647378404
added results/hazards/cox_ph_test_first_markdown.csv +23 −0
@@ -0,0 +1,23 @@
1+index,test_statistic,p,-log2(p)
2+age_years_entry,0.8186077084394746,0.36558755385373887,1.4517111390951396
3+body_Coupe,0.10767419193772326,0.7428077324657861,0.42893926139650074
4+body_Hatchback,0.21452637944487224,0.6432425717634052,0.6365652032345077
5+body_Pickup,0.010541947008119484,0.9182217221888216,0.12308553288347521
6+body_SUV,0.45501228395056703,0.4999642613800468,1.0001031235450866
7+body_Sedan,2.002994237252329,0.1569888230508847,2.67126624598104
8+body_Van,0.13532316740252293,0.7129754099942966,0.4880757748676314
9+body_Wagon,0.0039043091391384966,0.9501770279740785,0.0737317671414453
10+body_unknown,0.17203807386093378,0.6783068226231201,0.559990090923824
11+ldens_entry,1.8424022146886394,0.17466965380604002,2.517299111168445
12+lkm,7.219959611686393,0.007209732982763822,7.115838455372952
13+lprice,8.909123732402957,0.0028374938867816108,8.461167000459188
14+nocomp_entry,0.12211374066954826,0.726753179167042,0.46046261747185097
15+pricepos_entry,0.1982655816543443,0.6561244758120413,0.6079585550421583
16+pt_Diesel,2.2666333622011807,0.13218677949518673,2.9193502002693674
17+pt_HEV,0.31989146526423723,0.5716728776491948,0.806738249970271
18+pt_ICE,0.2665431210534278,0.6056601594808639,0.7234195813171831
19+pt_PHEV,2.6255278981573142,0.10515752632022816,3.2493759847483425
20+pt_unknown,1.868413965311333,0.171656840609659,2.5424007443385457
21+resid_A2,37.94793725058582,7.265784957034339e-10,30.358162281442706
22+src_direct_dealer,78.61061291492392,7.564194844035675e-19,60.197447277104786
23+src_full_portal,20.530677200409432,5.86831779499843e-06,17.378621567969713
added results/hazards/cutsize_ols.csv +22 −0
@@ -0,0 +1,22 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
2+age_at_cut,-0.01334867991159057,0.0026183402226803787,-5.098145686325519,4.371614257259182e-07,-0.01848903179385661,-0.008208328029324533
3+I(age_at_cut ** 2),0.00022824658836423858,4.290973837576299e-05,5.3192258215482555,1.3861080572219464e-07,0.00014400575804097401,0.00031248741868750314
4+ldens,-0.02672535010471304,0.011462483126498677,-2.3315497881022007,0.01999486791498173,-0.04922861236389452,-0.004222087845531564
5+resid_A2,2.224053963871681,0.22143338692052106,10.043896247091046,0.0,1.7893337096458077,2.658774218097555
6+lprice,-1.52632193795838,0.1166722226316213,-13.08213646342849,0.0,-1.755374019890907,-1.2972698560258529
7+lkm,-0.19384904071051606,0.018221295793775943,-10.638597984712552,0.0,-0.22962127203459481,-0.1580768093864373
8+age_years_entry,-0.06883788492710304,0.010427140556918388,-6.601798887368909,7.802136714474273e-11,-0.08930855223157536,-0.04836721762263072
9+C(powertrain)[T.Diesel],0.22408816919907276,0.08796198775335562,2.547556904096044,0.011051387814609681,0.05140030938650994,0.39677602901163556
10+C(powertrain)[T.HEV],0.18004548621357275,0.06701842060152745,2.6865074497065846,0.007384103643397255,0.048474248258522756,0.31161672416862274
11+C(powertrain)[T.ICE],0.03474634298761326,0.05941827566286054,0.5847753506810617,0.5588789754727839,-0.08190421391821154,0.15139689989343807
12+C(powertrain)[T.PHEV],0.09076863705025287,0.08697740080621762,1.0435887507432187,0.297020216496255,-0.0799862717565028,0.26152354585700854
13+C(powertrain)[T.unknown],0.06267217093507967,0.07013160204113601,0.8936366646568121,0.37181002925974926,-0.07501089616054914,0.20035523803070848
14+C(body_filled)[T.Coupe],0.16108909944191635,0.1131433208174124,1.423761458282434,0.1549416403803625,-0.06103500692583419,0.3832132058096669
15+C(body_filled)[T.Hatchback],-0.15242248799674515,0.13741906066133106,-1.109180104006023,0.26771676225589314,-0.4222049711939,0.11735999520040971
16+C(body_filled)[T.Pickup],0.06115285362601009,0.21197266181517227,0.28849405910339415,0.7730502495995852,-0.3549940098381779,0.4772997170901981
17+C(body_filled)[T.SUV],-0.0684838081326022,0.12882100251057457,-0.5316198973609179,0.5951505695174721,-0.3213864970172251,0.18441888075202073
18+C(body_filled)[T.Sedan],-0.0901286426620642,0.12154200499373657,-0.7415431616971334,0.4586020097543564,-0.3287411303487752,0.14848384502464684
19+C(body_filled)[T.Van],-0.14806440372400367,0.15060527976674784,-0.9831289045996303,0.3258688284636717,-0.44373420500586247,0.14760539755785515
20+C(body_filled)[T.Wagon],-0.04666294888303379,0.19088636611780255,-0.2444540688371451,0.8069476752223106,-0.42141298437234787,0.3280870866062803
21+C(source_class)[T.direct_dealer],-0.19122872481011444,0.04050379565287313,-4.721254433756991,2.8092189687800584e-06,-0.2707461841607669,-0.11171126545946194
22+C(source_class)[T.full_portal],-0.07278176956029168,0.032841568334219924,-2.216147804502237,0.026988167345921577,-0.13725666710838763,-0.008306872012195751
added results/hazards/empirical_hazard_exit.csv +72 −0
@@ -0,0 +1,72 @@
1+listing_age,events,at_risk,hazard,lo,hi
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72+70,2,1066,0.001876172607879925,0.0005146650718061527,0.006814887130571486
added results/hazards/empirical_hazard_exit_banner.csv +72 −0
@@ -0,0 +1,72 @@
1+listing_age,events,at_risk,hazard,lo,hi
2+0,24,6627,0.0036215482118605704,0.002434946587436338,0.005383285705198542
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added results/hazards/empirical_hazard_exit_direct_dealer.csv +72 −0
@@ -0,0 +1,72 @@
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added results/hazards/empirical_hazard_exit_full_portal.csv +72 −0
@@ -0,0 +1,72 @@
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added results/hazards/empirical_hazard_first_markdown.csv +72 −0
@@ -0,0 +1,72 @@
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\ No newline at end of file
added results/hedonic/coefs_A1_linear_mm.csv +39 −0
@@ -0,0 +1,39 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
2+age,-0.08509572749682125,0.0009856193877049052,-86.33731089135084,0.0,-0.08702974601922545,-0.08316170897441705
3+km,-0.03103901627724493,0.0004746652442712518,-65.39138193043559,0.0,-0.03197042183380544,-0.030107610720684425
4+C(transmission)[T.Manual],-0.03366444027836668,0.010603069088923616,-3.1749713216085595,0.001542337381773784,-0.05447017144408653,-0.012858709112646836
5+C(transmission)[T.unknown],0.04363431485115234,0.009359386586034414,4.662091308020247,3.5348321409944816e-06,0.02526898312012571,0.06199964658217896
6+C(drivetrain)[T.FWD],-0.02866781927705689,0.005544431455465087,-5.1705606800854795,2.79583066475908e-07,-0.039547306089952675,-0.017788332464161102
7+C(drivetrain)[T.RWD],-0.04324459113779039,0.02052059679624786,-2.107374925162876,0.035322856788239765,-0.08351085901949924,-0.002978323256081536
8+C(drivetrain)[T.unknown],-0.0008109880280454345,0.0049575484657059,-0.1635865052365068,0.8700883001044311,-0.010538871507202768,0.008916895451111899
9+C(powertrain)[T.BEV],0.05604383331265492,0.010996013124132762,5.09674121701951,4.0995193861803614e-07,0.03446705294493797,0.07762061368037186
10+C(powertrain)[T.Diesel],0.07855073242266543,0.009530332164547479,8.242182021196655,4.440892098500626e-16,0.05984996500592525,0.0972514998394056
11+C(powertrain)[T.HEV],0.07226065160315269,0.008164579540792633,8.850504945431334,0.0,0.05623981409070822,0.08828148911559716
12+C(powertrain)[T.PHEV],0.10613287709980947,0.016964332962872378,6.256236383245286,5.7426952082551e-10,0.07284484058215691,0.139420913617462
13+C(powertrain)[T.unknown],0.027960587066036675,0.005797342480216495,4.823000738951086,1.6240339033579687e-06,0.01658482896171628,0.03933634517035707
14+C(body)[T.Coupe],-0.03671702262490823,0.028908135072312803,-1.270127683195811,0.2043218808891143,-0.09344162582919753,0.020007580579381068
15+C(body)[T.Hatchback],-0.057844687320471815,0.024148257747454237,-2.3953979589508867,0.016777509808114877,-0.10522928461022203,-0.010460090030721597
16+C(body)[T.Pickup],-0.05338522400979397,0.03208775276030346,-1.6637258585412105,0.09646704782831672,-0.11634898969348356,0.009578541673895623
17+C(body)[T.SUV],-0.05160874871496092,0.023763564242492336,-2.171759597521898,0.030098150121669054,-0.09823848629585599,-0.00497901113406584
18+C(body)[T.Sedan],-0.06896269859799099,0.0230725392342069,-2.988951406603233,0.002864845974208663,-0.11423648155569163,-0.02368891564029036
19+C(body)[T.Van],-0.0740716592029875,0.028343288547973595,-2.6133756172158527,0.009094084172575956,-0.1296878998344469,-0.018455418571528118
20+C(body)[T.Wagon],-0.07947027968304358,0.02764349610522209,-2.874827387264919,0.004124470933306146,-0.1337133619097445,-0.025227197456342672
21+C(source_class)[T.banner],-0.011289545083890344,0.01054285718851615,-1.0708240548100687,0.28449580249735584,-0.03197712624961033,0.009398036081829642
22+C(source_class)[T.full_portal],-0.04087109979839923,0.012068395020043107,-3.3866226395904997,0.0007339355826543326,-0.06455214726521517,-0.017190052331583286
23+C(source_class)[T.small_portal],0.03636527526778945,0.02754316307659424,1.3203013454432222,0.18702353152960516,-0.017680929809176175,0.09041148034475507
24+C(region)[T.(missing)],0.02801132265050806,0.024207729415949897,1.157123089456381,0.24748650043073295,-0.01948997212902746,0.07551261743004357
25+C(region)[T.Abitibi-Témiscamingue],0.032613294025524646,0.01788484853427202,1.8235152488448025,0.06851093379811601,-0.002481011917685731,0.06770759996873502
26+C(region)[T.Bas-Saint-Laurent],0.024602927470991375,0.02264738581941843,1.0863473456568313,0.27757586825817104,-0.019836603858422015,0.06904245880040477
27+C(region)[T.Capitale-Nationale],0.007973876879048363,0.015899953710897953,0.5015031505143946,0.6161226922936045,-0.023225595621032748,0.039173349379129474
28+C(region)[T.Centre-du-Québec],0.015190869414926584,0.016489489343914428,0.9212455945782754,0.35713484380986005,-0.017165411532811113,0.04754715036266428
29+C(region)[T.Chaudière-Appalaches],0.003016990871095375,0.017623051108888114,0.17119571704435266,0.8641030175477251,-0.03156360655935003,0.03759758830154078
30+C(region)[T.Côte-Nord],0.015229150626749574,0.01685325460118266,0.9036326209467508,0.36639849271111213,-0.01784092385441497,0.048299225107914116
31+C(region)[T.Estrie],0.014457541204021295,0.016499691540329337,0.876230999148286,0.38110583771839757,-0.017918758867814175,0.04683384127585677
32+C(region)[T.Gaspésie–Îles-de-la-Madeleine],0.03137603197130474,0.020045681640440807,1.5652264928724455,0.11783256521831209,-0.00795833996825944,0.07071040391086891
33+C(region)[T.Lanaudière],-0.011413638544111579,0.018630130571682425,-0.6126440445597399,0.5402450209765555,-0.047970364243616774,0.025143087155393616
34+C(region)[T.Laurentides],-0.0076126800152041315,0.014800833227429179,-0.5143413143184514,0.60712213406655,-0.03665541797870883,0.021430057948300572
35+C(region)[T.Laval],0.010645861150194611,0.016150854407264006,0.6591515768606353,0.5099436568251408,-0.021045937901199574,0.0423376602015888
36+C(region)[T.Mauricie],0.02797458336235084,0.015593227953342325,1.7940213178474467,0.07309876364331958,-0.0026230206032725745,0.05857218732797426
37+C(region)[T.Montérégie],0.007953142618060877,0.015254400783235752,0.5213670947207054,0.6022215390779975,-0.021979602243678814,0.03788588747980057
38+C(region)[T.Outaouais],0.021560250851550892,0.014542550707623048,1.4825632232624255,0.138491989163678,-0.006975675675866429,0.05009617737896821
39+C(region)[T.Saguenay–Lac-Saint-Jean],0.034153647858040946,0.02132990110613386,1.6012098550339433,0.1096326794761524,-0.007700666631827453,0.07600796234790935
added results/hedonic/coefs_A2_spline_mm.csv +51 −0
@@ -0,0 +1,51 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
2+age,-0.08505418991227316,0.0009166118417031229,-92.79193879301961,0.0,-0.08685279929620264,-0.08325558052834368
3+km,-0.03155058993267895,0.00047037102042375966,-67.0759646379891,0.0,-0.03247356920566184,-0.03062761065969606
4+na0,-0.015334778230006951,0.020546430376717544,-0.7463475625130366,0.45562535250788794,-0.05565173771108128,0.024982181251067374
5+na1,-0.046507990362948996,0.01460509591346146,-3.184367335792897,0.001493603329466353,-0.07516664538788578,-0.017849335338012216
6+na2,-0.020881485790578486,0.014180485807646642,-1.4725508049462182,0.1411733067763623,-0.048706955288028736,0.006943983706871763
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8+na4,-0.043354072175338584,0.028575050159309234,-1.5172002125502695,0.1295185838143984,-0.09942508394330038,0.012716939592623204
9+na5,-0.5034110784316851,0.05468483279518835,-9.205680125549906,0.0,-0.610715663588786,-0.39610649327458414
10+nk0,-0.03738137514927169,0.034578792232573395,-1.0810491846519221,0.27992464943416717,-0.10523314987530416,0.030470399576760787
11+nk1,-0.07446688933980285,0.02268667727956059,-3.282406163854294,0.0010633343997734634,-0.11898351981381017,-0.029950258865795533
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13+nk3,-0.047689998472732906,0.02455302548289013,-1.9423267615620676,0.05236594771373415,-0.09586884586284641,0.0004888489173805952
14+nk4,-0.08911109013249176,0.03505154180464262,-2.542287315894586,0.011156363485037124,-0.15789051141280883,-0.02033166885217469
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17+C(transmission)[T.unknown],0.039651055140167826,0.007736174460255679,5.125408603939028,3.535355901362891e-07,0.024470849793837558,0.0548312604864981
18+C(drivetrain)[T.FWD],-0.023856359764856706,0.00544795114330637,-4.378959931417125,1.3123957925520457e-05,-0.03454652936961924,-0.013166190160094174
19+C(drivetrain)[T.RWD],-0.0394663097096315,0.020206196089528495,-1.9531785960487742,0.05106503002779417,-0.07911564899005935,0.00018302957079635584
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21+C(powertrain)[T.BEV],0.06044031823245008,0.011163343370004743,5.414177117838166,7.63739000930741e-08,0.03853519631736521,0.08234544014753495
22+C(powertrain)[T.Diesel],0.09109444692598412,0.009082228026212618,10.029966948976883,0.0,0.07327296588927715,0.10891592796269109
23+C(powertrain)[T.HEV],0.07511627532581869,0.008019148838053535,9.3671132488983,0.0,0.05938080727376868,0.0908517433778687
24+C(powertrain)[T.PHEV],0.11784887360308498,0.019318278276550718,6.100381820575312,1.488080103584366e-09,0.07994183923071134,0.15575590797545863
25+C(powertrain)[T.unknown],0.03617043194294278,0.005564405425743766,6.500322887257638,1.238966707006739e-10,0.025251751472772387,0.04708911241311318
26+C(body)[T.Coupe],-0.02957329168989379,0.028114116025575585,-1.051901886688907,0.29308774176021357,-0.08473984159015122,0.025593258210363637
27+C(body)[T.Hatchback],-0.061189981742188794,0.02625015863124378,-2.3310328368590705,0.01994108661973426,-0.11269900605946805,-0.009680957424909542
28+C(body)[T.Pickup],-0.05334275952008961,0.032451677185492064,-1.6437597112526796,0.10052664274184075,-0.11702063106233887,0.01033511202215965
29+C(body)[T.SUV],-0.06499591854494664,0.027771889024273938,-2.3403492102441126,0.019453104009069078,-0.11949093806781827,-0.01050089902207501
30+C(body)[T.Sedan],-0.06219347077686401,0.025458874398190667,-2.442899470107132,0.014734520688868935,-0.11214980814185564,-0.01223713341187238
31+C(body)[T.Van],-0.07450770701312982,0.028707163748046566,-2.595439510049119,0.009579435172951012,-0.13083795691182104,-0.01817745711443862
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33+C(source_class)[T.banner],-0.02088044412095713,0.010141620697317458,-2.058886320455682,0.03975253818271174,-0.04078070432330837,-0.0009801839186058918
34+C(source_class)[T.full_portal],-0.0319732950406011,0.010157445856949783,-3.1477691824194944,0.001691815163917365,-0.05190460795176744,-0.012041982129434754
35+C(source_class)[T.small_portal],0.04436420952922718,0.02348871682112915,1.8887455567312894,0.05920266298466692,-0.0017262123580214783,0.09045463141647583
36+C(region)[T.(missing)],0.027222670720390075,0.023367867632595244,1.1649616964800786,0.24430035608680267,-0.018630616455470306,0.07307595789625046
37+C(region)[T.Abitibi-Témiscamingue],0.0279553461163767,0.015299897220800126,1.827159079106202,0.06796094824272281,-0.0020666735242995116,0.05797736575705291
38+C(region)[T.Bas-Saint-Laurent],0.027250704078541563,0.01950183231777824,1.397340702888687,0.1626077414922129,-0.011016506767656128,0.06551791492473925
39+C(region)[T.Capitale-Nationale],0.009195232892148608,0.013324388177895203,0.6901054494496976,0.49028110669558855,-0.016950370428175134,0.035340836212472346
40+C(region)[T.Centre-du-Québec],0.010096978247849017,0.013799164504991726,0.7317093903907388,0.4645100157183337,-0.01698024859983032,0.03717420509552835
41+C(region)[T.Chaudière-Appalaches],-0.0011129357994559452,0.015192594552713809,-0.0732551504349298,0.9416170992649113,-0.030924402208153262,0.028698530609241373
42+C(region)[T.Côte-Nord],0.0057843733578359985,0.013973162862889516,0.4139630672450236,0.6789861016124643,-0.02163427945126425,0.033203026166936246
43+C(region)[T.Estrie],0.007387028990914035,0.013785364464234624,0.5358602603565019,0.592169162357949,-0.019663118910503583,0.03443717689233165
44+C(region)[T.Gaspésie–Îles-de-la-Madeleine],0.020714319325495233,0.017155186770717465,1.2074668496674676,0.22752562228116568,-0.012948217535848083,0.054376856186838546
45+C(region)[T.Lanaudière],-0.007850763762476313,0.017067977251575606,-0.459970367124293,0.645633174381357,-0.0413421749058657,0.025640647380913067
46+C(region)[T.Laurentides],-0.0130813194026723,0.01207682960862716,-1.0831749578819574,0.2789806317555228,-0.03677891752865131,0.010616278723306716
47+C(region)[T.Laval],0.00691156735523364,0.014452285663036323,0.47823351381096135,0.6325840406658669,-0.021447237790102436,0.03527037250056971
48+C(region)[T.Mauricie],0.01966348947629454,0.014095767595050336,1.3949924573954586,0.16331452816825753,-0.007995742836305112,0.0473227217888942
49+C(region)[T.Montérégie],0.004209298592528483,0.0128856330286059,0.3266660305461057,0.7439859099888047,-0.02107536327705769,0.02949396046211466
50+C(region)[T.Outaouais],0.013684107536571585,0.012101917486940473,1.1307387900585588,0.2584244913290772,-0.010062718944643776,0.037430934017786945
51+C(region)[T.Saguenay–Lac-Saint-Jean],0.025463602673954273,0.018198012934249937,1.3992518175448698,0.16203423223519087,-0.010245205944092107,0.061172411292000656
added results/hedonic/coefs_A2t_tensor_mm.csv +100 −0
@@ -0,0 +1,100 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
2+age,0.009691824674736041,0.01517251271002825,0.6387751890515961,0.5231091650805815,-0.02008023640549233,0.03946388575496441
3+km,0.07632641590636648,0.44157019125799557,0.17285228354957355,0.8628010113365556,-0.7901388134588418,0.9427916452715748
4+na0,0.1362208468226591,0.07931725759454365,1.7174175072844413,0.08619919078705363,-0.01941838597628079,0.291860079621599
5+na1,-0.092670439649736,0.16975549237307783,-0.5459053981362252,0.5852474821141489,-0.4257708946781967,0.24043001537872472
6+na2,0.40135695684578415,0.23920781286367557,1.6778588961662273,0.09367366607903671,-0.06802538947699083,0.8707393031685591
7+na3,0.4386028895152439,0.4692982298084537,0.9345931044620853,0.35021393885878727,-0.48227131436435317,1.359477093394841
8+na4,2.0206279572787924,0.8006314584366991,2.5237928587320813,0.011756930645889785,0.4495995364177534,3.5916563781398314
9+na5,1.3094496922547514,2.1255275042113833,0.6160586911532747,0.5379899468902001,-2.861338356666174,5.480237741175676
10+nk0,-6.951863136558691,6.999629110309986,-0.9931759278958169,0.32085413789892137,-20.686792175264067,6.783065902146687
11+nk1,-21.38007263538218,21.178557495253997,-1.0095150550349494,0.3129613723991731,-62.93741513957286,20.177269868808498
12+nk2,-42.99345041960283,42.90875561057903,-1.00197383512569,0.31658818189330384,-127.1905848909683,41.20368405176264
13+nk3,-68.47249450181724,68.15010531349856,-1.0047305750568638,0.3152591892532306,-202.19913140068968,65.2541423970552
14+nk4,-154.31654035852793,157.09137026924083,-0.9823362040450913,0.326161615976605,-462.5669903806522,153.93390966359632
15+nk5,-310.4189282685015,267.45199251757225,-1.1606528908103244,0.24604816073560887,-835.2230401147267,214.3851835777238
16+t0_0,-0.15919974879246923,0.09809608999250896,-1.6228959666448113,0.10491325647376382,-0.3516874954699355,0.03328799788499706
17+t0_1,0.044357030099070716,0.1179725260830659,0.3759945774818655,0.7069972433141989,-0.1871329886907921,0.2758470488889335
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21+t0_5,78.07940491523914,39.94699028633619,1.9545754099513748,0.050899565633391486,-0.3060449773169722,156.46485480779523
22+t0_6,-301.3091590147366,390.531692909054,-0.7715357408519075,0.44056387067278835,-1067.6247744479363,465.0064564184631
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added results/hedonic/coefs_A3_spline_x_powertrain.csv +86 −0
@@ -0,0 +1,86 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
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added results/hedonic/coefs_A4_spline_mmt.csv +53 −0
@@ -0,0 +1,53 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
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added results/hedonic/coefs_A5_spline_dealerFE.csv +36 −0
@@ -0,0 +1,36 @@
1+term,Estimate,Std. Error,t value,Pr(>|t|),2.5%,97.5%
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11+nk0,0.01811710611795711,1.5896205676260662,0.011397126136215721,0.9909087812633759,-3.101094690783046,3.1373289030189606
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13+nk2,0.07034021604949985,9.739406128462095,0.007222228452301634,0.9942389239793132,-19.040679803071914,19.18136023517091
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15+nk4,0.17122191021694227,36.02789916487677,0.004752481110079956,0.9962089900330522,-70.52404360240115,70.86648742283504
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17+nk6,0.4438468954908804,71.40500145054975,0.006215907660169415,0.9950416415664385,-139.66966661730183,140.55736040828359
18+C(transmission)[T.Manual],-0.05455392006172659,0.008413310470909388,-6.484239497681333,1.37289735135937e-10,-0.0710628265305466,-0.03804501359290658
19+C(transmission)[T.unknown],0.0041305749223210135,0.008260405387438866,0.5000450617837893,0.6171486264323511,-0.012078295582017451,0.020339445426659476
20+C(drivetrain)[T.FWD],-0.02379868545020988,0.004410793084821748,-5.3955569877229115,8.44949594736022e-08,-0.0324537054619425,-0.015143665438477262
21+C(drivetrain)[T.RWD],-0.06014121242972176,0.018712640032482138,-3.213935197028654,0.0013493627516920625,-0.09685984122026883,-0.023422583639174682
22+C(drivetrain)[T.unknown],-0.01509453504349103,0.004722563044538663,-3.1962590866726233,0.0014339618830798706,-0.024361321508221068,-0.0058277485787609894
23+C(powertrain)[T.BEV],0.0386848882131962,0.013857557045093269,2.7916095230431597,0.005340244011039941,0.011493081381056952,0.06587669504533544
24+C(powertrain)[T.Diesel],0.04749170779947143,0.013289116644217492,3.573729471336723,0.0003679974505927941,0.02141531557655228,0.07356810002239057
25+C(powertrain)[T.HEV],0.030693511312783042,0.009979539085734642,3.0756441804670325,0.0021550798738385613,0.011111293595038589,0.050275729030527495
26+C(powertrain)[T.PHEV],0.032548816743084394,0.01139308179276076,2.856893098385911,0.004362905874170542,0.010192893666353654,0.05490473981981513
27+C(powertrain)[T.unknown],0.022806512014463595,0.005489370047527231,4.154668353017507,3.523744728095579e-05,0.012035068716285431,0.03357795531264176
28+C(body)[T.Coupe],-0.10173457918154645,0.02192087754103457,-4.64099026104705,3.907610623743452e-06,-0.144748529313388,-0.05872062904970489
29+C(body)[T.Hatchback],-0.08900182192349917,0.029452260245759945,-3.021901245637411,0.002573101250983756,-0.1467941275056847,-0.03120951634131363
30+C(body)[T.Pickup],-0.05056572850274625,0.04104727197011832,-1.2318901129302136,0.21826725676378178,-0.1311101914817351,0.029978734476242608
31+C(body)[T.SUV],-0.06368162153337112,0.03148899058358286,-2.022345599308358,0.043395015525230285,-0.12547047410684964,-0.001892768959892592
32+C(body)[T.Sedan],-0.09607317212469488,0.0269165215879345,-3.5693011747758776,0.00037419673295713274,-0.14888975828274195,-0.04325658596664781
33+C(body)[T.Van],-0.07926627110116924,0.034015755674969665,-2.330281057360044,0.019980926718723868,-0.14601323483750317,-0.012519307364835294
34+C(body)[T.Wagon],-0.06499498015194982,0.03129425145866054,-2.076898379812908,0.03805481774467445,-0.12640170847002632,-0.003588251833873325
35+C(source_class)[T.banner],1.484689326550877,67.40105581029646,0.02202768649098921,0.9824300754601354,-130.7721351564316,133.74151380953333
36+C(source_class)[T.small_portal],-0.03427527771704855,120.2229314755172,-0.00028509767060561607,0.9997725793867329,-235.94012209025487,235.87157153482076
added results/hedonic/cv_grouped_dealer.json +221 −0
@@ -0,0 +1,221 @@
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218+ "coverage": 1.0
219+ }
220+ }
221+}
\ No newline at end of file
added results/hedonic/dispersion_by_region.csv +18 −0
@@ -0,0 +1,18 @@
1+region,iqr,sd,n
2+(missing),0.15241485938359473,0.2484220341867935,6041
3+Abitibi-Témiscamingue,0.15810661628892483,0.1737486909661265,3338
4+Bas-Saint-Laurent,0.16774938569269512,0.23701906136204803,4423
5+Capitale-Nationale,0.1432907008924193,0.1556641070392749,17015
6+Centre-du-Québec,0.15424733666676888,0.15601713650879184,3839
7+Chaudière-Appalaches,0.1655276984726986,0.18506214469314103,9176
8+Côte-Nord,0.12910711631212937,0.1213778956081262,2909
9+Estrie,0.1482428307961338,0.15278104289626362,4627
10+Gaspésie–Îles-de-la-Madeleine,0.15421419684757065,0.15342762913645613,741
11+Lanaudière,0.1468293295044321,0.14547547457817125,8549
12+Laurentides,0.14880399757153132,0.14037811824085974,12654
13+Laval,0.13149101325425122,0.15589498794484552,8968
14+Mauricie,0.13137130589,0.13060568958201285,7146
15+Montréal,0.16616812394131242,0.16277045115311317,12120
16+Montérégie,0.15899194868349997,0.14860388330561897,26490
17+Outaouais,0.13744509664027293,0.1308961761974694,4348
18+Saguenay–Lac-Saint-Jean,0.15557921163816668,0.16151495487206932,4009
added results/hedonic/region_premia_A2.csv +17 −0
@@ -0,0 +1,17 @@
1+region,Estimate,Std. Error,2.5%,97.5%
2+(missing),0.027222670720390075,0.023367867632595244,-0.018630616455470306,0.07307595789625046
3+Abitibi-Témiscamingue,0.0279553461163767,0.015299897220800126,-0.0020666735242995116,0.05797736575705291
4+Bas-Saint-Laurent,0.027250704078541563,0.01950183231777824,-0.011016506767656128,0.06551791492473925
5+Capitale-Nationale,0.009195232892148608,0.013324388177895203,-0.016950370428175134,0.035340836212472346
6+Centre-du-Québec,0.010096978247849017,0.013799164504991726,-0.01698024859983032,0.03717420509552835
7+Chaudière-Appalaches,-0.0011129357994559452,0.015192594552713809,-0.030924402208153262,0.028698530609241373
8+Côte-Nord,0.0057843733578359985,0.013973162862889516,-0.02163427945126425,0.033203026166936246
9+Estrie,0.007387028990914035,0.013785364464234624,-0.019663118910503583,0.03443717689233165
10+Gaspésie–Îles-de-la-Madeleine,0.020714319325495233,0.017155186770717465,-0.012948217535848083,0.054376856186838546
11+Lanaudière,-0.007850763762476313,0.017067977251575606,-0.0413421749058657,0.025640647380913067
12+Laurentides,-0.0130813194026723,0.01207682960862716,-0.03677891752865131,0.010616278723306716
13+Laval,0.00691156735523364,0.014452285663036323,-0.021447237790102436,0.03527037250056971
14+Mauricie,0.01966348947629454,0.014095767595050336,-0.007995742836305112,0.0473227217888942
15+Montérégie,0.004209298592528483,0.0128856330286059,-0.02107536327705769,0.02949396046211466
16+Outaouais,0.013684107536571585,0.012101917486940473,-0.010062718944643776,0.037430934017786945
17+Saguenay–Lac-Saint-Jean,0.025463602673954273,0.018198012934249937,-0.010245205944092107,0.061172411292000656
added results/hedonic/region_premia_A4.csv +17 −0
@@ -0,0 +1,17 @@
1+region,Estimate,Std. Error,2.5%,97.5%
2+(missing),0.030022810990545386,0.030814084901318168,-0.03044171689263337,0.09048733887372415
3+Abitibi-Témiscamingue,0.03047929987779571,0.015980299930405357,-0.0008778309218997297,0.061836430677491144
4+Bas-Saint-Laurent,0.03705788763974842,0.03160104246049697,-0.024950837237507205,0.09906661251700405
5+Capitale-Nationale,0.00018811997419042204,0.016371258446771893,-0.03193616396958555,0.032312403917966394
6+Centre-du-Québec,0.012483281752859931,0.01857910438467856,-0.023973318490486235,0.0489398819962061
7+Chaudière-Appalaches,0.011331880923327306,0.022847011596555538,-0.033499363429742725,0.05616312527639734
8+Côte-Nord,0.009238359867216087,0.014989264756033564,-0.020174125354786056,0.03865084508921823
9+Estrie,0.01498814113189832,0.017553497490383384,-0.019455975636573072,0.04943225790036972
10+Gaspésie–Îles-de-la-Madeleine,0.026904159239928394,0.019433415970901726,-0.01122880254040775,0.06503712102026454
11+Lanaudière,-0.01025317733712667,0.02617232279004195,-0.061609469311202134,0.041103114636948795
12+Laurentides,-0.005451159536868261,0.017770471226205547,-0.04032103012998026,0.02941871105624374
13+Laval,0.010861779879471841,0.021635377861634355,-0.031591952307198995,0.05331551206614267
14+Mauricie,0.024046332027682624,0.015349817349690657,-0.006073642721344193,0.054166306776709444
15+Montérégie,0.01291498969959945,0.013767253673263627,-0.01409962054333637,0.03992959994253527
16+Outaouais,0.02107466964130463,0.014319118697520394,-0.0070228303987413705,0.04917216968135063
17+Saguenay–Lac-Saint-Jean,0.023092594344520614,0.01930454446429379,-0.014787491037584102,0.06097267972662533
added results/hedonic/summary.json +91 −0
@@ -0,0 +1,91 @@
1+{
2+ "fits": {
3+ "A1_linear_mm": {
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added results/prediction/calibration_exit30_chronological_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
2+0,0.0,0.1,0,,
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11+9,0.9,1.0,1268,0.9720777465102893,0.9858044164037855
added results/prediction/calibration_exit30_chronological_logistic.csv +11 −0
@@ -0,0 +1,11 @@
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11+9,0.9,1.0,719,0.9225941545015905,0.933240611961057
added results/prediction/calibration_exit30_dealer_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
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added results/prediction/calibration_exit30_dealer_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
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added results/prediction/calibration_exit30_region_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
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added results/prediction/calibration_exit30_region_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
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11+9,0.9,1.0,5,0.9185676420535671,0.6
added results/prediction/calibration_markdown30_chronological_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
2+0,0.0,0.1,5814,0.04572482481250088,0.06019951840385277
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11+9,0.9,1.0,0,,
added results/prediction/calibration_markdown30_chronological_logistic.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
2+0,0.0,0.1,4627,0.07009799850649404,0.06894315971471796
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11+9,0.9,1.0,17,0.9181487635992029,0.6470588235294118
added results/prediction/calibration_markdown30_dealer_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
2+0,0.0,0.1,19268,0.07142794657957895,0.14199709362673862
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8+6,0.6,0.7,2,0.6291257507542802,0.5
9+7,0.7,0.8,0,,
10+8,0.8,0.9,0,,
11+9,0.9,1.0,0,,
added results/prediction/calibration_markdown30_dealer_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
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added results/prediction/calibration_markdown30_region_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
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9+7,0.7,0.8,2,0.7074297804368848,0.5
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11+9,0.9,1.0,0,,
added results/prediction/calibration_markdown30_region_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
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added results/prediction/calibration_markdown30cr_chronological_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
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added results/prediction/calibration_markdown30cr_chronological_logistic.csv +11 −0
@@ -0,0 +1,11 @@
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added results/prediction/calibration_markdown30cr_dealer_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
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10+8,0.8,0.9,0,,
11+9,0.9,1.0,0,,
added results/prediction/calibration_markdown30cr_dealer_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
1+bin,lo,hi,n,mean_pred,frac_pos
2+0,0.0,0.1,9,0.09126445391308836,0.0
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added results/prediction/calibration_markdown30cr_region_holdout_lightgbm.csv +11 −0
@@ -0,0 +1,11 @@
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added results/prediction/calibration_markdown30cr_region_holdout_logistic.csv +11 −0
@@ -0,0 +1,11 @@
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11+9,0.9,1.0,0,,
added results/prediction/cox_exit_summary.csv +38 −0
@@ -0,0 +1,38 @@
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added results/prediction/feature_importance_exit30_chronological.csv +24 −0
@@ -0,0 +1,24 @@
1+,gain_share
2+dealer_rate_exit30,0.2978827032022339
3+km10k,0.12699982777345603
4+age_years_entry,0.08054663196745093
5+model,0.07505869725566801
6+dealer_log_n_train,0.07050287748171483
7+resid_A2,0.05403080518047422
8+logp,0.053568728014089635
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11+dealer_rate_markdown30,0.031268308196356565
12+pricepos_25,0.027617342774513377
13+log_dens_50,0.018306946126070357
14+year,0.016797789210763716
15+log_own_stock,0.015454165269818102
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17+source_class,0.00978840151480924
18+log_dens_10,0.005912874930765868
19+powertrain,0.005612104340842328
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21+trim,0.005308941780744152
22+body,0.002916273803204054
23+drivetrain,0.0026996007562051032
24+week,0.0021863409531782505
added results/prediction/feature_importance_exit30_dealer_holdout.csv +24 −0
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added results/prediction/feature_importance_markdown30_chronological.csv +24 −0
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added results/prediction/feature_importance_markdown30_dealer_holdout.csv +24 −0
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added results/prediction/feature_importance_markdown30cr_chronological.csv +24 −0
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added results/prediction/feature_importance_markdown30cr_dealer_holdout.csv +24 −0
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added results/prediction/metrics.json +1610 −0
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1140+ },
1141+ "fold_sd": {
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1147+ "lightgbm": {
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1150+ "auc": 0.03832361227087986
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1152+ }
1153+ },
1154+ "region_holdout": {
1155+ "n_train": 39178,
1156+ "n_test": 28947,
1157+ "base_rate_train": 0.5919150624764695,
1158+ "models": {
1159+ "logistic": {
1160+ "log_loss": 0.6895086811014098,
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1162+ 0.6873192581802143,
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1164+ ],
1165+ "brier": 0.2478891684784745,
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1167+ 0.24687389591644682,
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1170+ "auc": 0.5843988559292631,
1171+ "auc_ci": [
1172+ 0.5766574148498544,
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1175+ "ece": 0.11532407638989386,
1176+ "n_test": 28947,
1177+ "base_rate": 0.6009949217535496,
1178+ "n_boot": 200
1179+ },
1180+ "lightgbm": {
1181+ "log_loss": 0.6890728325227832,
1182+ "log_loss_ci": [
1183+ 0.6844833481593796,
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1185+ ],
1186+ "brier": 0.24725910342919488,
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1191+ "auc": 0.6065048609213427,
1192+ "auc_ci": [
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1196+ "ece": 0.11224426208325944,
1197+ "n_test": 28947,
1198+ "base_rate": 0.6009949217535496,
1199+ "n_boot": 200,
1200+ "best_iteration": 500
1201+ }
1202+ },
1203+ "per_region": {
1204+ "Mont\u00e9r\u00e9gie": {
1205+ "logistic": {
1206+ "log_loss": 0.7185548741531423,
1207+ "brier": 0.2622670755352521,
1208+ "auc": 0.5507198070077464,
1209+ "n_test": 9137,
1210+ "base_rate": 0.6132209696837037
1211+ },
1212+ "lightgbm": {
1213+ "log_loss": 0.6821032233112184,
1214+ "brier": 0.24412024729940518,
1215+ "auc": 0.6097070996710166,
1216+ "n_test": 9137,
1217+ "base_rate": 0.6132209696837037
1218+ }
1219+ },
1220+ "Capitale-Nationale": {
1221+ "logistic": {
1222+ "log_loss": 0.6826432948911425,
1223+ "brier": 0.2443618673736587,
1224+ "auc": 0.6112885021555081,
1225+ "n_test": 5358,
1226+ "base_rate": 0.6362448674878686
1227+ },
1228+ "lightgbm": {
1229+ "log_loss": 0.692498946330963,
1230+ "brier": 0.2488109699935438,
1231+ "auc": 0.6229425293653461,
1232+ "n_test": 5358,
1233+ "base_rate": 0.6362448674878686
1234+ }
1235+ },
1236+ "Montr\u00e9al": {
1237+ "logistic": {
1238+ "log_loss": 0.6939403574317395,
1239+ "brier": 0.2502112164586107,
1240+ "auc": 0.5520071413269267,
1241+ "n_test": 4290,
1242+ "base_rate": 0.6508158508158508
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1244+ "lightgbm": {
1245+ "log_loss": 0.7407256390973135,
1246+ "brier": 0.2715057800974201,
1247+ "auc": 0.5115864849407614,
1248+ "n_test": 4290,
1249+ "base_rate": 0.6508158508158508
1250+ }
1251+ },
1252+ "Laurentides": {
1253+ "logistic": {
1254+ "log_loss": 0.6836822032908833,
1255+ "brier": 0.24524372752759388,
1256+ "auc": 0.5790217054117824,
1257+ "n_test": 4033,
1258+ "base_rate": 0.5383089511529878
1259+ },
1260+ "lightgbm": {
1261+ "log_loss": 0.6760231711527256,
1262+ "brier": 0.24097788216521682,
1263+ "auc": 0.6283442369165659,
1264+ "n_test": 4033,
1265+ "base_rate": 0.5383089511529878
1266+ }
1267+ },
1268+ "Laval": {
1269+ "logistic": {
1270+ "log_loss": 0.6679218283231959,
1271+ "brier": 0.23761150726507252,
1272+ "auc": 0.6305263465949894,
1273+ "n_test": 3309,
1274+ "base_rate": 0.5811423390752494
1275+ },
1276+ "lightgbm": {
1277+ "log_loss": 0.6602450076615012,
1278+ "brier": 0.23403708273939391,
1279+ "auc": 0.6507309931646904,
1280+ "n_test": 3309,
1281+ "base_rate": 0.5811423390752494
1282+ }
1283+ },
1284+ "Chaudi\u00e8re-Appalaches": {
1285+ "logistic": {
1286+ "log_loss": 0.6903095285183557,
1287+ "brier": 0.24763961671065904,
1288+ "auc": 0.5828296330786258,
1289+ "n_test": 2820,
1290+ "base_rate": 0.5315602836879433
1291+ },
1292+ "lightgbm": {
1293+ "log_loss": 0.6828410075829774,
1294+ "brier": 0.2441026582801894,
1295+ "auc": 0.6157178214696752,
1296+ "n_test": 2820,
1297+ "base_rate": 0.5315602836879433
1298+ }
1299+ }
1300+ },
1301+ "aggregation": "unweighted mean over the 6 held-out regions; CI = bootstrap within region, then averaged"
1302+ },
1303+ "drift": {
1304+ "weeks_4_5": {
1305+ "n_train": 24354,
1306+ "n_test": 16259,
1307+ "weeks_observable": [
1308+ 4,
1309+ 5
1310+ ],
1311+ "models": {
1312+ "logistic": {
1313+ "log_loss": 0.618162464970206,
1314+ "log_loss_ci": [
1315+ 0.6123170701522876,
1316+ 0.6248851843361714
1317+ ],
1318+ "brier": 0.21437804280362754,
1319+ "brier_ci": [
1320+ 0.21201373045830557,
1321+ 0.2173036145427639
1322+ ],
1323+ "auc": 0.6959144125378385,
1324+ "auc_ci": [
1325+ 0.687254674723287,
1326+ 0.7028942745380895
1327+ ],
1328+ "ece": 0.020602736684108267,
1329+ "n_test": 16259,
1330+ "base_rate": 0.5975767267359616,
1331+ "n_boot": 200
1332+ },
1333+ "lightgbm": {
1334+ "log_loss": 0.5954846295525256,
1335+ "log_loss_ci": [
1336+ 0.5882372598982487,
1337+ 0.60374242729879
1338+ ],
1339+ "brier": 0.20552943836890286,
1340+ "brier_ci": [
1341+ 0.20264678625904206,
1342+ 0.20868024684517003
1343+ ],
1344+ "auc": 0.7275086552544345,
1345+ "auc_ci": [
1346+ 0.720227611541182,
1347+ 0.7342970160743388
1348+ ],
1349+ "ece": 0.03206067711276024,
1350+ "n_test": 16259,
1351+ "base_rate": 0.5975767267359616,
1352+ "n_boot": 200
1353+ }
1354+ }
1355+ },
1356+ "weeks_6_7": {
1357+ "n_train": 24354,
1358+ "n_test": 3390,
1359+ "weeks_observable": [
1360+ 6
1361+ ],
1362+ "models": {
1363+ "logistic": {
1364+ "log_loss": 0.6335406964925955,
1365+ "log_loss_ci": [
1366+ 0.6212458123830099,
1367+ 0.6450765383488211
1368+ ],
1369+ "brier": 0.2219334974369145,
1370+ "brier_ci": [
1371+ 0.21648779673683372,
1372+ 0.22684994886530957
1373+ ],
1374+ "auc": 0.674931342556254,
1375+ "auc_ci": [
1376+ 0.6582693643726644,
1377+ 0.6910379276071871
1378+ ],
1379+ "ece": 0.022569791027503453,
1380+ "n_test": 3390,
1381+ "base_rate": 0.58023598820059,
1382+ "n_boot": 200
1383+ },
1384+ "lightgbm": {
1385+ "log_loss": 0.6185171938975021,
1386+ "log_loss_ci": [
1387+ 0.6036703816435322,
1388+ 0.6330047222841904
1389+ ],
1390+ "brier": 0.21666892021735712,
1391+ "brier_ci": [
1392+ 0.2107951018556213,
1393+ 0.22316674678411297
1394+ ],
1395+ "auc": 0.6953947798549575,
1396+ "auc_ci": [
1397+ 0.6785078625186961,
1398+ 0.7134433722139846
1399+ ],
1400+ "ece": 0.02787863043036501,
1401+ "n_test": 3390,
1402+ "base_rate": 0.58023598820059,
1403+ "n_boot": 200
1404+ }
1405+ }
1406+ }
1407+ },
1408+ "chronological_oct1_naive": {
1409+ "n_train": 49629,
1410+ "n_test": 3317,
1411+ "base_rate_train": 0.5878014870337908,
1412+ "models": {
1413+ "logistic": {
1414+ "log_loss": 0.6322114387021451,
1415+ "log_loss_ci": [
1416+ 0.6197245122439214,
1417+ 0.6447269181842753
1418+ ],
1419+ "brier": 0.22084303889969914,
1420+ "brier_ci": [
1421+ 0.21527341133434813,
1422+ 0.2263263065133749
1423+ ],
1424+ "auc": 0.7127074429684979,
1425+ "auc_ci": [
1426+ 0.6945860057032724,
1427+ 0.7332895663949947
1428+ ],
1429+ "ece": 0.08236096055258645,
1430+ "n_test": 3317,
1431+ "base_rate": 0.4615616520952668,
1432+ "n_boot": 200
1433+ },
1434+ "lightgbm": {
1435+ "log_loss": 0.629292096351042,
1436+ "log_loss_ci": [
1437+ 0.6157108059959672,
1438+ 0.6422784020925622
1439+ ],
1440+ "brier": 0.22257599524033,
1441+ "brier_ci": [
1442+ 0.21679288730122043,
1443+ 0.2278356290587968
1444+ ],
1445+ "auc": 0.728343608719535,
1446+ "auc_ci": [
1447+ 0.7147762501849771,
1448+ 0.7432568738375331
1449+ ],
1450+ "ece": 0.12296025739657858,
1451+ "n_test": 3317,
1452+ "base_rate": 0.4615616520952668,
1453+ "n_boot": 200,
1454+ "best_iteration": 416
1455+ }
1456+ }
1457+ }
1458+ }
1459+ },
1460+ "notes": {
1461+ "region_holdout_regions": [
1462+ "Mont\u00e9r\u00e9gie",
1463+ "Capitale-Nationale",
1464+ "Montr\u00e9al",
1465+ "Laurentides",
1466+ "Laval",
1467+ "Chaudi\u00e8re-Appalaches"
1468+ ],
1469+ "drift": "train = entries in weeks 0-3 (days 0-27); the week 6-7 window only contains observable entries up to day 44 (week 6), week 7 has no determinable 30-day outcome",
1470+ "chronological_oct1_naive": "Analysis-plan cutoff (entry before vs on/after 2026-10-01 UTC) applied to the looser rule entry day <= 51. Test entries (days 50-51) have only ~22-24 days of confirmable follow-up: exits at ages between ~23 and 30 days are coded 0 (exit30 base rate is a lower bound) and the last-day markdowns may be unclassified. Reported as a sensitivity only.",
1471+ "chronological": "primary chronological split: train entries before 2026-09-15 UTC, test 2026-09-15 to day 44 (2026-09-25); the plan's 2026-10-01 cutoff leaves no listing with a determinable 30-day outcome in the test fold",
1472+ "targets": {
1473+ "markdown30": "first sustained markdown (>=1 % and >=100 $) at listing age <= 30 d; exits before any cut count as 0",
1474+ "markdown30cr": "competing-risk-aware: listings that exit within 30 d without a prior cut are removed; event = markdown within 30 d among the rest",
1475+ "exit30": "disappearance confirmed by >= 7 d of absence at listing age <= 30 d (NOT a sale)"
1476+ },
1477+ "features": {
1478+ "numeric": [
1479+ "age_years_entry",
1480+ "km10k",
1481+ "year",
1482+ "logp",
1483+ "resid_A2",
1484+ "week",
1485+ "log_dens_10",
1486+ "log_dens_25",
1487+ "log_dens_50",
1488+ "log_own_stock",
1489+ "pricepos_25"
1490+ ],
1491+ "dealer_target_encoding": [
1492+ "dealer_rate_markdown30",
1493+ "dealer_rate_exit30",
1494+ "dealer_log_n_train",
1495+ "(training fold only; inner 5-fold OOF for training rows; smoothing m=20)"
1496+ ],
1497+ "categorical_lightgbm": [
1498+ "powertrain",
1499+ "body",
1500+ "region",
1501+ "source_class",
1502+ "make",
1503+ "model",
1504+ "trim",
1505+ "transmission",
1506+ "drivetrain"
1507+ ],
1508+ "one_hot_logistic": [
1509+ "powertrain",
1510+ "body",
1511+ "region",
1512+ "source_class"
1513+ ],
1514+ "competition_at_entry": "risk_set row with listing_age = 0 (dens_10/25/50, own_stock, pricepos_25; NaN when no competitor or no coordinates)",
1515+ "excluded": "dealer_id (identity), anything measured after entry"
1516+ },
1517+ "models": {
1518+ "logistic": "sklearn LogisticRegression L2, C=1, standardised numerics + missing indicators + one-hot coarse categoricals",
1519+ "lightgbm": {
1520+ "n_estimators": 2000,
1521+ "learning_rate": 0.05,
1522+ "num_leaves": 31,
1523+ "min_child_samples": 50,
1524+ "subsample": 0.8,
1525+ "subsample_freq": 1,
1526+ "colsample_bytree": 0.8,
1527+ "reg_lambda": 1.0,
1528+ "cat_l2": 10.0,
1529+ "cat_smooth": 50.0,
1530+ "max_cat_to_onehot": 4,
1531+ "n_jobs": 8,
1532+ "verbose": -1,
1533+ "random_state": 36,
1534+ "early_stopping": "100 rounds on inner validation (latest 15 % of training entries for chronological splits, 15 % of training dealers otherwise)"
1535+ }
1536+ },
1537+ "metrics": {
1538+ "ci": "200 bootstrap resamples of test rows, percentile 2.5/97.5",
1539+ "calibration": "10 equal-width probability bins; ECE = n-weighted |frac_pos - mean_pred|"
1540+ }
1541+ },
1542+ "samples": {
1543+ "n_S2": 80856,
1544+ "n_strict_observable": 44003,
1545+ "strict_rule": "entry_ts <= SNAPSHOT_TS - 30 - 7 days (entry day <= 44); every listing has a determined 30-day exit and markdown outcome",
1546+ "n_naive_observable": 52946,
1547+ "naive_rule": "entry day <= 51 (analysis-plan rule; exit at ages > snapshot-7d-entry cannot be confirmed)",
1548+ "n_without_coordinates": 573,
1549+ "base_rates_strict": {
1550+ "markdown30": 0.18387382678453743,
1551+ "markdown30cr": 0.39780716849402625,
1552+ "exit30": 0.5930504738313297
1553+ },
1554+ "n_markdown30cr": 20339,
1555+ "n_exit_before_cut_removed_in_cr": 23664
1556+ },
1557+ "survival": {
1558+ "model": "CoxPHFitter(penalizer=0.01)",
1559+ "split": "chronological, entry before vs on/after 2026-10-01 UTC",
1560+ "n_train": 49629,
1561+ "events_train": 34694,
1562+ "n_test": 31227,
1563+ "events_test": 9040,
1564+ "c_index_train": 0.6001305839001478,
1565+ "c_index_test": 0.6581044283827092,
1566+ "dynamic_auc_30d": null,
1567+ "dynamic_auc_note": "scikit-survival (sksurv) is not installed in the project environment; the 30-day cumulative/dynamic AUC was not computed.",
1568+ "covariates": [
1569+ "age_years_entry",
1570+ "logp",
1571+ "resid_A2",
1572+ "km10k",
1573+ "log_dens_25",
1574+ "pricepos_25f",
1575+ "pricepos_missing",
1576+ "coords_missing",
1577+ "powertrain=BEV",
1578+ "powertrain=Diesel",
1579+ "powertrain=HEV",
1580+ "powertrain=PHEV",
1581+ "powertrain=unknown",
1582+ "body=Convertible",
1583+ "body=Coupe",
1584+ "body=Hatchback",
1585+ "body=Pickup",
1586+ "body=Sedan",
1587+ "body=Van",
1588+ "body=Wagon",
1589+ "body=unknown",
1590+ "region=(missing)",
1591+ "region=Abitibi-T\u00e9miscamingue",
1592+ "region=Bas-Saint-Laurent",
1593+ "region=Capitale-Nationale",
1594+ "region=Centre-du-Qu\u00e9bec",
1595+ "region=Chaudi\u00e8re-Appalaches",
1596+ "region=C\u00f4te-Nord",
1597+ "region=Estrie",
1598+ "region=Gasp\u00e9sie\u2013\u00celes-de-la-Madeleine",
1599+ "region=Lanaudi\u00e8re",
1600+ "region=Laurentides",
1601+ "region=Laval",
1602+ "region=Mauricie",
1603+ "region=Mont\u00e9r\u00e9gie",
1604+ "region=Outaouais",
1605+ "region=Saguenay\u2013Lac-Saint-Jean"
1606+ ],
1607+ "seconds": 2.1
1608+ },
1609+ "seconds_total": 133.3
1610+}
\ No newline at end of file
added results/robustness/amli_variants.csv +10 −0
@@ -0,0 +1,10 @@
1+variant,b,se,lo,hi,p_normal,n,n_markets,r2,corr_equal_with_baseline
2+baseline_z_region_weekly,0.0096490977012689,0.0013056386786525556,0.00688126734752511,0.01241692805501269,1.4646737707617753e-13,68,17,0.6547239063742168,1.0
3+rank_standardisation,0.00456575801509579,0.002902489242755708,-0.0015872443115547228,0.010718760341746302,0.1157074555670082,68,17,0.5682641676602667,0.9180473580612152
4+min_listing_days_100,0.0096490977012689,0.0013056386786525556,0.00688126734752511,0.01241692805501269,1.4646737707617753e-13,68,17,0.6547239063742168,1.0
5+two_week_aggregation,-5.0647684885064045e-05,0.005156021981727472,-0.010980926006850408,0.010879630637080279,0.9921624934735607,34,17,0.6634647885843963,0.9964681085768937
6+province_level_pooled,-0.0006758958560584832,0.0029216647285782815,-0.013246804575888553,0.011895012863771585,0.8170512221747255,4,1,0.012580488821915314,0.8524653789516003
7+loo_drop_mdfreq,0.005761196960659998,0.0018642332556800935,0.001809199002959276,0.00971319491836072,0.0019989774961107617,68,17,0.5994945821085352,
8+loo_drop_mddepth,0.007800753894242435,0.0013483525519670683,0.004942374174108945,0.010659133614375925,7.234156792142602e-09,68,17,0.6228185105840963,
9+loo_drop_disp,0.007167096568502988,0.0010436470232336819,0.004954663713433418,0.009379529423572558,6.5402310598137945e-12,68,17,0.6486798987567317,
10+loo_drop_arrival,0.008207525301409847,0.0012430814345517705,0.00557231038093999,0.010842740221879704,4.041054673271348e-11,68,17,0.6440220390446187,
added results/robustness/hazards_variants.csv +20 −0
@@ -0,0 +1,20 @@
1+model,variant,n_rows,n_events,n_listings,joint_age_stat,joint_age_p,ldens_b,ldens_se,ldens_p,resid_A2_b,resid_A2_se,resid_A2_p,lprice_b,lprice_se,lprice_p,lkm_b,lkm_se,lkm_p,note
2+B,published_full,318323,15692,15692,169.80932895948362,1.414000625241716e-32,0.018919584034498692,0.01627908403935399,0.24515342232317672,0.2558872219208771,0.18605527145137654,0.16902945056234064,-0.13401256064719333,0.049295119471057545,0.00655634634372644,-0.07920955253798637,0.023550516594834613,0.0007699174282199426,"results/hazards/summary.json as published (fitted on listings with a NULL-free first_cut, i.e. eventually-cut listings only)"
3+B,baseline_full_nullfix,1371919,15692,79699,156.98639223265488,6.821573207521007e-30,0.019141862624844564,0.02851711282146282,0.5020668506227545,0.7440344265787721,0.22273242790630624,0.0008363207372747672,0.007090732683278286,0.09079788795260434,0.9377536091410988,0.11645746067072511,0.037041399231496214,0.0016666617954118814,"full S2 first-cut risk set, NULL first_cut coded 0, no subsample"
4+B,baseline_sub,800000,9189,76312,133.76744593180743,4.679498452964765e-25,-0.006616003069077046,0.031229131351022458,0.8322212715055906,0.8021738487514163,0.25847659801141776,0.0019126692267133305,-0.020077470905059756,0.09662055128583714,0.8353873926334734,0.10677245048231793,0.04115204145188865,0.00947053200240862,S2; 1 %/100 $; 48 h; exit 7 d; subsample
5+B,threshold_2pct_200,800000,6371,75868,126.45955308122453,1.531207032603548e-23,-0.021729407171294252,0.030208264257705758,0.47194380581854956,0.9491704270750956,0.26181270709106447,0.0002885373904489523,-0.20297585603820717,0.09286431164232177,0.02883572404062242,0.10280591580632405,0.038925993468652775,0.008264688401330015,"S2; rebuilt events (48 h, 2 %/200 $); lprice/resid_A2 from baseline entry price; subsample"
6+B,threshold_0.5pct_50,800000,10395,76584,161.91703510100746,6.353149769096158e-31,0.036677061125441085,0.030350591004945117,0.22687561783112165,0.570297002472363,0.20829317028766411,0.006182282755922169,0.07754896057244133,0.09414366161883087,0.41009297619984963,0.11748006183956124,0.03569389776580635,0.0009971818893878277,"S2; rebuilt events (48 h, 0.5 %/50 $); lprice/resid_A2 from baseline entry price; subsample"
7+B,transient_24h,800000,9116,76418,141.259209677654,1.2981802465697835e-26,0.003032809753676576,0.028294013827198046,0.9146389660213127,0.7609931760338264,0.23662266499185539,0.0012996272298859486,0.0155578420673418,0.09621853734205968,0.8715477963661776,0.10891715780788602,0.040425549918225785,0.007054398178560692,"S2; rebuilt events (24 h, 1 %/100 $); lprice/resid_A2 from baseline entry price; subsample"
8+B,transient_72h,800000,9092,76428,137.86355198731485,6.598593140088049e-26,0.029137693628915162,0.029624018436334235,0.3253203762033311,0.5984845235994212,0.2353862183232021,0.011004244803346722,0.0038395948994570926,0.09801192585204561,0.9687510468057174,0.1279363722500821,0.03754400491222568,0.0006552779984728735,"S2; rebuilt events (72 h, 1 %/100 $); lprice/resid_A2 from baseline entry price; subsample"
9+B,exit_confirm_3d,800000,9189,76312,133.76744593180743,4.679498452964765e-25,-0.006616003069077046,0.031229131351022458,0.8322212715055906,0.8021738487514163,0.25847659801141776,0.0019126692267133305,-0.020077470905059756,0.09662055128583714,0.8353873926334734,0.10677245048231793,0.04115204145188865,0.00947053200240862,S2; exit = inactive and last_seen <= snapshot - 3 d (exit on last observed listing-day); subsample
10+B,exit_confirm_14d,800000,9189,76312,133.76744593180743,4.679498452964765e-25,-0.006616003069077046,0.031229131351022458,0.8322212715055906,0.8021738487514163,0.25847659801141776,0.0019126692267133305,-0.020077470905059756,0.09662055128583714,0.8353873926334734,0.10677245048231793,0.04115204145188865,0.00947053200240862,S2; exit = inactive and last_seen <= snapshot - 14 d (exit on last observed listing-day); subsample
11+B,S2L_with_stock,800000,10588,111718,148.84828376793763,3.4089193876987325e-28,0.04498499163960471,0.02216729255790443,0.04242358840781761,0.08554580643531341,0.1489947228664341,0.565864092385099,0.14068957658871845,0.07097484078146367,0.047451742376013184,0.1722165346957676,0.028765630053018867,2.1389719033631545e-09,"S2L (S2 + left-censored stock, age from first_seen); subsample"
12+C,published_full,1663764,43311,79699,103.48350732513617,8.27234021842165e-19,0.032635174880585587,0.0298694350387374,0.27457196040694076,0.1803884357069441,0.2249175737951905,0.42254127819565435,-0.30650093117934923,0.11057049714499967,0.005571379024700772,-0.5117116922783111,0.07033764741721567,3.462268744139904e-13,results/hazards/summary.json as published (full S2 risk set)
13+C,baseline_sub,800000,20865,74869,100.17420063260735,3.933115107158467e-18,0.031464767056494966,0.029243152738446705,0.2819404899594391,0.19955329392715446,0.22746179270353808,0.38032117412298494,-0.3066006665293555,0.1095492572392021,0.005130129949932886,-0.5045885162759294,0.07162722759258074,1.859325947473793e-12,S2; 1 %/100 $; 48 h; exit 7 d; subsample
14+C,threshold_2pct_200,800000,20865,74869,100.7274582657925,3.0312985324267937e-18,0.031422627228575445,0.029261983865009614,0.2828953412509042,0.18277192069229206,0.22638102007421698,0.41945671281390295,-0.3049451147167821,0.10971671010832208,0.005446172523043425,-0.5038295535997543,0.07159485941504065,1.9609782065608486e-12,"S2; rebuilt events (48 h, 2 %/200 $); lprice/resid_A2 from baseline entry price; subsample"
15+C,threshold_0.5pct_50,800000,20865,74869,101.25825121744026,2.3609021965996056e-18,0.03228504578775424,0.02911857456480592,0.26754062061668493,0.20735369679905744,0.22820380453617545,0.3635434433064225,-0.30619130113227666,0.10951103049435536,0.005174165194309282,-0.504408486986445,0.07153285252685256,1.7709570077417082e-12,"S2; rebuilt events (48 h, 0.5 %/50 $); lprice/resid_A2 from baseline entry price; subsample"
16+C,transient_24h,800000,20865,74869,100.65408366464628,3.1378457039833425e-18,0.0315419494088607,0.029243932993100942,0.28077457154762986,0.20024142558836805,0.22765063479481412,0.3790762121120491,-0.30655834042428,0.10956753450741917,0.005143700023825034,-0.5045162310412573,0.07161072380124243,1.851143499531596e-12,"S2; rebuilt events (24 h, 1 %/100 $); lprice/resid_A2 from baseline entry price; subsample"
17+C,transient_72h,800000,20647,74905,80.57012438025802,3.7533875975030806e-14,0.03918883853030006,0.031006218179021224,0.2062650028679729,0.20927625175978945,0.22376909715416715,0.3496682175604764,-0.30946817350970696,0.11316391633304414,0.006243914404089707,-0.5159182860353899,0.06983252634322544,1.4912467173195228e-13,"S2; rebuilt events (72 h, 1 %/100 $); lprice/resid_A2 from baseline entry price; subsample"
18+C,exit_confirm_3d,800000,23268,74869,80.40631185894367,4.049585548007091e-14,0.029529302810218358,0.028392656389660292,0.29832451684275585,0.14089342174164418,0.22240084134448446,0.5263998755660579,-0.29005743067228823,0.10428697473349403,0.005413517385436609,-0.48612910366259626,0.07021165078039074,4.39789359105172e-12,S2; exit = inactive and last_seen <= snapshot - 3 d (exit on last observed listing-day); subsample
19+C,exit_confirm_14d,800000,17852,74869,126.25607966444633,1.6871484079423688e-23,0.03016852128975833,0.03077012211746359,0.32686475971536466,0.19299848973265102,0.23727767537786695,0.4159964200740617,-0.3220997556739144,0.11670697115458697,0.005781877484351966,-0.5239853800109883,0.06774756909133911,1.0390900114106216e-14,S2; exit = inactive and last_seen <= snapshot - 14 d (exit on last observed listing-day); subsample
20+C,S2L_with_stock,800000,17579,109402,188.13335052721516,2.0111331142463394e-36,0.05712034643444522,0.02312345230613535,0.013502455966931838,-0.38060700148324794,0.17414934703058135,0.02885069045624567,-0.17126562158097483,0.07761803263239593,0.02734771938852374,-0.3135216555163537,0.0659302106292122,1.980965330002837e-06,"S2L (S2 + left-censored stock, age from first_seen); subsample"
added results/robustness/hedonic_heterogeneity.csv +13 −0
@@ -0,0 +1,13 @@
1+group_var,group,n,age_b,age_se,age_lo,age_hi,km_b,km_se,km_lo,km_hi,H1_joint_p,H4_region_p
2+powertrain,ICE,110876,-0.08516249862217497,0.0009263349069031461,-0.08698019709449835,-0.08334480014985159,-0.03223503394475152,0.0005157987436971462,-0.03324715880527203,-0.031222909084231015,3.9880510166131804e-130,0.009473186106586816
3+powertrain,BEV,8346,-0.09418856780166687,0.003094018394964743,-0.10026482820537322,-0.08811230739796053,-0.028880770501467725,0.0014709471233133838,-0.031769524419634626,-0.02599201658330082,1.0932118317260773e-36,9.27804943609742e-06
4+powertrain,HEV,4875,-0.07307116211043929,0.002360265309545413,-0.07770565752024597,-0.06843666670063261,-0.02824593928732538,0.0011679069917627863,-0.030539181309955556,-0.025952697264695204,4.506128497348465e-15,0.05307699887003208
5+yband,<=2015,10123,-0.09343903335129783,0.002701290484702805,-0.09874130120478122,-0.08813676549781443,-0.03812717636568413,0.0009369493583932014,-0.03996628122740978,-0.03628807150395848,6.516959514946951e-14,0.025915859915874046
6+yband,2016-2020,37339,-0.09715633314620731,0.0013501128772381652,-0.09980577125766235,-0.09450689503475226,-0.03576818244174664,0.0005023290923762917,-0.0367539443467489,-0.034782420536744386,2.9999775836146458e-64,0.00011162111764795073
7+yband,2021+,88931,-0.07511547679817891,0.0018728961933155994,-0.07879099341511109,-0.07143996018124674,-0.027496371088819128,0.0005717442365599215,-0.028618406282019542,-0.026374335895618713,4.080245062699649e-31,3.355673618094612e-06
8+body,SUV,77286,-0.08910022283624779,0.0009558184351022697,-0.0909758559074516,-0.08722458976504398,-0.030587006406574448,0.000446760635618739,-0.03146369908407955,-0.029710313729069352,4.5832553461729925e-84,7.3272112081570585e-06
9+body,Sedan,28309,-0.08088105729138274,0.0010924868587925886,-0.08302500936835515,-0.07873710521441034,-0.031277746832098,0.0006765784050562001,-0.03260549889161298,-0.029949994772583012,1.352993809132424e-114,0.031669077967971235
10+body,Pickup,14422,-0.08820582112770353,0.0022001937323767114,-0.09252460692441111,-0.08388703533099595,-0.025470801639652384,0.0009190192307819184,-0.027274755147198553,-0.023666848132106216,2.6029464533379603e-50,0.020561096256730516
11+source_class,direct_dealer,28380,-0.08541911708376818,0.0015212141310459158,-0.08843180195668132,-0.08240643221085503,-0.030394130382526856,0.0006620574912062043,-0.03170530055117654,-0.02908296021387717,6.634378380125708e-106,7.1150555295757435e-22
12+source_class,full_portal,92475,-0.08441764676764998,0.0011422691601119638,-0.08665940239964522,-0.08217589113565475,-0.032440529739186874,0.0005775169276453068,-0.03357393320513929,-0.03130712627323446,1.5832137231501645e-85,0.05027649257737147
13+source_class,banner,15309,-0.08187672462440206,0.0012558947713418663,-0.08536364751493769,-0.07838980173386642,-0.02689103091885741,0.0005923141122999401,-0.028535558536692155,-0.025246503301022665,0.0,0.0
added results/robustness/hedonic_region_premia_variants.csv +129 −0
@@ -0,0 +1,129 @@
1+variant,region,est,se,lo,hi
2+baseline_S1,(missing),0.02722267072039877,0.02336786742165347,-0.018630616041543927,0.07307595748234147
3+baseline_S1,Abitibi-Témiscamingue,0.027955346116406706,0.015299897846591736,-0.002066674752220759,0.05797736698503417
4+baseline_S1,Bas-Saint-Laurent,0.027250704078567438,0.01950183283661084,-0.011016507785702609,0.06551791594283748
5+baseline_S1,Capitale-Nationale,0.009195232892166982,0.013324389440942368,-0.01695037290655424,0.03534083869088821
6+baseline_S1,Centre-du-Québec,0.010096978247867578,0.013799165721595178,-0.016980250987075687,0.03717420748281084
7+baseline_S1,Chaudière-Appalaches,-0.0011129357994365443,0.015192595469393519,-0.03092440400687642,0.02869853240800333
8+baseline_S1,Côte-Nord,0.005784373357869974,0.013973164620431347,-0.021634282899943333,0.033203029615683284
9+baseline_S1,Estrie,0.007387028990940261,0.013785365191405101,-0.019663120337357946,0.03443717831923847
10+baseline_S1,Gaspésie–Îles-de-la-Madeleine,0.020714319325508695,0.01715518676767113,-0.012948217529856993,0.054376856180874386
11+baseline_S1,Lanaudière,-0.00785076376244159,0.01706797773261729,-0.041342175849748614,0.02564064832486544
12+baseline_S1,Laurentides,-0.013081319402649772,0.012076831135059411,-0.03677892052385015,0.010616281718550606
13+baseline_S1,Laval,0.006911567355249812,0.014452287139353448,-0.021447240686969887,0.03527037539746951
14+baseline_S1,Mauricie,0.01966348947630822,0.014095769209876853,-0.007995746004963267,0.04732272495757971
15+baseline_S1,Montérégie,0.004209298592543848,0.012885634330258945,-0.021075365831193685,0.02949396301628138
16+baseline_S1,Outaouais,0.013684107536590802,0.012101918549465119,-0.010062721029549403,0.03743093610273101
17+baseline_S1,Saguenay–Lac-Saint-Jean,0.025463602673988495,0.01819801408167168,-0.010245208195570916,0.06117241354354791
18+plus_sampled_portals,(missing),0.015594039866526216,0.01284324494717696,-0.009591245792044005,0.04077932552509644
19+plus_sampled_portals,Abitibi-Témiscamingue,0.03198881326444284,0.011620531497452645,0.009201238436471668,0.054776388092414016
20+plus_sampled_portals,Bas-Saint-Laurent,0.02791017293071161,0.014505763894978065,-0.0005352712614198687,0.05635561712284309
21+plus_sampled_portals,Capitale-Nationale,0.011573223035353237,0.008282812257895678,-0.004669166923678305,0.02781561299438478
22+plus_sampled_portals,Centre-du-Québec,0.01312137830542365,0.008703193870275247,-0.0039453695971754955,0.030188126208022796
23+plus_sampled_portals,Chaudière-Appalaches,0.0035191122101329024,0.009833742013045887,-0.015764612635875176,0.02280283705614098
24+plus_sampled_portals,Côte-Nord,0.00809371836194003,0.009855138686116026,-0.011231964830703253,0.027419401554583313
25+plus_sampled_portals,Estrie,0.010130951701571677,0.009069380898221792,-0.007653879905046812,0.027915783308190167
26+plus_sampled_portals,Gaspésie–Îles-de-la-Madeleine,0.017403243246235427,0.013267582736321441,-0.00861415832068076,0.04342064481315161
27+plus_sampled_portals,Lanaudière,0.0005304156989695708,0.010377007405169674,-0.019818639143104666,0.020879470541043804
28+plus_sampled_portals,Laurentides,-0.005612250986292502,0.007286868765246879,-0.019901620391303734,0.008677118418718729
29+plus_sampled_portals,Laval,0.008445069794765832,0.008423070574728214,-0.008072363254228276,0.02496250284375994
30+plus_sampled_portals,Mauricie,0.01956058522266215,0.008576501635289918,0.002742277659085704,0.0363788927862386
31+plus_sampled_portals,Montérégie,0.006543970440222698,0.0075194934317634535,-0.008201570181762584,0.02128951106220798
32+plus_sampled_portals,Outaouais,0.016856036596288402,0.007197077487038706,0.0027427456668199183,0.030969327525756887
33+plus_sampled_portals,Saguenay–Lac-Saint-Jean,0.027023421927823903,0.013458998623103126,0.0006306585416779933,0.05341618531396981
34+plus_kijiji,(missing),0.025291513672849743,0.02259479901854591,-0.019044780757754204,0.06962780810345369
35+plus_kijiji,Abitibi-Témiscamingue,0.03230756803596063,0.01452031262714345,0.0038153094709238965,0.060799826600997364
36+plus_kijiji,Bas-Saint-Laurent,0.030646093643963635,0.018620490643248073,-0.00589167573047188,0.06718386301839915
37+plus_kijiji,Capitale-Nationale,0.011764395626579544,0.012050294565362718,-0.011881108251852865,0.03540989950501195
38+plus_kijiji,Centre-du-Québec,0.01272803575844542,0.012495511176617527,-0.011791087519999657,0.03724715903689049
39+plus_kijiji,Chaudière-Appalaches,0.0008560838587286921,0.013909343229571812,-0.02643730946848978,0.028149477185947167
40+plus_kijiji,Côte-Nord,0.008855844552445605,0.012767399806329403,-0.016196787978697173,0.03390847708358838
41+plus_kijiji,Estrie,0.008966220080621513,0.011920721338895976,-0.014425030740979655,0.032357470902222685
42+plus_kijiji,Gaspésie–Îles-de-la-Madeleine,0.029433116186786366,0.01738299705280041,-0.00467640055583602,0.06354263292940876
43+plus_kijiji,Lanaudière,-0.005925328848454676,0.015804169360052585,-0.036936815403536875,0.02508615770662752
44+plus_kijiji,Laurentides,-0.010763727631819556,0.01077261559267153,-0.0319021257345143,0.010374670470875189
45+plus_kijiji,Laval,0.0021135923046353847,0.013085600245814198,-0.02356342411066435,0.027790608719935116
46+plus_kijiji,Mauricie,0.01925623660576885,0.0130671011297856,-0.006384480205614218,0.04489695341715192
47+plus_kijiji,Montérégie,0.005860516100062409,0.011340907664669966,-0.016393004319322106,0.028114036519446925
48+plus_kijiji,Outaouais,0.012378532628881915,0.011186928856261978,-0.009572845258495716,0.03432991051625955
49+plus_kijiji,Saguenay–Lac-Saint-Jean,0.02740153078666884,0.017227621813210723,-0.006403103298488005,0.06120616487182569
50+plus_sampled_and_kijiji,(missing),0.015072842064218077,0.0126800633092265,-0.0097924429746427,0.039938127103078855
51+plus_sampled_and_kijiji,Abitibi-Témiscamingue,0.03442579427109656,0.011395327843569744,0.012079843148324682,0.056771745393868436
52+plus_sampled_and_kijiji,Bas-Saint-Laurent,0.02977957610775538,0.01427110116980974,0.001794305864858993,0.057764846350651766
53+plus_sampled_and_kijiji,Capitale-Nationale,0.012611545969583884,0.007945284974629402,-0.002968957912079252,0.02819204985124702
54+plus_sampled_and_kijiji,Centre-du-Québec,0.014142413802891645,0.008321896168093845,-0.002176615151237408,0.030461442757020698
55+plus_sampled_and_kijiji,Chaudière-Appalaches,0.004358845429906793,0.009467644427632087,-0.014206967026909274,0.022924657886722863
56+plus_sampled_and_kijiji,Côte-Nord,0.00990466142390408,0.009540721521925272,-0.008804453375739377,0.02861377622354754
57+plus_sampled_and_kijiji,Estrie,0.01100413303667426,0.008385307455036399,-0.005439243855320394,0.027447509928668914
58+plus_sampled_and_kijiji,Gaspésie–Îles-de-la-Madeleine,0.023779552572123067,0.01374828147853045,-0.0031804814136223505,0.050739586557868484
59+plus_sampled_and_kijiji,Lanaudière,0.0009611371152535073,0.010017059308117873,-0.018682064095444425,0.020604338325951443
60+plus_sampled_and_kijiji,Laurentides,-0.004686201580036019,0.006926462834102171,-0.01826882088412939,0.008896417724057352
61+plus_sampled_and_kijiji,Laval,0.0060949692783625435,0.008157745500934599,-0.009902164348535466,0.022092102905260553
62+plus_sampled_and_kijiji,Mauricie,0.01920387491519171,0.008445750045757846,0.0026419716239098855,0.03576577820647353
63+plus_sampled_and_kijiji,Montérégie,0.00733899220303613,0.0070890872532249636,-0.006562529494298846,0.021240513900371107
64+plus_sampled_and_kijiji,Outaouais,0.016027562667435227,0.007066363523208152,0.0021706016327905555,0.029884523702079897
65+plus_sampled_and_kijiji,Saguenay–Lac-Saint-Jean,0.02792943405192016,0.013152894882657895,0.0021369381969911574,0.05372192990684916
66+price_1000_500000,(missing),0.026183215304922736,0.02376965177325792,-0.020458467452176478,0.07282489806202194
67+price_1000_500000,Abitibi-Témiscamingue,0.02800218921778516,0.01562558076718551,-0.002658898626103183,0.058663277061673506
68+price_1000_500000,Bas-Saint-Laurent,0.02684371778848829,0.01945357479605897,-0.011328800378147034,0.06501623595512362
69+price_1000_500000,Capitale-Nationale,0.009238460225448981,0.01375551685170166,-0.017753119595624034,0.036230040046522
70+price_1000_500000,Centre-du-Québec,0.009992468275990438,0.01440280688989686,-0.018269247805441986,0.038254184357422866
71+price_1000_500000,Chaudière-Appalaches,-0.0007794688525527648,0.015498907687885309,-0.031191994132927586,0.029633056427822053
72+price_1000_500000,Côte-Nord,0.004495574375459647,0.01433580854539004,-0.023634675096383922,0.03262582384730322
73+price_1000_500000,Estrie,0.006192252143714681,0.013846377096792659,-0.020977616983705455,0.03336212127113482
74+price_1000_500000,Gaspésie–Îles-de-la-Madeleine,0.01920353395612386,0.01768107662271557,-0.01549092326636119,0.05389799117860891
75+price_1000_500000,Lanaudière,-0.008392486022886107,0.017357428762527476,-0.042451869540299454,0.02566689749452724
76+price_1000_500000,Laurentides,-0.012384189586342048,0.01233426526025529,-0.03658693739260015,0.011818558219916047
77+price_1000_500000,Laval,0.007779392649946875,0.014818661098816117,-0.02129832781685219,0.03685711311674594
78+price_1000_500000,Mauricie,0.020118514977479105,0.014317506860931127,-0.007975822257677283,0.04821285221263549
79+price_1000_500000,Montérégie,0.005582892307814388,0.013099378164940638,-0.020121188110049128,0.0312869727256779
80+price_1000_500000,Outaouais,0.014435560321025413,0.012360269813567845,-0.009818214573783,0.03868933521583383
81+price_1000_500000,Saguenay–Lac-Saint-Jean,0.02478240617421409,0.018488765808168696,-0.01149692840001355,0.06106174074844173
82+reweighted_to_otogo_mmy,(missing),0.029553656139787304,0.02551926191783764,-0.020521231448420628,0.07962854372799524
83+reweighted_to_otogo_mmy,Abitibi-Témiscamingue,0.02045755576273783,0.014680347941049896,-0.008348793545900127,0.04926390507137579
84+reweighted_to_otogo_mmy,Bas-Saint-Laurent,0.01811144183857978,0.01957019515613365,-0.02028995572839662,0.05651283940555618
85+reweighted_to_otogo_mmy,Capitale-Nationale,0.003196565752375278,0.013253954344620374,-0.022810858601939384,0.02920399010668994
86+reweighted_to_otogo_mmy,Centre-du-Québec,0.003457972918601276,0.014149510803005423,-0.024306747125804603,0.031222692963007153
87+reweighted_to_otogo_mmy,Chaudière-Appalaches,-0.004393741420505536,0.015063083412744054,-0.03395110914741947,0.025163626306408397
88+reweighted_to_otogo_mmy,Côte-Nord,-0.002691054716612693,0.014119559011042369,-0.030397002190697207,0.025014892757471825
89+reweighted_to_otogo_mmy,Estrie,0.0049748322813139585,0.013690250608420707,-0.02188870955928606,0.03183837412191398
90+reweighted_to_otogo_mmy,Gaspésie–Îles-de-la-Madeleine,0.012934096591646758,0.017094103603265152,-0.020608617892441033,0.04647681107573455
91+reweighted_to_otogo_mmy,Lanaudière,-0.013043532992734815,0.017064657027403996,-0.046528466261172546,0.020441400275702917
92+reweighted_to_otogo_mmy,Laurentides,-0.017329862110535728,0.012384787017856906,-0.04163177255448916,0.006972048333417701
93+reweighted_to_otogo_mmy,Laval,0.0026167348770272814,0.014755349479481747,-0.02633678536539432,0.03157025511944889
94+reweighted_to_otogo_mmy,Mauricie,0.016606706772612643,0.014216903047774022,-0.01129025295380166,0.04450366649902694
95+reweighted_to_otogo_mmy,Montérégie,0.0008424228096003863,0.01298822386324833,-0.024643574866062397,0.026328420485263168
96+reweighted_to_otogo_mmy,Outaouais,0.006763851102078603,0.012185448892356199,-0.017146910324374643,0.03067461252853185
97+reweighted_to_otogo_mmy,Saguenay–Lac-Saint-Jean,0.019297654876611718,0.01793795719245877,-0.015900901918112803,0.05449621167133624
98+direct_banner_only_unweighted,(missing),0.00539128554740795,0.014773404908655287,-0.023854145014599967,0.03463671610941587
99+direct_banner_only_unweighted,Abitibi-Témiscamingue,0.08855453851133051,0.019881165103408524,0.04919778648845019,0.12791129053421083
100+direct_banner_only_unweighted,Bas-Saint-Laurent,0.07050547989015095,0.02270863851755785,0.025551461840196424,0.11545949794010547
101+direct_banner_only_unweighted,Capitale-Nationale,0.029215387882629895,0.022410459682333874,-0.015148355381347962,0.07357913114660775
102+direct_banner_only_unweighted,Centre-du-Québec,0.03741677153345052,0.01813664508571293,0.0015134711256179073,0.07332007194128314
103+direct_banner_only_unweighted,Chaudière-Appalaches,0.012798331687068959,0.027451447353309236,-0.04154455015782437,0.06714121353196228
104+direct_banner_only_unweighted,Côte-Nord,0.034914767097859754,0.019588453191532955,-0.0038625324598400984,0.07369206665555961
105+direct_banner_only_unweighted,Estrie,0.029356252592830197,0.0203779070384631,-0.010983849704323378,0.06969635488998377
106+direct_banner_only_unweighted,Gaspésie–Îles-de-la-Madeleine,0.05390201631829499,0.025747024978112838,0.002933208800230361,0.10487082383635962
107+direct_banner_only_unweighted,Lanaudière,0.03709908748141625,0.02111507092057252,-0.004700304347185873,0.07889847931001837
108+direct_banner_only_unweighted,Laurentides,-0.0059330545796727205,0.01647757447834407,-0.03855205901475718,0.026685949855411742
109+direct_banner_only_unweighted,Laval,0.015479372165060422,0.020433936187553382,-0.024971645428823984,0.05593038975894483
110+direct_banner_only_unweighted,Mauricie,0.028981451986026672,0.022534968726136403,-0.015628769365933163,0.07359167333798651
111+direct_banner_only_unweighted,Montérégie,-0.0026795500277797045,0.019641245015409294,-0.041561356273610246,0.036202256218050836
112+direct_banner_only_unweighted,Outaouais,0.01829681171399932,0.021630141843842033,-0.024522214451719128,0.06111583787971777
113+direct_banner_only_unweighted,Saguenay–Lac-Saint-Jean,0.026707948501300757,0.01989228666156095,-0.01267081975674669,0.0660867167593482
114+direct_banner_only_reweighted,(missing),-0.002411644258633349,0.021456188695445196,-0.04488982911935349,0.040066540602086795
115+direct_banner_only_reweighted,Abitibi-Témiscamingue,0.06525610187779048,0.027421657927284404,0.01096769720553422,0.11954450655004673
116+direct_banner_only_reweighted,Bas-Saint-Laurent,0.04524739166764457,0.028927178176177,-0.012021587437087339,0.10251637077237648
117+direct_banner_only_reweighted,Capitale-Nationale,0.011796593041685744,0.028463128320495173,-0.044553676974771424,0.06814686305814291
118+direct_banner_only_reweighted,Centre-du-Québec,0.018177791476284653,0.026079680044939192,-0.03345381401442084,0.06980939696699014
119+direct_banner_only_reweighted,Chaudière-Appalaches,-0.0021086765845645134,0.031048976843260127,-0.06357831580171983,0.0593609626325908
120+direct_banner_only_reweighted,Côte-Nord,0.01102940029415874,0.027290065952005525,-0.042998483354003886,0.06505728394232137
121+direct_banner_only_reweighted,Estrie,0.01184049747795019,0.027364114996080015,-0.042333985784319274,0.06601498074021965
122+direct_banner_only_reweighted,Gaspésie–Îles-de-la-Madeleine,0.03376879051414811,0.03270423294647403,-0.03097786475381597,0.09851544578211219
123+direct_banner_only_reweighted,Lanaudière,0.013937463144445317,0.028513472188291825,-0.04251247583714003,0.07038740212603066
124+direct_banner_only_reweighted,Laurentides,-0.021601518214139012,0.025868244137397703,-0.07281453055700142,0.029611494128723395
125+direct_banner_only_reweighted,Laval,0.005804001762112585,0.029479748960836303,-0.05255893695830698,0.06416694048253216
126+direct_banner_only_reweighted,Mauricie,0.013338188085583307,0.030695355542762256,-0.0474313644951963,0.07410774066636291
127+direct_banner_only_reweighted,Montérégie,-0.01809095660410335,0.02693501520456079,-0.07141592364862444,0.03523401044041774
128+direct_banner_only_reweighted,Outaouais,-0.0009431942784990279,0.027889081911152063,-0.05615698801574238,0.054270599458744316
129+direct_banner_only_reweighted,Saguenay–Lac-Saint-Jean,0.003463022966910448,0.02648319287195545,-0.04896744259642793,0.05589348853024883
added results/robustness/hedonic_variants.csv +9 −0
@@ -0,0 +1,9 @@
1+variant,n,n_dealers,r2,rmse,age_b,age_se,age_lo,age_hi,km_b,km_se,km_lo,km_hi,H1_joint_stat,H1_joint_p,H4_region_stat,H4_region_df,H4_region_p,H4_region_p_A4_mmt,age_b_A4,region_premia_max_abs,region_premia_corr_with_baseline
2+baseline_S1,136393,1046,0.9280014597158732,0.1616760947428576,-0.08505418991227306,0.000916611868937161,-0.08685279934964217,-0.08325558047490396,-0.03155058993268052,0.0004703710294337211,-0.03247356922334309,-0.030627610642017956,663.8015588050786,2.453325670732276e-134,34.92463190263317,16,0.004069742562954826,0.5340937064633859,-0.08304609705447548,0.027955346116406706,1.0
3+plus_sampled_portals,231536,2348,0.9294730837928272,0.16450157472536325,-0.08651562905726723,0.0006402911201263733,-0.08777122410597595,-0.08526003400855851,-0.031940344172204864,0.00035274803605351624,-0.032632074345540864,-0.031248613998868864,1602.2370651916997,0.0,41.72374868977306,16,0.00043380115263381167,0.013781325877415903,-0.08479148033418463,0.03198881326444284,0.9440014879643905
4+plus_kijiji,146154,1047,0.9304919036479814,0.17913723960197267,-0.08790000445277865,0.001282762640696246,-0.09041708557732445,-0.08538292332823286,-0.03253957665459621,0.0004690703858562808,-0.03346000275437833,-0.03161915055481409,928.6141227228941,4.107167903797069e-191,37.271834760887955,16,0.0019199165020918594,0.4064627263763767,-0.08720620660081191,0.03230756803596063,0.9747629814696241
5+plus_sampled_and_kijiji,241297,2349,0.9305632845368418,0.17498810378298962,-0.08804333323958234,0.0008377695451712086,-0.08968617823408652,-0.08640048824507816,-0.03249115831307417,0.00033186046343144503,-0.03314192833015658,-0.03184038829599176,2020.8937118034041,0.0,43.107709781696606,16,0.0002692013556466114,0.010172455764048443,-0.08721606914415701,0.03442579427109656,0.9291550302387362
6+price_1000_500000,136695,1046,0.9268116080824476,0.16622453669934223,-0.08570695167932091,0.0009496528725780688,-0.08757039538622395,-0.08384350797241787,-0.03197232308350167,0.00047869927245417583,-0.032911644358160866,-0.03103300180884247,659.1160896902433,2.4652277761391827e-133,33.13158715234462,16,0.007099070172701061,0.5390717149888316,-0.08396785114680001,0.02800218921778516,0.9978305459211027
7+reweighted_to_otogo_mmy,130660,1045,0.926773813108368,0.16366423380641934,-0.08457293538246352,0.0009120829222530037,-0.08636265994228784,-0.0827832108226392,-0.03153284033049531,0.00044706007502278756,-0.03241007898551668,-0.030655601675473945,596.6105057518923,5.611952731913164e-120,30.938971502432846,16,0.013699730806122037,0.4747053083393441,-0.08259937018138454,0.029553656139787304,0.9743424074738103
8+direct_banner_only_unweighted,43689,123,0.942375171728972,0.1221416382390889,-0.08491246075471708,0.0011809428068437814,-0.0872502549916447,-0.08257466651778946,-0.029402949945637833,0.0005227379364633468,-0.03043776190114103,-0.028368137990134636,404.4834936813797,4.2512324907302643e-79,167.39932343069316,16,2.778190487708472e-27,,,,
9+direct_banner_only_reweighted,37956,122,0.938828267685913,0.12691674988040774,-0.0834045913928368,0.0013769862976326028,-0.08613069896635629,-0.08067848381931732,-0.029606159284854088,0.0005426716488757093,-0.030680520950237267,-0.02853179761947091,309.4511002926671,4.856762514089193e-59,248.2759183589775,16,1.1680470889405644e-43,,,,
added results/validation/exit_validation.json +26 −0
@@ -0,0 +1,26 @@
1+{
2+ "n_reliable_vin_listings": 131860,
3+ "pairs_overlapping": 17898,
4+ "pairs_both_exited": 8541,
5+ "both_exited_within_1d": 0.8029504741833509,
6+ "both_exited_within_3d": 0.8981383912890762,
7+ "both_exited_within_7d": 0.9384147055379932,
8+ "median_abs_exit_gap_days": 0.36759831440117624,
9+ "direct_exits_with_pair": 9557,
10+ "direct_exit_but_otogo_still_listed_7d": 0.11415716228942137,
11+ "otogo_exits_with_pair": 8733,
12+ "otogo_exit_but_direct_still_listed_7d": 0.028398030459177832,
13+ "all_exits_n": 68796,
14+ "all_exits_relisted_any_30d": 0.11641665213093784,
15+ "all_exits_relisted_other_dealer_30d": 0.05650037792894936,
16+ "S2_exits_n": 34053,
17+ "S2_exits_relisted_any_30d": 0.12944527648077997,
18+ "S2_exits_relisted_other_dealer_30d": 0.0575279711038675,
19+ "exits_observable_30d_n": 47398,
20+ "exits_observable_30d_relisted_any": 0.10167095658044643,
21+ "exits_observable_30d_relisted_other_dealer": 0.058019325709945564,
22+ "transfer_gap_days_median": 1.121260796026499,
23+ "transfer_gap_days_q75": 8.668015104736442,
24+ "vins_total": 95968,
25+ "vins_at_2plus_locations_5km": 10139
26+}
\ No newline at end of file
added scripts/01_ingest.py +6 −0
@@ -0,0 +1,6 @@
1+#!/usr/bin/env python
2+"""Step 01 — extract the read-only snapshot to Parquet (data/interim) + manifest."""
3+import json, sys
4+from ucml import ingest
5+m = ingest.run()
6+print(json.dumps(m, indent=2))
added scripts/02_clean.py +5 −0
@@ -0,0 +1,5 @@
1+#!/usr/bin/env python
2+"""Step 02 — cleaning, normalisation, price paths, sample flags, exclusion log."""
3+import json
4+from ucml import cleaning
5+print(json.dumps(cleaning.run(), indent=2))
added scripts/03_features.py +5 −0
@@ -0,0 +1,5 @@
1+#!/usr/bin/env python
2+"""Step 03 — listing-day risk set with time-varying local competition."""
3+import json
4+from ucml import features
5+print(json.dumps(features.build(), indent=2))
added scripts/04_hedonic.py +8 −0
@@ -0,0 +1,8 @@
1+#!/usr/bin/env python
2+"""Step 04 — Model A hedonic asking prices (H1, H4) + grouped CV."""
3+import json
4+from ucml import hedonic
5+df = hedonic.load()
6+r = hedonic.fit_all(df)
7+print(json.dumps(r["tests"], indent=2, default=float))
8+print(json.dumps(hedonic.grouped_cv(df), indent=2))
added scripts/05_descriptives.py +19 −0
@@ -0,0 +1,19 @@
1+#!/usr/bin/env python
2+"""Step 05 — descriptive figures (fig01, fig03, fig04) and tables (tab01-tab03)."""
3+import json
4+from ucml import descriptives
5+s = descriptives.run()
6+c, d = s["coverage"], s["dynamics"]
7+print(json.dumps({
8+ "S0": s["S0_n"], "S1": s["S1_n"], "S2": s["S2_n"], "S2L": s["S2L_n"],
9+ "active_canonical_auto": s["active_canonical_auto_at_snapshot"],
10+ "active_s2l_range_full_days": [c["active_s2l_min"], c["active_s2l_max"]],
11+ "entries_per_day_mean": round(c["entries_per_full_day_mean"], 1),
12+ "exits_per_day_mean": round(c["exits_per_full_day_mean_confirmable"], 1),
13+ "crawl_cycles_per_day_mean": round(c["crawl_cycles_per_full_day_mean"], 2),
14+ "max_gap_hours": round(c["max_gap_hours_same_source"], 1),
15+ "S2_completed_median_days": round(d["S2_completed_duration_median_days"], 1),
16+ "S2_share_with_cut": round(d["S2_share_with_cut"], 4),
17+ "S2_first_cut_median_pct": round(d["S2_first_cut_pct_median"], 2),
18+ "S2L_left_censored_share": round(d["S2L_left_censored_share"], 4),
19+}, indent=2))
added scripts/06_hazards.py +6 −0
@@ -0,0 +1,6 @@
1+#!/usr/bin/env python
2+"""Step 06 — Models B and C (markdown and exit hazards)."""
3+import json
4+from ucml import hazards
5+r = hazards.run()
6+print(json.dumps({k: {kk: vv for kk, vv in v.items() if kk != "coef" and kk != "cox_coef"} for k, v in r.items()}, indent=2, default=float))
added scripts/07_competition.py +6 −0
@@ -0,0 +1,6 @@
1+#!/usr/bin/env python
2+"""Step 07 — Model D competitor price-response event study (association)."""
3+import json
4+from ucml import competition
5+r = competition.run()
6+print(json.dumps({k: {kk: vv for kk, vv in v.items() if kk != "coef"} for k, v in r.items()}, indent=2, default=float))
added scripts/07b_competition_dow.py +18 −0
@@ -0,0 +1,18 @@
1+#!/usr/bin/env python
2+"""Step 07b — Model D robustness: dealer x day-of-week fixed effects (repricing calendars) and
3+day-of-week FE; also competitor-cut COUNT instead of indicator."""
4+import json
5+import numpy as np, pandas as pd, polars as pl
6+from ucml import competition as cp
7+from ucml import config as C
8+df = cp.load("25")
9+df["dow"] = (df["t"] + 2) % 7 # day 0 = 2026-08-12 (Wednesday) -> 0..6 arbitrary labels
10+df["dealer_dow"] = pd.factorize(df["dealer_id"].astype(str) + "|" + df["dow"].astype(str))[0]
11+res = {}
12+res["dow_fe"] = cp.fit(df, fe="mkt_week + mmy_week + dow", name="dow_fe")
13+res["dealer_dow_fe"] = cp.fit(df, fe="mkt_week + mmy_week + dealer_dow", name="dealer_dow_fe")
14+res["dealer_dow_fe_firstcut"] = cp.fit(df[df["in_first_cut_risk"]], y="first_cut", fe="mkt_week + mmy_week + dealer_dow", name="dealer_dow_fe_firstcut")
15+json.dump(res, open(cp.OUT / "summary_dow.json", "w"), indent=2, default=float)
16+for k, v in res.items():
17+ print(k, "leads p=%.3g sum=%.4f | lags p=%.3g sum=%.4f | lag0-7 sum=%.4f" % (v["leads_joint"]["p"], v["leads_joint"]["sum"], v["lags_joint"]["p"], v["lags_joint"]["sum"], v["lag0_7_sum"]["sum"]))
18+ print({kk: round(vv["b"]*100, 3) for kk, vv in v["coef"].items()})
added scripts/08_prediction.py +39 −0
@@ -0,0 +1,39 @@
1+#!/usr/bin/env python
2+"""Step 08 — Model F: prediction and economic value (markdown / exit within 30 days,
3+Cox time-to-exit). Idempotent: every output under results/prediction/, the figure
4+fig10_prediction and paper/tables/tab08_prediction.tex are rebuilt from scratch on
5+each run (stale files from a previous run are removed first).
6+
7+Usage: .venv/bin/python scripts/08_prediction.py [--n-boot 200]
8+"""
9+import argparse
10+import json
11+import shutil
12+
13+from ucml import config as C
14+from ucml import prediction
15+
16+ap = argparse.ArgumentParser()
17+ap.add_argument("--n-boot", type=int, default=prediction.N_BOOT, help="bootstrap resamples for the CIs (default 200)")
18+args = ap.parse_args()
19+
20+if prediction.OUT.exists():
21+ shutil.rmtree(prediction.OUT)
22+prediction.OUT.mkdir(parents=True, exist_ok=True)
23+for stem in ("fig10_prediction.pdf", "fig10_prediction.png"):
24+ (C.FIGURES / stem).unlink(missing_ok=True)
25+(C.ROOT / "paper" / "figures" / "fig10_prediction.pdf").unlink(missing_ok=True)
26+(C.TABLES / "tab08_prediction.tex").unlink(missing_ok=True)
27+
28+stats = C.PROCESSED / "cleaning_stats.json"
29+if stats.exists():
30+ s = json.loads(stats.read_text())
31+ print("cleaning_stats.json:", {k: v for k, v in s.items() if "S2" in k or "S1" in k}, flush=True)
32+
33+m = prediction.run_all(n_boot=args.n_boot)
34+for target in ("markdown30", "markdown30cr", "exit30"):
35+ for split in ("chronological", "dealer_holdout", "region_holdout"):
36+ r = m["targets"][target][split]
37+ print(f"{target:13s} {split:16s} n_test {r['n_test']:6d} base {r['models']['logistic']['base_rate']:.3f} | "
38+ + " | ".join(f"{k} logloss {r['models'][k]['log_loss']:.4f} brier {r['models'][k]['brier']:.4f} auc {r['models'][k]['auc']:.3f}" for k in ("logistic", "lightgbm")))
39+print("survival:", {k: m["survival"][k] for k in ("n_train", "n_test", "events_test", "c_index_train", "c_index_test", "dynamic_auc_30d")})
added scripts/09_amli.py +7 −0
@@ -0,0 +1,7 @@
1+#!/usr/bin/env python
2+"""Step 09 — Automobile Market Liquidity Index (construction, weights, internal validation, bootstrap)."""
3+import json, sys
4+from ucml import amli
5+reps = int(sys.argv[1]) if len(sys.argv) > 1 else 200
6+r = amli.run(reps=reps)
7+print(json.dumps({k: v for k, v in r.items() if k not in ("component_corr",)}, indent=2, default=float))
added scripts/10_validation.py +5 −0
@@ -0,0 +1,5 @@
1+#!/usr/bin/env python
2+"""Step 10 — H5 validation of listing exit as a sale proxy (cross-source consistency, relisting)."""
3+import json
4+from ucml import validation
5+print(json.dumps(validation.run(), indent=2))
added scripts/11_figures.py +5 −0
@@ -0,0 +1,5 @@
1+#!/usr/bin/env python
2+"""Step 11 — model-based figures 2, 5, 6, 7, 8, 9 (inputs: results/)."""
3+import sys
4+from ucml import figures
5+print(figures.run(sys.argv[1:] or None))
added scripts/12_robustness.py +19 −0
@@ -0,0 +1,19 @@
1+#!/usr/bin/env python
2+"""Step 12 — Phase 6 robustness suite (hedonic, hazards, AMLI variants; Holm; Table 9).
3+
4+Usage: .venv/bin/python scripts/12_robustness.py [hedonic|hazards|amli|all] (default all)
5+Writes results/robustness/*.{json,csv} and paper/tables/tab09_robustness.tex; logs runtime per block.
6+"""
7+import json, sys, time
8+from ucml import robustness as R
9+
10+t0 = time.time()
11+what = sys.argv[1] if len(sys.argv) > 1 else "all"
12+if what == "all":
13+ S = R.run()
14+ print(json.dumps({"seconds_total": S["seconds_total"], "hedonic_s": S["hedonic"]["seconds"], "hazards_s": S["hazards"]["seconds"], "amli_s": S["amli"]["seconds"]}, indent=2))
15+else:
16+ fn = {"hedonic": R.run_hedonic, "hazards": R.run_hazards, "amli": R.run_amli, "holm": R.holm_primary_tests}[what]
17+ out = fn()
18+ print(json.dumps(out if not hasattr(out, "to_dict") else out.to_dict(orient="records"), indent=2, default=float)[:6000])
19+ print(f"[{what}] {time.time() - t0:.1f} s")
added scripts/13_tables.py +4 −0
@@ -0,0 +1,4 @@
1+#!/usr/bin/env python
2+"""Step 13 — LaTeX table fragments for model results (tables 4-7)."""
3+from ucml import tables
4+print(tables.run())
added src/ucml/__init__.py +0 −0
added src/ucml/amli.py +175 −0
@@ -0,0 +1,175 @@
1+"""Automobile Market Liquidity Index (AMLI) — Phase 5 (analysis_plan §4, DECISIONS D-013).
2+
3+Higher AMLI = more liquid. Market-week panel (region x ISO-like week of the panel,
4+weeks 0..10 complete; week 11 partial is dropped). Components computed with
5+information available in week w, from the reliable-source listing-day universe (S2L):
6+ exit = confirmed exits / listing-days (+)
7+ mdfreq = sustained price cuts / listing-days (+)
8+ mddepth = median sustained cut size (%) (+)
9+ disp = IQR of hedonic residuals among active listings (-)
10+ arrival = new entries (S2) / active stock (+)
11+ stale = share of active stock with listing age > 30 d (-) [5-component variant only: mechanically censored before week 5]
12+Weights: equal, PCA (first component, signed), supervised ridge on next-week exit (fit weeks 0-5).
13+"""
14+from __future__ import annotations
15+
16+import json
17+import time
18+
19+import numpy as np
20+import pandas as pd
21+import polars as pl
22+
23+from . import config as C
24+
25+OUT = C.RESULTS / "amli"
26+WEEKS = 11 # complete weeks 0..10 (days 0..76)
27+WEEK_MIN = 2 # weeks 0-1 excluded: portal start-up (otogo day 6, autousagee day 11), DECISIONS D-015
28+MIN_LISTING_DAYS = 30
29+PRIMARY = ["exit", "mdfreq", "mddepth", "disp", "arrival"]
30+SIGN = {"exit": 1, "mdfreq": 1, "mddepth": 1, "disp": -1, "arrival": 1, "stale": -1}
31+
32+
33+def listing_days() -> pd.DataFrame:
34+ RS = pl.read_parquet(C.PROCESSED / "risk_set.parquet").select(["listing_id", "t", "listing_age", "cut", "exit", "s2_dynamic", "left_censored", "logp"])
35+ L = pl.read_parquet(C.PROCESSED / "listings.parquet").select(["listing_id", "dealer_id", "region", "entry_price", "body_filled"])
36+ R = pl.read_parquet(C.RESULTS / "hedonic" / "residuals.parquet").select(["listing_id", "resid_A2"])
37+ E = pl.read_parquet(C.PROCESSED / "price_events.parquet").filter(pl.col("kind") == "cut").with_columns(
38+ ((pl.col("ts") / 86400).floor() - (C.WINDOW_START_TS // 86400)).cast(pl.Int32).alias("t")).select(["listing_id", "t", "change_pct"])
39+ d = RS.join(L, on="listing_id", how="left").join(R, on="listing_id", how="left").join(E, on=["listing_id", "t"], how="left")
40+ d = d.with_columns([(pl.col("t") // 7).alias("week"), (pl.col("listing_age") == 0).cast(pl.Int8).alias("entry"),
41+ (pl.col("listing_age") > 30).cast(pl.Int8).alias("stale_flag"), pl.col("region").fill_null("(missing)")])
42+ # price tier (terciles of entry price over S2L)
43+ q = d.select(pl.col("entry_price").quantile(0.333).alias("q1"), pl.col("entry_price").quantile(0.667).alias("q2")).row(0)
44+ d = d.with_columns(pl.when(pl.col("entry_price") < q[0]).then(pl.lit("low")).when(pl.col("entry_price") < q[1]).then(pl.lit("mid")).otherwise(pl.lit("high")).alias("tier"))
45+ return d.filter((pl.col("week") < WEEKS) & (pl.col("week") >= WEEK_MIN)).to_pandas()
46+
47+
48+def components(d: pd.DataFrame, by: list[str], w: np.ndarray | None = None) -> pd.DataFrame:
49+ """Aggregate listing-days to market-week components; w = optional row weights (bootstrap)."""
50+ d = d.copy()
51+ d["w"] = 1.0 if w is None else w
52+ d["w_exit"] = d["w"] * d["exit"]; d["w_cut"] = d["w"] * d["cut"]; d["w_entry"] = d["w"] * d["entry"] * d["s2_dynamic"]
53+ d["w_stale"] = d["w"] * d["stale_flag"]
54+ g = d.groupby(by + ["week"])
55+ out = g.agg(ld=("w", "sum"), exits=("w_exit", "sum"), cuts=("w_cut", "sum"), entries=("w_entry", "sum"), stale=("w_stale", "sum")).reset_index()
56+ out["exit"] = out["exits"] / out["ld"]; out["mdfreq"] = out["cuts"] / out["ld"]; out["arrival"] = out["entries"] / out["ld"]; out["stale"] = out["stale"] / out["ld"]
57+ # depth: weighted median of cut sizes; dispersion: weighted IQR of residuals (approximate by unweighted when w is None)
58+ cutd = d[d["cut"] == 1].dropna(subset=["change_pct"])
59+ if w is None:
60+ dep = cutd.groupby(by + ["week"])["change_pct"].median().rename("mddepth").reset_index()
61+ rr = d.dropna(subset=["resid_A2"]).groupby(by + ["week"])["resid_A2"].agg(lambda s: s.quantile(0.75) - s.quantile(0.25)).rename("disp").reset_index()
62+ else:
63+ def wq(s, ww, q):
64+ o = np.argsort(s.values); cs = np.cumsum(ww.values[o]); return s.values[o][np.searchsorted(cs, q * cs[-1])]
65+ dep = cutd.groupby(by + ["week"]).apply(lambda x: wq(x["change_pct"], x["w"], 0.5)).rename("mddepth").reset_index()
66+ rr = d.dropna(subset=["resid_A2"]).groupby(by + ["week"]).apply(lambda x: wq(x["resid_A2"], x["w"], 0.75) - wq(x["resid_A2"], x["w"], 0.25)).rename("disp").reset_index()
67+ out = out.merge(dep, on=by + ["week"], how="left").merge(rr, on=by + ["week"], how="left")
68+ out["mddepth"] = -out["mddepth"] * 100 # positive % cut
69+ out.loc[out["ld"] < MIN_LISTING_DAYS, PRIMARY + ["stale"]] = np.nan
70+ return out
71+
72+
73+def build_index(comp: pd.DataFrame, cols: list[str], fit_weeks=range(WEEK_MIN, 6)) -> tuple[pd.DataFrame, dict]:
74+ Z = comp.copy()
75+ mu, sd = {}, {}
76+ for c in cols:
77+ mu[c] = float(Z[c].mean()); sd[c] = float(Z[c].std()); Z[f"z_{c}"] = SIGN[c] * (Z[c] - mu[c]) / sd[c]
78+ zc = [f"z_{c}" for c in cols]
79+ ok = Z[zc].notna().sum(axis=1) >= min(3, len(cols))
80+ Z["amli_equal"] = Z.loc[ok, zc].mean(axis=1)
81+ # PCA on complete rows
82+ comp_rows = Z[zc].dropna()
83+ X = comp_rows.values - comp_rows.values.mean(0)
84+ U, S, Vt = np.linalg.svd(X, full_matrices=False)
85+ v = Vt[0]; v = v * np.sign(v[cols.index("exit")] if "exit" in cols else v[0])
86+ expl = float(S[0] ** 2 / (S ** 2).sum())
87+ Z["amli_pca"] = (Z[zc].fillna(Z[zc].mean()) @ v) / np.abs(v).sum()
88+ Z.loc[~ok, "amli_pca"] = np.nan
89+ # supervised: ridge of next-week exit on the other components (fit weeks)
90+ weights = {"equal": {c: 1 / len(cols) for c in cols}, "pca": dict(zip(cols, v.tolist())), "pca_explained": expl}
91+ if "exit" in cols:
92+ others = [f"z_{c}" for c in cols if c != "exit"]
93+ Z = Z.sort_values([*[c for c in Z.columns if c in ("region", "tier", "body_filled", "market")], "week"])
94+ key = [c for c in ("region", "tier", "body_filled", "market") if c in Z.columns]
95+ Z["exit_next"] = Z.groupby(key)["exit"].shift(-1) if key else Z["exit"].shift(-1)
96+ fitrows = Z[Z["week"].isin(list(fit_weeks))].dropna(subset=others + ["exit_next"])
97+ A = fitrows[others].values; y = fitrows["exit_next"].values; lam = 1.0
98+ beta = np.linalg.solve(A.T @ A + lam * np.eye(A.shape[1]), A.T @ (y - y.mean()))
99+ Z["amli_supervised"] = Z[others].fillna(0) @ beta / np.abs(beta).sum()
100+ weights["supervised"] = dict(zip([c for c in cols if c != "exit"], beta.tolist()))
101+ weights["supervised_fit_weeks"] = list(fit_weeks)
102+ return Z, {"mu": mu, "sd": sd, "weights": weights}
103+
104+
105+def predictive_validation(Z: pd.DataFrame, key: list[str], eval_weeks=range(6, WEEKS)) -> dict:
106+ """Leave-exit-out index (equal weights on non-exit components) vs next-week exit intensity, with market FE."""
107+ import pyfixest as pf
108+ others = [f"z_{c}" for c in PRIMARY if c != "exit"]
109+ Z = Z.copy(); Z["amli_noexit"] = Z[others].mean(axis=1)
110+ Z["mkt"] = Z[key].astype(str).agg("|".join, axis=1) if key else "all"
111+ Z["exit_next"] = Z.groupby("mkt")["exit"].shift(-1)
112+ d = Z[Z["week"].isin(list(eval_weeks))].dropna(subset=["amli_noexit", "exit_next", "exit"])
113+ res = {}
114+ for name, f in {"noexit_fe": "exit_next ~ amli_noexit | mkt", "noexit_fe_lagexit": "exit_next ~ amli_noexit + exit | mkt", "noexit_pooled": "exit_next ~ amli_noexit"}.items():
115+ m = pf.feols(f, data=d, vcov={"CRV1": "mkt"} if "|" in f or len(d["mkt"].unique()) > 1 else "hetero")
116+ t = m.tidy().reset_index(); t = t.rename(columns={t.columns[0]: "term"}); r = t[t["term"] == "amli_noexit"].iloc[0]
117+ res[name] = {"b": float(r["Estimate"]), "se": float(r["Std. Error"]), "lo": float(r["2.5%"]), "hi": float(r["97.5%"]), "n": int(m._N), "r2": float(m._r2)}
118+ return res
119+
120+
121+def bootstrap(d: pd.DataFrame, key: list[str], reps: int = 200, seed: int = 36) -> pd.DataFrame:
122+ """Block bootstrap over dealers: index values per market-week (equal weights, 5 components)."""
123+ rng = np.random.default_rng(seed)
124+ dealers = d["dealer_id"].unique(); idx = pd.factorize(d["dealer_id"])[0]
125+ rows = []
126+ for r in range(reps):
127+ draw = rng.integers(0, len(dealers), len(dealers)); mult = np.bincount(draw, minlength=len(dealers)).astype(float)
128+ w = mult[idx]
129+ comp = components(d[w > 0], key, w[w > 0])
130+ Z, _ = build_index(comp, PRIMARY)
131+ Z["rep"] = r; rows.append(Z[key + ["week", "amli_equal", "amli_pca", "exit"]])
132+ if r % 20 == 0:
133+ print("boot", r, flush=True)
134+ B = pd.concat(rows)
135+ q = B.groupby(key + ["week"]).agg(amli_equal_lo=("amli_equal", lambda s: s.quantile(0.025)), amli_equal_hi=("amli_equal", lambda s: s.quantile(0.975)),
136+ amli_pca_lo=("amli_pca", lambda s: s.quantile(0.025)), amli_pca_hi=("amli_pca", lambda s: s.quantile(0.975))).reset_index()
137+ return q
138+
139+
140+def run(reps: int = 200) -> dict:
141+ OUT.mkdir(parents=True, exist_ok=True); t0 = time.time()
142+ d = listing_days()
143+ out = {"listing_days": int(len(d)), "weeks": WEEKS}
144+ # --- region x week (primary) --------------------------------------------
145+ comp = components(d, ["region"])
146+ Z, meta = build_index(comp, PRIMARY)
147+ Z.to_csv(OUT / "index_region_week.csv", index=False); out["region"] = meta
148+ out["region_validation"] = predictive_validation(Z, ["region"])
149+ # 5-component variant with staleness (weeks >= 6)
150+ Z5, meta5 = build_index(comp[comp["week"] >= 6], PRIMARY + ["stale"]); Z5.to_csv(OUT / "index_region_week_5comp.csv", index=False); out["region_5comp"] = meta5
151+ # leave-one-component-out
152+ loo = {}
153+ for c in PRIMARY:
154+ cols = [x for x in PRIMARY if x != c]; Zc, mc = build_index(comp, cols)
155+ loo[c] = {"corr_with_full_equal": float(Zc["amli_equal"].corr(Z["amli_equal"])), "pca_explained": mc["weights"]["pca_explained"]}
156+ out["leave_one_out"] = loo
157+ out["corr_equal_pca"] = float(Z["amli_equal"].corr(Z["amli_pca"])); out["corr_equal_supervised"] = float(Z["amli_equal"].corr(Z["amli_supervised"]))
158+ # --- province x week, daily province -------------------------------------
159+ d["market"] = "Quebec"; compP = components(d, ["market"]); ZP, metaP = build_index(compP, PRIMARY); ZP.to_csv(OUT / "index_province_week.csv", index=False); out["province"] = metaP
160+ # --- segments -----------------------------------------------------------
161+ for seg in ["tier", "body_filled"]:
162+ dd = d.dropna(subset=[seg]); cs = components(dd, [seg]); Zs, ms = build_index(cs, PRIMARY); Zs.to_csv(OUT / f"index_{seg}_week.csv", index=False)
163+ out[f"segment_{seg}"] = {"pca_explained": ms["weights"]["pca_explained"], "validation": predictive_validation(Zs, [seg])}
164+ # --- component correlations ------------------------------------------------
165+ out["component_corr"] = comp[PRIMARY].corr().round(3).to_dict()
166+ # --- bootstrap --------------------------------------------------------------
167+ if reps:
168+ q = bootstrap(d, ["region"], reps=reps); q.to_csv(OUT / "bootstrap_region_week.csv", index=False)
169+ out["seconds"] = round(time.time() - t0, 1)
170+ json.dump(out, open(OUT / "summary.json", "w"), indent=2, default=float)
171+ return out
172+
173+
174+if __name__ == "__main__":
175+ print(json.dumps(run(), indent=2, default=float))
added src/ucml/cleaning.py +380 −0
@@ -0,0 +1,380 @@
1+"""Cleaning, normalisation, linkage keys and sample construction (Phase 3).
2+
3+Produces in data/processed:
4+ listings.parquet one row per listing (anonymised ids, normalised fields, sample flags)
5+ price_events.parquet sustained price events (entry / cut / rise / minor)
6+ exclusions.csv auditable exclusion log (uid, rule, value)
7+ sample_construction.csv attrition table for the paper
8+ sources_registry.csv public registry of sources with class and quality flags
9+ id_maps/ (interim) uid -> listing_id, dealer key -> dealer_id (restricted)
10+"""
11+from __future__ import annotations
12+
13+import csv
14+import hashlib
15+import json
16+import math
17+import re
18+from pathlib import Path
19+
20+import numpy as np
21+import polars as pl
22+
23+from . import config as C
24+from . import pricepaths
25+
26+# ----------------------------------------------------------------------------- helpers
27+
28+def _norm(s: str | None) -> str:
29+ return (s or "").strip().lower()
30+
31+
32+def powertrain(fuel: str | None, engine: str | None, title: str | None, make: str | None) -> str:
33+ f = _norm(fuel)
34+ if "rechargeable" in f or "plug" in f:
35+ return "PHEV"
36+ if "hybride" in f or "hybrid" in f:
37+ return "HEV"
38+ if "électrique" in f or "electrique" in f or f in {"ev", "electric"}:
39+ return "BEV"
40+ if f.startswith("diesel"):
41+ return "Diesel"
42+ if f.startswith("essence") or "flex" in f or "polycarb" in f or "éthanol" in f or "propane" in f:
43+ return "ICE"
44+ if _norm(make) in {"tesla", "polestar"}:
45+ return "BEV"
46+ e = _norm(engine) + " " + _norm(title)
47+ if re.search(r"\b(électrique|electric|\bev\b|kwh)", e):
48+ return "BEV"
49+ return "unknown"
50+
51+
52+_BODY = [
53+ (r"vus|sport utility|suv|crossover|utilitaire", "SUV"),
54+ (r"camionnette|pickup|pick-up|truck|camion", "Pickup"),
55+ (r"fourgon|minivan|van", "Van"),
56+ (r"berline|sedan|4dr car|4 portes|intermédiaire|compact", "Sedan"),
57+ (r"hayon|hatch|5 portes", "Hatchback"),
58+ (r"coupé|coupe|2 portes|2dr|voiture 2 portes", "Coupe"),
59+ (r"cabriolet|convertible|décapotable", "Convertible"),
60+ (r"familiale|wagon", "Wagon"),
61+]
62+
63+
64+def body_segment(body: str | None) -> str | None:
65+ b = _norm(body)
66+ if not b or b in {"n/d", "autre", "other", "custom", "n.d."}:
67+ return None
68+ for pat, lab in _BODY:
69+ if re.search(pat, b):
70+ return lab
71+ return None
72+
73+
74+def transmission_norm(t: str | None) -> str:
75+ s = _norm(t)
76+ if not s or s in {"n/a", "0", "null", "na", "unknown", "other", "transmission"}:
77+ return "unknown"
78+ if "manuel" in s or "manual" in s:
79+ return "Manual"
80+ return "Automatic"
81+
82+
83+def drivetrain_norm(d: str | None) -> str:
84+ s = _norm(d)
85+ if not s or s in {"n/a", "null", "other", "not defined"}:
86+ return "unknown"
87+ if "intégrale" in s or "4x4" in s or "four" in s or "4 roues" in s or "quatre" in s or "awd" in s:
88+ return "AWD/4x4"
89+ if "traction" in s or "fwd" in s or "4x2" in s:
90+ return "FWD"
91+ if "propulsion" in s or "rwd" in s:
92+ return "RWD"
93+ return "unknown"
94+
95+
96+def source_class(src: str) -> str:
97+ if src in C.SAMPLED_PORTALS:
98+ return "sampled_portal"
99+ if src in C.PRIVATE_MARKETPLACE:
100+ return "private_marketplace"
101+ if src in C.FULL_PORTALS:
102+ return "full_portal"
103+ if src in C.BANNERS:
104+ return "banner"
105+ if src in C.SMALL_PORTALS:
106+ return "small_portal"
107+ return "direct_dealer"
108+
109+
110+def _hid(prefix: str, key: str, n: int = 10) -> str:
111+ return prefix + hashlib.sha1(key.encode()).hexdigest()[:n]
112+
113+
114+def vehicle_age_years(ts: float, year: int) -> float:
115+ """Age at observation: years since 1 October of (model year - 1), floored at 0."""
116+ import datetime as dt
117+ ref = dt.datetime(year - 1, 10, 1, tzinfo=dt.timezone.utc).timestamp()
118+ return max(0.0, (ts - ref) / (365.25 * 86400))
119+
120+
121+# ----------------------------------------------------------------------------- region recovery
122+
123+def build_city_region_map(v: pl.DataFrame) -> dict[str, str]:
124+ d = (v.filter((pl.col("region") != "") & pl.col("region").is_not_null() & (pl.col("city") != "") & pl.col("city").is_not_null())
125+ .with_columns(pl.col("city").str.to_lowercase().str.strip_chars())
126+ .group_by(["city", "region"]).len().sort(["city", "len"], descending=[False, True])
127+ .unique(subset=["city"], keep="first"))
128+ return dict(zip(d["city"].to_list(), d["region"].to_list()))
129+
130+
131+def nearest_city_region(lat: float, lng: float, cities: list[tuple[str, float, float]], cmap: dict[str, str]) -> str | None:
132+ best, bd = None, 1e9
133+ for name, clat, clng in cities:
134+ d = (lat - clat) ** 2 + ((lng - clng) * math.cos(math.radians(lat))) ** 2
135+ if d < bd:
136+ bd, best = d, name
137+ if best is None or bd > (0.5 ** 2): # > ~50 km from any known city -> leave missing
138+ return None
139+ return cmap.get(best)
140+
141+
142+REGION_FIX = {"Saguenay-Lac-Saint-Jean": "Saguenay–Lac-Saint-Jean"}
143+
144+
145+# ----------------------------------------------------------------------------- main
146+
147+def run(interim: Path = C.INTERIM, out: Path = C.PROCESSED, registry: Path = C.RAW / "registry") -> dict:
148+ out.mkdir(parents=True, exist_ok=True)
149+ (interim / "id_maps").mkdir(exist_ok=True)
150+ v = pl.read_parquet(interim / "vehicles.parquet")
151+ pl_log = pl.read_parquet(interim / "price_log.parquet")
152+ sync = pl.read_parquet(interim / "sync_log.parquet")
153+ snap_ts = C.SNAPSHOT_TS
154+ excl: list[tuple[str, str, str]] = []
155+
156+ # --- sources registry ---------------------------------------------------
157+ reg = json.loads((registry / "sources.json").read_text())["sources"]
158+ reg_rows = []
159+ for s in reg:
160+ reg_rows.append({"source": s["id"], "name": s.get("name"), "type": s.get("type", "direct"),
161+ "connector": s.get("connector"), "platform": s.get("platform"), "region": s.get("region"),
162+ "city": s.get("city"), "status": s.get("status"), "class": source_class(s["id"]),
163+ "in_dynamic_sample": source_class(s["id"]) in {"direct_dealer", "full_portal", "banner"} and s["id"] not in C.FLICKER_OR_FAILED})
164+ # sync quality per source
165+ sq = (sync.group_by("source").agg([pl.len().alias("n_syncs"), pl.col("ok").sum().alias("n_ok"),
166+ pl.col("found").mean().alias("mean_found"), pl.col("added").sum().alias("added"),
167+ pl.col("removed").sum().alias("removed")]))
168+ inact = v.group_by("source").agg([(pl.col("active") == 0).sum().alias("n_inactive"), pl.len().alias("n_listings")])
169+ sq = sq.join(inact, on="source", how="left").with_columns(
170+ ((pl.col("removed") - pl.col("n_inactive")) / pl.col("removed").clip(lower_bound=1)).alias("reactivation_share"))
171+ regdf = pl.DataFrame(reg_rows).join(sq, on="source", how="left")
172+ regdf.write_csv(out / "sources_registry.csv")
173+
174+ # first non-empty crawl per source: entries within 24 h of it are the source's start-up stock (left-censored, D-014)
175+ ff = sync.filter(pl.col("found") > 0).group_by("source").agg(pl.col("ts").min().alias("first_found"))
176+ first_found = dict(zip(ff["source"].to_list(), ff["first_found"].to_list()))
177+
178+ # crawl interval per source (median seconds) for interval-censoring midpoints
179+ iv = (sync.sort(["source", "ts"]).with_columns((pl.col("ts") - pl.col("ts").shift(1).over("source")).alias("dt"))
180+ .filter(pl.col("dt").is_not_null()).group_by("source").agg(pl.col("dt").median().alias("crawl_median_s")))
181+ med_iv = dict(zip(iv["source"].to_list(), iv["crawl_median_s"].to_list()))
182+
183+ # --- price paths ---------------------------------------------------------
184+ events, psum = pricepaths.build(pl_log, v)
185+
186+ # --- region recovery -----------------------------------------------------
187+ cmap = build_city_region_map(v)
188+ cmap = {k: REGION_FIX.get(r, r) for k, r in cmap.items()}
189+ gps = json.loads((registry / "villes_gps.json").read_text())
190+ cities = [(name, c[0], c[1]) for name, c in gps.items() if name in cmap]
191+
192+ # --- per-listing table ---------------------------------------------------
193+ v = v.join(psum, on="uid", how="left")
194+ rows = []
195+ dealer_keys: dict[str, str] = {}
196+ vin_re = re.compile(r"^[A-HJ-NPR-Z0-9]{17}$")
197+ for r in v.iter_rows(named=True):
198+ uid, src = r["uid"], r["source"]
199+ cls = source_class(src)
200+ region = REGION_FIX.get(r["region"] or "", r["region"] or "") or None
201+ city = (r["city"] or "").strip() or None
202+ if region is None and city:
203+ region = cmap.get(city.lower())
204+ region_recovered = False
205+ if region is None and r["lat"] is not None and r["lng"] is not None:
206+ region = nearest_city_region(r["lat"], r["lng"], cities, cmap)
207+ region_recovered = region is not None
208+ dealer_key = src if cls in {"direct_dealer", "banner", "small_portal"} else f"{src}|{(r['dealer_name'] or '').strip().lower()}"
209+ if cls == "private_marketplace":
210+ dealer_key = "kijiji|private"
211+ dealer_id = dealer_keys.setdefault(dealer_key, _hid("d", dealer_key, 8))
212+ vin = (r["vin"] or "").strip().upper()
213+ vin_ok = bool(vin_re.match(vin))
214+ year = r["year"]
215+ entry_ts = r["first_seen"]
216+ entry_price = r["entry_price"] if r["entry_price"] is not None else r["price"]
217+ # exit variables (D-004)
218+ active = int(r["active"] or 0)
219+ last_seen = r["last_seen"]
220+ exit_confirmed = (active == 0) and (last_seen <= snap_ts - C.EXIT_CONFIRM_DAYS * 86400)
221+ half_iv = 0.5 * med_iv.get(src, 3.3 * 3600)
222+ if exit_confirmed:
223+ end_ts = min(last_seen + half_iv, snap_ts)
224+ else:
225+ end_ts = last_seen if active == 0 else snap_ts
226+ rows.append({
227+ "listing_id": _hid("l", uid), "uid": uid, "title": r["title"], "source": src, "source_class": cls,
228+ "dealer_id": dealer_id, "kind": r["kind"], "make": (r["make"] or "").strip() or None,
229+ "model": (r["model"] or "").strip() or None, "trim": (r["trim"] or "").strip() or None, "year": year,
230+ "price_raw": r["price"], "entry_price": entry_price, "last_price": r["last_price"] if r["last_price"] is not None else r["price"],
231+ "mileage_km": r["mileage_km"], "transmission": transmission_norm(r["transmission"]),
232+ "drivetrain": drivetrain_norm(r["drivetrain"]), "powertrain": powertrain(r["fuel"], r["engine"], r["title"], r["make"]),
233+ "body": body_segment(r["body_type"]), "exterior_color": (r["exterior_color"] or "").strip().lower() or None,
234+ "region": region, "region_recovered": region_recovered, "city": city,
235+ "lat_r": round(r["lat"], 2) if r["lat"] is not None else None, "lng_r": round(r["lng"], 2) if r["lng"] is not None else None,
236+ "lat": r["lat"], "lng": r["lng"], # exact coords stay in interim-only columns (dropped from release)
237+ "vin_ok": vin_ok, "vin_hash": _hid("v", vin, 12) if vin_ok else None,
238+ "has_stock_number": bool((r["stock_number"] or "").strip()), "is_canonical": r["dup_of"] is None,
239+ "n_dup_sources": len(json.loads(r["dup_sources"])) if r["dup_sources"] else 0,
240+ "entry_ts": entry_ts, "last_seen_ts": last_seen, "end_ts": end_ts, "active": active,
241+ "exit_confirmed": exit_confirmed,
242+ "left_censored": (entry_ts < C.LEFT_CENSOR_CUTOFF_TS) or (entry_ts < first_found.get(src, C.WINDOW_START_TS) + C.SOURCE_STARTUP_SECONDS),
243+ "duration_days": (end_ts - entry_ts) / 86400,
244+ "n_cuts": r["n_cuts"] or 0, "n_rises": r["n_rises"] or 0, "n_transients": r["n_transients"] or 0,
245+ "oscillating": bool(r["oscillating"]) if r["oscillating"] is not None else False,
246+ "first_cut_ts": r["first_cut_ts"], "first_cut_pct": r["first_cut_pct"], "total_change_pct": r["total_change_pct"],
247+ "age_years_entry": vehicle_age_years(entry_ts, int(year)) if year and 1980 <= year <= 2027 else None,
248+ })
249+ L = pl.DataFrame(rows)
250+
251+ # --- coordinates outside the Quebec bounding box are errors -> null (flagged) ---
252+ inbox = pl.col("lat").is_between(44.0, 63.0) & pl.col("lng").is_between(-80.0, -56.0)
253+ L = L.with_columns([
254+ (pl.col("lat").is_not_null() & ~inbox).alias("coords_out_of_bbox"),
255+ pl.when(inbox).then(pl.col("lat")).otherwise(None).alias("lat"),
256+ pl.when(inbox).then(pl.col("lng")).otherwise(None).alias("lng"),
257+ ])
258+
259+ # --- coordinates: fill missing from the dealer's median location (flagged) ---
260+ dm = (L.filter(pl.col("lat").is_not_null()).group_by("dealer_id")
261+ .agg(pl.col("lat").median().alias("_dlat"), pl.col("lng").median().alias("_dlng")))
262+ L = L.join(dm, on="dealer_id", how="left").with_columns([
263+ (pl.col("lat").is_null() & pl.col("_dlat").is_not_null()).alias("coords_from_dealer"),
264+ pl.when(pl.col("lat").is_null()).then(pl.col("_dlat")).otherwise(pl.col("lat")).alias("lat"),
265+ pl.when(pl.col("lng").is_null()).then(pl.col("_dlng")).otherwise(pl.col("lng")).alias("lng"),
266+ ]).drop(["_dlat", "_dlng"])
267+
268+ # --- coordinates: fill missing from city centroid (flagged) --------------
269+ gps_l = {k: v for k, v in gps.items()}
270+ L = L.with_columns([
271+ pl.col("city").str.to_lowercase().str.strip_chars().alias("_city_l")])
272+ fill_lat = pl.col("_city_l").map_elements(lambda c: gps_l.get(c, [None, None])[0] if c else None, return_dtype=pl.Float64)
273+ fill_lng = pl.col("_city_l").map_elements(lambda c: gps_l.get(c, [None, None])[1] if c else None, return_dtype=pl.Float64)
274+ L = L.with_columns([
275+ (pl.col("lat").is_null() & fill_lat.is_not_null()).alias("coords_from_city"),
276+ pl.when(pl.col("lat").is_null()).then(fill_lat).otherwise(pl.col("lat")).alias("lat"),
277+ pl.when(pl.col("lng").is_null()).then(fill_lng).otherwise(pl.col("lng")).alias("lng"),
278+ ]).drop("_city_l")
279+ L = L.with_columns([pl.col("lat").round(2).alias("lat_r"), pl.col("lng").round(2).alias("lng_r")])
280+
281+ # --- powertrain recovery: title keywords, then make-model(-trim) modal (>= 90 % agreement) ---
282+ kw = pl.col("title").fill_null("").str.to_lowercase()
283+ L = L.with_columns(
284+ pl.when(pl.col("powertrain") != "unknown").then(pl.col("powertrain"))
285+ .when(kw.str.contains("rechargeable|plug-in|phev")).then(pl.lit("PHEV"))
286+ .when(kw.str.contains("hybrid")).then(pl.lit("HEV"))
287+ .when(kw.str.contains(r"électrique|electric|\bev\b")).then(pl.lit("BEV"))
288+ .when(kw.str.contains("diesel|tdi")).then(pl.lit("Diesel"))
289+ .otherwise(pl.lit("unknown")).alias("powertrain"))
290+ known = L.filter(pl.col("powertrain") != "unknown")
291+ def modal(keys):
292+ g = known.group_by(keys + ["powertrain"]).len()
293+ tot = g.group_by(keys).agg(pl.col("len").sum().alias("tot"))
294+ g = g.join(tot, on=keys).with_columns((pl.col("len") / pl.col("tot")).alias("sh")).filter(pl.col("sh") >= 0.9)
295+ return g.select(keys + [pl.col("powertrain").alias("pt_fill")])
296+ L = L.with_columns(pl.col("powertrain").alias("_pt0"))
297+ for keys in (["make", "model", "trim", "year"], ["make", "model", "year"], ["make", "model"]):
298+ m = modal(keys)
299+ L = L.join(m, on=keys, how="left").with_columns(
300+ pl.when((pl.col("powertrain") == "unknown") & pl.col("pt_fill").is_not_null()).then(pl.col("pt_fill")).otherwise(pl.col("powertrain")).alias("powertrain")).drop("pt_fill")
301+ L = L.with_columns(((pl.col("_pt0") == "unknown") & (pl.col("powertrain") != "unknown")).alias("powertrain_recovered")).drop("_pt0")
302+
303+ # --- body recovery from make-model modal body ----------------------------
304+ mm = (L.filter(pl.col("body").is_not_null()).group_by(["make", "model", "body"]).len()
305+ .sort(["make", "model", "len"], descending=[False, False, True]).unique(subset=["make", "model"], keep="first")
306+ .rename({"body": "body_mm"}).select(["make", "model", "body_mm"]))
307+ L = L.join(mm, on=["make", "model"], how="left").with_columns(
308+ pl.when(pl.col("body").is_null()).then(pl.col("body_mm")).otherwise(pl.col("body")).alias("body_filled"),
309+ pl.col("body").is_null().alias("body_recovered")).drop("body_mm")
310+
311+ # --- sample flags + exclusion log ---------------------------------------
312+ def flag(cond: pl.Expr, name: str):
313+ return pl.when(cond).then(pl.lit(name)).otherwise(pl.lit(None))
314+
315+ L = L.with_columns([
316+ (pl.col("kind") == "auto").alias("f_auto"),
317+ pl.col("is_canonical").alias("f_canonical"),
318+ pl.col("year").is_between(C.YEAR_MIN, C.YEAR_MAX).fill_null(False).alias("f_year"),
319+ pl.col("mileage_km").is_between(C.KM_MIN, C.KM_MAX).fill_null(False).alias("f_km"),
320+ pl.col("entry_price").is_between(C.PRICE_MIN, C.PRICE_MAX).fill_null(False).alias("f_price"),
321+ (pl.col("make").is_not_null() & pl.col("model").is_not_null()).alias("f_mm"),
322+ (~pl.col("uid").is_in(list(C.UID_COLLISIONS))).alias("f_nocollision"),
323+ (~pl.col("source").is_in(list(C.SAMPLED_PORTALS | C.PRIVATE_MARKETPLACE))).alias("f_not_sampled_or_private"),
324+ (~pl.col("source").is_in(list(C.FLICKER_OR_FAILED | C.SMALL_PORTALS))).alias("f_source_reliable"),
325+ ])
326+ L = L.with_columns([
327+ (pl.col("f_auto") & pl.col("f_canonical") & pl.col("f_year") & pl.col("f_km") & pl.col("f_price") & pl.col("f_mm") & pl.col("f_nocollision")).alias("s1_all_sources"),
328+ ])
329+ L = L.with_columns([
330+ (pl.col("s1_all_sources") & pl.col("f_not_sampled_or_private")).alias("s1_hedonic"),
331+ ])
332+ L = L.with_columns([
333+ (pl.col("s1_hedonic") & pl.col("f_source_reliable") & ~pl.col("oscillating") & ~pl.col("left_censored")).alias("s2_dynamic"),
334+ (pl.col("s1_hedonic") & pl.col("f_source_reliable") & ~pl.col("oscillating")).alias("s2l_dynamic_with_stock"),
335+ ])
336+
337+ # exclusion log (sequential attrition, S0 -> S1 -> S2)
338+ steps = [
339+ ("kind != auto", ~pl.col("f_auto")), ("VIN duplicate (non-canonical)", ~pl.col("f_canonical")),
340+ (f"model year outside [{C.YEAR_MIN},{C.YEAR_MAX}] or missing", ~pl.col("f_year")),
341+ (f"mileage outside [{C.KM_MIN:.0f},{C.KM_MAX:.0f}] km or missing", ~pl.col("f_km")),
342+ (f"entry price outside [{C.PRICE_MIN:.0f},{C.PRICE_MAX:.0f}] $ or missing/placeholder", ~pl.col("f_price")),
343+ ("make or model missing", ~pl.col("f_mm")), ("uid identity collision", ~pl.col("f_nocollision")),
344+ ("sampled portal (autotrader/cargurus) or private marketplace (kijiji)", ~pl.col("f_not_sampled_or_private")),
345+ ("flickering/failed connector or small portal", ~pl.col("f_source_reliable")),
346+ ("oscillating price path", pl.col("oscillating")), ("left-censored stock (day 1 or source start-up day)", pl.col("left_censored")),
347+ ]
348+ remaining = L
349+ attrition = [("S0 all listings in snapshot", len(L))]
350+ for name, cond in steps:
351+ dropped = remaining.filter(cond)
352+ for uid in dropped["uid"].to_list():
353+ excl.append((uid, name, ""))
354+ remaining = remaining.filter(~cond)
355+ attrition.append((f"− {name}", len(remaining)))
356+ with open(out / "exclusions.csv", "w", newline="") as f:
357+ w = csv.writer(f); w.writerow(["uid", "rule", "value"]); w.writerows(excl)
358+ with open(out / "sample_construction.csv", "w", newline="") as f:
359+ w = csv.writer(f); w.writerow(["step", "listings_remaining"]); w.writerows(attrition)
360+ stats = {"S0": len(L), "S1_all_sources": int(L["s1_all_sources"].sum()), "S1_hedonic": int(L["s1_hedonic"].sum()),
361+ "S2_dynamic": int(L["s2_dynamic"].sum()), "S2L": int(L["s2l_dynamic_with_stock"].sum()),
362+ "exit_confirmed_in_S2": int(L.filter(pl.col("s2_dynamic"))["exit_confirmed"].sum()),
363+ "region_recovered": int(L["region_recovered"].sum()), "region_missing": int(L["region"].is_null().sum()),
364+ "body_recovered": int(L["body_recovered"].sum()), "body_missing_after": int(L["body_filled"].is_null().sum()),
365+ "oscillating": int(L["oscillating"].sum()), "n_dealer_ids": L["dealer_id"].n_unique(),
366+ "powertrain_recovered": int(L["powertrain_recovered"].sum()), "powertrain_unknown_after": int((L["powertrain"] == "unknown").sum()),
367+ "coords_from_city": int(L["coords_from_city"].sum()), "coords_from_dealer": int(L["coords_from_dealer"].sum()), "coords_out_of_bbox": int(L["coords_out_of_bbox"].sum()), "coords_missing_after": int(L["lat"].is_null().sum())}
368+ # restricted id maps stay in interim
369+ pl.DataFrame({"dealer_key": list(dealer_keys.keys()), "dealer_id": list(dealer_keys.values())}).write_csv(interim / "id_maps" / "dealers.csv")
370+ L.select(["uid", "listing_id"]).write_csv(interim / "id_maps" / "listings.csv")
371+ L.write_parquet(interim / "listings_full.parquet") # includes exact lat/lng, uid
372+ L.drop(["lat", "lng", "uid", "city", "title"]).write_parquet(out / "listings.parquet") # release-grade
373+ events = events.join(L.select(["uid", "listing_id"]), on="uid", how="left").drop("uid")
374+ events.write_parquet(out / "price_events.parquet")
375+ (out / "cleaning_stats.json").write_text(json.dumps(stats, indent=2))
376+ return stats
377+
378+
379+if __name__ == "__main__":
380+ print(json.dumps(run(), indent=2))
added src/ucml/competition.py +118 −0
@@ -0,0 +1,118 @@
1+"""Model D — competitor price-response event study / distributed lags (H3, association only).
2+
3+Outcome: sustained own price cut on day t (S2 listing-days). Treatment: indicator of
4+>= 1 sustained cut among local competitors (same make-model, year +-1, other dealer,
5+within 25 km; 10/50 km robustness) on day t-k, k = -7..+14. Linear probability
6+model with market x week and make-model-year x week fixed effects (DECISIONS D-011),
7+SEs clustered by dealer (primary) and by region (secondary). Placebo: competitor-cut
8+series permuted across listings within make-model (geography broken).
9+"""
10+from __future__ import annotations
11+
12+import json
13+import time
14+
15+import numpy as np
16+import pandas as pd
17+import polars as pl
18+import pyfixest as pf
19+
20+from . import config as C
21+
22+OUT = C.RESULTS / "competition"
23+LEADS, LAGS = 7, 14
24+
25+
26+def load(radius: str = "25") -> pd.DataFrame:
27+ RS = pl.read_parquet(C.PROCESSED / "risk_set.parquet").filter(pl.col("s2_dynamic"))
28+ L = pl.read_parquet(C.PROCESSED / "listings.parquet").select(["listing_id", "dealer_id", "make", "model", "year", "region", "powertrain", "body_filled", "source_class"])
29+ RS = RS.join(L, on="listing_id", how="left").sort(["listing_id", "t"])
30+ RS = RS.with_columns([(pl.col(f"ccut_{radius}") > 0).cast(pl.Int8).alias("cc")])
31+ shifts = []
32+ for k in range(-LEADS, LAGS + 1):
33+ name = f"cc_m{abs(k)}" if k < 0 else f"cc_p{k}" # m = lead (future competitor cut), p = lag (past)
34+ shifts.append(pl.col("cc").shift(k).over("listing_id").fill_null(0).alias(name))
35+ RS = RS.with_columns(shifts)
36+ RS = RS.with_columns([
37+ (pl.col("t") // 7).alias("week"),
38+ pl.concat_str([pl.col("region").fill_null("(missing)"), pl.lit("|"), (pl.col("t") // 7).cast(pl.Utf8)]).alias("mkt_week"),
39+ pl.concat_str([pl.col("make"), pl.lit("|"), pl.col("model"), pl.lit("|"), pl.col("year").cast(pl.Utf8), pl.lit("|"), (pl.col("t") // 7).cast(pl.Utf8)]).alias("mmy_week"),
40+ pl.concat_str([pl.col("make"), pl.lit("|"), pl.col("model")]).alias("mm"),
41+ pl.col("listing_age").clip(upper_bound=60).alias("age_c"),
42+ (pl.col(f"dens_{radius}") + 1).log().alias("ldens"),
43+ ])
44+ df = RS.to_pandas()
45+ for c in ["mkt_week", "mmy_week", "mm", "dealer_id", "region"]:
46+ df[c] = pd.factorize(df[c])[0]
47+ return df
48+
49+
50+def lag_names():
51+ return [f"cc_m{k}" for k in range(LEADS, 0, -1)] + [f"cc_p{k}" for k in range(0, LAGS + 1)]
52+
53+
54+def fit(df: pd.DataFrame, y: str = "cut", fe: str = "mkt_week + mmy_week", cluster: str = "dealer_id", name: str = "main") -> dict:
55+ t0 = time.time()
56+ rhs = " + ".join(lag_names()) + " + ldens + C(age_c)"
57+ m = pf.feols(f"{y} ~ {rhs} | {fe}", data=df, vcov={"CRV1": cluster}, fixef_rm="none")
58+ t = m.tidy().reset_index(); t = t.rename(columns={t.columns[0]: "term"})
59+ t = t[t["term"].isin(lag_names())].copy()
60+ t["k"] = t["term"].map(lambda s: -int(s[4:]) if s.startswith("cc_m") else int(s[4:]))
61+ t = t.sort_values("k"); t.to_csv(OUT / f"eventstudy_{name}.csv", index=False)
62+ names = list(m._coefnames)
63+ def joint(cols):
64+ idx = [names.index(c) for c in cols]; b = np.asarray(m.coef())[idx]; V = np.asarray(m._vcov)[np.ix_(idx, idx)]
65+ W = float(b @ np.linalg.solve(V, b)); from scipy.stats import chi2
66+ return {"stat": W, "df": len(idx), "p": float(chi2.sf(W, len(idx))), "sum": float(b.sum())}
67+ leads = [f"cc_m{k}" for k in range(1, LEADS + 1)]; lags = [f"cc_p{k}" for k in range(0, LAGS + 1)]
68+ out = {"n": int(m._N), "base_rate": float(df[y].mean()), "fe": fe, "cluster": cluster, "seconds": round(time.time() - t0, 1),
69+ "leads_joint": joint(leads), "lags_joint": joint(lags), "lag0_7_sum": joint([f"cc_p{k}" for k in range(0, 8)]),
70+ "coef": {r.term: {"b": float(r.Estimate), "se": float(r["Std. Error"]), "lo": float(r["2.5%"]), "hi": float(r["97.5%"])} for _, r in t.iterrows()}}
71+ return out
72+
73+
74+def placebo(df: pd.DataFrame, seed: int = 36) -> dict:
75+ """Permute the competitor-cut series across listings within make-model (breaks geography)."""
76+ rng = np.random.default_rng(seed)
77+ cols = lag_names()
78+ first = df.groupby("listing_id")["mm"].first()
79+ perm = {}
80+ for mm, ids in first.groupby(first).groups.items():
81+ ids = np.array(list(ids)); sh = ids.copy(); rng.shuffle(sh); perm.update(dict(zip(ids, sh)))
82+ # map each listing's series to another listing's series of the same make-model, aligned on listing_age
83+ src = df[["listing_id", "listing_age"] + cols].copy(); src["listing_id"] = src["listing_id"].map(perm)
84+ d2 = df.drop(columns=cols).merge(src, on=["listing_id", "listing_age"], how="left")
85+ d2[cols] = d2[cols].fillna(0)
86+ return fit(d2, name="placebo_permuted")
87+
88+
89+def selection_check(df: pd.DataFrame) -> dict:
90+ """Do listings that ever receive a competitor cut differ at entry? (selection into treatment)"""
91+ g = df.groupby("listing_id").agg(any_cc=("cc", "max"), ldens=("ldens", "first"), logp=("logp", "first"), pp=("pricepos_25", "first"))
92+ out = {}
93+ for v in ["ldens", "logp", "pp"]:
94+ a, b = g.loc[g.any_cc == 1, v].dropna(), g.loc[g.any_cc == 0, v].dropna()
95+ out[v] = {"mean_treated": float(a.mean()), "mean_untreated": float(b.mean()), "std_diff": float((a.mean() - b.mean()) / np.sqrt((a.var() + b.var()) / 2))}
96+ out["share_ever_treated"] = float(g.any_cc.mean())
97+ return out
98+
99+
100+def run() -> dict:
101+ OUT.mkdir(parents=True, exist_ok=True)
102+ res = {}
103+ df = load("25")
104+ print("main", flush=True); res["main_25km"] = fit(df)
105+ print("cluster region", flush=True); res["main_25km_cluster_region"] = fit(df, cluster="region", name="main_cluster_region")
106+ print("first cut only", flush=True)
107+ res["firstcut_25km"] = fit(df[df["in_first_cut_risk"]], y="first_cut", name="firstcut")
108+ print("no FE / coarse", flush=True); res["coarse_fe"] = fit(df, fe="region + week", name="coarse_fe")
109+ print("placebo", flush=True); res["placebo_permuted"] = placebo(df)
110+ res["selection"] = selection_check(df)
111+ for r in ("10", "50"):
112+ print("radius", r, flush=True); d = load(r); res[f"main_{r}km"] = fit(d, name=f"main_{r}km")
113+ json.dump(res, open(OUT / "summary.json", "w"), indent=2, default=float)
114+ return res
115+
116+
117+if __name__ == "__main__":
118+ print(json.dumps(run(), indent=2, default=float))
added src/ucml/config.py +53 −0
@@ -0,0 +1,53 @@
1+"""Project-wide constants and paths (UQO WP36)."""
2+from __future__ import annotations
3+
4+import os
5+from pathlib import Path
6+
7+ROOT = Path(os.environ.get("UCML_ROOT", Path(__file__).resolve().parents[2]))
8+DATA = ROOT / "data"
9+RAW = DATA / "raw"
10+INTERIM = DATA / "interim"
11+PROCESSED = DATA / "processed"
12+SYNTHETIC = DATA / "synthetic"
13+RESULTS = ROOT / "results"
14+FIGURES = ROOT / "figures"
15+TABLES = ROOT / "paper" / "tables"
16+
17+SNAPSHOT = Path(os.environ.get("UCML_SNAPSHOT", RAW / "autoka-snapshot-20261101.db"))
18+SNAPSHOT_DATE = "2026-11-01"
19+# last sync in the snapshot (UTC) — used as the right-censoring point
20+def _manifest_ts() -> tuple[float, float]:
21+ """(first sync ts, last sync ts) from data/interim/manifest.json when available."""
22+ import json
23+ m = INTERIM / "manifest.json"
24+ if m.exists():
25+ d = json.loads(m.read_text())
26+ return float(d["sync_ts_min"]), float(d["sync_ts_max"])
27+ return 1786537835.64518, 1793515782.732466 # 2026-08-12 12:30:35 UTC, 2026-11-01 06:49:42 UTC
28+
29+
30+WINDOW_START_TS, SNAPSHOT_TS = _manifest_ts()
31+
32+# --- Source classification (DECISIONS.md D-003) ---------------------------------
33+SAMPLED_PORTALS = {"autotrader", "cargurus"} # capped sliding windows -> no exit information
34+PRIVATE_MARKETPLACE = {"kijiji"} # private sellers, no VIN
35+FULL_PORTALS = {"otogo", "autousagee"} # full-inventory portals (dealer-identified)
36+BANNERS = {"hgregoire", "automobileendirect", "megacentredeliquidation", "clubautozone", "leprixdugros"}
37+SMALL_PORTALS = {"okaze", "classeauto", "ototr"}
38+FLICKER_OR_FAILED = {"leprixdugros", "albioccasion", "baiecomeaugm", "septilesgm",
39+ "stjeromeautodepot", "excelmoto"}
40+UID_COLLISIONS = {"classeauto:-98"}
41+
42+# --- Cleaning thresholds (analysis_plan.md §1-2) --------------------------------
43+PRICE_MIN, PRICE_MAX = 2_000.0, 250_000.0
44+KM_MIN, KM_MAX = 1_000.0, 400_000.0
45+YEAR_MIN, YEAR_MAX = 2005, 2026
46+TRANSIENT_SECONDS = 48 * 3600 # A->B->A level shorter than this is removed
47+OSCILLATION_MAX_TRANSIENTS = 3 # >= 3 transients -> oscillating uid
48+MARKDOWN_MIN_PCT, MARKDOWN_MIN_ABS = 0.01, 100.0
49+EXIT_CONFIRM_DAYS = 7
50+LEFT_CENSOR_CUTOFF_TS = 1786579200.0 # 2026-08-13 00:00:00 UTC: entries before this are the day-1 stock
51+SOURCE_STARTUP_SECONDS = 24 * 3600 # entries within 24 h of a source's first non-empty crawl = start-up stock (D-014)
52+
53+RESTRICTED_COLUMNS = ["description", "images", "url", "carfax_url", "features"]
added src/ucml/descriptives.py +458 −0
@@ -0,0 +1,458 @@
1+"""Descriptive figures and tables for WP36 (Phase 4a).
2+
3+Inputs (read-only): data/processed/listings.parquet, data/processed/price_events.parquet,
4+data/interim/sync_log.parquet, data/processed/sources_registry.csv,
5+data/processed/sample_construction.csv.
6+
7+Outputs: figures/fig01_coverage, fig03_listing_age_and_cuts, fig04_price_paths (PDF + PNG,
8+copied to paper/figures/), paper/tables/tab01_sources.tex, tab02_sample_construction.tex,
9+tab03_descriptives.tex, results/descriptives/*.csv|json (every number quoted in captions is
10+in results/descriptives/summary.json).
11+
12+Conventions: day index t = UTC calendar days since 2026-08-12 (day 0, partial: crawl started
13+12:30 UTC); day 81 (2026-11-01) is partial (snapshot 06:49 UTC). "Exit" = observed
14+disappearance confirmed by >= 7 days of absence before the snapshot — not a sale.
15+"""
16+from __future__ import annotations
17+
18+import hashlib
19+import json
20+import math
21+from pathlib import Path
22+
23+import matplotlib.pyplot as plt
24+import numpy as np
25+import pandas as pd
26+import polars as pl
27+
28+from . import config as C
29+from . import plotstyle as PS
30+
31+OUT = C.RESULTS / "descriptives"
32+DAY0 = math.floor(C.WINDOW_START_TS / 86400)
33+T = int(math.floor(C.SNAPSHOT_TS / 86400) - DAY0) + 1 # number of day indices (82)
34+DAY_LABELS = {0: "2026-08-12", 81: "2026-11-01"}
35+WITHIN_DAYS = 30 # exit-within-30-days horizon
36+CLASS_SHORT = {"direct_dealer": "Dealer websites", "full_portal": "Full-inventory portals", "banner": "Dealer groups",
37+ "sampled_portal": "Sampled portals", "private_marketplace": "Private sellers (Kijiji)", "small_portal": "Small portals"}
38+
39+
40+# ----------------------------------------------------------------------------- helpers
41+def day_of(ts: pl.Expr) -> pl.Expr:
42+ return ((ts / 86400).floor() - DAY0).cast(pl.Int32)
43+
44+
45+def fmt_int(x) -> str:
46+ """Integer with thin-space thousands separators (LaTeX)."""
47+ return f"{int(round(float(x))):,}".replace(",", "\\,")
48+
49+
50+def fmt1(x) -> str:
51+ return "--" if x is None or (isinstance(x, float) and np.isnan(x)) else f"{float(x):.1f}"
52+
53+
54+def fmt_pct(x) -> str:
55+ return "--" if x is None or (isinstance(x, float) and np.isnan(x)) else f"{100 * float(x):.1f}"
56+
57+
58+def write_tex(path: Path, lines: list[str]) -> None:
59+ path.parent.mkdir(parents=True, exist_ok=True)
60+ path.write_text("\n".join(lines) + "\n", encoding="utf-8")
61+
62+
63+def load() -> tuple[pl.DataFrame, pl.DataFrame, pl.DataFrame, pd.DataFrame, pd.DataFrame]:
64+ L = pl.read_parquet(C.PROCESSED / "listings.parquet")
65+ E = pl.read_parquet(C.PROCESSED / "price_events.parquet")
66+ S = pl.read_parquet(C.INTERIM / "sync_log.parquet")
67+ reg = pd.read_csv(C.PROCESSED / "sources_registry.csv")
68+ sc = pd.read_csv(C.PROCESSED / "sample_construction.csv")
69+ L = L.with_columns(day_of(pl.col("entry_ts")).alias("e_day"),
70+ pl.min_horizontal(day_of(pl.col("end_ts")), pl.lit(T - 1)).alias("x_day"))
71+ return L, E, S, reg, sc
72+
73+
74+# ----------------------------------------------------------------------------- fig01
75+def daily_coverage(L: pl.DataFrame, S: pl.DataFrame) -> tuple[pd.DataFrame, dict]:
76+ """Daily active stock / entries / confirmed exits in S2L, and crawl cycles per day."""
77+ U = L.filter(pl.col("s2l_dynamic_with_stock"))
78+ e = U["e_day"].to_numpy(); x = U["x_day"].to_numpy(); ex = U["exit_confirmed"].to_numpy()
79+ days = np.arange(T)
80+ entries = np.bincount(e, minlength=T)[:T]
81+ exits = np.bincount(x[ex], minlength=T)[:T]
82+ ends = np.bincount(x, minlength=T)[:T]
83+ # observed-active on day t: entered on or before t and not yet ended before t (ends are inclusive days)
84+ active = np.cumsum(entries) - np.concatenate([[0], np.cumsum(ends)[:-1]])
85+ # crawl cycles per day = sync rows / distinct sources logged that day (one row per source per cycle)
86+ sd = S.with_columns(day_of(pl.col("ts")).alias("day"))
87+ agg = (sd.group_by("day").agg(pl.len().alias("rows"), pl.col("source").n_unique().alias("n_sources"),
88+ (pl.col("ok") == 0).sum().alias("failed_rows"))
89+ .with_columns((pl.col("rows") / pl.col("n_sources")).alias("cycles")).sort("day").to_pandas())
90+ cyc = np.zeros(T); cyc[agg["day"].to_numpy()] = agg["cycles"].to_numpy()
91+ nsrc = np.zeros(T, dtype=int); nsrc[agg["day"].to_numpy()] = agg["n_sources"].to_numpy()
92+ fail = np.zeros(T); fail[agg["day"].to_numpy()] = (agg["failed_rows"] / agg["rows"]).to_numpy()
93+ # largest gap between consecutive crawls of the same source (hours)
94+ gaps = (sd.sort(["source", "ts"]).with_columns(pl.col("ts").diff().over("source").alias("dt"))
95+ .filter(pl.col("dt").is_not_null()).select((pl.col("dt") / 3600).alias("h")))
96+ # connector start-up stock: for portal sources first crawled after day 0, the first crawl day's entries are
97+ # initial stock, not an entry flow (not flagged left_censored because the cut-off is 2026-08-13 00:00 UTC)
98+ first_day = sd.group_by("source").agg(pl.col("day").min().alias("first")).filter(pl.col("first") > 0)
99+ st = (U.join(first_day, on="source", how="inner").filter(pl.col("e_day") == pl.col("first"))
100+ .group_by(["source", "e_day"]).agg(pl.len().alias("n")).filter(pl.col("n") >= 1000).sort("e_day"))
101+ startup_src = {str(int(r["e_day"])): r["source"] for r in st.iter_rows(named=True)}
102+ startup_n = {str(int(r["e_day"])): int(entries[int(r["e_day"])]) for r in st.iter_rows(named=True)}
103+ startup_listings = {r["source"]: int(r["n"]) for r in st.iter_rows(named=True)}
104+ cov = pd.DataFrame({"day": days, "active_s2l": active, "entries_s2l": entries, "exits_confirmed_s2l": exits,
105+ "crawl_cycles": cyc, "sources_logged": nsrc, "failed_share": fail})
106+ cov["date_utc"] = pd.to_datetime((DAY0 + days) * 86400, unit="s").strftime("%Y-%m-%d")
107+ full = cov[(cov.day >= 1) & (cov.day <= T - 2)]
108+ info = {"T_days": int(T), "day0_initial_stock_s2l": int(entries[0]),
109+ "connector_startup_entries": startup_n, "connector_startup_source": startup_src, "connector_startup_listings_s2l": startup_listings,
110+ "connector_startup_listings_s2_total": int(L.filter(pl.col("s2_dynamic")).join(first_day, on="source", how="inner")
111+ .filter((pl.col("e_day") == pl.col("first")) & pl.col("source").is_in(list(startup_listings))).height),
112+ "entries_per_full_day_mean_excl_startup": float(full[~full.day.isin([int(k) for k in startup_n])].entries_s2l.mean()), "entries_day0_left_censored": int(U["left_censored"].sum()),
113+ "active_s2l_min": int(full.active_s2l.min()), "active_s2l_max": int(full.active_s2l.max()),
114+ "active_s2l_mean_full_days": float(full.active_s2l.mean()), "active_s2l_at_last_full_day": int(active[T - 2]),
115+ "entries_per_full_day_mean": float(full.entries_s2l.mean()), "entries_per_full_day_median": float(full.entries_s2l.median()),
116+ "exits_per_full_day_mean_confirmable": float(cov[(cov.day >= 1) & (cov.day <= T - 1 - C.EXIT_CONFIRM_DAYS)].exits_confirmed_s2l.mean()),
117+ "crawl_cycles_per_full_day_mean": float(full.crawl_cycles.mean()), "crawl_cycles_per_full_day_min": float(full.crawl_cycles.min()),
118+ "crawl_cycles_per_full_day_max": float(full.crawl_cycles.max()), "crawl_cycles_total": int(round(cyc.sum())),
119+ "days_with_fewer_than_4_cycles": [int(d) for d in full.day[full.crawl_cycles < 4]],
120+ "max_gap_hours_same_source": float(gaps["h"].max()), "median_gap_hours_same_source": float(gaps["h"].median()),
121+ "sources_logged_min": int(full.sources_logged.min()), "sources_logged_max": int(full.sources_logged.max()),
122+ "failed_source_cycles_share": float((S["ok"] == 0).mean())}
123+ return cov, info
124+
125+
126+def region_stock(L: pl.DataFrame) -> pd.DataFrame:
127+ """Active canonical auto listings by region at the snapshot (all sources)."""
128+ A = L.filter((pl.col("active") == 1) & pl.col("is_canonical") & (pl.col("kind") == "auto"))
129+ r = (A.group_by(pl.col("region").fill_null("Region missing")).agg(pl.len().alias("active_canonical"))
130+ .sort("active_canonical", descending=True).to_pandas())
131+ r["share"] = r.active_canonical / r.active_canonical.sum()
132+ return r
133+
134+
135+def fig01(cov: pd.DataFrame, reg: pd.DataFrame, info: dict) -> None:
136+ fig = plt.figure(figsize=(6.3, 4.6))
137+ gs = fig.add_gridspec(3, 2, width_ratios=[1.35, 1.0])
138+ ax_a1 = fig.add_subplot(gs[0, 0]); ax_a2 = fig.add_subplot(gs[1, 0], sharex=ax_a1); ax_b = fig.add_subplot(gs[2, 0], sharex=ax_a1)
139+ ax_c = fig.add_subplot(gs[:, 1])
140+ d = cov.day.to_numpy()
141+ c_act, c_ent, c_ex = PS.CAT[0], PS.CAT[2], PS.CAT[1]
142+ # (a1) active stock
143+ ax_a1.plot(d, cov.active_s2l / 1000, color=c_act, lw=1.4)
144+ ax_a1.set_ylabel("Active listings\n(thousands)")
145+ ax_a1.set_ylim(0, None)
146+ ax_a1.set_title("(a) Dynamic-reliable universe (S2L): stock and flows", loc="left")
147+ # (a2) entries / exits (day 0 = initial left-censored stock, not an entry flow)
148+ m = d >= 1
149+ ent = cov.entries_s2l.to_numpy().astype(float)
150+ spikes = {int(k): int(v) for k, v in info["connector_startup_entries"].items()}
151+ ycap = float(np.quantile(ent[m & ~np.isin(d, list(spikes))], 1.0)) * 1.5
152+ ax_a2.plot(d[m], np.minimum(ent[m], ycap), color=c_ent, lw=1.1, label="Entries")
153+ mx = m & (d <= T - 1 - C.EXIT_CONFIRM_DAYS)
154+ ax_a2.plot(d[mx], cov.exits_confirmed_s2l[mx], color=c_ex, lw=1.1, label="Confirmed exits")
155+ ax_a2.plot(list(spikes), [ycap] * len(spikes), ls="none", marker="^", ms=4, color=c_ent, clip_on=False)
156+ note = "\u25b2 truncated: connector\n start-up stock\n" + "\n".join(
157+ f" day {k}: {v:,} ({info['connector_startup_source'][str(k)]})".replace(",", "\u2009") for k, v in spikes.items())
158+ ax_a2.text(0.14, 0.97, note, transform=ax_a2.transAxes, fontsize=6, color=PS.TEXT2, ha="left", va="top", linespacing=1.1)
159+ ax_a2.axvspan(T - 1 - C.EXIT_CONFIRM_DAYS, T - 1, color=PS.GRID, alpha=0.6, lw=0, zorder=0)
160+ ax_a2.set_ylabel("Listings per day")
161+ ax_a2.set_ylim(0, ycap)
162+ ax_a2.legend(loc="upper right", bbox_to_anchor=(1.0, 1.02), ncol=1, handlelength=1.4, labelspacing=0.3)
163+ # (b) crawl cycles per day
164+ ax_b.bar(d, cov.crawl_cycles, width=0.8, color=PS.CAT[0], alpha=0.85)
165+ ax_b.set_ylabel("Crawl cycles\nper day")
166+ ax_b.set_xlabel("Day index (0 = 2026-08-12 UTC; days 0 and 81 are partial)")
167+ ax_b.set_title("(b) Crawl cycles per day, all sources", loc="left")
168+ ax_b.set_ylim(0, max(12, cov.crawl_cycles.max() * 1.1))
169+ ax_b.set_xlim(-1, T)
170+ for ax in (ax_a1, ax_a2):
171+ plt.setp(ax.get_xticklabels(), visible=False)
172+ # (c) regions
173+ rr = reg.sort_values("active_canonical", ascending=True)
174+ cols = [PS.color_for(PS.REGIONS, k) for k in rr.region]
175+ ax_c.barh(rr.region, rr.active_canonical / 1000, color=cols, height=0.72)
176+ ax_c.set_xlabel("Active canonical listings (thousands)")
177+ ax_c.set_title("(c) Stock by region, 2026-11-01", loc="left")
178+ ax_c.tick_params(axis="y", labelsize=7)
179+ ax_c.grid(axis="y", visible=False)
180+ for y, v in enumerate(rr.active_canonical):
181+ ax_c.text(v / 1000 + 0.08, y, f"{v:,}".replace(",", " "), va="center", fontsize=6, color=PS.TEXT2)
182+ ax_c.set_xlim(0, rr.active_canonical.max() / 1000 * 1.22)
183+ PS.save(fig, "fig01_coverage")
184+
185+
186+# ----------------------------------------------------------------------------- fig03
187+def duration_and_cuts(L: pl.DataFrame) -> tuple[pd.DataFrame, pd.DataFrame, pd.DataFrame, dict]:
188+ S2 = L.filter(pl.col("s2_dynamic"))
189+ done = S2.filter(pl.col("exit_confirmed"))["duration_days"].to_numpy()
190+ done = np.sort(done)
191+ ecdf = pd.DataFrame({"duration_days": done, "ecdf": np.arange(1, done.size + 1) / done.size})
192+ fc = -100 * S2.filter(pl.col("n_cuts") >= 1)["first_cut_pct"].to_numpy()
193+ cuts = pd.DataFrame({"first_cut_pct": np.sort(fc)})
194+ bc = (S2.group_by("source_class").agg(pl.len().alias("n"), (pl.col("n_cuts") >= 1).sum().alias("n_with_cut"),
195+ pl.col("exit_confirmed").sum().alias("n_exit_confirmed"))
196+ .with_columns((pl.col("n_with_cut") / pl.col("n")).alias("share_with_cut")).to_pandas())
197+ bc["order"] = bc.source_class.map(PS.CLASS_ORDER.index); bc = bc.sort_values("order").drop(columns="order")
198+ S2L = L.filter(pl.col("s2l_dynamic_with_stock"))
199+ q = lambda a, p: float(np.quantile(a, p))
200+ info = {"S2_n": S2.height, "S2_exit_confirmed": int(S2["exit_confirmed"].sum()),
201+ "S2_right_censored_share": float(1 - S2["exit_confirmed"].mean()),
202+ "S2L_n": S2L.height, "S2L_left_censored_n": int(S2L["left_censored"].sum()), "S2L_left_censored_share": float(S2L["left_censored"].mean()),
203+ "S2_completed_duration_median_days": q(done, .5), "S2_completed_duration_p25_days": q(done, .25), "S2_completed_duration_p75_days": q(done, .75),
204+ "S2_completed_duration_mean_days": float(done.mean()),
205+ "S2_completed_share_within_7d": float((done <= 7).mean()), "S2_completed_share_within_30d": float((done <= 30).mean()),
206+ "S2_share_with_cut": float((S2["n_cuts"] >= 1).mean()), "S2_n_with_cut": int((S2["n_cuts"] >= 1).sum()),
207+ "S2_share_with_2plus_cuts": float((S2["n_cuts"] >= 2).mean()),
208+ "S2_first_cut_pct_median": q(fc, .5), "S2_first_cut_pct_mean": float(fc.mean()), "S2_first_cut_pct_p25": q(fc, .25), "S2_first_cut_pct_p75": q(fc, .75),
209+ "S2_first_cut_share_le_5pct": float((fc <= 5).mean()), "S2_first_cut_share_gt_10pct": float((fc > 10).mean()),
210+ "S2_share_with_cut_by_class": {r.source_class: float(r.share_with_cut) for r in bc.itertuples()}}
211+ return ecdf, cuts, bc, info
212+
213+
214+def fig03(ecdf: pd.DataFrame, cuts: pd.DataFrame, bc: pd.DataFrame, info: dict) -> None:
215+ fig, axes = plt.subplots(1, 3, figsize=(6.3, 2.3), gridspec_kw={"width_ratios": [1.15, 1.15, 1.0]})
216+ a, b, c = axes
217+ # (a) ECDF of completed durations
218+ x = ecdf.duration_days.to_numpy(); y = ecdf.ecdf.to_numpy()
219+ a.step(x, y, where="post", color=PS.CAT[0], lw=1.2)
220+ med = info["S2_completed_duration_median_days"]
221+ a.axvline(med, color=PS.NEUTRAL, lw=0.7, ls=(0, (3, 2)))
222+ a.text(med + 1.5, 0.08, f"median {med:.1f} d", fontsize=7, color=PS.TEXT2)
223+ a.set_xlim(0, 75); a.set_ylim(0, 1)
224+ a.set_xlabel("Completed duration (days)"); a.set_ylabel("Cumulative share of spells")
225+ a.set_title("(a) Completed spells, S2", loc="left")
226+ # (b) first markdown size
227+ v = cuts.first_cut_pct.to_numpy()
228+ bins = np.arange(1, 25.5, 0.5)
229+ b.hist(np.clip(v, 1, 25.4), bins=bins, color=PS.CAT[1], alpha=0.9, lw=0, density=False, weights=np.full(v.size, 100 / v.size))
230+ medc = info["S2_first_cut_pct_median"]
231+ b.axvline(medc, color=PS.NEUTRAL, lw=0.7, ls=(0, (3, 2)))
232+ b.text(medc + 0.6, b.get_ylim()[1] * 0.97, f"median {medc:.1f} %", fontsize=7, color=PS.TEXT2, va="top")
233+ b.set_xlim(1, 25.5)
234+ b.set_xlabel("First markdown (% of entry price; \u2265 25 pooled)"); b.set_ylabel("Share of first markdowns (%)")
235+ b.set_title("(b) First markdown size, S2", loc="left")
236+ # (c) share with >=1 cut by source class
237+ labels = [CLASS_SHORT[k] for k in bc.source_class]
238+ cols = [PS.color_for(PS.CLASS_ORDER, k) for k in bc.source_class]
239+ c.bar(range(len(bc)), 100 * bc.share_with_cut, color=cols, width=0.65)
240+ c.set_xticks(range(len(bc)), [l.replace(" ", "\n", 1) for l in labels], fontsize=7)
241+ for i, (s, n) in enumerate(zip(bc.share_with_cut, bc.n)):
242+ c.text(i, 100 * s + 0.6, f"{100 * s:.1f} %", ha="center", fontsize=7, color=PS.TEXT2)
243+ c.set_ylabel("Listings with ≥1 markdown (%)")
244+ c.set_ylim(0, max(100 * bc.share_with_cut) * 1.25)
245+ c.grid(axis="x", visible=False)
246+ c.set_title("(c) Markdown incidence", loc="left"); c.set_xlabel("Source class (S2)")
247+ PS.save(fig, "fig03_listing_age_and_cuts")
248+
249+
250+# ----------------------------------------------------------------------------- fig04
251+def select_paths(L: pl.DataFrame) -> tuple[pl.DataFrame, dict]:
252+ """Deterministic selection: S2 listings with >= 3 sustained cuts and 30 <= duration <= 70 days,
253+ 2 per body type among {SUV, Sedan, Pickup} (the three largest body types), chosen by the
254+ lowest SHA-256 of the anonymised listing_id (hex, ascending)."""
255+ bodies = ["SUV", "Sedan", "Pickup"]
256+ cand = (L.filter(pl.col("s2_dynamic") & (pl.col("n_cuts") >= 3) & (pl.col("duration_days") >= 30) & (pl.col("duration_days") <= 70)
257+ & pl.col("body_filled").is_in(bodies))
258+ .with_columns(pl.col("listing_id").map_elements(lambda s: hashlib.sha256(s.encode()).hexdigest(), return_dtype=pl.String).alias("h")))
259+ picks = (cand.sort(["body_filled", "h"]).group_by("body_filled", maintain_order=True).head(2)
260+ .with_columns(pl.col("body_filled").replace_strict({b: i for i, b in enumerate(bodies)}, return_dtype=pl.Int64).alias("bo"))
261+ .sort(["bo", "h"]).drop("bo"))
262+ picks = picks.with_columns(pl.Series("label", [f"Listing {chr(65 + i)}" for i in range(picks.height)]))
263+ rule = {"rule": "S2 listings with n_cuts >= 3 and 30 <= duration_days <= 70, body in {SUV, Sedan, Pickup}; "
264+ "within each body type the two listings with the lowest SHA-256(listing_id) hex digest; labelled A-F in body order.",
265+ "n_candidates": cand.height, "n_candidates_by_body": {b: int(cand.filter(pl.col("body_filled") == b).height) for b in bodies},
266+ "selected": [{"label": r["label"], "listing_id": r["listing_id"], "sha256_prefix": r["h"][:12], "make": r["make"], "body": r["body_filled"],
267+ "year": int(r["year"]), "n_cuts": int(r["n_cuts"]), "duration_days": round(float(r["duration_days"]), 1),
268+ "exit_confirmed": bool(r["exit_confirmed"]), "entry_price": float(r["entry_price"]), "last_price": float(r["last_price"]),
269+ "total_change_pct": round(100 * float(r["total_change_pct"]), 1)}
270+ for r in picks.iter_rows(named=True)]}
271+ return picks, rule
272+
273+
274+def fig04(picks: pl.DataFrame, E: pl.DataFrame) -> pd.DataFrame:
275+ ev = (E.filter(pl.col("listing_id").is_in(picks["listing_id"].to_list())).join(
276+ picks.select(["listing_id", "label", "entry_ts", "end_ts", "exit_confirmed", "make", "body_filled", "year"]), on="listing_id")
277+ .with_columns(((pl.col("ts") - pl.col("entry_ts")) / 86400).alias("days"))
278+ .sort(["label", "ts"]))
279+ fig, axes = plt.subplots(2, 3, figsize=(6.3, 3.7), sharex=True)
280+ rows = []
281+ for ax, (lab, g) in zip(axes.flat, ev.group_by("label", maintain_order=True)):
282+ lab = lab[0] if isinstance(lab, tuple) else lab
283+ d = g["days"].to_numpy(); p = g["price"].to_numpy() / 1000
284+ d0 = np.concatenate([[0.0], d[1:]]) if d.size else d # entry level starts at day 0
285+ dur = float((g["end_ts"][0] - g["entry_ts"][0]) / 86400)
286+ body = g["body_filled"][0]; col = PS.color_for(PS.BODY_ORDER, body)
287+ ax.step(np.concatenate([d0, [dur]]), np.concatenate([p, [p[-1]]]), where="post", color=col, lw=1.3)
288+ ax.plot(d[1:], p[1:], ls="none", marker="o", ms=2.6, color=col, mfc=PS.SURFACE, mew=0.9)
289+ if bool(g["exit_confirmed"][0]):
290+ ax.plot([dur], [p[-1]], ls="none", marker="x", ms=4.5, color=PS.TEXT2, mew=0.9)
291+ ax.set_title(f"{lab} — {g['make'][0]} {body} {int(g['year'][0])}", loc="left", fontsize=8)
292+ ax.set_xlim(0, 72)
293+ lo, hi = p.min(), p.max(); pad = max(0.6, 0.12 * (hi - lo))
294+ ax.set_ylim(lo - pad, hi + pad)
295+ for r in g.iter_rows(named=True):
296+ rows.append({"label": lab, "days_since_entry": round(r["days"], 3), "price_cad": r["price"], "kind": r["kind"]})
297+ rows.append({"label": lab, "days_since_entry": round(dur, 3), "price_cad": g["price"][-1], "kind": "exit" if bool(g["exit_confirmed"][0]) else "censored"})
298+ for ax in axes[1]:
299+ ax.set_xlabel("Days since entry")
300+ fig.supylabel("Asking price (CAD, thousands)", fontsize=9)
301+ from matplotlib.lines import Line2D
302+ h = [Line2D([], [], color=PS.TEXT2, lw=1.2, label="Sustained price level"),
303+ Line2D([], [], ls="none", marker="o", ms=2.6, color=PS.TEXT2, mfc=PS.SURFACE, label="Price change"),
304+ Line2D([], [], ls="none", marker="x", ms=4.5, color=PS.TEXT2, label="Confirmed exit")]
305+ fig.legend(handles=h, loc="outside lower center", ncol=3, fontsize=7, handlelength=1.6, columnspacing=1.6)
306+ PS.save(fig, "fig04_price_paths")
307+ return pd.DataFrame(rows)
308+
309+
310+# ----------------------------------------------------------------------------- tables
311+def tab01(L: pl.DataFrame, reg: pd.DataFrame) -> pd.DataFrame:
312+ by = (L.group_by("source_class").agg(pl.col("source").n_unique().alias("n_sources_with_listings"), pl.col("dealer_id").n_unique().alias("n_dealers"),
313+ pl.len().alias("listings"), pl.col("active").sum().alias("active_at_snapshot"),
314+ pl.col("s1_hedonic").any().alias("in_s1"), pl.col("s2_dynamic").any().alias("in_s2"),
315+ pl.col("source").filter(pl.col("s2_dynamic")).n_unique().alias("n_sources_in_s2"),
316+ pl.col("s1_hedonic").sum().alias("n_s1"), pl.col("s2_dynamic").sum().alias("n_s2"))
317+ .to_pandas().set_index("source_class"))
318+ rg = reg.groupby("class").agg(n_sources=("source", "nunique"), found_per_cycle=("mean_found", "sum"), mean_found_per_source=("mean_found", "mean"))
319+ t = by.join(rg).reindex(PS.CLASS_ORDER)
320+ t["label"] = [PS.CLASS_LABEL[k] for k in t.index]
321+ t.index.name = "source_class"
322+ rows = [r"\begin{tabular}{lrrrrrcc}", r"\toprule",
323+ r"Source class & Sources & Dealers & Listings & Active & Found/cycle & S1 & S2 \\", r"\midrule"]
324+ for k, r in t.iterrows():
325+ s1 = "Yes" if r.in_s1 else "No"
326+ s2 = ("Yes" if r.n_sources_in_s2 == r.n_sources else f"Yes ({int(r.n_sources_in_s2)} of {int(r.n_sources)})") if r.in_s2 else "No"
327+ rows.append(f"{CLASS_SHORT[k]} & {fmt_int(r.n_sources)} & {fmt_int(r.n_dealers)} & {fmt_int(r.listings)} & {fmt_int(r.active_at_snapshot)} & {fmt_int(r.found_per_cycle)} & {s1} & {s2} \\\\")
328+ rows += [r"\midrule",
329+ f"All sources & {fmt_int(t.n_sources.sum())} & {fmt_int(L['dealer_id'].n_unique())} & {fmt_int(t.listings.sum())} & {fmt_int(t.active_at_snapshot.sum())} & {fmt_int(t.found_per_cycle.sum())} & & \\\\",
330+ r"\bottomrule", r"\end{tabular}"]
331+ write_tex(C.TABLES / "tab01_sources.tex", rows)
332+ return t.reset_index()
333+
334+
335+def tab02(sc: pd.DataFrame) -> pd.DataFrame:
336+ sc = sc.copy()
337+ sc["dropped"] = (-sc.listings_remaining.diff()).fillna(0).astype(int)
338+ rows = [r"\begin{tabular}{lrr}", r"\toprule", r"Step & Remaining & Dropped \\", r"\midrule"]
339+ marks = {"− make or model missing": None, "− uid identity collision": "S1 (all sources)",
340+ "− sampled portal (autotrader/cargurus) or private marketplace (kijiji)": "S1 hedonic",
341+ "− oscillating price path": "S2L", "− left-censored day-1 stock": "S2 dynamic"}
342+ for _, r in sc.iterrows():
343+ step = r.step.replace("−", "$-$").replace("[", "[").replace("$", r"\$").replace(r"\$-\$", "$-$")
344+ tag = marks.get(r.step)
345+ step = step if tag is None else f"{step} \\hfill $\\Rightarrow$ {tag}"
346+ rows.append(f"{step} & {fmt_int(r.listings_remaining)} & {fmt_int(r.dropped) if r.dropped else '--'} \\\\")
347+ rows += [r"\bottomrule", r"\end{tabular}"]
348+ write_tex(C.TABLES / "tab02_sample_construction.tex", rows)
349+ return sc
350+
351+
352+def tab03(L: pl.DataFrame) -> tuple[dict, pd.DataFrame]:
353+ S1 = L.filter(pl.col("s1_hedonic")); S2 = L.filter(pl.col("s2_dynamic"))
354+
355+ def block(D: pl.DataFrame, dyn_completed: pl.DataFrame) -> dict:
356+ n = D.height
357+ d = {"N": n, "entry_price_mean": D["entry_price"].mean(), "entry_price_median": D["entry_price"].median(),
358+ "mileage_mean_km": D["mileage_km"].mean(), "mileage_median_km": D["mileage_km"].median(),
359+ "age_mean_years": D["age_years_entry"].mean(), "age_median_years": D["age_years_entry"].median(),
360+ "dealers": D["dealer_id"].n_unique(), "active_share": D["active"].mean(),
361+ "powertrain_share": {k: D.filter(pl.col("powertrain") == k).height / n for k in PS.POWERTRAIN_ORDER},
362+ "body_share": {k: D.filter(pl.col("body_filled") == k).height / n for k in PS.BODY_ORDER},
363+ "body_missing_share": D["body_filled"].null_count() / n,
364+ "duration_median_completed": dyn_completed["duration_days"].median(), "n_completed": dyn_completed.height,
365+ "share_with_cut": (D["n_cuts"] >= 1).mean(),
366+ "first_cut_pct_median": -100 * D.filter(pl.col("n_cuts") >= 1)["first_cut_pct"].median()}
367+ return d
368+ s1 = block(S1, S1.filter(pl.col("exit_confirmed") & ~pl.col("left_censored")))
369+ s2 = block(S2, S2.filter(pl.col("exit_confirmed")))
370+ top_bodies = sorted(PS.BODY_ORDER, key=lambda k: -s1["body_share"][k])[:5]
371+ # by region (S1), with exit-within-30d among S2 entrants observed long enough to confirm an exit at day 30
372+ horizon = (WITHIN_DAYS + C.EXIT_CONFIRM_DAYS) * 86400
373+ S2e = S2.filter(pl.col("entry_ts") <= C.SNAPSHOT_TS - horizon).with_columns(
374+ (pl.col("exit_confirmed") & (pl.col("duration_days") <= WITHIN_DAYS)).alias("exit30"))
375+ rs1 = S1.group_by(pl.col("region").fill_null("Region missing")).agg(pl.len().alias("N"), pl.col("entry_price").median().alias("median_price"),
376+ pl.col("mileage_km").median().alias("median_km"), pl.col("age_years_entry").median().alias("median_age"))
377+ rs2 = S2e.group_by(pl.col("region").fill_null("Region missing")).agg(pl.len().alias("n_s2_entrants_37d"), pl.col("exit30").mean().alias("share_exit_30d"))
378+ R = rs1.join(rs2, on="region", how="left").to_pandas().set_index("region")
379+ order = [r for r in PS.REGIONS if r in R.index] + [r for r in R.index if r not in PS.REGIONS]
380+ R = R.reindex(order).reset_index()
381+ tot = {"N": S1.height, "median_price": S1["entry_price"].median(), "median_km": S1["mileage_km"].median(), "median_age": S1["age_years_entry"].median(),
382+ "n_s2_entrants_37d": S2e.height, "share_exit_30d": S2e["exit30"].mean()}
383+
384+ rows = [r"\begin{tabular}{lrr}", r"\toprule", r" & S1 hedonic & S2 dynamic \\", r"\midrule",
385+ f"Listings (N) & {fmt_int(s1['N'])} & {fmt_int(s2['N'])} \\\\",
386+ f"Dealers & {fmt_int(s1['dealers'])} & {fmt_int(s2['dealers'])} \\\\",
387+ f"Entry asking price, mean (\\$) & {fmt_int(s1['entry_price_mean'])} & {fmt_int(s2['entry_price_mean'])} \\\\",
388+ f"Entry asking price, median (\\$) & {fmt_int(s1['entry_price_median'])} & {fmt_int(s2['entry_price_median'])} \\\\",
389+ f"Mileage, mean (km) & {fmt_int(s1['mileage_mean_km'])} & {fmt_int(s2['mileage_mean_km'])} \\\\",
390+ f"Vehicle age at entry, mean (years) & {fmt1(s1['age_mean_years'])} & {fmt1(s2['age_mean_years'])} \\\\",
391+ r"\addlinespace", r"\multicolumn{3}{l}{\textit{Powertrain (\% of listings)}} \\"]
392+ for k in PS.POWERTRAIN_ORDER:
393+ rows.append(f"\\quad {k if k != 'unknown' else 'Unknown'} & {fmt_pct(s1['powertrain_share'][k])} & {fmt_pct(s2['powertrain_share'][k])} \\\\")
394+ rows += [r"\addlinespace", r"\multicolumn{3}{l}{\textit{Body type, top 5 (\% of listings)}} \\"]
395+ for k in top_bodies:
396+ rows.append(f"\\quad {k} & {fmt_pct(s1['body_share'][k])} & {fmt_pct(s2['body_share'][k])} \\\\")
397+ rows += [r"\addlinespace", r"\multicolumn{3}{l}{\textit{Listing dynamics}} \\",
398+ f"\\quad Completed spells (confirmed exits) & {fmt_int(s1['n_completed'])} & {fmt_int(s2['n_completed'])} \\\\",
399+ f"\\quad Duration of completed spells, median (days) & {fmt1(s1['duration_median_completed'])} & {fmt1(s2['duration_median_completed'])} \\\\",
400+ f"\\quad Listings with $\\geq 1$ sustained markdown (\\%) & {fmt_pct(s1['share_with_cut'])} & {fmt_pct(s2['share_with_cut'])} \\\\",
401+ f"\\quad First markdown, median (\\% of entry price) & {fmt1(s1['first_cut_pct_median'])} & {fmt1(s2['first_cut_pct_median'])} \\\\",
402+ r"\bottomrule", r"\end{tabular}", "",
403+ r"\begin{tabular}{lrrrrr}", r"\toprule",
404+ r"Region & N (S1) & Median price (\$) & Median km & Median age (y) & Exit $\leq$ 30 d (\%) \\", r"\midrule"]
405+ for _, r in R.iterrows():
406+ rows.append(f"{r.region} & {fmt_int(r.N)} & {fmt_int(r.median_price)} & {fmt_int(r.median_km)} & {fmt1(r.median_age)} & {fmt_pct(r.share_exit_30d)} \\\\")
407+ rows += [r"\midrule",
408+ f"All regions & {fmt_int(tot['N'])} & {fmt_int(tot['median_price'])} & {fmt_int(tot['median_km'])} & {fmt1(tot['median_age'])} & {fmt_pct(tot['share_exit_30d'])} \\\\",
409+ r"\bottomrule", r"\end{tabular}"]
410+ write_tex(C.TABLES / "tab03_descriptives.tex", rows)
411+ info = {"S1": s1, "S2": s2, "top_bodies_S1": top_bodies, "region_total": tot,
412+ "exit30_definition": f"S2 listings with entry_ts <= snapshot - {WITHIN_DAYS + C.EXIT_CONFIRM_DAYS} days (so that an exit at day {WITHIN_DAYS} is confirmable); "
413+ f"exit within {WITHIN_DAYS} d = exit_confirmed and duration_days <= {WITHIN_DAYS}",
414+ "S1_duration_note": "S1 completed spells exclude left-censored day-1 stock (duration unknown) but include all S1 sources"}
415+ return info, R
416+
417+
418+# ----------------------------------------------------------------------------- main
419+def run() -> dict:
420+ PS.setup()
421+ OUT.mkdir(parents=True, exist_ok=True)
422+ L, E, S, reg, sc = load()
423+ cov, cinfo = daily_coverage(L, S)
424+ rstock = region_stock(L)
425+ fig01(cov, rstock, cinfo)
426+ cov.to_csv(OUT / "fig01_daily_coverage.csv", index=False)
427+ rstock.to_csv(OUT / "fig01_region_stock.csv", index=False)
428+
429+ ecdf, cuts, bc, dinfo = duration_and_cuts(L)
430+ fig03(ecdf, cuts, bc, dinfo)
431+ ecdf.iloc[::25].to_csv(OUT / "fig03_duration_ecdf.csv", index=False)
432+ bc.to_csv(OUT / "fig03_cut_share_by_class.csv", index=False)
433+
434+ picks, rule = select_paths(L)
435+ paths = fig04(picks, E)
436+ (OUT / "fig04_selection.json").write_text(json.dumps(rule, indent=2, ensure_ascii=False), encoding="utf-8")
437+ paths.to_csv(OUT / "fig04_price_paths.csv", index=False)
438+
439+ t1 = tab01(L, reg); t1.to_csv(OUT / "tab01_sources.csv", index=False)
440+ t2 = tab02(sc); t2.to_csv(OUT / "tab02_sample_construction.csv", index=False)
441+ t3info, R = tab03(L); R.to_csv(OUT / "tab03_by_region.csv", index=False)
442+
443+ A = L.filter((pl.col("active") == 1) & pl.col("is_canonical") & (pl.col("kind") == "auto"))
444+ summary = {
445+ "snapshot_date": C.SNAPSHOT_DATE, "window_start_utc": "2026-08-12 12:30 UTC", "window_end_utc": "2026-11-01 06:49 UTC",
446+ "window_days": round((C.SNAPSHOT_TS - C.WINDOW_START_TS) / 86400, 1),
447+ "S0_n": L.height, "S1_n": t3info["S1"]["N"], "S2_n": t3info["S2"]["N"], "S2L_n": dinfo["S2L_n"],
448+ "active_canonical_auto_at_snapshot": A.height, "active_canonical_region_missing": int(A["region"].null_count()),
449+ "active_canonical_top_regions": rstock.head(5).to_dict(orient="records"),
450+ "sources_total": int(reg["source"].nunique()), "dealers_total": int(L["dealer_id"].n_unique()),
451+ "coverage": cinfo, "dynamics": dinfo, "fig04": rule, "tab01": t1.to_dict(orient="records"), "tab03": t3info,
452+ "tab03_by_region": R.to_dict(orient="records"),
453+ "notes": ["exit = observed disappearance from the source confirmed by >= 7 days of absence before the snapshot; it is NOT a sale",
454+ "day index 0 = 2026-08-12 UTC (partial day, crawl started 12:30 UTC); day 81 = 2026-11-01 (partial, snapshot 06:49 UTC)",
455+ "crawl cycles per day = sync_log rows / distinct sources logged that day (one row per source per cycle)",
456+ "S2 contains no left-censored listing by construction; the left-censored share refers to S2L"]}
457+ (OUT / "summary.json").write_text(json.dumps(summary, indent=2, ensure_ascii=False, default=float), encoding="utf-8")
458+ return summary
added src/ucml/features.py +137 −0
@@ -0,0 +1,137 @@
1+"""Listing-day risk set with time-varying local competition (Phase 3, analysis_plan §2).
2+
3+Competitor set of listing i on day t: listings of the same make & model, model year
4+within ±1, at a *different* dealer, within radius R km, active on day t, drawn from
5+the reliable-source universe (S2L: direct dealers, full portals, banners; not
6+oscillating). Radii 10 / 25 / 50 km. Day index: UTC calendar days since the first
7+crawl day (2026-08-12 = day 0).
8+"""
9+from __future__ import annotations
10+
11+import json
12+import math
13+import time
14+from pathlib import Path
15+
16+import numpy as np
17+import polars as pl
18+
19+from . import config as C
20+
21+RADII = (10.0, 25.0, 50.0)
22+DAY0 = math.floor(C.WINDOW_START_TS / 86400)
23+
24+
25+def day_of(ts: np.ndarray) -> np.ndarray:
26+ return (np.floor(ts / 86400) - DAY0).astype(np.int32)
27+
28+
29+def haversine_km(lat1, lng1, lat2, lng2):
30+ R = 6371.0
31+ p1, p2 = np.radians(lat1), np.radians(lat2)
32+ dphi = p2[None, :] - p1[:, None]
33+ dl = np.radians(lng2)[None, :] - np.radians(lng1)[:, None]
34+ a = np.sin(dphi / 2) ** 2 + np.cos(p1)[:, None] * np.cos(p2)[None, :] * np.sin(dl / 2) ** 2
35+ return 2 * R * np.arcsin(np.sqrt(np.clip(a, 0, 1)))
36+
37+
38+def daily_price_matrix(ev: pl.DataFrame, ids: list[str], e_day: np.ndarray, T: int) -> tuple[np.ndarray, np.ndarray]:
39+ """Step-function log price per listing-day (nan when no price yet) and daily cut indicator."""
40+ n = len(ids)
41+ P = np.full((n, T), np.nan, dtype=np.float32)
42+ K = np.zeros((n, T), dtype=np.float32)
43+ idx = {l: i for i, l in enumerate(ids)}
44+ g = ev.filter(pl.col("listing_id").is_in(ids)).sort(["listing_id", "ts"]).group_by("listing_id", maintain_order=True).agg([pl.col("ts"), pl.col("price"), pl.col("kind")])
45+ days = np.arange(T)
46+ for lid, ts, pr, kd in g.iter_rows():
47+ i = idx[lid]
48+ d = day_of(np.asarray(ts, dtype=np.float64))
49+ d = np.minimum(d, T - 1)
50+ # price at end of each day = last event with day <= t
51+ pos = np.searchsorted(d, days, side="right") - 1
52+ valid = pos >= 0
53+ P[i, valid] = np.log(np.asarray(pr, dtype=np.float64))[pos[valid]]
54+ for dd, k in zip(d, kd):
55+ if k == "cut" and 0 <= dd < T:
56+ K[i, dd] = 1.0
57+ return P, K
58+
59+
60+def build(listings_full: Path = C.INTERIM / "listings_full.parquet", events: Path = C.PROCESSED / "price_events.parquet",
61+ out: Path = C.PROCESSED / "risk_set.parquet") -> dict:
62+ t0 = time.time()
63+ L = pl.read_parquet(listings_full)
64+ E = pl.read_parquet(events)
65+ T = int(day_of(np.array([C.SNAPSHOT_TS]))[0]) + 1
66+ U = L.filter(pl.col("s2l_dynamic_with_stock") & pl.col("lat").is_not_null()).select(
67+ ["listing_id", "dealer_id", "make", "model", "year", "lat", "lng", "entry_ts", "end_ts", "exit_confirmed", "s2_dynamic", "left_censored", "first_cut_ts"])
68+ U = U.with_columns([
69+ pl.Series("e_day", day_of(U["entry_ts"].to_numpy())),
70+ pl.Series("x_day", np.minimum(day_of(U["end_ts"].to_numpy()), T - 1)),
71+ ])
72+ parts = []
73+ n_groups = 0
74+ for (mk, md), G in U.group_by(["make", "model"]):
75+ n_groups += 1
76+ ids = G["listing_id"].to_list()
77+ n = len(ids)
78+ e = G["e_day"].to_numpy(); x = G["x_day"].to_numpy(); y = G["year"].to_numpy()
79+ lat = G["lat"].to_numpy(); lng = G["lng"].to_numpy()
80+ dealer = pl.Series(G["dealer_id"]).cast(pl.Categorical).to_physical().to_numpy()
81+ A = ((np.arange(T)[None, :] >= e[:, None]) & (np.arange(T)[None, :] <= x[:, None])).astype(np.float32)
82+ P, K = daily_price_matrix(E, ids, e, T)
83+ Pf = np.where(np.isnan(P), 0.0, P).astype(np.float32)
84+ has_p = (~np.isnan(P)).astype(np.float32) * A
85+ D = haversine_km(lat, lng, lat, lng)
86+ same_year = np.abs(y[:, None] - y[None, :]) <= 1
87+ other_dealer = dealer[:, None] != dealer[None, :]
88+ base = same_year & other_dealer
89+ cols = {}
90+ for R in RADII:
91+ M = (base & (D <= R)).astype(np.float32)
92+ dens = M @ A # n x T competitor counts
93+ ccut = M @ K # competitor cuts on day t
94+ sp = M @ (Pf * has_p); cnt = M @ has_p
95+ mean_lp = np.where(cnt > 0, sp / np.maximum(cnt, 1), np.nan)
96+ tag = f"{int(R)}"
97+ cols[f"dens_{tag}"] = dens; cols[f"ccut_{tag}"] = ccut; cols[f"cmlp_{tag}"] = mean_lp
98+ # within-dealer same-model stock (own-dealer substitutes)
99+ Mo = (same_year & ~other_dealer).astype(np.float32) - np.eye(n, dtype=np.float32)
100+ own = Mo @ A
101+ # explode to listing-days
102+ rows_i, rows_t = [], []
103+ for i in range(n):
104+ tt = np.arange(e[i], x[i] + 1)
105+ rows_i.append(np.full(len(tt), i)); rows_t.append(tt)
106+ ii = np.concatenate(rows_i); tt = np.concatenate(rows_t)
107+ part = {"listing_id": np.array(ids, dtype=object)[ii], "t": tt.astype(np.int16), "listing_age": (tt - e[ii]).astype(np.int16),
108+ "logp": P[ii, tt], "cut": K[ii, tt].astype(np.int8), "own_stock": own[ii, tt].astype(np.int16)}
109+ for k, v in cols.items():
110+ part[k] = v[ii, tt]
111+ parts.append(pl.DataFrame(part))
112+ RS = pl.concat(parts)
113+ # exit indicator: last day of the spell for confirmed exits
114+ last = U.select(["listing_id", "x_day", "exit_confirmed", "e_day", "s2_dynamic", "left_censored", "first_cut_ts"])
115+ RS = RS.join(last, on="listing_id", how="left").with_columns([
116+ ((pl.col("t") == pl.col("x_day")) & pl.col("exit_confirmed")).cast(pl.Int8).alias("exit"),
117+ (pl.col("t") == pl.col("x_day")).cast(pl.Int8).alias("last_day"),
118+ ])
119+ # risk set for the first markdown: days up to and including the first cut day
120+ fc_day = pl.when(pl.col("first_cut_ts").is_not_null()).then(((pl.col("first_cut_ts") / 86400).floor() - DAY0).cast(pl.Int32)).otherwise(None)
121+ RS = RS.with_columns(fc_day.alias("first_cut_day"))
122+ RS = RS.with_columns([
123+ (pl.col("first_cut_day").is_null() | (pl.col("t") <= pl.col("first_cut_day"))).alias("in_first_cut_risk"),
124+ ((pl.col("t") == pl.col("first_cut_day"))).cast(pl.Int8).alias("first_cut"),
125+ ])
126+ for tag in ("10", "25", "50"):
127+ RS = RS.with_columns((pl.col("logp") - pl.col(f"cmlp_{tag}")).alias(f"pricepos_{tag}"))
128+ RS = RS.drop(["x_day", "first_cut_ts"]).sort(["listing_id", "t"])
129+ RS.write_parquet(out, compression="zstd")
130+ stats = {"listing_days": RS.height, "listings": RS["listing_id"].n_unique(), "groups": n_groups, "T": T,
131+ "exits": int(RS["exit"].sum()), "first_cuts": int(RS["first_cut"].sum()), "seconds": round(time.time() - t0, 1)}
132+ (C.PROCESSED / "risk_set_stats.json").write_text(json.dumps(stats, indent=2))
133+ return stats
134+
135+
136+if __name__ == "__main__":
137+ print(json.dumps(build(), indent=2))
added src/ucml/figures.py +14 −0
@@ -0,0 +1,176 @@
1+"""Model-based figures: 2 (depreciation surface), 5 (hazards), 6 (event study), 7 (regional premia map),
2+8 (AMLI series + components), 9 (AMLI internal validation). Descriptive figures 1, 3, 4 live in descriptives.py;
3+figure 10 in prediction.py. Every plotted number is read from results/ files produced by the pipeline."""
4+from __future__ import annotations
5+
6+import json
7+
8+import matplotlib.pyplot as plt
9+import numpy as np
10+import pandas as pd
11+import polars as pl
12+
13+from . import config as C
14+from . import plotstyle as ps

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