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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:52 .\ndrwxr-xr-x 30 simon-pierreboucher staff 960 oct. 30 00:09 ..\n-rw-r--r-- 1 simon-pierreboucher staff 517 oct. 29 23:52 bundle.json\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:46 data\n-rw-r--r-- 1 simon-pierreboucher staff 1862 oct. 29 23:47 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 5520 oct. 29 23:47 TASK.md\ndata:\nip_predictors.csv\nREADME.md","is_error":false}]},"uuid":"c81cd8e2-e309-4029-876b-3d635398ffe0","timestamp":"2026-10-30T04:09:34.945Z","toolUseResult":{"stdout":"# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth with standardised predictors\n\n## Scientific question\nDoes a fixed-penalty ridge regression on eight standardised monthly predictors forecast next month's US industrial production growth better than two simple baselines — the naive (last value) forecast and the expanding historical mean — over 60 rolling origins from 2021-09 to 2026-08? The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\n{\n \"task_id\": \"F3-conf-03\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n \"files\": {\n \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n }\n}month,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\n7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b data/ip_predictors.csv\n225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e data/README.md","is_error":false}]},"uuid":"05c5d091-ebbf-4265-83e4-d5924e39c111","timestamp":"2026-10-30T04:09:36.720Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-03\n\n## ip_predictors.csv\nOne row per month (`month`, YYYY-MM-01), 1992-01 to 2026-09 (417 data rows), built from six FRED series (Federal Reserve Bank of St. Louis), each retrieved with `https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>` on 2026-10-30 (UTC times below). SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\n{\n \"task_id\": \"F3-conf-03\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n \"files\": {\n \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n 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out/state\ncat > contract.md <<'EOF'\n# Contract — F3-conf-03\n**Question.** Does a fixed-penalty (λ=50) ridge regression on 8 standardised predictors forecast next-month US IP growth (100·Δln INDPRO) better than (a) naive last value and (b) expanding historical mean, over 60 origins t=2021-09..2026-08, h=1?\n\n**Estimands.** Out-of-sample RMSE and MAE (percent/month) of naive, mean, ridge over the 60 targets 2021-10..2026-09; relative improvements 1−RMSE_ridge/RMSE_baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), 60 squared/absolute errors as sample of loss; not a real-time evaluation.\n\n**Specification.** Training rows at origin t: s in [1992-03, t−1], target y_{s+1}, features (ip_s, ip_{s−1}, ip_{s−2}, oil_s, ff_s, permit_s, neworder_s, tcu_s). Standardise with training mean and sample SD (ddof=1), centre y with training mean; b=(Z'Z+50I)^{-1}Z'(y−ȳ); forecast ȳ+z̃_t·b. Mean baseline: mean ip_growth over 1992-04..t. Naive: ip_growth_t.\n\n**Information rules.** Only values dated ≤ t at origin t; all scaling stats recomputed per origin from training window → normalisation_window=\"expanding\". Target of last training row is ip_{t}, which is ≤ t (allowed).\n\n**Outputs.** out/forecasts.csv (180 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json (task_id, n_origins, first_origin, last_origin, lambda, normalisation_window, n_features, rmse_/mae_ naive/mean/ridge, rel_improvement_vs_naive/mean, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md; out/analysis.py; out/claims.json; plus protocol files.\n\n**Invalidity.** Missing months/target values in the evaluation window, any use of data dated > t, full-sample scaling, missing feature values inside training rows.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30: P0 contract written. Read TASK.md, data/README.md.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nph={f\"P{i}\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"} for i in range(6)}\nph[\"P0\"]={\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written with estimands, outputs, info rules\"],\"notes\":\"\"}\njson.dump(ph,open(\"phases.json\",\"w\"),indent=2)\nEOF","description":"Write contract, log, phase ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":490,"cache_read_input_tokens":9254,"output_tokens":1080,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":490},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmPRw92WRxb5vCsRitV","requestRef":"514c7bed-77dc-48c2-99fe-656b075e8d86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"212ee444-d37e-49e0-ab13-ee8307cbb9cb","timestamp":"2026-10-30T04:09:57.157Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"212ee444-d37e-49e0-ab13-ee8307cbb9cb","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014hHv2835vbpkCpioebdQNL","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"97524092-568d-4035-a3d8-079b2c1c5405","timestamp":"2026-10-30T04:09:57.202Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"212ee444-d37e-49e0-ab13-ee8307cbb9cb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027/out/state","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"f340a7ccdd9f09255bfbccdd9ba42e68a815cfb917cb74f277cce72bb70cf43f","message":{"role":"assistant","content":[{"type":"text","text":"Now the contract, ledger, and audit files."},{"type":"tool_use","id":"toolu_014hHv2835vbpkCpioebdQNL","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract — F3-conf-03\n**Question.** Does a fixed-penalty (λ=50) ridge regression on 8 standardised predictors forecast next-month US IP growth (100·Δln INDPRO) better than (a) naive last value and (b) expanding historical mean, over 60 origins t=2021-09..2026-08, h=1?\n\n**Estimands.** Out-of-sample RMSE and MAE (percent/month) of naive, mean, ridge over the 60 targets 2021-10..2026-09; relative improvements 1−RMSE_ridge/RMSE_baseline. 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Now the analysis script (baseline + ridge together, baseline computed first)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":218,"cache_read_input_tokens":11701,"output_tokens":2807,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":218},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmR36W9c3SpJeuZDJJ5","requestRef":"2e4d213e-b87e-4423-acbc-6bece5e6ac70","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4237eb5a-87d2-4e8b-b382-9ce436d02b90","timestamp":"2026-10-30T04:10:09.797Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"4237eb5a-87d2-4e8b-b382-9ce436d02b90","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmR3wrrXbLF6e6GvhQh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014W6ELRdrZs1iypuQUBfAjF","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, hashlib\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nLAMBDA = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nd = raw.copy()\nd[\"month\"] = pd.to_datetime(d[\"month\"])\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique and (d.index.to_series().diff().dropna().dt.days.between(28, 31)).all()\n# Lags use only past rows (shift forward in time); next-month target kept in a separate column\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1) # target of row s = ip_growth of s+1 (only used when s+1 <= t)\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows, n_train_last = [], None\nfor t in origins:\n tgt = t + pd.DateOffset(months=1)\n actual = float(d.loc[tgt, \"ip_growth\"])\n avail = d.loc[:t] # information set: rows dated <= t\n # --- baselines ---\n naive = float(avail.loc[t, \"ip_growth\"])\n mean_f = float(avail.loc[MEAN_START:t, \"ip_growth\"].mean())\n # --- ridge ---\n tr = avail.loc[TRAIN_START:t - pd.DateOffset(months=1)] # s+1 <= t\n X = tr[FEATS].to_numpy(float); y = tr[\"y_next\"].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n assert tr.index.max() + pd.DateOffset(months=1) <= t\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (avail.loc[t, FEATS].to_numpy(float) - mu) / sd\n ridge = float(ybar + zt @ b)\n n_train_last = len(tr)\n for m, f in ((\"naive\", naive), (\"mean\", mean_f), (\"ridge\", ridge)):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nfc.to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.10f\")\n\ndef err(m):\n e = fc.loc[fc.model == m, \"actual\"] - fc.loc[fc.model == m, \"forecast\"]\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrmse_n, mae_n = err(\"naive\"); rmse_m, mae_m = err(\"mean\"); rmse_r, mae_r = err(\"ridge\")\n\n# exclusion ledger (row-level, for the training set at the last origin)\nlast = origins[-1]\nex = []\nn0 = n_raw\nn1 = int((d.index >= TRAIN_START).sum()); ex.append((\"1\", \"month >= 1992-03-01 (training start per spec)\", n0, n1))\nn2 = int(((d.index >= TRAIN_START) & (d.index <= last - pd.DateOffset(months=1))).sum())\nex.append((\"2\", \"s+1 <= last origin 2026-08-01 (target observed at origin)\", n1, n2))\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b_, n_removed=a - b_,\n rationale=(\"rows 1992-01/02 lack full lag/neworder features; spec starts at 1992-03\" if s == \"1\"\n else \"rows dated after 2026-07 have targets after the last origin (information rule)\"))\n for s, r, a, b_ in ex]).to_csv(\"out/exclusions.csv\", index=False)\nassert n2 == n_train_last\n\nres = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": len(origins),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n \"lambda\": LAMBDA,\n \"normalisation_window\": \"expanding\",\n \"n_features\": len(FEATS),\n \"baseline_note\": \"naive and mean are the task-required baselines\",\n \"rmse_naive\": rmse_n, \"mae_naive\": mae_n,\n \"rmse_mean\": rmse_m, \"mae_mean\": mae_m,\n \"rmse_ridge\": rmse_r, \"mae_ridge\": mae_r,\n \"rel_improvement_vs_naive\": 1 - rmse_r / rmse_n,\n \"rel_improvement_vs_mean\": 1 - rmse_r / rmse_m,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"data/README.md states the first month (1992-01) of every change series is empty, but ip_growth, oil_chg and permit_chg have values at 1992-01; only neworder_chg is missing (1992-01, 1992-02).\",\n \"handling\": \"Values used as given. Only ip_growth 1992-01 enters the analysis (lag-2 feature of the s=1992-03 training row, as specified); missing neworder_chg rows precede the training window.\"},\n {\"issue\": \"Extreme COVID-19 observations (ip_growth 2020-04 = -14.14, 2020-06 = +6.35; oil_chg 2020-03..05) lie inside every training window.\",\n \"handling\": \"Kept; the task fully specifies the model without outlier treatment. Noted as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data; publication lags ignored.\",\n \"handling\": \"Errors computed on the data as given; no real-time validity claimed.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": n_raw},\n \"input_sha256\": {\"ip_predictors.csv\": sha},\n \"n_analysed\": n_train_last,\n \"random_seed\": None,\n}\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\nprint(json.dumps({k: res[k] for k in res if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_\"))}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":218,"cache_read_input_tokens":11701,"output_tokens":2807,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":218},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmR36W9c3SpJeuZDJJ5","requestRef":"2e4d213e-b87e-4423-acbc-6bece5e6ac70","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e4ef8049-c5ce-4775-8c14-5d8e376d21da","timestamp":"2026-10-30T04:10:30.294Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"e4ef8049-c5ce-4775-8c14-5d8e376d21da","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014W6ELRdrZs1iypuQUBfAjF","type":"tool_result","content":"{\n \"n_origins\": 60,\n \"n_features\": 8,\n \"rmse_naive\": 0.8830699166753823,\n \"mae_naive\": 0.6989594000000001,\n \"rmse_mean\": 0.5439355781141625,\n \"mae_mean\": 0.42929777256194207,\n \"rmse_ridge\": 0.5607989303521244,\n \"mae_ridge\": 0.45141289074453916,\n \"rel_improvement_vs_naive\": 0.3649439078805412,\n \"rel_improvement_vs_mean\": -0.031002480654837017,\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,month >= 1992-03-01 (training start per spec),417,415,2,rows 1992-01/02 lack full lag/neworder features; spec starts at 1992-03\n2,s+1 <= last origin 2026-08-01 (target observed at origin),415,413,2,rows dated after 2026-07 have targets after the last origin (information rule)\n 181 out/forecasts.csv","is_error":false}]},"uuid":"b089dd31-2b35-4a99-a880-a978b24e22f0","timestamp":"2026-10-30T04:10:30.602Z","toolUseResult":{"stdout":"{\n \"n_origins\": 60,\n \"n_features\": 8,\n \"rmse_naive\": 0.8830699166753823,\n \"mae_naive\": 0.6989594000000001,\n \"rmse_mean\": 0.5439355781141625,\n \"mae_mean\": 0.42929777256194207,\n \"rmse_ridge\": 0.5607989303521244,\n \"mae_ridge\": 0.45141289074453916,\n \"rel_improvement_vs_naive\": 0.3649439078805412,\n \"rel_improvement_vs_mean\": -0.031002480654837017,\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,month >= 1992-03-01 (training start per spec),417,415,2,rows 1992-01/02 lack full lag/neworder features; spec starts at 1992-03\n2,s+1 <= last origin 2026-08-01 (target observed at origin),415,413,2,rows dated after 2026-07 have targets after the last origin (information rule)\n 181 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e4ef8049-c5ce-4775-8c14-5d8e376d21da","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"747b7ccfa510afa323195e0ebe242556b4d017b4124ce1407911027db6c9d21b","message":{"role":"assistant","content":[{"type":"text","text":"Good—files are in place. Now the analysis script (baseline + ridge together, baseline computed first)."},{"type":"tool_use","id":"toolu_014W6ELRdrZs1iypuQUBfAjF","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, hashlib\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nLAMBDA = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA)\nn_raw = len(raw)\nsha = hashlib.sha256(open(DATA, \"rb\").read()).hexdigest()\nd = raw.copy()\nd[\"month\"] = pd.to_datetime(d[\"month\"])\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique and (d.index.to_series().diff().dropna().dt.days.between(28, 31)).all()\n# Lags use only past rows (shift forward in time); next-month target kept in a separate column\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1) # target of row s = ip_growth of s+1 (only used when s+1 <= t)\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows, n_train_last = [], None\nfor t in origins:\n tgt = t + pd.DateOffset(months=1)\n actual = float(d.loc[tgt, \"ip_growth\"])\n avail = d.loc[:t] # information set: rows dated <= t\n # --- baselines ---\n naive = float(avail.loc[t, \"ip_growth\"])\n mean_f = float(avail.loc[MEAN_START:t, \"ip_growth\"].mean())\n # --- ridge ---\n tr = avail.loc[TRAIN_START:t - pd.DateOffset(months=1)] # s+1 <= t\n X = tr[FEATS].to_numpy(float); y = tr[\"y_next\"].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n assert tr.index.max() + pd.DateOffset(months=1) <= t\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (avail.loc[t, FEATS].to_numpy(float) - mu) / sd\n ridge = float(ybar + zt @ b)\n n_train_last = len(tr)\n for m, f in ((\"naive\", naive), (\"mean\", mean_f), (\"ridge\", ridge)):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nfc.to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.10f\")\n\ndef err(m):\n e = fc.loc[fc.model == m, \"actual\"] - fc.loc[fc.model == m, \"forecast\"]\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrmse_n, mae_n = err(\"naive\"); rmse_m, mae_m = err(\"mean\"); rmse_r, mae_r = err(\"ridge\")\n\n# exclusion ledger (row-level, for the training set at the last origin)\nlast = origins[-1]\nex = []\nn0 = n_raw\nn1 = int((d.index >= TRAIN_START).sum()); ex.append((\"1\", \"month >= 1992-03-01 (training start per spec)\", n0, n1))\nn2 = int(((d.index >= TRAIN_START) & (d.index <= last - pd.DateOffset(months=1))).sum())\nex.append((\"2\", \"s+1 <= last origin 2026-08-01 (target observed at origin)\", n1, n2))\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b_, n_removed=a - b_,\n rationale=(\"rows 1992-01/02 lack full lag/neworder features; spec starts at 1992-03\" if s == \"1\"\n else \"rows dated after 2026-07 have targets after the last origin (information rule)\"))\n for s, r, a, b_ in ex]).to_csv(\"out/exclusions.csv\", index=False)\nassert n2 == n_train_last\n\nres = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": len(origins),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n \"lambda\": LAMBDA,\n \"normalisation_window\": \"expanding\",\n \"n_features\": len(FEATS),\n \"baseline_note\": \"naive and mean are the task-required baselines\",\n \"rmse_naive\": rmse_n, \"mae_naive\": mae_n,\n \"rmse_mean\": rmse_m, \"mae_mean\": mae_m,\n \"rmse_ridge\": rmse_r, \"mae_ridge\": mae_r,\n \"rel_improvement_vs_naive\": 1 - rmse_r / rmse_n,\n \"rel_improvement_vs_mean\": 1 - rmse_r / rmse_m,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"data/README.md states the first month (1992-01) of every change series is empty, but ip_growth, oil_chg and permit_chg have values at 1992-01; only neworder_chg is missing (1992-01, 1992-02).\",\n \"handling\": \"Values used as given. Only ip_growth 1992-01 enters the analysis (lag-2 feature of the s=1992-03 training row, as specified); missing neworder_chg rows precede the training window.\"},\n {\"issue\": \"Extreme COVID-19 observations (ip_growth 2020-04 = -14.14, 2020-06 = +6.35; oil_chg 2020-03..05) lie inside every training window.\",\n \"handling\": \"Kept; the task fully specifies the model without outlier treatment. Noted as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data; publication lags ignored.\",\n \"handling\": \"Errors computed on the data as given; no real-time validity claimed.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": n_raw},\n \"input_sha256\": {\"ip_predictors.csv\": sha},\n \"n_analysed\": n_train_last,\n \"random_seed\": None,\n}\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\nprint(json.dumps({k: res[k] for k in res if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_\"))}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && wc -l out/forecasts.csv","description":"Write and run analysis 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P3 ridge implemented in out/analysis.py; rmse_ridge 0.561. Decision: add DM test (HLN) for Uncertainty section.\nEOF","description":"Update phase ledger and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1190,"cache_read_input_tokens":15117,"output_tokens":577,"output_tokens_details":{"thinking_tokens":17},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1190},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmTSu3dDKGawtVjwtEk","requestRef":"a09003af-7d27-4448-b853-9cad8afd8af1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7f95e22a-d7e8-4c76-aa84-dde5c051ca9c","timestamp":"2026-10-30T04:10:46.666Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"7f95e22a-d7e8-4c76-aa84-dde5c051ca9c","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Pc1ZrNYvqzxCuhYPdNqjCK","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"f5e6847b-4c57-442a-a7ed-62c7d22deb62","timestamp":"2026-10-30T04:10:46.701Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7f95e22a-d7e8-4c76-aa84-dde5c051ca9c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}64{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"d6800ef9a9bd925adfc6f73578f449c4d59558e099729d26e324f18f66a014f3","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Pc1ZrNYvqzxCuhYPdNqjCK","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P1\"]={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README/bundle\",\"417x7 schema, date range, gaps, duplicates checked\",\"missingness and extremes tabulated\",\"exclusions.csv: 417-2-2=413 = n_analysed (asserted in script)\"],\"notes\":\"README/data 1992-01 discrepancy recorded\"}\np[\"P2\"]={\"status\":\"passed\",\"checks\":[\"naive and expanding-mean baselines computed with same 60-origin evaluation: rmse_naive=0.8831, rmse_mean=0.5439\"],\"notes\":\"baselines are task-required keys\"}\np[\"P3\"]={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"no randomness: random_seed=null\",\"per-origin standardisation from training window\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30: P2 baselines computed (rmse_naive 0.883, rmse_mean 0.544). P3 ridge implemented in out/analysis.py; rmse_ridge 0.561. Decision: add DM test (HLN) for Uncertainty section.\nEOF","description":"Update phase ledger and log"}}]}}65{"type":"api-request","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","id":"32857a98-b187-4542-bf10-f94cb4a24d97","timestamp":"2026-10-30T04:10:46.704Z","version":"2.1.314","querySource":"sdk","shapeHash":"f3d8889d0113d7901dc9f36f9d04a90b0d5b30c922f25bfdb63e2a15eca5dca2","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"a09003af-7d27-4448-b853-9cad8afd8af1","keep":20,"tail":["d6800ef9a9bd925adfc6f73578f449c4d59558e099729d26e324f18f66a014f3","262aa9ca159881c566603fbeb63deb8dc49e0c11559c495a0ab95d9d25ed5352"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3c65215d11851f1a2abf2d283a04f041eabceb33e886fa42b53d19cf950df7c3"}66{"parentUuid":"f5e6847b-4c57-442a-a7ed-62c7d22deb62","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmTsarCoMuHhi7bRPgM","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification: clean rerun reproducibility, independent ridge recomputation, and a leakage test (perturbing future data)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":592,"cache_read_input_tokens":16307,"output_tokens":1853,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":592},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmTsFkqcQR8bgpxny44","requestRef":"32857a98-b187-4542-bf10-f94cb4a24d97","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0402948f-d42d-4b5e-9352-98f12b3b4721","timestamp":"2026-10-30T04:10:48.135Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"0402948f-d42d-4b5e-9352-98f12b3b4721","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmTsarCoMuHhi7bRPgM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019WdmZKV8YpreofB1CFbwoe","name":"Bash","input":{"command":"shasum -a 256 out/results.json out/forecasts.csv > /tmp/h1; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; shasum -a 256 out/results.json out/forecasts.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json, numpy as np, pandas as pd\nr=json.load(open(\"out/results.json\")); fc=pd.read_csv(\"out/forecasts.csv\")\nd=pd.read_csv(\"data/ip_predictors.csv\",parse_dates=[\"month\"]).set_index(\"month\")\nok=lambda n,c: print((\"PASS\" if c else \"FAIL\"), n)\nok(\"180 rows, 60 per model\", len(fc)==180 and (fc.model.value_counts()==60).all())\nok(\"horizon all 1\", (fc.horizon==1).all())\nok(\"target = origin+1 month\", (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\nok(\"actual matches data\", np.allclose(fc.actual, d.loc[pd.to_datetime(fc.target_date),\"ip_growth\"].values))\nnv=fc[fc.model==\"naive\"]; ok(\"naive = ip at origin\", np.allclose(nv.forecast, d.loc[pd.to_datetime(nv.origin_date),\"ip_growth\"].values))\n# independent ridge via augmented least squares (sqrt(lam) rows), at origin 2021-09 and 2026-08\ndef ridge_aug(t):\n t=pd.Timestamp(t); s=d.index[(d.index>=pd.Timestamp(\"1992-03-01\"))&(d.index<=t-pd.DateOffset(months=1))]\n ip=d.ip_growth\n def feat(m): return np.array([ip[m],ip[m-pd.DateOffset(months=1)],ip[m-pd.DateOffset(months=2)],d.oil_chg[m],d.fedfunds[m],d.permit_chg[m],d.neworder_chg[m],d.tcu[m]])\n X=np.array([feat(m) for m in s]); y=np.array([ip[m+pd.DateOffset(months=1)] for m in s])\n mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd\n A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.concatenate([y-y.mean(),np.zeros(8)])\n b=np.linalg.lstsq(A,bb,rcond=None)[0]\n return y.mean()+((feat(t)-mu)/sd)@b, len(s)\nrg=fc[fc.model==\"ridge\"].set_index(\"origin_date\").forecast\nfor t in [\"2021-09-01\",\"2024-01-01\",\"2026-08-01\"]:\n f,n=ridge_aug(t); ok(f\"independent ridge at {t} ({f:.6f} vs {rg[t]:.6f})\", abs(f-rg[t])<1e-8)\nok(\"n_analysed = 413 = training rows at last origin\", ridge_aug(\"2026-08-01\")[1]==r[\"n_analysed\"]==413)\nmn=fc[fc.model==\"mean\"].set_index(\"origin_date\").forecast\nok(\"mean at 2021-09 independent\", abs(d.loc[\"1992-04-01\":\"2021-09-01\",\"ip_growth\"].mean()-mn[\"2021-09-01\"])<1e-10)\ne=lambda m:(fc[fc.model==m].actual-fc[fc.model==m].forecast)\nfor m in [\"naive\",\"mean\",\"ridge\"]:\n ok(f\"rmse/mae {m} recomputed\", np.isclose(np.sqrt((e(m)**2).mean()),r[f\"rmse_{m}\"]) and np.isclose(e(m).abs().mean(),r[f\"mae_{m}\"]))\n ok(f\"mae<=rmse {m}\", r[f\"mae_{m}\"]<=r[f\"rmse_{m}\"])\nok(\"rel improvements identities\", np.isclose(r[\"rel_improvement_vs_naive\"],1-r[\"rmse_ridge\"]/r[\"rmse_naive\"]) and np.isclose(r[\"rel_improvement_vs_mean\"],1-r[\"rmse_ridge\"]/r[\"rmse_mean\"]))\nok(\"DM sign consistent with RMSE ordering\", np.sign(r[\"dm_stat_ridge_vs_mean\"])==np.sign(r[\"rmse_mean\"]-r[\"rmse_ridge\"]) and np.sign(r[\"dm_stat_ridge_vs_naive\"])==np.sign(r[\"rmse_naive\"]-r[\"rmse_ridge\"]))\nex=pd.read_csv(\"out/exclusions.csv\"); ok(\"exclusions add up\", 417-ex.n_removed.sum()==r[\"n_analysed\"])\nEOF","description":"Rerun reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":592,"cache_read_input_tokens":16307,"output_tokens":1853,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":592},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmTsFkqcQR8bgpxny44","requestRef":"32857a98-b187-4542-bf10-f94cb4a24d97","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4c3bf535-737b-4241-8e36-6a60504f66f7","timestamp":"2026-10-30T04:11:00.873Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"4c3bf535-737b-4241-8e36-6a60504f66f7","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019WdmZKV8YpreofB1CFbwoe","type":"tool_result","content":"IDENTICAL\nPASS 180 rows, 60 per model\nPASS horizon all 1\nPASS target = origin+1 month\nPASS actual matches data\nPASS naive = ip at origin\nPASS independent ridge at 2021-09-01 (0.112470 vs 0.112470)\nPASS independent ridge at 2024-01-01 (0.272711 vs 0.272711)\nPASS independent ridge at 2026-08-01 (0.258667 vs 0.258667)\nPASS n_analysed = 413 = training rows at last origin\nPASS mean at 2021-09 independent\nPASS rmse/mae naive recomputed\nPASS mae<=rmse naive\nPASS rmse/mae mean recomputed\nPASS mae<=rmse mean\nPASS rmse/mae ridge recomputed\nPASS mae<=rmse ridge\nPASS rel improvements identities\nPASS DM sign consistent with RMSE ordering\nPASS exclusions add up","is_error":false}]},"uuid":"2a2c99e1-a84d-428b-9e4d-9d1d0ff243c1","timestamp":"2026-10-30T04:11:01.972Z","toolUseResult":{"stdout":"IDENTICAL\nPASS 180 rows, 60 per model\nPASS horizon all 1\nPASS target = origin+1 month\nPASS actual matches data\nPASS naive = ip at origin\nPASS independent ridge at 2021-09-01 (0.112470 vs 0.112470)\nPASS independent ridge at 2024-01-01 (0.272711 vs 0.272711)\nPASS independent ridge at 2026-08-01 (0.258667 vs 0.258667)\nPASS n_analysed = 413 = training rows at last origin\nPASS mean at 2021-09 independent\nPASS rmse/mae naive recomputed\nPASS mae<=rmse naive\nPASS rmse/mae mean recomputed\nPASS mae<=rmse mean\nPASS rmse/mae ridge recomputed\nPASS mae<=rmse ridge\nPASS rel improvements identities\nPASS DM sign consistent with RMSE ordering\nPASS exclusions add up","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4c3bf535-737b-4241-8e36-6a60504f66f7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}69{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"8d54cdeb4150521af1ed73e41144b727b48e31e116536ac2579b693058435e5b","message":{"role":"assistant","content":[{"type":"text","text":"Now verification: clean rerun reproducibility, independent ridge recomputation, and a leakage test (perturbing future data)."},{"type":"tool_use","id":"toolu_019WdmZKV8YpreofB1CFbwoe","name":"Bash","input":{"command":"shasum -a 256 out/results.json out/forecasts.csv > /tmp/h1; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; shasum -a 256 out/results.json out/forecasts.csv | diff - /tmp/h1 && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json, numpy as np, pandas as pd\nr=json.load(open(\"out/results.json\")); fc=pd.read_csv(\"out/forecasts.csv\")\nd=pd.read_csv(\"data/ip_predictors.csv\",parse_dates=[\"month\"]).set_index(\"month\")\nok=lambda n,c: print((\"PASS\" if c else \"FAIL\"), n)\nok(\"180 rows, 60 per model\", len(fc)==180 and (fc.model.value_counts()==60).all())\nok(\"horizon all 1\", (fc.horizon==1).all())\nok(\"target = origin+1 month\", (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\nok(\"actual matches data\", np.allclose(fc.actual, d.loc[pd.to_datetime(fc.target_date),\"ip_growth\"].values))\nnv=fc[fc.model==\"naive\"]; ok(\"naive = ip at origin\", np.allclose(nv.forecast, d.loc[pd.to_datetime(nv.origin_date),\"ip_growth\"].values))\n# independent ridge via augmented least squares (sqrt(lam) rows), at origin 2021-09 and 2026-08\ndef ridge_aug(t):\n t=pd.Timestamp(t); s=d.index[(d.index>=pd.Timestamp(\"1992-03-01\"))&(d.index<=t-pd.DateOffset(months=1))]\n ip=d.ip_growth\n def feat(m): return np.array([ip[m],ip[m-pd.DateOffset(months=1)],ip[m-pd.DateOffset(months=2)],d.oil_chg[m],d.fedfunds[m],d.permit_chg[m],d.neworder_chg[m],d.tcu[m]])\n X=np.array([feat(m) for m in s]); y=np.array([ip[m+pd.DateOffset(months=1)] for m in s])\n mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd\n A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.concatenate([y-y.mean(),np.zeros(8)])\n b=np.linalg.lstsq(A,bb,rcond=None)[0]\n return y.mean()+((feat(t)-mu)/sd)@b, len(s)\nrg=fc[fc.model==\"ridge\"].set_index(\"origin_date\").forecast\nfor t in [\"2021-09-01\",\"2024-01-01\",\"2026-08-01\"]:\n f,n=ridge_aug(t); ok(f\"independent ridge at {t} ({f:.6f} vs {rg[t]:.6f})\", abs(f-rg[t])<1e-8)\nok(\"n_analysed = 413 = training rows at last origin\", ridge_aug(\"2026-08-01\")[1]==r[\"n_analysed\"]==413)\nmn=fc[fc.model==\"mean\"].set_index(\"origin_date\").forecast\nok(\"mean at 2021-09 independent\", abs(d.loc[\"1992-04-01\":\"2021-09-01\",\"ip_growth\"].mean()-mn[\"2021-09-01\"])<1e-10)\ne=lambda m:(fc[fc.model==m].actual-fc[fc.model==m].forecast)\nfor m in [\"naive\",\"mean\",\"ridge\"]:\n ok(f\"rmse/mae {m} recomputed\", np.isclose(np.sqrt((e(m)**2).mean()),r[f\"rmse_{m}\"]) and np.isclose(e(m).abs().mean(),r[f\"mae_{m}\"]))\n ok(f\"mae<=rmse {m}\", r[f\"mae_{m}\"]<=r[f\"rmse_{m}\"])\nok(\"rel improvements identities\", np.isclose(r[\"rel_improvement_vs_naive\"],1-r[\"rmse_ridge\"]/r[\"rmse_naive\"]) and np.isclose(r[\"rel_improvement_vs_mean\"],1-r[\"rmse_ridge\"]/r[\"rmse_mean\"]))\nok(\"DM sign consistent with RMSE ordering\", np.sign(r[\"dm_stat_ridge_vs_mean\"])==np.sign(r[\"rmse_mean\"]-r[\"rmse_ridge\"]) and np.sign(r[\"dm_stat_ridge_vs_naive\"])==np.sign(r[\"rmse_naive\"]-r[\"rmse_ridge\"]))\nex=pd.read_csv(\"out/exclusions.csv\"); ok(\"exclusions add up\", 417-ex.n_removed.sum()==r[\"n_analysed\"])\nEOF","description":"Rerun reproducibility and consistency checks"}}]}}70{"type":"api-request","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","id":"61189953-180a-404d-8833-b556f4853728","timestamp":"2026-10-30T04:11:01.975Z","version":"2.1.314","querySource":"sdk","shapeHash":"f3d8889d0113d7901dc9f36f9d04a90b0d5b30c922f25bfdb63e2a15eca5dca2","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"32857a98-b187-4542-bf10-f94cb4a24d97","keep":22,"tail":["8d54cdeb4150521af1ed73e41144b727b48e31e116536ac2579b693058435e5b","b186e76761ae70d0c84d7e573fb7baf625af477ca5c9b691844c474eac718a4b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c267caeb1ab8218063ab46267b55a54d62116f97d7f96b40e7904a0362f960c9"}71{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"2a2c99e1-a84d-428b-9e4d-9d1d0ff243c1","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba"}72{"type":"atis-latch","atis":"","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba"}73{"parentUuid":"2a2c99e1-a84d-428b-9e4d-9d1d0ff243c1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmUzqgcjRKREDAVwgnt","type":"message","role":"assistant","content":[{"type":"text","text":"Leakage test: corrupt all data after an origin and confirm that origin's forecasts don't change."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2184,"cache_read_input_tokens":16899,"output_tokens":557,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2184},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmUzZZwLx5jZbzXK1g1","requestRef":"61189953-180a-404d-8833-b556f4853728","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6443500e-7e5c-44bc-afbf-db651bfc63bd","timestamp":"2026-10-30T04:11:03.423Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"6443500e-7e5c-44bc-afbf-db651bfc63bd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmUzqgcjRKREDAVwgnt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BGTPubGUsS8ryMN7P6r35u","name":"Bash","input":{"command":"rm -rf /tmp/leak && mkdir -p /tmp/leak/data /tmp/leak/out && cp out/analysis.py /tmp/leak/out/ && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np\nd=pd.read_csv(\"data/ip_predictors.csv\")\nm=d.month>\"2024-01-01\"\nfor c in d.columns[1:]: d.loc[m,c]=d.loc[m,c]*7+100\nd.to_csv(\"/tmp/leak/data/ip_predictors.csv\",index=False)\nEOF\ncd /tmp/leak && ~/wp29/venv/bin/python out/analysis.py >/dev/null; cd /Users/simon-pierreboucher/wp29/runs/RC0027\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\na=pd.read_csv('out/forecasts.csv');b=pd.read_csv('/tmp/leak/out/forecasts.csv')\nk=a.origin_date<='2024-01-01'\nprint('forecasts at origins<=2024-01 unchanged:', np.allclose(a[k].forecast,b[k].forecast), '| later changed:', not np.allclose(a[~k].forecast,b[~k].forecast))\"","description":"Test for look-ahead leakage"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2184,"cache_read_input_tokens":16899,"output_tokens":557,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2184},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmUzZZwLx5jZbzXK1g1","requestRef":"61189953-180a-404d-8833-b556f4853728","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"412199b5-373d-4337-ac93-73a8cfd363f8","timestamp":"2026-10-30T04:11:07.065Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}75{"parentUuid":"412199b5-373d-4337-ac93-73a8cfd363f8","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BGTPubGUsS8ryMN7P6r35u","type":"tool_result","content":"forecasts at origins<=2024-01 unchanged: True | later changed: True","is_error":false}]},"uuid":"48e52d83-577b-4119-82d7-7c39896ca886","timestamp":"2026-10-30T04:11:08.239Z","toolUseResult":{"stdout":"forecasts at origins<=2024-01 unchanged: True | later changed: True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"412199b5-373d-4337-ac93-73a8cfd363f8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}76{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"ed739c9a717a0a2b452202ed07b5cac5381d5a42030cbb13f56eec28652eabec","message":{"role":"assistant","content":[{"type":"text","text":"Leakage test: corrupt all data after an origin and confirm that origin's forecasts don't change."},{"type":"tool_use","id":"toolu_01BGTPubGUsS8ryMN7P6r35u","name":"Bash","input":{"command":"rm -rf /tmp/leak && mkdir -p /tmp/leak/data /tmp/leak/out && cp out/analysis.py /tmp/leak/out/ && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np\nd=pd.read_csv(\"data/ip_predictors.csv\")\nm=d.month>\"2024-01-01\"\nfor c in d.columns[1:]: d.loc[m,c]=d.loc[m,c]*7+100\nd.to_csv(\"/tmp/leak/data/ip_predictors.csv\",index=False)\nEOF\ncd /tmp/leak && ~/wp29/venv/bin/python out/analysis.py >/dev/null; cd /Users/simon-pierreboucher/wp29/runs/RC0027\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\na=pd.read_csv('out/forecasts.csv');b=pd.read_csv('/tmp/leak/out/forecasts.csv')\nk=a.origin_date<='2024-01-01'\nprint('forecasts at origins<=2024-01 unchanged:', np.allclose(a[k].forecast,b[k].forecast), '| later changed:', not np.allclose(a[~k].forecast,b[~k].forecast))\"","description":"Test for look-ahead leakage"}}]}}77{"type":"api-request","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","id":"a6a1402a-3152-4e9a-bd20-537cdf9769df","timestamp":"2026-10-30T04:11:08.242Z","version":"2.1.314","querySource":"sdk","shapeHash":"f3d8889d0113d7901dc9f36f9d04a90b0d5b30c922f25bfdb63e2a15eca5dca2","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"61189953-180a-404d-8833-b556f4853728","keep":24,"tail":["ed739c9a717a0a2b452202ed07b5cac5381d5a42030cbb13f56eec28652eabec","f10f8c1a520ece1cb29cfea631b2a22b8089e6ef4aa75716fa1fce87a90b5074"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ff936c22f7d1a634cdfdd3e3980a4d24fcdd1d24b8c65b0fb38434f112e7e529"}78{"parentUuid":"48e52d83-577b-4119-82d7-7c39896ca886","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmVTXnmTMkFgR2zEKSv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XbDBthCCahRefjnbWhrn3z","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4), 2026-10-30\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, forecasts.csv, exclusions.csv) reproduces byte-identical SHA-256 of results.json and forecasts.csv | PASS |\n| 2 | forecasts.csv: 180 rows, 60 per model, horizon=1, target_date = origin+1 month | PASS |\n| 3 | `actual` equals data ip_growth at target month; naive equals ip_growth at origin | PASS |\n| 4 | Independent ridge recomputation (augmented least squares, separate feature construction) at origins 2021-09, 2024-01, 2026-08 matches to <1e-8 | PASS |\n| 5 | Expanding mean at 2021-09 recomputed independently | PASS |\n| 6 | RMSE/MAE recomputed from forecasts.csv; MAE ≤ RMSE for all models | PASS |\n| 7 | rel_improvement identities 1 − RMSE_ridge/RMSE_base hold | PASS |\n| 8 | DM statistic signs agree with RMSE ordering | PASS |\n| 9 | Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed = training rows at last origin | PASS |\n| 10 | Leakage test: multiplying all values dated > 2024-01 by 7 and adding 100 leaves every forecast at origins ≤ 2024-01 unchanged (later ones change) | PASS |\n| 11 | Script asserts: no NaN in training X/y; last training row s satisfies s+1 ≤ t | PASS (asserts did not fire) |\n| 12 | Magnitudes: RMSEs 0.54–0.88 %/month vs full-sample SD of ip_growth 1.04 (inflated by 2020); plausible | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json & forecasts.csv\",\"independent ridge recomputation at 3 origins matches\",\"metric identities, counts, exclusions add up\",\"future-data perturbation leakage test passed\",\"see out/state/checks.md (12 checks, all PASS)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\necho \"- 2026-10-30: P4 verification passed (12 checks, see checks.md).\" >> out/state/log.md; 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positive stat => ridge more accurate\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"data/README.md states the first month (1992-01) of every change series is empty, but ip_growth, oil_chg and permit_chg have values at 1992-01; only neworder_chg is missing (1992-01, 1992-02).\",\n \"handling\": \"Values used as given. Only ip_growth 1992-01 enters the analysis (lag-2 feature of the s=1992-03 training row, as specified); missing neworder_chg rows precede the training window.\"\n },\n {","is_error":false}]},"uuid":"e1643c12-b32c-427e-885a-1a74eba3ef5f","timestamp":"2026-10-30T04:11:17.328Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": 60,\n \"first_origin\": \"2021-09-01\",\n \"last_origin\": \"2026-08-01\",\n \"lambda\": 50.0,\n \"normalisation_window\": \"expanding\",\n \"n_features\": 8,\n \"baseline_note\": \"naive and mean are the task-required baselines\",\n \"rmse_naive\": 0.8830699166753823,\n \"mae_naive\": 0.6989594000000001,\n \"rmse_mean\": 0.5439355781141625,\n \"mae_mean\": 0.42929777256194207,\n \"rmse_ridge\": 0.5607989303521244,\n \"mae_ridge\": 0.45141289074453916,\n \"rel_improvement_vs_naive\": 0.3649439078805412,\n \"rel_improvement_vs_mean\": -0.031002480654837017,\n \"dm_stat_ridge_vs_naive\": 3.3649594921847754,\n \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571726,\n \"dm_stat_ridge_vs_mean\": -0.5392198591267081,\n \"dm_pvalue_ridge_vs_mean\": 0.591764258342193,\n \"dm_note\": \"Diebold-Mariano, squared-error loss, h=1, HLN correction, t(59) reference; positive stat => ridge more accurate\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"data/README.md states the first month (1992-01) of every change series is empty, but ip_growth, oil_chg and permit_chg have values at 1992-01; only neworder_chg is missing (1992-01, 1992-02).\",\n \"handling\": \"Values used as given. Only ip_growth 1992-01 enters the analysis (lag-2 feature of the s=1992-03 training row, as specified); missing neworder_chg rows precede the training window.\"\n },\n {","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9fabff13-4964-40a9-ba79-00023a245186","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}80{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"65c9eeba0634853714756c04a77524e250eeadc1f238de537c556464989bcbfc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01XbDBthCCahRefjnbWhrn3z","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4), 2026-10-30\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, forecasts.csv, exclusions.csv) reproduces byte-identical SHA-256 of results.json and forecasts.csv | PASS |\n| 2 | forecasts.csv: 180 rows, 60 per model, horizon=1, target_date = origin+1 month | PASS |\n| 3 | `actual` equals data ip_growth at target month; naive equals ip_growth at origin | PASS |\n| 4 | Independent ridge recomputation (augmented least squares, separate feature construction) at origins 2021-09, 2024-01, 2026-08 matches to <1e-8 | PASS |\n| 5 | Expanding mean at 2021-09 recomputed independently | PASS |\n| 6 | RMSE/MAE recomputed from forecasts.csv; MAE ≤ RMSE for all models | PASS |\n| 7 | rel_improvement identities 1 − RMSE_ridge/RMSE_base hold | PASS |\n| 8 | DM statistic signs agree with RMSE ordering | PASS |\n| 9 | Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed = training rows at last origin | PASS |\n| 10 | Leakage test: multiplying all values dated > 2024-01 by 7 and adding 100 leaves every forecast at origins ≤ 2024-01 unchanged (later ones change) | PASS |\n| 11 | Script asserts: no NaN in training X/y; last training row s satisfies s+1 ≤ t | PASS (asserts did not fire) |\n| 12 | Magnitudes: RMSEs 0.54–0.88 %/month vs full-sample SD of ip_growth 1.04 (inflated by 2020); plausible | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open(\"out/state/phases.json\"))\np[\"P4\"]={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json & forecasts.csv\",\"independent ridge recomputation at 3 origins matches\",\"metric identities, counts, exclusions add up\",\"future-data perturbation leakage test passed\",\"see out/state/checks.md (12 checks, all PASS)\"],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nEOF\necho \"- 2026-10-30: P4 verification passed (12 checks, see checks.md).\" >> out/state/log.md; cat out/results.json | head -30","description":"Record checks and update ledger"}}]}}81{"type":"api-request","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","id":"f83a3ba9-0f52-4a23-bbea-56f316fce734","timestamp":"2026-10-30T04:11:17.332Z","version":"2.1.314","querySource":"sdk","shapeHash":"f3d8889d0113d7901dc9f36f9d04a90b0d5b30c922f25bfdb63e2a15eca5dca2","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"a6a1402a-3152-4e9a-bd20-537cdf9769df","keep":26,"tail":["65c9eeba0634853714756c04a77524e250eeadc1f238de537c556464989bcbfc","78487f69fade26897c8c9a8996b6a40aaa789391500b23d0c1879f4fb8c09956"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4a68a80e73e8d60b636fe96e934dd218ae63090870df5ef97350f46a790cb176"}82{"parentUuid":"e1643c12-b32c-427e-885a-1a74eba3ef5f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmW8X5Hgnoh9gz9C4LR","type":"message","role":"assistant","content":[{"type":"text","text":"Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1650,"cache_read_input_tokens":19671,"output_tokens":2924,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1650},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmW8FCmRrtEzbRpHCTX","requestRef":"f83a3ba9-0f52-4a23-bbea-56f316fce734","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3e046150-37d7-4290-a0c3-fa3604f8e0e0","timestamp":"2026-10-30T04:11:18.407Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}83{"parentUuid":"3e046150-37d7-4290-a0c3-fa3604f8e0e0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmW8X5Hgnoh9gz9C4LR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01NhBKb86SDLMPNgn3zGK7Vu","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03: Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a ridge regression with a fixed penalty (λ = 50, key `lambda`) on 8 standardised monthly predictors (key `n_features`) forecast next month's US industrial production growth (100 × Δln INDPRO) better than two baselines: the naive (last value) forecast and the expanding historical mean? The evaluation uses 60 rolling origins (`n_origins`) from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, at horizon h = 1.\n\n## Data and exclusions\n- The input is `data/ip_predictors.csv`: 417 data rows (`n_input_rows`), 1992-01 to 2026-09. Its SHA-256 (`input_sha256`) matches `data/README.md`. There are no gaps and no duplicate months. The only missing cells are `neworder_chg` for 1992-01 and 1992-02. The full audit is in `out/state/data_audit.md`.\n- Exclusions (`out/exclusions.csv`):\n - 2 rows dropped because they come before the specified training start of 1992-03.\n - 2 rows dropped because their targets fall after the last origin.\n - This leaves 413 training rows at the last origin (`n_analysed`).\n- Data issues (`data_issues`):\n 1. The README says the 1992-01 values of every change series are empty, but the file has values for `ip_growth`, `oil_chg` and `permit_chg`. I used the values as given. Only `ip_growth` for 1992-01 enters the analysis, as a lag-2 feature, which the specification requires.\n 2. The extreme COVID-19 observations of 2020 are kept in every training window.\n 3. The data are the current vintage, not real-time.\n\n## Method\nFor each origin t = 2021-09, …, 2026-08, the target is `ip_growth` of month t+1. Only data dated ≤ t are used.\n- **Naive forecast:** `ip_growth` of month t.\n- **Expanding mean:** the mean of `ip_growth` over 1992-04 to t.\n- **Ridge regression:**\n - Training rows are s = 1992-03, …, t−1, with target y_{s+1}.\n - The features are z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s).\n - Each feature is standardised with the mean and sample SD (ddof = 1) of that origin's training rows; this is recomputed at every origin (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ.\n - Coefficients: b = (Z′Z + 50·I)⁻¹ Z′(y − ȳ).\n - Forecast: ȳ + ((z_t − μ)/σ)·b.\n- The method is deterministic (`random_seed` = null).\n- Forecast errors are actual − forecast. RMSE and MAE are computed over the 60 targets, 2021-10 to 2026-09.\n- Implementation: `out/analysis.py`. Forecasts: `out/forecasts.csv` (180 rows).\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.883 (`rmse_naive`) | 0.699 (`mae_naive`) |\n| expanding mean | 0.544 (`rmse_mean`) | 0.429 (`mae_mean`) |\n| ridge (λ=50) | 0.561 (`rmse_ridge`) | 0.451 (`mae_ridge`) |\n\n- **Ridge vs naive:** ridge's RMSE is 36.5% lower than the naive forecast's (`rel_improvement_vs_naive` = 0.365).\n- **Ridge vs expanding mean:** ridge does *not* beat the expanding mean. Its RMSE is 3.1% higher (`rel_improvement_vs_mean` = −0.031), and its MAE is also higher (`mae_ridge` vs `mae_mean`).\n\n**Answer:** over these 60 origins, ridge is better than the naive baseline but slightly worse than the expanding historical mean.\n\n## Uncertainty\nI ran Diebold–Mariano tests on squared-error loss (h = 1, Harvey–Leybourne–Newbold small-sample correction, t(59) reference distribution; see `dm_note`):\n- **Ridge vs naive:** statistic 3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). Ridge's advantage over the naive forecast is statistically distinguishable from zero.\n- **Ridge vs mean:** statistic −0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). The small RMSE difference between ridge and the expanding mean is not statistically distinguishable from zero.\n\nCaveats on these tests:\n- 60 targets is a short sample.\n- The DM test treats the loss differential as stationary. With h = 1, it does not adjust for autocorrelation in the loss differential.\n- The test ignores that the models are re-estimated on expanding windows, i.e. it does not account for parameter-estimation effects.\n\n## Limitations and open issues\n- **Not a real-time evaluation.** All series are current-vintage (revised) data, and publication lags are ignored. INDPRO, TCU and NEWORDER are revised substantially, so these errors do not show historical real-time performance.\n- **Specification taken as given.** λ = 50 is fixed by the task and not tuned. Other penalties, or outlier handling for the 2020 observations that sit in every training window, could change the ridge-vs-mean ranking. I did not explore these, because the task does not require it.\n- **Minor data discrepancy.** The README and the data file disagree about which 1992-01 values are present. This does not materially affect the results: only one 1992 value enters the analysis, as a lag feature.\n- **Status.** All required outputs were produced (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has RMSE 0.561 and MAE 0.451 percent per month, the naive forecast 0.883/0.699 and the expanding mean 0.544/0.429.\",\"results_keys\":[\"rmse_ridge\",\"mae_ridge\",\"rmse_naive\",\"mae_naive\",\"rmse_mean\",\"mae_mean\",\"n_origins\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; publication lags ignored.\"},\n {\"id\":\"C2\",\"text\":\"Ridge improves on the naive forecast by 36.5% in RMSE, and the Diebold-Mariano test rejects equal accuracy (p=0.0014).\",\"results_keys\":[\"rel_improvement_vs_naive\",\"dm_stat_ridge_vs_naive\",\"dm_pvalue_ridge_vs_naive\"],\"status\":\"supported\",\"caveat\":\"60 targets; the DM test ignores parameter-estimation effects.\"},\n {\"id\":\"C3\",\"text\":\"Ridge does not beat the expanding historical mean: its RMSE is 3.1% higher (and its MAE is also higher); the difference is not statistically significant (DM p=0.59).\",\"results_keys\":[\"rel_improvement_vs_mean\",\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"dm_stat_ridge_vs_mean\",\"dm_pvalue_ridge_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Only lambda=50 was evaluated, as specified; the ranking may depend on lambda and on the treatment of 2020 outliers.\"},\n {\"id\":\"C4\",\"text\":\"All standardisation statistics and the target mean were computed from each origin's training window only (expanding), with 413 training rows at the last origin.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Verified by a test that perturbs future data (out/state/checks.md).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write report and claims ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1650,"cache_read_input_tokens":19671,"output_tokens":2924,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1650},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmW8FCmRrtEzbRpHCTX","requestRef":"f83a3ba9-0f52-4a23-bbea-56f316fce734","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"82744e60-5f63-4e41-a35e-44e5988d8e82","timestamp":"2026-10-30T04:11:41.537Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}84{"parentUuid":"82744e60-5f63-4e41-a35e-44e5988d8e82","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NhBKb86SDLMPNgn3zGK7Vu","type":"tool_result","content":"ok","is_error":false}]},"uuid":"4dce9d52-6a85-496c-90f7-f2a371c8fed9","timestamp":"2026-10-30T04:11:41.581Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"82744e60-5f63-4e41-a35e-44e5988d8e82","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}85{"type":"api-request-blob","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","hash":"7465a2f57211ad51d4594dab02370a725938579451bd1f5764c597235f6b14d0","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_01NhBKb86SDLMPNgn3zGK7Vu","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03: Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a ridge regression with a fixed penalty (λ = 50, key `lambda`) on 8 standardised monthly predictors (key `n_features`) forecast next month's US industrial production growth (100 × Δln INDPRO) better than two baselines: the naive (last value) forecast and the expanding historical mean? The evaluation uses 60 rolling origins (`n_origins`) from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, at horizon h = 1.\n\n## Data and exclusions\n- The input is `data/ip_predictors.csv`: 417 data rows (`n_input_rows`), 1992-01 to 2026-09. Its SHA-256 (`input_sha256`) matches `data/README.md`. There are no gaps and no duplicate months. The only missing cells are `neworder_chg` for 1992-01 and 1992-02. The full audit is in `out/state/data_audit.md`.\n- Exclusions (`out/exclusions.csv`):\n - 2 rows dropped because they come before the specified training start of 1992-03.\n - 2 rows dropped because their targets fall after the last origin.\n - This leaves 413 training rows at the last origin (`n_analysed`).\n- Data issues (`data_issues`):\n 1. The README says the 1992-01 values of every change series are empty, but the file has values for `ip_growth`, `oil_chg` and `permit_chg`. I used the values as given. Only `ip_growth` for 1992-01 enters the analysis, as a lag-2 feature, which the specification requires.\n 2. The extreme COVID-19 observations of 2020 are kept in every training window.\n 3. The data are the current vintage, not real-time.\n\n## Method\nFor each origin t = 2021-09, …, 2026-08, the target is `ip_growth` of month t+1. Only data dated ≤ t are used.\n- **Naive forecast:** `ip_growth` of month t.\n- **Expanding mean:** the mean of `ip_growth` over 1992-04 to t.\n- **Ridge regression:**\n - Training rows are s = 1992-03, …, t−1, with target y_{s+1}.\n - The features are z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s).\n - Each feature is standardised with the mean and sample SD (ddof = 1) of that origin's training rows; this is recomputed at every origin (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ.\n - Coefficients: b = (Z′Z + 50·I)⁻¹ Z′(y − ȳ).\n - Forecast: ȳ + ((z_t − μ)/σ)·b.\n- The method is deterministic (`random_seed` = null).\n- Forecast errors are actual − forecast. RMSE and MAE are computed over the 60 targets, 2021-10 to 2026-09.\n- Implementation: `out/analysis.py`. Forecasts: `out/forecasts.csv` (180 rows).\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.883 (`rmse_naive`) | 0.699 (`mae_naive`) |\n| expanding mean | 0.544 (`rmse_mean`) | 0.429 (`mae_mean`) |\n| ridge (λ=50) | 0.561 (`rmse_ridge`) | 0.451 (`mae_ridge`) |\n\n- **Ridge vs naive:** ridge's RMSE is 36.5% lower than the naive forecast's (`rel_improvement_vs_naive` = 0.365).\n- **Ridge vs expanding mean:** ridge does *not* beat the expanding mean. Its RMSE is 3.1% higher (`rel_improvement_vs_mean` = −0.031), and its MAE is also higher (`mae_ridge` vs `mae_mean`).\n\n**Answer:** over these 60 origins, ridge is better than the naive baseline but slightly worse than the expanding historical mean.\n\n## Uncertainty\nI ran Diebold–Mariano tests on squared-error loss (h = 1, Harvey–Leybourne–Newbold small-sample correction, t(59) reference distribution; see `dm_note`):\n- **Ridge vs naive:** statistic 3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). Ridge's advantage over the naive forecast is statistically distinguishable from zero.\n- **Ridge vs mean:** statistic −0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). The small RMSE difference between ridge and the expanding mean is not statistically distinguishable from zero.\n\nCaveats on these tests:\n- 60 targets is a short sample.\n- The DM test treats the loss differential as stationary. With h = 1, it does not adjust for autocorrelation in the loss differential.\n- The test ignores that the models are re-estimated on expanding windows, i.e. it does not account for parameter-estimation effects.\n\n## Limitations and open issues\n- **Not a real-time evaluation.** All series are current-vintage (revised) data, and publication lags are ignored. INDPRO, TCU and NEWORDER are revised substantially, so these errors do not show historical real-time performance.\n- **Specification taken as given.** λ = 50 is fixed by the task and not tuned. Other penalties, or outlier handling for the 2020 observations that sit in every training window, could change the ridge-vs-mean ranking. I did not explore these, because the task does not require it.\n- **Minor data discrepancy.** The README and the data file disagree about which 1992-01 values are present. This does not materially affect the results: only one 1992 value enters the analysis, as a lag feature.\n- **Status.** All required outputs were produced (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has RMSE 0.561 and MAE 0.451 percent per month, the naive forecast 0.883/0.699 and the expanding mean 0.544/0.429.\",\"results_keys\":[\"rmse_ridge\",\"mae_ridge\",\"rmse_naive\",\"mae_naive\",\"rmse_mean\",\"mae_mean\",\"n_origins\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; publication lags ignored.\"},\n {\"id\":\"C2\",\"text\":\"Ridge improves on the naive forecast by 36.5% in RMSE, and the Diebold-Mariano test rejects equal accuracy (p=0.0014).\",\"results_keys\":[\"rel_improvement_vs_naive\",\"dm_stat_ridge_vs_naive\",\"dm_pvalue_ridge_vs_naive\"],\"status\":\"supported\",\"caveat\":\"60 targets; the DM test ignores parameter-estimation effects.\"},\n {\"id\":\"C3\",\"text\":\"Ridge does not beat the expanding historical mean: its RMSE is 3.1% higher (and its MAE is also higher); the difference is not statistically significant (DM p=0.59).\",\"results_keys\":[\"rel_improvement_vs_mean\",\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"dm_stat_ridge_vs_mean\",\"dm_pvalue_ridge_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Only lambda=50 was evaluated, as specified; the ranking may depend on lambda and on the treatment of 2020 outliers.\"},\n {\"id\":\"C4\",\"text\":\"All standardisation statistics and the target mean were computed from each origin's training window only (expanding), with 413 training rows at the last origin.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Verified by a test that perturbs future data (out/state/checks.md).\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write report and claims ledger"}}]}}86{"type":"api-request","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","id":"42e928b9-9404-464d-ba18-61a3ab90877e","timestamp":"2026-10-30T04:11:41.584Z","version":"2.1.314","querySource":"sdk","shapeHash":"f3d8889d0113d7901dc9f36f9d04a90b0d5b30c922f25bfdb63e2a15eca5dca2","params":{"model":"claude-opus-5-5","betas":["claude-code-20250219","interleaved-thinking-2025-05-14","context-management-2025-06-27","prompt-caching-scope-2026-01-05","per-turn-control-2026-07-01","thinking-token-count-2026-05-13","mid-conversation-system-clear-at-2026-08-21","effort-2025-11-24","thinking-binding-controls-2026-08-01","thinking-display-updates-2026-08-18","cache-diagnosis-2026-04-07"],"max_tokens":128000,"thinking":{"type":"adaptive","display":"updates"},"context_management":{"edits":[{"type":"clear_thinking_20251015","keep":"all"}]},"output_config":{"effort":"medium"}},"base":"f83a3ba9-0f52-4a23-bbea-56f316fce734","keep":28,"tail":["7465a2f57211ad51d4594dab02370a725938579451bd1f5764c597235f6b14d0","a3c3464d434ab0e5b92212579fc4ecafbc0f930a365567e8534738f2e3668372"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d6161554640b0e7734901e0eb0a77095486ba15c05252df101d2d367f1aa447d"}87{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4dce9d52-6a85-496c-90f7-f2a371c8fed9","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba"}88{"type":"atis-latch","atis":"","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba"}89{"parentUuid":"4dce9d52-6a85-496c-90f7-f2a371c8fed9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXvDUC4a2dEJdgqVJy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CfCiYTv12B1eVD4ADRRN3f","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,re\nr=json.load(open(\"out/results.json\"))\nkeys=set(re.findall(r\"`([a-z_0-9]+)`\",open(\"out/report.md\").read()))\nmissing=[k for k in keys if k not in r and k not in {\"ip_growth\",\"oil_chg\",\"permit_chg\",\"neworder_chg\"}]\ncl=json.load(open(\"out/claims.json\")); bad=[k for c in cl for k in c[\"results_keys\"] if k not in r]\nprint(\"report keys not in results:\",missing,\"| claim keys missing:\",bad)\np=json.load(open(\"out/state/phases.json\"))\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"all backticked result keys in report exist in results.json: \"+str(missing==[]),\"all claims.json results_keys exist in results.json: \"+str(bad==[])],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nprint({k:v[\"status\"] for k,v in p.items()})\nEOF\necho \"- 2026-10-30: P5 report.md and claims.json written; all phases passed.\" >> out/state/log.md; ls out","description":"Cross-check report keys and finalize ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2931,"cache_read_input_tokens":21321,"output_tokens":486,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2931},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmXuv6jPmP8tGdFZGiG","requestRef":"42e928b9-9404-464d-ba18-61a3ab90877e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"71e9b5f6-3667-4587-9879-1e2f5bc252f8","timestamp":"2026-10-30T04:11:46.165Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}90{"parentUuid":"71e9b5f6-3667-4587-9879-1e2f5bc252f8","isSidechain":false,"promptId":"1593c1d7-9f8f-4d5a-8a36-ea8844bd117a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CfCiYTv12B1eVD4ADRRN3f","type":"tool_result","content":"report keys not in results: [] | claim keys missing: []\n{'P0': 'passed', 'P1': 'passed', 'P2': 'passed', 'P3': 'passed', 'P4': 'passed', 'P5': 'passed'}\nanalysis.py\nclaims.json\nexclusions.csv\nforecasts.csv\nreport.md\nresults.json\nstate","is_error":false}]},"uuid":"1ed976fc-1aff-4781-b78a-3decb6b044d1","timestamp":"2026-10-30T04:11:46.197Z","toolUseResult":{"stdout":"report keys not in results: [] | claim keys missing: []\n{'P0': 'passed', 'P1': 'passed', 'P2': 'passed', 'P3': 'passed', 'P4': 'passed', 'P5': 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json,re\nr=json.load(open(\"out/results.json\"))\nkeys=set(re.findall(r\"`([a-z_0-9]+)`\",open(\"out/report.md\").read()))\nmissing=[k for k in keys if k not in r and k not in {\"ip_growth\",\"oil_chg\",\"permit_chg\",\"neworder_chg\"}]\ncl=json.load(open(\"out/claims.json\")); bad=[k for c in cl for k in c[\"results_keys\"] if k not in r]\nprint(\"report keys not in results:\",missing,\"| claim keys missing:\",bad)\np=json.load(open(\"out/state/phases.json\"))\np[\"P5\"]={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"all backticked result keys in report exist in results.json: \"+str(missing==[]),\"all claims.json results_keys exist in results.json: \"+str(bad==[])],\"notes\":\"\"}\njson.dump(p,open(\"out/state/phases.json\",\"w\"),indent=2)\nprint({k:v[\"status\"] for k,v in p.items()})\nEOF\necho \"- 2026-10-30: P5 report.md and claims.json written; all phases passed.\" >> out/state/log.md; ls out","description":"Cross-check report keys and finalize 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over the 60 origins from 2021-09 to 2026-08, the ridge forecast beats the naive forecast but is slightly worse than the expanding historical mean. All required outputs are written, and re-running `out/analysis.py` from a clean state reproduces them byte for byte.\n\n| Model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| Naive | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge (λ = 50) | 0.561 | 0.451 |\n\n- **Ridge vs naive:** ridge's RMSE is 36.5% lower (`rel_improvement_vs_naive` = 0.365). I added a Diebold–Mariano test, which the task did not ask for, to fill the report's Uncertainty section. It says this gain is unlikely to be chance (p = 0.0014).\n- **Ridge vs expanding mean:** ridge's RMSE is 3.1% higher (`rel_improvement_vs_mean` = −0.031), and its MAE is also higher. The difference is small and not statistically significant (p = 0.59).\n\n**Checks:**\n- I recomputed the ridge forecasts a second, independent way at three origins, and they matched.\n- To check that no future data leaks in, I distorted every value after 2024-01. The forecasts made at or before that date did not change.\n- The row counts add up: 417 rows read, 2 dropped because they come before the 1992-03 training start, and 2 because their targets fall after the last origin. That leaves 413 training rows at the last origin.\n\nAll 12 checks are listed in `out/state/checks.md`.\n\n**Limitations:**\n- These are revised, current-vintage data with publication delays ignored, so the errors don't show how the model would have done in real time.\n- The penalty was fixed at 50 as the task specifies; I didn't try other values or special treatment of the extreme 2020 observations. Either could change the ranking against the mean.\n- `data/README.md` says the 1992-01 values of every change series are empty, but the file has values for three of them. Only one of those values (IP growth in 1992-01) is used, as a lag that the task's model requires, so it doesn't materially affect the results. This is recorded under `data_issues` in `results.json`.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv` (180 rows)\n- `exclusions.csv`\n- `report.md`\n- `claims.json`\n- `state/` (contract, data audit, checks, phase ledger, log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":608,"cache_read_input_tokens":24252,"output_tokens":902,"output_tokens_details":{"thinking_tokens":49},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":608},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmYFeFuid99F3eFAmFf","requestRef":"088f876a-3cbd-4f18-b76e-6478ecf5a603","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"697c253a-1e9a-4b7e-97ea-5536c538031f","timestamp":"2026-10-30T04:11:55.062Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0027","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","version":"2.1.314","gitBranch":"HEAD"}95{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"697c253a-1e9a-4b7e-97ea-5536c538031f","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba"}96{"type":"cost-state","sessionId":"17565932-c65a-42e3-a6cc-0fdf25582bba","totalCostUSD":0.4649420000000001,"totalAPIDuration":138215,"totalAPIDurationWithoutRetries":138206,"totalToolDuration":4105,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":142504,"startTime":1793333372566,"modelUsage":{"claude-opus-5-5":{"inputTokens":34,"outputTokens":15343,"thinkingTokens":829,"cacheReadInputTokens":205830,"cacheCreationInputTokens":23356,"cacheCreationEphemeral5mInputTokens":23356,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.4649420000000001}},"hasUnknownModelCost":false}97