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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 31 simon-pierreboucher staff 992 oct. 30 00:08 ..\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\n\n./data:\ntotal 64\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:46 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:52 ..\n-rw-r--r-- 1 simon-pierreboucher staff 28378 oct. 29 23:50 ip_predictors.csv\n-rw-r--r-- 1 simon-pierreboucher staff 2224 oct. 29 23:50 README.md\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"8e8c3d49-fa1b-44e4-9eba-41bf3a390f07","timestamp":"2026-10-30T04:08:19.653Z","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}{\n \"task_id\": \"F3-conf-03\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"lambda\": {\"type\": \"number\", \"nullable\": false},\n \"normalisation_window\": {\"type\": \"string\", \"nullable\": false},\n \"n_features\": {\"type\": \"integer\", \"nullable\": false},\n \"rmse_naive\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_mean\": {\"type\": \"number\", \"nullable\": false},\n \"mae_mean\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_ridge\": {\"type\": \"number\", \"nullable\": false},\n \"mae_ridge\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_vs_naive\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_vs_mean\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,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":"0149999f-c7e4-4ef8-af36-547683eb7ba7","timestamp":"2026-10-30T04:08:21.219Z","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 }\n}{\n \"task_id\": \"F3-conf-03\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"lambda\": {\"type\": \"number\", \"nullable\": false},\n \"normalisation_window\": {\"type\": \"string\", \"nullable\": false},\n \"n_features\": {\"type\": \"integer\", \"nullable\": false},\n \"rmse_naive\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_mean\": {\"type\": \"number\", \"nullable\": false},\n \"mae_mean\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_ridge\": {\"type\": \"number\", \"nullable\": false},\n \"mae_ridge\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_vs_naive\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_vs_mean\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a1a79a9a-0b23-4978-a4ca-a6ea773d5487","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","hash":"353a41683baffcafc77e556637d13738dd59664a49918779676e6b067256c1b9","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01LvvQnSCn7VkuBEFAqhvGDH","name":"Bash","input":{"command":"cat data/README.md; 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pd.set_option('display.width',200); pd.set_option('display.max_columns',20)\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month'])\nprint(d.describe().T.to_string())\"\ncat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-03\n\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 rolling origins 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; rel. improvement 1 − RMSE_ridge/RMSE_baseline for each baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), squared/absolute loss; 60 points ⇒ sizable sampling noise.\n\n**Information rules.** At origin t only data dated ≤ t. Ridge training rows s ∈ [1992-03, t−1], target y_{s+1}; features z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_s, ff_s, permit_s, neworder_s, tcu_s). μ, σ (ddof=1), ȳ computed on training rows of each origin only. b=(Z'Z+50I)^{-1}Z'(y−ȳ); forecast ȳ + ((z_t−μ)/σ)·b. Naive = ip_t; mean = mean ip over 1992-04..t.\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 (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus out/exclusions.csv and out/state/*.\n\n**Invalidating conditions.** Missing feature values inside training/origin rows; missing target months; using full-sample scaling (leakage); data not covering 2026-09 target.\nEOF","description":"Stats and write 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count mean min 25% 50% 75% max std\nmonth 417 2009-05-01 16:27:37.553956864 1992-01-01 00:00:00 2000-09-01 00:00:00 2009-05-01 00:00:00 2018-01-01 00:00:00 2026-09-01 00:00:00 NaN\nip_growth 417.0 0.124761 -14.142248 -0.207893 0.177862 0.532071 6.347249 1.039866\noil_chg 417.0 0.385489 -56.812501 -4.730747 1.292382 5.653178 54.562104 9.511083\nfedfunds 417.0 2.66482 0.05 0.2 2.4 4.83 6.54 2.151342\npermit_chg 417.0 0.066146 -24.780968 -2.654512 0.071454 3.083222 17.052698 4.943466\nneworder_chg 415.0 0.230273 -11.402067 -1.270999 0.370381 2.054636 9.096596 2.955474\ntcu 417.0 78.266985 64.0847 76.1216 77.8036 80.8334 84.9892 3.618664","is_error":false}]},"uuid":"8897c839-1721-4d8c-97fb-01176918d4fe","timestamp":"2026-10-30T04:08:37.994Z","toolUseResult":{"stdout":" count mean min 25% 50% 75% max std\nmonth 417 2009-05-01 16:27:37.553956864 1992-01-01 00:00:00 2000-09-01 00:00:00 2009-05-01 00:00:00 2018-01-01 00:00:00 2026-09-01 00:00:00 NaN\nip_growth 417.0 0.124761 -14.142248 -0.207893 0.177862 0.532071 6.347249 1.039866\noil_chg 417.0 0.385489 -56.812501 -4.730747 1.292382 5.653178 54.562104 9.511083\nfedfunds 417.0 2.66482 0.05 0.2 2.4 4.83 6.54 2.151342\npermit_chg 417.0 0.066146 -24.780968 -2.654512 0.071454 3.083222 17.052698 4.943466\nneworder_chg 415.0 0.230273 -11.402067 -1.270999 0.370381 2.054636 9.096596 2.955474\ntcu 417.0 78.266985 64.0847 76.1216 77.8036 80.8334 84.9892 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-c \"\nimport pandas as pd; pd.set_option('display.width',200); pd.set_option('display.max_columns',20)\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month'])\nprint(d.describe().T.to_string())\"\ncat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-03\n\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 rolling origins 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; rel. improvement 1 − RMSE_ridge/RMSE_baseline for each baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), squared/absolute loss; 60 points ⇒ sizable sampling noise.\n\n**Information rules.** At origin t only data dated ≤ t. Ridge training rows s ∈ [1992-03, t−1], target y_{s+1}; features z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_s, ff_s, permit_s, neworder_s, tcu_s). μ, σ (ddof=1), ȳ computed on training rows of each origin only. b=(Z'Z+50I)^{-1}Z'(y−ȳ); forecast ȳ + ((z_t−μ)/σ)·b. Naive = ip_t; mean = mean ip over 1992-04..t.\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 (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus out/exclusions.csv and out/state/*.\n\n**Invalidating conditions.** Missing feature values inside training/origin rows; missing target months; using full-sample scaling (leakage); data not covering 2026-09 target.\nEOF","description":"Stats and write contract"}}]}}38{"type":"api-request","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","id":"bcddf0ac-e5b6-4eb2-8817-72b61e3a1f29","timestamp":"2026-10-30T04:08:37.997Z","version":"2.1.314","querySource":"sdk","shapeHash":"036ffa53cf5357a10e750df9f8825a659859c168831ad105bb1027d1bccd7755","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":"fbcd4ed0-3a9b-47c5-b001-378c0a4ccc57","keep":10,"tail":["b82e96cd3d4ad0bdac9e2dc53413eae3be83008a8e52cb69cf3759100156aab4","2cc99448eb501935147f34cbe46388e07ac32135d87764c2a7a35e662be61c43"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5c3801764f8103176af7371f4c00f0c42d57a3d1694a013e97efbf1c23e96a0d"}39{"parentUuid":"8897c839-1721-4d8c-97fb-01176918d4fe","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmJN1Ms8rxCY461Wgeq","type":"message","role":"assistant","content":[{"type":"text","text":"Now the audit and analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1410,"cache_read_input_tokens":10635,"output_tokens":3658,"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":1410},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmJMfWa2GJ3E68N5APU","requestRef":"bcddf0ac-e5b6-4eb2-8817-72b61e3a1f29","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"11652e72-d088-4304-9054-4e56502462bb","timestamp":"2026-10-30T04:08:39.216Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"11652e72-d088-4304-9054-4e56502462bb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmJN1Ms8rxCY461Wgeq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GTbobKDdzQGHxNw6PC7vGK","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/ip_predictors.csv | 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json) | 417 | 7 |\n| data/README.md | 225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e | — | — |\n\nSchema: month (YYYY-MM-01, 1992-01-01..2026-09-01, 417 consecutive months, no gaps, no duplicates); ip_growth, oil_chg, permit_chg, neworder_chg (percent per month, 100×Δln); fedfunds (percent, level); tcu (percent of capacity, level). All float64.\n\nMissing values: only neworder_chg at 1992-01 and 1992-02 (2 cells). README states that the first month (1992-01) of *every* change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01 — a minor README/data inconsistency. It has no effect: the earliest value used is ip_growth for 1992-01 (as lag 2 of training row 1992-03), which is present; neworder_chg is first needed at 1992-03, present.\n\nRanges (min / max): ip_growth −14.14 / 6.35; oil_chg −56.8 / 54.6; fedfunds 0.05 / 6.54; permit_chg −24.8 / 17.1; neworder_chg −11.4 / 9.1; tcu 64.1 / 85.0. Magnitudes plausible for units.\n\nAnomalies: |z|>5 outliers — ip_growth 2020-04 (−14.14) and 2020-06 (+6.35); oil_chg 2020-03/04/05 (COVID); permit_chg 2008-07. These are genuine economic events; retained (ridge spec fixes training rows; no robustification allowed by the spec). They lie in every training window and influence μ, σ, b.\n\nInformation availability: the file contains data up to 2026-09, which is the target of the last origin (2026-08). The script masks all rows dated > t at every origin before computing any statistic. No input violates the rules per se; the vintage (current, revised) and ignored publication lags are accepted stylisations stated in TASK.md/README.\nEOF\ncat > 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, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAM = 50.0\nFIRST_TRAIN = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nORIGINS = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\ndf = raw.sort_values(\"month\").set_index(\"month\")\nassert df.index.is_unique and (df.index == pd.date_range(df.index[0], df.index[-1], freq=\"MS\")).all()\n\n# Feature matrix z_s (dated s; uses only values dated <= s)\nF = pd.DataFrame({\n \"ip_l0\": df[\"ip_growth\"], \"ip_l1\": df[\"ip_growth\"].shift(1), \"ip_l2\": df[\"ip_growth\"].shift(2),\n \"oil_chg\": df[\"oil_chg\"], \"fedfunds\": df[\"fedfunds\"], \"permit_chg\": df[\"permit_chg\"],\n \"neworder_chg\": df[\"neworder_chg\"], \"tcu\": df[\"tcu\"],\n})\ny_next = df[\"ip_growth\"].shift(-1) # target of row s = ip_growth_{s+1}\n\nrows, excl = [], []\nn_train_last = None\nfor t in ORIGINS:\n avail = df.loc[:t] # information set: values dated <= t only\n Fa = F.loc[:t]\n ya = avail[\"ip_growth\"].shift(-1) # recomputed inside the info set\n s_idx = Fa.index[(Fa.index >= FIRST_TRAIN) & (Fa.index <= t - pd.offsets.MonthBegin(1))]\n X = Fa.loc[s_idx].to_numpy(float)\n y = ya.loc[s_idx].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\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 + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = Fa.loc[t].to_numpy(float)\n assert np.isfinite(zt).all()\n f_ridge = ybar + ((zt - mu) / sd) @ b\n f_naive = avail.loc[t, \"ip_growth\"]\n f_mean = avail.loc[MEAN_START:t, \"ip_growth\"].mean()\n tgt = t + pd.offsets.MonthBegin(1)\n actual = df.loc[tgt, \"ip_growth\"] # used only for scoring\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n \"horizon\": 1, \"forecast\": float(f), \"actual\": float(actual), \"model\": m})\n n_train_last = len(s_idx)\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\ndef err(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\n\nrn, an = err(\"naive\"); rm, am = err(\"mean\"); rr, ar = err(\"ridge\")\n\n# Exclusion ledger (rows of the input file vs ridge training rows at the last origin)\nt_last = ORIGINS[-1]\nn1 = n_raw\nn2 = int((df.index >= FIRST_TRAIN).sum())\nn3 = int(((df.index >= FIRST_TRAIN) & (df.index <= t_last - pd.offsets.MonthBegin(1))).sum())\npd.DataFrame([\n {\"step\": 1, \"rule\": \"drop months before 1992-03-01 (lags of ip_growth / neworder_chg unavailable; spec start)\",\n \"n_before\": n1, \"n_after\": n2, \"n_removed\": n1 - n2, \"rationale\": \"TASK.md training-row definition s >= 1992-03-01\"},\n {\"step\": 2, \"rule\": \"drop months s with s+1 > last origin 2026-08-01 (target not in info set)\",\n \"n_before\": n2, \"n_after\": n3, \"n_removed\": n2 - n3, \"rationale\": \"information-availability rule at last origin\"},\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n3 == 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\": LAM,\n \"normalisation_window\": \"expanding\",\n \"n_features\": F.shape[1],\n \"rmse_naive\": rn, \"mae_naive\": an,\n \"rmse_mean\": rm, \"mae_mean\": am,\n \"rmse_ridge\": rr, \"mae_ridge\": ar,\n \"rel_improvement_vs_naive\": 1 - rr / rn,\n \"rel_improvement_vs_mean\": 1 - rr / rm,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02 (series starts 1992-02)\",\n \"handling\": \"Not needed: first ridge training row is 1992-03 per spec; no imputation.\"},\n {\"issue\": \"README says the first month of every change series is empty, but ip_growth, oil_chg, permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as lag 2 of training row 1992-03); noted as documentation inconsistency.\"},\n {\"issue\": \"Extreme COVID-19 observations (ip_growth 2020-04 = -14.1, 2020-06 = +6.3; oil_chg 2020-03..05 ~ +/-55) inside every training window\",\n \"handling\": \"Retained as genuine data, as the ridge specification fixes the training rows; they affect mu, sigma and coefficients.\"},\n {\"issue\": \"Current-vintage (revised) data; publication lags ignored\",\n \"handling\": \"Accepted per TASK.md; results are not real-time forecast accuracy.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": n_raw},\n \"n_analysed\": n_train_last,\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"mae_naive\", \"rmse_mean\", \"mae_mean\", \"rmse_ridge\", \"mae_ridge\",\n \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write audit and analysis script, run it"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1410,"cache_read_input_tokens":10635,"output_tokens":3658,"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":1410},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmJMfWa2GJ3E68N5APU","requestRef":"bcddf0ac-e5b6-4eb2-8817-72b61e3a1f29","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4cdc8c29-44e3-4784-aeec-244bb854bfab","timestamp":"2026-10-30T04:09:07.298Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"4cdc8c29-44e3-4784-aeec-244bb854bfab","isSidechain":false,"promptId":"5f0f08e6-4468-417f-bcbc-49946e901507","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GTbobKDdzQGHxNw6PC7vGK","type":"tool_result","content":"{\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_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop months before 1992-03-01 (lags of ip_growth / neworder_chg unavailable; spec start),417,415,2,TASK.md training-row definition s >= 1992-03-01\n2,drop months s with s+1 > last origin 2026-08-01 (target not in info set),415,413,2,information-availability rule at last origin","is_error":false}]},"uuid":"f3d92286-2f49-4914-b73d-5e76455d5016","timestamp":"2026-10-30T04:09:07.599Z","toolUseResult":{"stdout":"{\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_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop months before 1992-03-01 (lags of ip_growth / neworder_chg unavailable; spec start),417,415,2,TASK.md training-row definition s >= 1992-03-01\n2,drop months s with s+1 > last origin 2026-08-01 (target not in info set),415,413,2,information-availability rule at last origin","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4cdc8c29-44e3-4784-aeec-244bb854bfab","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","hash":"7625a49958b99fbc325d32c05f599dc44e5d68984856d2410a5ad3e449e907d1","message":{"role":"assistant","content":[{"type":"text","text":"Now the audit and analysis script."},{"type":"tool_use","id":"toolu_01GTbobKDdzQGHxNw6PC7vGK","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/ip_predictors.csv | 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json) | 417 | 7 |\n| data/README.md | 225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e | — | — |\n\nSchema: month (YYYY-MM-01, 1992-01-01..2026-09-01, 417 consecutive months, no gaps, no duplicates); ip_growth, oil_chg, permit_chg, neworder_chg (percent per month, 100×Δln); fedfunds (percent, level); tcu (percent of capacity, level). All float64.\n\nMissing values: only neworder_chg at 1992-01 and 1992-02 (2 cells). README states that the first month (1992-01) of *every* change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01 — a minor README/data inconsistency. It has no effect: the earliest value used is ip_growth for 1992-01 (as lag 2 of training row 1992-03), which is present; neworder_chg is first needed at 1992-03, present.\n\nRanges (min / max): ip_growth −14.14 / 6.35; oil_chg −56.8 / 54.6; fedfunds 0.05 / 6.54; permit_chg −24.8 / 17.1; neworder_chg −11.4 / 9.1; tcu 64.1 / 85.0. Magnitudes plausible for units.\n\nAnomalies: |z|>5 outliers — ip_growth 2020-04 (−14.14) and 2020-06 (+6.35); oil_chg 2020-03/04/05 (COVID); permit_chg 2008-07. These are genuine economic events; retained (ridge spec fixes training rows; no robustification allowed by the spec). They lie in every training window and influence μ, σ, b.\n\nInformation availability: the file contains data up to 2026-09, which is the target of the last origin (2026-08). The script masks all rows dated > t at every origin before computing any statistic. No input violates the rules per se; the vintage (current, revised) and ignored publication lags are accepted stylisations stated in TASK.md/README.\nEOF\ncat > 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, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAM = 50.0\nFIRST_TRAIN = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nORIGINS = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\ndf = raw.sort_values(\"month\").set_index(\"month\")\nassert df.index.is_unique and (df.index == pd.date_range(df.index[0], df.index[-1], freq=\"MS\")).all()\n\n# Feature matrix z_s (dated s; uses only values dated <= s)\nF = pd.DataFrame({\n \"ip_l0\": df[\"ip_growth\"], \"ip_l1\": df[\"ip_growth\"].shift(1), \"ip_l2\": df[\"ip_growth\"].shift(2),\n \"oil_chg\": df[\"oil_chg\"], \"fedfunds\": df[\"fedfunds\"], \"permit_chg\": df[\"permit_chg\"],\n \"neworder_chg\": df[\"neworder_chg\"], \"tcu\": df[\"tcu\"],\n})\ny_next = df[\"ip_growth\"].shift(-1) # target of row s = ip_growth_{s+1}\n\nrows, excl = [], []\nn_train_last = None\nfor t in ORIGINS:\n avail = df.loc[:t] # information set: values dated <= t only\n Fa = F.loc[:t]\n ya = avail[\"ip_growth\"].shift(-1) # recomputed inside the info set\n s_idx = Fa.index[(Fa.index >= FIRST_TRAIN) & (Fa.index <= t - pd.offsets.MonthBegin(1))]\n X = Fa.loc[s_idx].to_numpy(float)\n y = ya.loc[s_idx].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\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 + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = Fa.loc[t].to_numpy(float)\n assert np.isfinite(zt).all()\n f_ridge = ybar + ((zt - mu) / sd) @ b\n f_naive = avail.loc[t, \"ip_growth\"]\n f_mean = avail.loc[MEAN_START:t, \"ip_growth\"].mean()\n tgt = t + pd.offsets.MonthBegin(1)\n actual = df.loc[tgt, \"ip_growth\"] # used only for scoring\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append({\"origin_date\": t.strftime(\"%Y-%m-%d\"), \"target_date\": tgt.strftime(\"%Y-%m-%d\"),\n \"horizon\": 1, \"forecast\": float(f), \"actual\": float(actual), \"model\": m})\n n_train_last = len(s_idx)\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\ndef err(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\n\nrn, an = err(\"naive\"); rm, am = err(\"mean\"); rr, ar = err(\"ridge\")\n\n# Exclusion ledger (rows of the input file vs ridge training rows at the last origin)\nt_last = ORIGINS[-1]\nn1 = n_raw\nn2 = int((df.index >= FIRST_TRAIN).sum())\nn3 = int(((df.index >= FIRST_TRAIN) & (df.index <= t_last - pd.offsets.MonthBegin(1))).sum())\npd.DataFrame([\n {\"step\": 1, \"rule\": \"drop months before 1992-03-01 (lags of ip_growth / neworder_chg unavailable; spec start)\",\n \"n_before\": n1, \"n_after\": n2, \"n_removed\": n1 - n2, \"rationale\": \"TASK.md training-row definition s >= 1992-03-01\"},\n {\"step\": 2, \"rule\": \"drop months s with s+1 > last origin 2026-08-01 (target not in info set)\",\n \"n_before\": n2, \"n_after\": n3, \"n_removed\": n2 - n3, \"rationale\": \"information-availability rule at last origin\"},\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n3 == 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\": LAM,\n \"normalisation_window\": \"expanding\",\n \"n_features\": F.shape[1],\n \"rmse_naive\": rn, \"mae_naive\": an,\n \"rmse_mean\": rm, \"mae_mean\": am,\n \"rmse_ridge\": rr, \"mae_ridge\": ar,\n \"rel_improvement_vs_naive\": 1 - rr / rn,\n \"rel_improvement_vs_mean\": 1 - rr / rm,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02 (series starts 1992-02)\",\n \"handling\": \"Not needed: first ridge training row is 1992-03 per spec; no imputation.\"},\n {\"issue\": \"README says the first month of every change series is empty, but ip_growth, oil_chg, permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as lag 2 of training row 1992-03); noted as documentation inconsistency.\"},\n {\"issue\": \"Extreme COVID-19 observations (ip_growth 2020-04 = -14.1, 2020-06 = +6.3; oil_chg 2020-03..05 ~ +/-55) inside every training window\",\n \"handling\": \"Retained as genuine data, as the ridge specification fixes the training rows; they affect mu, sigma and coefficients.\"},\n {\"issue\": \"Current-vintage (revised) data; publication lags ignored\",\n \"handling\": \"Accepted per TASK.md; results are not real-time forecast accuracy.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": n_raw},\n \"n_analysed\": n_train_last,\n \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"mae_naive\", \"rmse_mean\", \"mae_mean\", \"rmse_ridge\", \"mae_ridge\",\n \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv","description":"Write audit and analysis script, run 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''rn, an = err(\"naive\")''','''def dm(base):\n \"\"\"Diebold-Mariano test (squared loss, h=1, HLN small-sample correction), d = e_ridge^2 - e_base^2.\"\"\"\n from scipy import stats\n e_r = (fc.loc[fc.model == \"ridge\", \"forecast\"].to_numpy() - fc.loc[fc.model == \"ridge\", \"actual\"].to_numpy())\n e_b = (fc.loc[fc.model == base, \"forecast\"].to_numpy() - fc.loc[fc.model == base, \"actual\"].to_numpy())\n d = e_r ** 2 - e_b ** 2\n n = len(d)\n stat = d.mean() / np.sqrt(d.var(ddof=0) / n) * np.sqrt((n + 1 - 2 + 0) / n)\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\ndm_n, p_n = dm(\"naive\"); dm_m, p_m = dm(\"mean\")\nrn, an = err(\"naive\")''')\ns=s.replace(''' \"feasibility\": \"complete\",''',''' \"dm_stat_ridge_vs_naive\": dm_n, \"dm_pvalue_ridge_vs_naive\": p_n,\n \"dm_stat_ridge_vs_mean\": dm_m, \"dm_pvalue_ridge_vs_mean\": p_m,\n \"feasibility\": \"complete\",''')\ns=s.replace('\"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]','\"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\",\\n \"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\", \"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"]')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py","description":"Add Diebold-Mariano tests and 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\"rel_improvement_vs_mean\": -0.031002480654837017,\n \"n_analysed\": 413,\n \"dm_stat_ridge_vs_naive\": -3.3649594921847754,\n \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571726,\n \"dm_stat_ridge_vs_mean\": 0.5392198591267084,\n \"dm_pvalue_ridge_vs_mean\": 0.5917642583421929\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d0e9be24-0177-49be-900a-912c0c55e5f3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","hash":"0f6dfecc2429be18ada8d4d9c443f212e5c31c21436ef103134b99d312ad3b79","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01DSCUEdQHK7G9Zqfe5FLEUP","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''rn, an = err(\"naive\")''','''def dm(base):\n \"\"\"Diebold-Mariano test (squared loss, h=1, HLN small-sample correction), d = e_ridge^2 - e_base^2.\"\"\"\n from scipy import stats\n e_r = (fc.loc[fc.model == \"ridge\", \"forecast\"].to_numpy() - fc.loc[fc.model == \"ridge\", \"actual\"].to_numpy())\n e_b = (fc.loc[fc.model == base, \"forecast\"].to_numpy() - fc.loc[fc.model == base, \"actual\"].to_numpy())\n d = e_r ** 2 - e_b ** 2\n n = len(d)\n stat = d.mean() / np.sqrt(d.var(ddof=0) / n) * np.sqrt((n + 1 - 2 + 0) / n)\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\ndm_n, p_n = dm(\"naive\"); dm_m, p_m = dm(\"mean\")\nrn, an = err(\"naive\")''')\ns=s.replace(''' \"feasibility\": \"complete\",''',''' \"dm_stat_ridge_vs_naive\": dm_n, \"dm_pvalue_ridge_vs_naive\": p_n,\n \"dm_stat_ridge_vs_mean\": dm_m, \"dm_pvalue_ridge_vs_mean\": p_m,\n \"feasibility\": \"complete\",''')\ns=s.replace('\"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]','\"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\",\\n \"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\", \"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"]')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py","description":"Add Diebold-Mariano tests and rerun"}}]}}50{"type":"api-request","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","id":"938dbb97-1227-436a-81bb-dba69a7a7f68","timestamp":"2026-10-30T04:09:17.374Z","version":"2.1.314","querySource":"sdk","shapeHash":"036ffa53cf5357a10e750df9f8825a659859c168831ad105bb1027d1bccd7755","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":"8b886ad5-c015-4975-802f-162d266177f4","keep":14,"tail":["0f6dfecc2429be18ada8d4d9c443f212e5c31c21436ef103134b99d312ad3b79","fce0f313ecf4f510613fcd313632f9677c2ed96015424d7f4a8b28d78e5f03e6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ec691af7547b214c249b1be1e7468249f409e1357d985e0e398348769f28dc17"}51{"parentUuid":"e4c3d01c-3488-4afb-b98a-8ce4c81fa721","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmMGGQnGU38VhkYUeyb","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1396,"cache_read_input_tokens":16028,"output_tokens":1761,"output_tokens_details":{"thinking_tokens":140},"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":1396},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":895,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmMFyJYT5SWkFERRQK4","requestRef":"938dbb97-1227-436a-81bb-dba69a7a7f68","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"adfaa467-e21b-43de-9986-53c4087454a7","timestamp":"2026-10-30T04:09:19.191Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"adfaa467-e21b-43de-9986-53c4087454a7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmMGGQnGU38VhkYUeyb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TieNW23KEBfNfdQxhEZwAW","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json, subprocess, shutil, os, tempfile\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'])\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon', fc.horizon.unique())\nprint('origin<target all', (pd.to_datetime(fc.origin_date)<pd.to_datetime(fc.target_date)).all())\n# independent ridge via augmented least squares at origins\nip=d.ip_growth.values\ndef indep(t):\n i=d.index[d.month==t][0]\n rows=[k for k in range(len(d)) if d.month[k]>=pd.Timestamp('1992-03-01') and k+1<=i]\n def z(k): return [ip[k],ip[k-1],ip[k-2]]+list(d.loc[k,['oil_chg','fedfunds','permit_chg','neworder_chg','tcu']])\n X=np.array([z(k) for k in rows]); y=np.array([ip[k+1] for k in rows])\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()+((np.array(z(i))-mu)/sd)@b, ip[i], ip[4:i+1].mean(), len(rows)\nmx=0\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n fr,fn,fm,n=indep(t); s=fc[fc.origin_date==t.strftime('%Y-%m-%d')].set_index('model').forecast\n mx=max(mx,abs(fr-s.ridge),abs(fn-s.naive),abs(fm-s['mean']))\nprint('max abs diff independent vs script',mx,'n last',n)\n# metrics recompute\nfor m in ['naive','mean','ridge']:\n e=fc[fc.model==m].eval('forecast-actual'); print(m, np.isclose(np.sqrt((e**2).mean()),r['rmse_'+m]), np.isclose(e.abs().mean(),r['mae_'+m]), np.sqrt((e**2).mean())>=e.abs().mean())\nprint('rel', np.isclose(1-r['rmse_ridge']/r['rmse_naive'],r['rel_improvement_vs_naive']), np.isclose(1-r['rmse_ridge']/r['rmse_mean'],r['rel_improvement_vs_mean']))\n# leakage test: perturb all data after 2024-01 and check origins <= 2024-01 unchanged\ntmp=tempfile.mkdtemp(); os.makedirs(tmp+'/data'); os.makedirs(tmp+'/out')\nd2=pd.read_csv('data/ip_predictors.csv'); m=d2.month>'2024-01-01'\nfor c in d2.columns[1:]: d2.loc[m,c]=d2.loc[m,c]*3+7\nd2.to_csv(tmp+'/data/ip_predictors.csv',index=False); shutil.copy('out/analysis.py',tmp+'/out/')\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],cwd=tmp,capture_output=True)\nf2=pd.read_csv(tmp+'/out/forecasts.csv'); keep=fc.origin_date<='2024-01-01'\nprint('leak test: forecasts for origins<=2024-01 unchanged', np.allclose(fc[keep].forecast.values,f2[keep].forecast.values), 'n',keep.sum()//3)\nEOF","description":"Reproducibility, independent check, leakage test"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1396,"cache_read_input_tokens":16028,"output_tokens":1761,"output_tokens_details":{"thinking_tokens":140},"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":1396},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmMFyJYT5SWkFERRQK4","requestRef":"938dbb97-1227-436a-81bb-dba69a7a7f68","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6d136da8-68a2-45fd-aab7-677c87255648","timestamp":"2026-10-30T04:09:31.088Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"6d136da8-68a2-45fd-aab7-677c87255648","isSidechain":false,"promptId":"5f0f08e6-4468-417f-bcbc-49946e901507","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TieNW23KEBfNfdQxhEZwAW","type":"tool_result","content":"IDENTICAL\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon [1]\norigin<target all True\nmax abs diff independent vs script 0.0017741505532578028 n last 413\nnaive True True True\nmean True True True\nridge True True True\nrel True True\nleak test: forecasts for origins<=2024-01 unchanged True n 29","is_error":false}]},"uuid":"e11fccba-4440-4998-a7f1-d222e6130536","timestamp":"2026-10-30T04:09:34.960Z","toolUseResult":{"stdout":"IDENTICAL\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon [1]\norigin<target all True\nmax abs diff independent vs script 0.0017741505532578028 n last 413\nnaive True True True\nmean True True True\nridge True True True\nrel True True\nleak test: forecasts for origins<=2024-01 unchanged True n 29","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6d136da8-68a2-45fd-aab7-677c87255648","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","hash":"74c3da82de8a071e27948d6058fd58c1cfe9121595f5567003a0fc211e797187","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01TieNW23KEBfNfdQxhEZwAW","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json, subprocess, shutil, os, tempfile\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'])\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon', fc.horizon.unique())\nprint('origin<target all', (pd.to_datetime(fc.origin_date)<pd.to_datetime(fc.target_date)).all())\n# independent ridge via augmented least squares at origins\nip=d.ip_growth.values\ndef indep(t):\n i=d.index[d.month==t][0]\n rows=[k for k in range(len(d)) if d.month[k]>=pd.Timestamp('1992-03-01') and k+1<=i]\n def z(k): return [ip[k],ip[k-1],ip[k-2]]+list(d.loc[k,['oil_chg','fedfunds','permit_chg','neworder_chg','tcu']])\n X=np.array([z(k) for k in rows]); y=np.array([ip[k+1] for k in rows])\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()+((np.array(z(i))-mu)/sd)@b, ip[i], ip[4:i+1].mean(), len(rows)\nmx=0\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n fr,fn,fm,n=indep(t); s=fc[fc.origin_date==t.strftime('%Y-%m-%d')].set_index('model').forecast\n mx=max(mx,abs(fr-s.ridge),abs(fn-s.naive),abs(fm-s['mean']))\nprint('max abs diff independent vs script',mx,'n last',n)\n# metrics recompute\nfor m in ['naive','mean','ridge']:\n e=fc[fc.model==m].eval('forecast-actual'); print(m, np.isclose(np.sqrt((e**2).mean()),r['rmse_'+m]), np.isclose(e.abs().mean(),r['mae_'+m]), np.sqrt((e**2).mean())>=e.abs().mean())\nprint('rel', np.isclose(1-r['rmse_ridge']/r['rmse_naive'],r['rel_improvement_vs_naive']), np.isclose(1-r['rmse_ridge']/r['rmse_mean'],r['rel_improvement_vs_mean']))\n# leakage test: perturb all data after 2024-01 and check origins <= 2024-01 unchanged\ntmp=tempfile.mkdtemp(); os.makedirs(tmp+'/data'); os.makedirs(tmp+'/out')\nd2=pd.read_csv('data/ip_predictors.csv'); m=d2.month>'2024-01-01'\nfor c in d2.columns[1:]: d2.loc[m,c]=d2.loc[m,c]*3+7\nd2.to_csv(tmp+'/data/ip_predictors.csv',index=False); shutil.copy('out/analysis.py',tmp+'/out/')\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],cwd=tmp,capture_output=True)\nf2=pd.read_csv(tmp+'/out/forecasts.csv'); keep=fc.origin_date<='2024-01-01'\nprint('leak test: forecasts for origins<=2024-01 unchanged', np.allclose(fc[keep].forecast.values,f2[keep].forecast.values), 'n',keep.sum()//3)\nEOF","description":"Reproducibility, independent check, leakage 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passes. Now the state files, report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":734,"cache_read_input_tokens":19788,"output_tokens":4121,"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":734},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmPT5r7MoV1CiEc6MfN","requestRef":"c9cb692b-78ae-4c66-8399-f0526dadf8d6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c893985b-96f8-4e27-a32a-68f68fbeb812","timestamp":"2026-10-30T04:09:48.302Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"c893985b-96f8-4e27-a32a-68f68fbeb812","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmPTPwq4fVgfSdByC83","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015Ww7B2m1zvq5RnBsUurP2K","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, forecasts.csv, exclusions.csv) regenerates byte-identical results.json and forecasts.csv | PASS (cmp identical) |\n| 2 | forecasts.csv has 180 rows, 60 per model, horizon always 1 | PASS |\n| 3 | origin_date < target_date for every row; first_origin 2021-09-01 < last_origin 2026-08-01 | PASS |\n| 4 | Independent ridge implementation (augmented least squares, explicit loops) matches script forecasts | PASS (max abs diff 4.7e-11) |\n| 5 | Naive and expanding-mean baselines recomputed independently | PASS (max diff 0 / 5.0e-11). Note: a first attempt showed 1.8e-3 diff caused by an off-by-one bug in the *checker* (mean started 1992-05); corrected checker confirms the script. |\n| 6 | Leakage test: all data after 2024-01 multiplied by 3 and shifted by +7; forecasts for the 29 origins ≤ 2024-01 unchanged | PASS — no future information used, scaling stats recomputed per origin |\n| 7 | RMSE/MAE recomputed from forecasts.csv; RMSE ≥ MAE for each model; RMSE > 0 | PASS |\n| 8 | rel_improvement identities 1 − rmse_ridge/rmse_baseline | PASS |\n| 9 | Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed (training rows at last origin) | PASS |\n| 10 | Magnitudes: RMSEs 0.54–0.88 %/month, comparable to sd of ip_growth excl. COVID; ridge between the two baselines | PASS (plausible) |\n| 11 | Input SHA-256 matches README / bundle.json | PASS |\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over the 60 origins 2021-09 to 2026-08 (h=1), the ridge forecast has lower RMSE than the naive forecast (0.561 vs 0.883 percent/month), a relative improvement of about 36%.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data, publication lags ignored; 60 evaluation points.\"},\n {\"id\": \"C2\", \"text\": \"The improvement of ridge over the naive forecast is statistically significant under a Diebold-Mariano test with squared loss (HLN-corrected, p about 0.001).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\"], \"status\": \"supported\", \"caveat\": \"DM test treats the ridge model's estimation error as part of the forecast; asymptotic approximation with n=60.\"},\n {\"id\": \"C3\", \"text\": \"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3.1% higher (0.561 vs 0.544) and its MAE is also higher (0.451 vs 0.429).\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"rel_improvement_vs_mean\", \"mae_ridge\", \"mae_mean\"], \"status\": \"supported\", \"caveat\": \"Single fixed lambda=50; no tuning was permitted.\"},\n {\"id\": \"C4\", \"text\": \"The RMSE difference between ridge and the expanding mean is not statistically distinguishable from zero (DM p about 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Low power with 60 out-of-sample errors.\"}\n]\nEOF\ncat > out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next-month US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last value) and expanding-mean forecasts? The test uses `n_origins` = 60 rolling origins from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, at horizon h = 1. Estimands: out-of-sample RMSE and MAE of each forecast, and 1 − RMSE_ridge/RMSE_baseline.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, with 417 data rows (`n_input_rows`) covering 1992-01 to 2026-09. Its SHA-256 matches the README. The full audit is in `out/state/data_audit.md`.\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. Neither month is used.\n- Data issues (`data_issues`):\n - The README says the first month of every change series is empty, but three of these columns are filled for 1992-01. These values were used as given.\n - Extreme COVID-era observations (ip_growth −14.1 in 2020-04) fall inside every training window. They were kept, because the specification fixes the training rows.\n - The data are the current vintage, not real-time.\n- Exclusion ledger (`out/exclusions.csv`), for the ridge training set at the last origin: 417 rows → minus 2 months before 1992-03 → minus 2 months whose target lies after the origin → 413 training rows (`n_analysed`).\n\n## Method\n- **Naive forecast:** ip_growth_t.\n- **Expanding-mean forecast:** the mean of ip_growth over 1992-04..t.\n- **Ridge forecast:**\n - Training rows are s = 1992-03 … t−1, with target ip_growth_{s+1}.\n - The features are ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s.\n - Each feature is z-scored with the mean and the ddof=1 standard deviation of that origin's training rows. These statistics are recomputed at every origin (`normalisation_window` = \"expanding\").\n - The target is centred on its training mean ȳ, which serves as the unpenalised intercept.\n - The coefficients are b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), and the forecast is ȳ + z̃_t·b.\n- The method has no randomness (`random_seed` = null).\n- Code: `out/analysis.py`. It writes `out/results.json`, `out/forecasts.csv` (180 rows) and `out/exclusions.csv`.\n\n## Results\n| model | RMSE | MAE |\n|---|---|---|\n| naive | 0.8831 (`rmse_naive`) | 0.6990 (`mae_naive`) |\n| expanding mean | 0.5439 (`rmse_mean`) | 0.4293 (`mae_mean`) |\n| ridge | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge improves RMSE by 0.365 (`rel_improvement_vs_naive`), i.e. ~36% (C1).\n- Compared with the expanding mean, ridge is slightly worse: the relative improvement is −0.031 (`rel_improvement_vs_mean`), and its MAE is also higher (C3).\n- So ridge beats the naive forecast but not the expanding mean over this window.\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error loss differential (ridge minus baseline), with the Harvey–Leybourne–Newbold small-sample correction and t(59) reference.\n- **Ridge vs naive:** DM = −3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). Ridge is significantly better (C2).\n- **Ridge vs mean:** DM = 0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). There is no significant difference (C4).\n\nOnly 60 errors are evaluated, so the relative-improvement figures carry sizeable sampling error. The ~3% gap to the mean is well within noise.\n\n## Limitations and open issues\n- The data are the current vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast accuracy.\n- λ is fixed at 50 by the specification and was not tuned. Other penalties or feature sets could rank differently; this was not evaluated.\n- COVID outliers stay in every training window and can distort the scaling statistics and the coefficients.\n- The evaluation window (2021-10 to 2026-09 targets) is a single, fairly calm post-pandemic period.\n- The DM test is applied to forecasts from estimated models (with the mean nested in ridge as λ→∞), so its p-values are approximate.\n- Feasibility: complete.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"TASK.md read fully\", \"contract.md written with estimands, outputs, rules\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256 matches README/bundle\", \"417 rows x 7 cols, no gaps/duplicates\", \"missing values located (neworder_chg 1992-01/02)\", \"outliers flagged\", \"exclusions.csv sums: 417-2-2=413\"], \"notes\": \"README/data minor inconsistency on 1992-01 change values noted\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive and expanding-mean baselines computed with same 60-origin evaluation: rmse_naive, rmse_mean, mae_*\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py reads only data/, deterministic, no RNG\", \"ridge per spec with per-origin standardisation\", \"DM tests added\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"passed\", \"checks\": [\"clean re-run byte-identical\", \"independent ridge/baseline reimplementation max diff 5e-11\", \"leakage perturbation test passed\", \"metric identities & counts\", \"see checks.md\"], \"notes\": \"checker off-by-one found and fixed (not in script)\"},\n \"P5\": {\"status\": \"passed\", \"checks\": [\"report.md has 6 required sections, all numbers cite keys\", \"claims.json keys exist in results.json\"], \"notes\": \"\"}\n}\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data audited; decision to retain COVID outliers (spec fixes training rows).\n- 2026-10-30 P2/P3 passed: baselines + ridge in analysis.py; decision to add HLN-DM tests for the Uncertainty section (extra keys dm_*).\n- 2026-10-30 P4 passed: reproducibility, independent recomputation, leakage test (checks.md).\n- 2026-10-30 P5 passed: report, claims written.\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r])\"","description":"Write checks, claims, report, phases, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":734,"cache_read_input_tokens":19788,"output_tokens":4121,"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":734},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmPT5r7MoV1CiEc6MfN","requestRef":"c9cb692b-78ae-4c66-8399-f0526dadf8d6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"44def07c-d37e-4c4e-aafe-76cbdb6186f2","timestamp":"2026-10-30T04:10:20.933Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"44def07c-d37e-4c4e-aafe-76cbdb6186f2","isSidechain":false,"promptId":"5f0f08e6-4468-417f-bcbc-49946e901507","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015Ww7B2m1zvq5RnBsUurP2K","type":"tool_result","content":"[]","is_error":false}]},"uuid":"eaf4ce6a-819e-41bc-99e5-3ff3c8facee8","timestamp":"2026-10-30T04:10:20.993Z","toolUseResult":{"stdout":"[]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"44def07c-d37e-4c4e-aafe-76cbdb6186f2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","hash":"ef53cce264cce70c1dd90b6fcce36b8b637cc09deb35e4563f538a5393fb73b2","message":{"role":"assistant","content":[{"type":"text","text":"Verification passes. Now the state files, report and claims."},{"type":"tool_use","id":"toolu_015Ww7B2m1zvq5RnBsUurP2K","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, forecasts.csv, exclusions.csv) regenerates byte-identical results.json and forecasts.csv | PASS (cmp identical) |\n| 2 | forecasts.csv has 180 rows, 60 per model, horizon always 1 | PASS |\n| 3 | origin_date < target_date for every row; first_origin 2021-09-01 < last_origin 2026-08-01 | PASS |\n| 4 | Independent ridge implementation (augmented least squares, explicit loops) matches script forecasts | PASS (max abs diff 4.7e-11) |\n| 5 | Naive and expanding-mean baselines recomputed independently | PASS (max diff 0 / 5.0e-11). Note: a first attempt showed 1.8e-3 diff caused by an off-by-one bug in the *checker* (mean started 1992-05); corrected checker confirms the script. |\n| 6 | Leakage test: all data after 2024-01 multiplied by 3 and shifted by +7; forecasts for the 29 origins ≤ 2024-01 unchanged | PASS — no future information used, scaling stats recomputed per origin |\n| 7 | RMSE/MAE recomputed from forecasts.csv; RMSE ≥ MAE for each model; RMSE > 0 | PASS |\n| 8 | rel_improvement identities 1 − rmse_ridge/rmse_baseline | PASS |\n| 9 | Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed (training rows at last origin) | PASS |\n| 10 | Magnitudes: RMSEs 0.54–0.88 %/month, comparable to sd of ip_growth excl. COVID; ridge between the two baselines | PASS (plausible) |\n| 11 | Input SHA-256 matches README / bundle.json | PASS |\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over the 60 origins 2021-09 to 2026-08 (h=1), the ridge forecast has lower RMSE than the naive forecast (0.561 vs 0.883 percent/month), a relative improvement of about 36%.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data, publication lags ignored; 60 evaluation points.\"},\n {\"id\": \"C2\", \"text\": \"The improvement of ridge over the naive forecast is statistically significant under a Diebold-Mariano test with squared loss (HLN-corrected, p about 0.001).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\"], \"status\": \"supported\", \"caveat\": \"DM test treats the ridge model's estimation error as part of the forecast; asymptotic approximation with n=60.\"},\n {\"id\": \"C3\", \"text\": \"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3.1% higher (0.561 vs 0.544) and its MAE is also higher (0.451 vs 0.429).\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"rel_improvement_vs_mean\", \"mae_ridge\", \"mae_mean\"], \"status\": \"supported\", \"caveat\": \"Single fixed lambda=50; no tuning was permitted.\"},\n {\"id\": \"C4\", \"text\": \"The RMSE difference between ridge and the expanding mean is not statistically distinguishable from zero (DM p about 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Low power with 60 out-of-sample errors.\"}\n]\nEOF\ncat > out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next-month US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last value) and expanding-mean forecasts? The test uses `n_origins` = 60 rolling origins from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, at horizon h = 1. Estimands: out-of-sample RMSE and MAE of each forecast, and 1 − RMSE_ridge/RMSE_baseline.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, with 417 data rows (`n_input_rows`) covering 1992-01 to 2026-09. Its SHA-256 matches the README. The full audit is in `out/state/data_audit.md`.\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. Neither month is used.\n- Data issues (`data_issues`):\n - The README says the first month of every change series is empty, but three of these columns are filled for 1992-01. These values were used as given.\n - Extreme COVID-era observations (ip_growth −14.1 in 2020-04) fall inside every training window. They were kept, because the specification fixes the training rows.\n - The data are the current vintage, not real-time.\n- Exclusion ledger (`out/exclusions.csv`), for the ridge training set at the last origin: 417 rows → minus 2 months before 1992-03 → minus 2 months whose target lies after the origin → 413 training rows (`n_analysed`).\n\n## Method\n- **Naive forecast:** ip_growth_t.\n- **Expanding-mean forecast:** the mean of ip_growth over 1992-04..t.\n- **Ridge forecast:**\n - Training rows are s = 1992-03 … t−1, with target ip_growth_{s+1}.\n - The features are ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s.\n - Each feature is z-scored with the mean and the ddof=1 standard deviation of that origin's training rows. These statistics are recomputed at every origin (`normalisation_window` = \"expanding\").\n - The target is centred on its training mean ȳ, which serves as the unpenalised intercept.\n - The coefficients are b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), and the forecast is ȳ + z̃_t·b.\n- The method has no randomness (`random_seed` = null).\n- Code: `out/analysis.py`. It writes `out/results.json`, `out/forecasts.csv` (180 rows) and `out/exclusions.csv`.\n\n## Results\n| model | RMSE | MAE |\n|---|---|---|\n| naive | 0.8831 (`rmse_naive`) | 0.6990 (`mae_naive`) |\n| expanding mean | 0.5439 (`rmse_mean`) | 0.4293 (`mae_mean`) |\n| ridge | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge improves RMSE by 0.365 (`rel_improvement_vs_naive`), i.e. ~36% (C1).\n- Compared with the expanding mean, ridge is slightly worse: the relative improvement is −0.031 (`rel_improvement_vs_mean`), and its MAE is also higher (C3).\n- So ridge beats the naive forecast but not the expanding mean over this window.\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error loss differential (ridge minus baseline), with the Harvey–Leybourne–Newbold small-sample correction and t(59) reference.\n- **Ridge vs naive:** DM = −3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). Ridge is significantly better (C2).\n- **Ridge vs mean:** DM = 0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). There is no significant difference (C4).\n\nOnly 60 errors are evaluated, so the relative-improvement figures carry sizeable sampling error. The ~3% gap to the mean is well within noise.\n\n## Limitations and open issues\n- The data are the current vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast accuracy.\n- λ is fixed at 50 by the specification and was not tuned. Other penalties or feature sets could rank differently; this was not evaluated.\n- COVID outliers stay in every training window and can distort the scaling statistics and the coefficients.\n- The evaluation window (2021-10 to 2026-09 targets) is a single, fairly calm post-pandemic period.\n- The DM test is applied to forecasts from estimated models (with the mean nested in ridge as λ→∞), so its p-values are approximate.\n- Feasibility: complete.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"TASK.md read fully\", \"contract.md written with estimands, outputs, rules\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256 matches README/bundle\", \"417 rows x 7 cols, no gaps/duplicates\", \"missing values located (neworder_chg 1992-01/02)\", \"outliers flagged\", \"exclusions.csv sums: 417-2-2=413\"], \"notes\": \"README/data minor inconsistency on 1992-01 change values noted\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive and expanding-mean baselines computed with same 60-origin evaluation: rmse_naive, rmse_mean, mae_*\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py reads only data/, deterministic, no RNG\", \"ridge per spec with per-origin standardisation\", \"DM tests added\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"passed\", \"checks\": [\"clean re-run byte-identical\", \"independent ridge/baseline reimplementation max diff 5e-11\", \"leakage perturbation test passed\", \"metric identities & counts\", \"see checks.md\"], \"notes\": \"checker off-by-one found and fixed (not in script)\"},\n \"P5\": {\"status\": \"passed\", \"checks\": [\"report.md has 6 required sections, all numbers cite keys\", \"claims.json keys exist in results.json\"], \"notes\": \"\"}\n}\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data audited; decision to retain COVID outliers (spec fixes training rows).\n- 2026-10-30 P2/P3 passed: baselines + ridge in analysis.py; decision to add HLN-DM tests for the Uncertainty section (extra keys dm_*).\n- 2026-10-30 P4 passed: reproducibility, independent recomputation, leakage test (checks.md).\n- 2026-10-30 P5 passed: report, claims written.\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nprint([k for x in c for k in x['results_keys'] if k not in r])\"","description":"Write checks, claims, report, phases, log"}}]}}72{"type":"api-request","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","id":"1fea4aab-9e68-4796-892c-9ff70b933142","timestamp":"2026-10-30T04:10:20.997Z","version":"2.1.314","querySource":"sdk","shapeHash":"036ffa53cf5357a10e750df9f8825a659859c168831ad105bb1027d1bccd7755","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":"c9cb692b-78ae-4c66-8399-f0526dadf8d6","keep":22,"tail":["ef53cce264cce70c1dd90b6fcce36b8b637cc09deb35e4563f538a5393fb73b2","c664e23ea13fd2db8aeee3d9aeffdfb7dcc72a869a524464a1537f51d11c40e9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"16702444a83183c589bd8acd8e020af62a26983f6c9e3bce24ff07c9c1db8296"}73{"parentUuid":"eaf4ce6a-819e-41bc-99e5-3ff3c8facee8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmRxG5aucH338QSTtuk","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the analysis: over the 60 origins (2021-09 to 2026-08, one month ahead), the ridge model beats the naive forecast but not the expanding historical mean.\n\n| Forecast | RMSE (% per month) | MAE (% per month) |\n|---|---|---|\n| Naive (last value) | 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. That gap is statistically significant (Diebold–Mariano test, p ≈ 0.001).\n- **Ridge vs expanding mean:** ridge's RMSE is 3.1% higher, so it does slightly worse. That gap is not statistically significant (p ≈ 0.59), and with only 60 forecast errors it is well within noise.\n\n**Checks:**\n- **Rerun:** rerunning `out/analysis.py` from scratch produces identical outputs.\n- **Independent check:** a separate implementation reproduces every forecast to within about 5e-11.\n- **No future data:** I altered all data after January 2024 and confirmed the forecasts for earlier origins did not change. This shows the scaling statistics are recomputed at each origin from past data only.\n\n**Additions and data notes:**\n- The Diebold–Mariano tests weren't required by the task. I added them, under `dm_*` keys in `results.json`, so the report's Uncertainty section has something to rest on.\n- I kept the extreme COVID-era months (for example −14.1% in April 2020) in every training window, because the task fixes the training rows.\n- The README says the first month of every change series is empty, but three of them have values for January 1992. I used them as given.\n\n**Limitations:**\n- The results use today's revised data and ignore publication delays, so they don't measure how well the forecasts would have done in real time.\n- The penalty was fixed at 50 and never tuned.\n- The test period is a single, fairly calm stretch after the pandemic.\n\nEverything is in `out/`. Open `report.md` first. `results.json` and `forecasts.csv` (180 rows) are written by `analysis.py`, alongside `claims.json`, `exclusions.csv`, and the working notes in `state/`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4128,"cache_read_input_tokens":20522,"output_tokens":766,"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":4128},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmRwyDWhmsqVFfY7ceT","requestRef":"1fea4aab-9e68-4796-892c-9ff70b933142","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bec88dc1-8ca7-492e-929d-f61053d5b8b6","timestamp":"2026-10-30T04:10:28.317Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0026","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","version":"2.1.314","gitBranch":"HEAD"}74{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"bec88dc1-8ca7-492e-929d-f61053d5b8b6","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d"}75{"type":"atis-latch","atis":"","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d"}76{"type":"cost-state","sessionId":"35701796-31db-4282-8ef1-b71d70122b1d","totalCostUSD":0.43553379999999997,"totalAPIDuration":122102,"totalAPIDurationWithoutRetries":122094,"totalToolDuration":8364,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":130643,"startTime":1793333297680,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":14518,"thinkingTokens":853,"cacheReadInputTokens":146699,"cacheCreationInputTokens":23146,"cacheCreationEphemeral5mInputTokens":23146,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.43553379999999997}},"hasUnknownModelCost":false}77