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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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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\ndata:\nip_predictors.csv\nREADME.md","is_error":false}]},"uuid":"e74e4374-76c6-49ab-8550-b5f19ae1963f","timestamp":"2026-10-30T04:08:08.655Z","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 \"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}\n{\n \"task_id\": \"F3-conf-03\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n \"files\": {\n \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n }\n}month,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\n7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b data/ip_predictors.csv\n225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e data/README.md","is_error":false}]},"uuid":"7cf64f69-7cbe-4b74-afa9-403ec10896b1","timestamp":"2026-10-30T04:08:10.568Z","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 \"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}\n{\n \"task_id\": \"F3-conf-03\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n \"files\": {\n \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n 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7)\nmonth datetime64[ns]\nip_growth float64\noil_chg float64\nfedfunds float64\npermit_chg float64\nneworder_chg float64\ntcu float64\ndtype: object\nmonth 0\nip_growth 0\noil_chg 0\nfedfunds 0\npermit_chg 0\nneworder_chg 2\ntcu 0\ndtype: int64\n count ... std\nmonth 417 ... NaN\nip_growth 417.0 ... 1.039866\noil_chg 417.0 ... 9.511083\nfedfunds 417.0 ... 2.151342\npermit_chg 417.0 ... 4.943466\nneworder_chg 415.0 ... 2.955474\ntcu 417.0 ... 3.618664\n\n[7 rows x 8 columns]\ndup months 0\ngaps 0 0\n month ip_growth oil_chg fedfunds permit_chg neworder_chg tcu\n0 1992-01-01 -0.525126 -3.708965 4.03 1.496754 NaN 79.3278\n1 1992-02-01 0.703489 1.164034 4.06 6.209822 NaN 79.7329\n month ip_growth\n200 2008-09-01 -4.504760\n338 2020-03-01 -3.990543\n339 2020-04-01 -14.142248\n341 2020-06-01 6.347249\n342 2020-07-01 3.678711\n349 2021-02-01 -3.360058","is_error":false}]},"uuid":"35101129-8e37-48db-819f-d0dd38ccbdff","timestamp":"2026-10-30T04:08:16.705Z","toolUseResult":{"stdout":"(417, 7)\nmonth datetime64[ns]\nip_growth float64\noil_chg float64\nfedfunds float64\npermit_chg float64\nneworder_chg float64\ntcu float64\ndtype: object\nmonth 0\nip_growth 0\noil_chg 0\nfedfunds 0\npermit_chg 0\nneworder_chg 2\ntcu 0\ndtype: int64\n count ... std\nmonth 417 ... NaN\nip_growth 417.0 ... 1.039866\noil_chg 417.0 ... 9.511083\nfedfunds 417.0 ... 2.151342\npermit_chg 417.0 ... 4.943466\nneworder_chg 415.0 ... 2.955474\ntcu 417.0 ... 3.618664\n\n[7 rows x 8 columns]\ndup months 0\ngaps 0 0\n month ip_growth oil_chg fedfunds permit_chg neworder_chg tcu\n0 1992-01-01 -0.525126 -3.708965 4.03 1.496754 NaN 79.3278\n1 1992-02-01 0.703489 1.164034 4.06 6.209822 NaN 79.7329\n month ip_growth\n200 2008-09-01 -4.504760\n338 2020-03-01 -3.990543\n339 2020-04-01 -14.142248\n341 2020-06-01 6.347249\n342 2020-07-01 3.678711\n349 2021-02-01 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-p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nReads only data/ip_predictors.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\nDeterministic: no randomness used (random_seed = null).\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\" / \"ip_predictors.csv\"\nOUT = ROOT / \"out\"\nLAM = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").reset_index(drop=True)\nassert d[\"month\"].is_unique\nassert (d[\"month\"].diff().dropna().dt.days.between(28, 31)).all(), \"non-contiguous months\"\nd = d.set_index(\"month\")\n# Lags/leads by row position (months verified contiguous). Lags use only past values.\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1) # target of feature row s is ip_growth_{s+1}\n\norigins = d.index[(d.index >= FIRST_ORIGIN) & (d.index <= LAST_ORIGIN)]\nassert len(origins) == 60\n\nrows = []\nn_train_last = None\nfor t in origins:\n t_next = d.index[d.index.get_loc(t) + 1]\n actual = float(d.loc[t_next, \"ip_growth\"])\n # Information set: rows dated <= t only.\n info = d.loc[d.index <= t]\n # naive\n f_naive = float(info.loc[t, \"ip_growth\"])\n # expanding mean 1992-04..t\n f_mean = float(info.loc[info.index >= MEAN_START, \"ip_growth\"].mean())\n # ridge training rows: TRAIN_START <= s, s+1 <= t (so target y_{s+1} dated <= t)\n s_idx = info.index[(info.index >= TRAIN_START) & (info.index < t)]\n tr = info.loc[s_idx, FEATS].copy()\n y = info[\"ip_growth\"].shift(-1).loc[s_idx].to_numpy() # ip_growth_{s+1}, all dated <= t\n X = tr.to_numpy(dtype=float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n mu = X.mean(axis=0)\n sd = X.std(axis=0, ddof=1)\n Z = (X - mu) / sd\n ybar = y.mean()\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (info.loc[t, FEATS].to_numpy(dtype=float) - mu) / sd\n f_ridge = float(ybar + zt @ b)\n n_train_last = len(s_idx)\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=t_next.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nfc.to_csv(OUT / \"forecasts.csv\", index=False, float_format=\"%.10f\")\n\ndef metrics(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrn, an = metrics(\"naive\"); rm, am = metrics(\"mean\"); rr, ar = metrics(\"ridge\")\n\n# Exclusion ledger: raw rows -> training rows at the last origin\nlast = origins[-1]\nn1 = int((d.index >= TRAIN_START).sum())\nn2 = int(((d.index >= TRAIN_START) & (d.index < last)).sum())\nassert n2 == n_train_last\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop months before 1992-03-01 (lags ip_growth_{s-2} and neworder_chg undefined/not in spec)\",\n n_before=n_raw, n_after=n1, n_removed=n_raw - n1, rationale=\"TASK.md training window starts at s=1992-03-01\"),\n dict(step=2, rule=\"keep feature months s with s+1 <= last origin 2026-08-01 (s <= 2026-07-01)\",\n n_before=n1, n_after=n2, n_removed=n1 - n2, rationale=\"target y_{s+1} must be dated <= origin; later months unavailable\"),\n])\nexcl.to_csv(OUT / \"exclusions.csv\", index=False)\n\nres = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": int(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\": len(FEATS),\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 (NEWORDER starts 1992-02)\",\n \"handling\": \"Not used: training rows start at s=1992-03 per TASK.md; no imputation needed.\"},\n {\"issue\": \"data/README.md states the first month (1992-01) of each change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given; only ip_growth_1992-01 enters (as lag ip_growth_{s-2} for s=1992-03). oil_chg/permit_chg 1992-01 are not used.\"},\n {\"issue\": \"Extreme ip_growth values in training windows (e.g. 2020-04 = -14.1, 2020-06 = +6.3, 2008-09 = -4.5)\",\n \"handling\": \"Kept: the ridge specification is fixed by TASK.md; no outlier treatment. Noted as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data with publication lags ignored\",\n \"handling\": \"Errors computed on data as given; no real-time validity claimed.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n \"n_analysed\": int(n_train_last),\n \"random_seed\": None,\n \"sha256_input\": hashlib.sha256(DATA.read_bytes()).hexdigest(),\n \"baseline_best_rmse_model\": \"naive\" if rn < rm else \"mean\",\n}\nwith open(OUT / \"results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\", \"mae_naive\", \"mae_mean\", \"mae_ridge\",\n \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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-p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nReads only data/ip_predictors.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\nDeterministic: no randomness used (random_seed = null).\"\"\"\nimport json, hashlib\nfrom pathlib import Path\nimport numpy as np\nimport pandas as pd\n\nROOT = Path(__file__).resolve().parent.parent\nDATA = ROOT / \"data\" / \"ip_predictors.csv\"\nOUT = ROOT / \"out\"\nLAM = 50.0\nFIRST_ORIGIN, LAST_ORIGIN = pd.Timestamp(\"2021-09-01\"), pd.Timestamp(\"2026-08-01\")\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").reset_index(drop=True)\nassert d[\"month\"].is_unique\nassert (d[\"month\"].diff().dropna().dt.days.between(28, 31)).all(), \"non-contiguous months\"\nd = d.set_index(\"month\")\n# Lags/leads by row position (months verified contiguous). Lags use only past values.\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1) # target of feature row s is ip_growth_{s+1}\n\norigins = d.index[(d.index >= FIRST_ORIGIN) & (d.index <= LAST_ORIGIN)]\nassert len(origins) == 60\n\nrows = []\nn_train_last = None\nfor t in origins:\n t_next = d.index[d.index.get_loc(t) + 1]\n actual = float(d.loc[t_next, \"ip_growth\"])\n # Information set: rows dated <= t only.\n info = d.loc[d.index <= t]\n # naive\n f_naive = float(info.loc[t, \"ip_growth\"])\n # expanding mean 1992-04..t\n f_mean = float(info.loc[info.index >= MEAN_START, \"ip_growth\"].mean())\n # ridge training rows: TRAIN_START <= s, s+1 <= t (so target y_{s+1} dated <= t)\n s_idx = info.index[(info.index >= TRAIN_START) & (info.index < t)]\n tr = info.loc[s_idx, FEATS].copy()\n y = info[\"ip_growth\"].shift(-1).loc[s_idx].to_numpy() # ip_growth_{s+1}, all dated <= t\n X = tr.to_numpy(dtype=float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n mu = X.mean(axis=0)\n sd = X.std(axis=0, ddof=1)\n Z = (X - mu) / sd\n ybar = y.mean()\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (info.loc[t, FEATS].to_numpy(dtype=float) - mu) / sd\n f_ridge = float(ybar + zt @ b)\n n_train_last = len(s_idx)\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=t_next.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nfc.to_csv(OUT / \"forecasts.csv\", index=False, float_format=\"%.10f\")\n\ndef metrics(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrn, an = metrics(\"naive\"); rm, am = metrics(\"mean\"); rr, ar = metrics(\"ridge\")\n\n# Exclusion ledger: raw rows -> training rows at the last origin\nlast = origins[-1]\nn1 = int((d.index >= TRAIN_START).sum())\nn2 = int(((d.index >= TRAIN_START) & (d.index < last)).sum())\nassert n2 == n_train_last\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop months before 1992-03-01 (lags ip_growth_{s-2} and neworder_chg undefined/not in spec)\",\n n_before=n_raw, n_after=n1, n_removed=n_raw - n1, rationale=\"TASK.md training window starts at s=1992-03-01\"),\n dict(step=2, rule=\"keep feature months s with s+1 <= last origin 2026-08-01 (s <= 2026-07-01)\",\n n_before=n1, n_after=n2, n_removed=n1 - n2, rationale=\"target y_{s+1} must be dated <= origin; later months unavailable\"),\n])\nexcl.to_csv(OUT / \"exclusions.csv\", index=False)\n\nres = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": int(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\": len(FEATS),\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 (NEWORDER starts 1992-02)\",\n \"handling\": \"Not used: training rows start at s=1992-03 per TASK.md; no imputation needed.\"},\n {\"issue\": \"data/README.md states the first month (1992-01) of each change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given; only ip_growth_1992-01 enters (as lag ip_growth_{s-2} for s=1992-03). oil_chg/permit_chg 1992-01 are not used.\"},\n {\"issue\": \"Extreme ip_growth values in training windows (e.g. 2020-04 = -14.1, 2020-06 = +6.3, 2008-09 = -4.5)\",\n \"handling\": \"Kept: the ridge specification is fixed by TASK.md; no outlier treatment. Noted as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data with publication lags ignored\",\n \"handling\": \"Errors computed on data as given; no real-time validity claimed.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n \"n_analysed\": int(n_train_last),\n \"random_seed\": None,\n \"sha256_input\": hashlib.sha256(DATA.read_bytes()).hexdigest(),\n \"baseline_best_rmse_model\": \"naive\" if rn < rm else \"mean\",\n}\nwith open(OUT / \"results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\", \"mae_naive\", \"mae_mean\", \"mae_ridge\",\n \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''rn, an = metrics(\"naive\")''','''def dm_test(m1, m2):\n \"\"\"Diebold-Mariano test on squared-error loss differential d = e1^2 - e2^2 (h=1:\n plain sample variance, i.e. Newey-West with 0 lags), with the Harvey-Leybourne-Newbold\n small-sample correction and t_{n-1} reference distribution. Positive stat => m1 worse.\"\"\"\n from scipy import stats\n e1 = (fc.loc[fc.model == m1, \"forecast\"] - fc.loc[fc.model == m1, \"actual\"]).to_numpy()\n e2 = (fc.loc[fc.model == m2, \"forecast\"] - fc.loc[fc.model == m2, \"actual\"]).to_numpy()\n dd = e1 ** 2 - e2 ** 2\n n = len(dd)\n stat = dd.mean() / np.sqrt(dd.var(ddof=0) / n)\n stat *= np.sqrt((n + 1 - 2 * 1 + 1 * 0) / n) # HLN correction for h=1: sqrt((n+1-2h+h(h-1)/n)/n)\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\nrn, an = metrics(\"naive\")''')\ns=s.replace(''' \"sha256_input\"''',''' \"dm_stat_naive_vs_ridge\": dm_test(\"naive\", \"ridge\")[0],\n \"dm_pvalue_naive_vs_ridge\": dm_test(\"naive\", \"ridge\")[1],\n \"dm_stat_mean_vs_ridge\": dm_test(\"mean\", \"ridge\")[0],\n \"dm_pvalue_mean_vs_ridge\": dm_test(\"mean\", \"ridge\")[1],\n \"sha256_input\"''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep dm_ out/results.json","description":"Add Diebold-Mariano tests and rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3388,"cache_read_input_tokens":10712,"output_tokens":1041,"output_tokens_details":{"thinking_tokens":218},"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":3388},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmJiMy8J8X1PnQmU7Nt","requestRef":"d2510f3d-ac2c-4237-a5b2-8c412911821d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"993a3b1e-fee0-451c-a2d8-de39ec611b61","timestamp":"2026-10-30T04:08:51.517Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0025","sessionId":"5cd01f1f-3722-4772-bc28-11628f31cc55","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"993a3b1e-fee0-451c-a2d8-de39ec611b61","isSidechain":false,"promptId":"12d4050f-a17b-42d9-875c-a3b0ad8bc51d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014iPw4Gm6aYFMDUyw7FAesb","type":"tool_result","content":" \"dm_stat_naive_vs_ridge\": 3.3649594921847754,\n \"dm_pvalue_naive_vs_ridge\": 0.0013506687241571726,\n \"dm_stat_mean_vs_ridge\": -0.5392198591267084,\n \"dm_pvalue_mean_vs_ridge\": 0.5917642583421929,","is_error":false}]},"uuid":"9bcdd44a-62ac-4b6f-9da9-de3f24f0e64c","timestamp":"2026-10-30T04:08:52.297Z","toolUseResult":{"stdout":" \"dm_stat_naive_vs_ridge\": 3.3649594921847754,\n \"dm_pvalue_naive_vs_ridge\": 0.0013506687241571726,\n \"dm_stat_mean_vs_ridge\": -0.5392198591267084,\n \"dm_pvalue_mean_vs_ridge\": 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''rn, an = metrics(\"naive\")''','''def dm_test(m1, m2):\n \"\"\"Diebold-Mariano test on squared-error loss differential d = e1^2 - e2^2 (h=1:\n plain sample variance, i.e. Newey-West with 0 lags), with the Harvey-Leybourne-Newbold\n small-sample correction and t_{n-1} reference distribution. Positive stat => m1 worse.\"\"\"\n from scipy import stats\n e1 = (fc.loc[fc.model == m1, \"forecast\"] - fc.loc[fc.model == m1, \"actual\"]).to_numpy()\n e2 = (fc.loc[fc.model == m2, \"forecast\"] - fc.loc[fc.model == m2, \"actual\"]).to_numpy()\n dd = e1 ** 2 - e2 ** 2\n n = len(dd)\n stat = dd.mean() / np.sqrt(dd.var(ddof=0) / n)\n stat *= np.sqrt((n + 1 - 2 * 1 + 1 * 0) / n) # HLN correction for h=1: sqrt((n+1-2h+h(h-1)/n)/n)\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\nrn, an = metrics(\"naive\")''')\ns=s.replace(''' \"sha256_input\"''',''' \"dm_stat_naive_vs_ridge\": dm_test(\"naive\", \"ridge\")[0],\n \"dm_pvalue_naive_vs_ridge\": dm_test(\"naive\", \"ridge\")[1],\n \"dm_stat_mean_vs_ridge\": dm_test(\"mean\", \"ridge\")[0],\n \"dm_pvalue_mean_vs_ridge\": dm_test(\"mean\", \"ridge\")[1],\n \"sha256_input\"''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep dm_ out/results.json","description":"Add Diebold-Mariano tests and 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out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.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 REPRO_OK\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month'])\nfc=pd.read_csv('out/forecasts.csv'); r=json.load(open('out/results.json'))\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon uniq',fc.horizon.unique())\nprint('target=origin+1 month', (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\n# independent ridge, with future data corrupted (leakage test)\nrng=np.random.default_rng(0)\nmaxdiff=0\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n x=d.copy(); fut=x.month>t\n x.loc[fut,x.columns[1:]]=rng.normal(size=(fut.sum(),6))*1e3\n ip=x.set_index('month').ip_growth\n S=pd.date_range('1992-03-01',t-pd.DateOffset(months=1),freq='MS')\n def feat(s):\n row=x.set_index('month').loc[s]\n return [ip[s],ip[s-pd.DateOffset(months=1)],ip[s-pd.DateOffset(months=2)],row.oil_chg,row.fedfunds,row.permit_chg,row.neworder_chg,row.tcu]\n X=np.array([feat(s) for s in S]); y=np.array([ip[s+pd.DateOffset(months=1)] for s in S])\n mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd; yb=y.mean()\n A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.concatenate([y-yb,np.zeros(8)])\n b=np.linalg.lstsq(A,bb,rcond=None)[0]\n f=yb+((np.array(feat(t))-mu)/sd)@b\n mean=ip[(ip.index>=pd.Timestamp('1992-04-01'))&(ip.index<=t)].mean()\n g=fc[fc.origin_date==t.strftime('%Y-%m-%d')].set_index('model').forecast\n maxdiff=max(maxdiff,abs(f-g['ridge']),abs(mean-g['mean']),abs(ip[t]-g['naive']), abs(yb-mean))\nprint('max abs diff independent+corrupted-future vs script:',maxdiff)\nfor m in ['naive','mean','ridge']:\n e=fc[fc.model==m]; e=e.forecast-e.actual\n print(m, np.sqrt((e**2).mean())-r['rmse_'+m], np.abs(e).mean()-r['mae_'+m])\nprint('rel', 1-r['rmse_ridge']/r['rmse_naive']-r['rel_improvement_vs_naive'], 1-r['rmse_ridge']/r['rmse_mean']-r['rel_improvement_vs_mean'])\nprint('actual check', (fc.actual.values==np.repeat(d.set_index('month').ip_growth.loc[pd.to_datetime(fc.target_date[::3])].values,3)).all())\nEOF\ncat out/exclusions.csv","description":"Reproducibility and independent verification checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1166,"cache_read_input_tokens":14100,"output_tokens":1510,"output_tokens_details":{"thinking_tokens":138},"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":1166},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmKQcQ6fTcW82LR7rPt","requestRef":"516dc220-b9d2-48d8-81b0-5d0f11f2c7bd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"31d9e33d-3854-4beb-a6e4-5d144bdbf520","timestamp":"2026-10-30T04:09:04.570Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0025","sessionId":"5cd01f1f-3722-4772-bc28-11628f31cc55","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"31d9e33d-3854-4beb-a6e4-5d144bdbf520","isSidechain":false,"promptId":"12d4050f-a17b-42d9-875c-a3b0ad8bc51d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Dxy8GjXDQ2AQZcm8aTuYMX","type":"tool_result","content":"REPRO_OK\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon uniq [1]\ntarget=origin+1 month True\nmax abs diff independent+corrupted-future vs script: 4.962404098751705e-11\nnaive 0.0 0.0\nmean -1.6331380692236053e-13 4.724609592443585e-12\nridge 1.4273027204581012e-12 -1.2058687381966138e-12\nrel 0.0 0.0\nactual check True\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop months before 1992-03-01 (lags ip_growth_{s-2} and neworder_chg undefined/not in spec),417,415,2,TASK.md training window starts at s=1992-03-01\n2,keep feature months s with s+1 <= last origin 2026-08-01 (s <= 2026-07-01),415,413,2,target y_{s+1} must be dated <= origin; later months unavailable","is_error":false}]},"uuid":"9cf4dc37-8a7c-4361-a7ac-93e7b5896031","timestamp":"2026-10-30T04:09:08.957Z","toolUseResult":{"stdout":"REPRO_OK\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon uniq [1]\ntarget=origin+1 month True\nmax abs diff independent+corrupted-future vs script: 4.962404098751705e-11\nnaive 0.0 0.0\nmean -1.6331380692236053e-13 4.724609592443585e-12\nridge 1.4273027204581012e-12 -1.2058687381966138e-12\nrel 0.0 0.0\nactual check True\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop months before 1992-03-01 (lags ip_growth_{s-2} and neworder_chg undefined/not in spec),417,415,2,TASK.md training window starts at s=1992-03-01\n2,keep feature months s with s+1 <= last origin 2026-08-01 (s <= 2026-07-01),415,413,2,target y_{s+1} must be dated <= origin; later months 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out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.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 REPRO_OK\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month'])\nfc=pd.read_csv('out/forecasts.csv'); r=json.load(open('out/results.json'))\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon uniq',fc.horizon.unique())\nprint('target=origin+1 month', (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\n# independent ridge, with future data corrupted (leakage test)\nrng=np.random.default_rng(0)\nmaxdiff=0\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n x=d.copy(); fut=x.month>t\n x.loc[fut,x.columns[1:]]=rng.normal(size=(fut.sum(),6))*1e3\n ip=x.set_index('month').ip_growth\n S=pd.date_range('1992-03-01',t-pd.DateOffset(months=1),freq='MS')\n def feat(s):\n row=x.set_index('month').loc[s]\n return [ip[s],ip[s-pd.DateOffset(months=1)],ip[s-pd.DateOffset(months=2)],row.oil_chg,row.fedfunds,row.permit_chg,row.neworder_chg,row.tcu]\n X=np.array([feat(s) for s in S]); y=np.array([ip[s+pd.DateOffset(months=1)] for s in S])\n mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd; yb=y.mean()\n A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.concatenate([y-yb,np.zeros(8)])\n b=np.linalg.lstsq(A,bb,rcond=None)[0]\n f=yb+((np.array(feat(t))-mu)/sd)@b\n mean=ip[(ip.index>=pd.Timestamp('1992-04-01'))&(ip.index<=t)].mean()\n g=fc[fc.origin_date==t.strftime('%Y-%m-%d')].set_index('model').forecast\n maxdiff=max(maxdiff,abs(f-g['ridge']),abs(mean-g['mean']),abs(ip[t]-g['naive']), abs(yb-mean))\nprint('max abs diff independent+corrupted-future vs script:',maxdiff)\nfor m in ['naive','mean','ridge']:\n e=fc[fc.model==m]; e=e.forecast-e.actual\n print(m, np.sqrt((e**2).mean())-r['rmse_'+m], np.abs(e).mean()-r['mae_'+m])\nprint('rel', 1-r['rmse_ridge']/r['rmse_naive']-r['rel_improvement_vs_naive'], 1-r['rmse_ridge']/r['rmse_mean']-r['rel_improvement_vs_mean'])\nprint('actual check', (fc.actual.values==np.repeat(d.set_index('month').ip_growth.loc[pd.to_datetime(fc.target_date[::3])].values,3)).all())\nEOF\ncat out/exclusions.csv","description":"Reproducibility and independent verification 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out && cat > state/contract.md <<'EOF'\n# Contract — F3-conf-03\n**Question.** Over 60 rolling origins t = 2021-09 … 2026-08, does a fixed-penalty (λ=50) ridge regression on 8 standardised predictors forecast US IP growth at t+1 better than (a) naive (ip_growth_t) and (b) expanding mean of ip_growth over 1992-04..t?\n**Estimands.** Out-of-sample RMSE and MAE (percent/month) of naive, mean, ridge over the 60 h=1 forecasts; rel. improvements 1 − RMSE_ridge/RMSE_baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), fixed model spec; 60 forecasts is a small sample.\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.\n**Information rules.** At origin t use only values dated ≤ t; training rows s ∈ [1992-03, t−1] with target ip_growth_{s+1}; standardisation μ, σ (ddof=1) and ȳ recomputed per origin from training rows only.\n**Invalidity.** Any use of data dated > t (incl. full-sample scaling); wrong origin set; missing target months. Impossible if months non-contiguous or required values missing in the training window.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- data/ip_predictors.csv — SHA-256 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json). data/README.md SHA-256 225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e.\n- 417 rows × 7 columns; month 1992-01-01 … 2026-09-01, contiguous monthly, no duplicates.\n- Schema: month (date), ip_growth, oil_chg, permit_chg, neworder_chg (100×Δln, percent/month), fedfunds (percent, level), tcu (percent of capacity, level). All float.\n- Missing: neworder_chg 1992-01, 1992-02 only. **Discrepancy with README**: README says first month of every change series is empty, but ip_growth, oil_chg, permit_chg are populated for 1992-01. Used as given (only ip_growth_1992-01 enters, as lag-2 for s=1992-03).\n- Anomalies: large ip_growth values 2008-09 (−4.50), 2020-03 (−3.99), 2020-04 (−14.14), 2020-06 (+6.35), 2020-07 (+3.68), 2021-02 (−3.36). Genuine events (crisis, COVID, Texas freeze); kept (spec fixed).\n- Availability: file contains 2026-09 (target of last origin) — it is used only as an `actual`, never as an input to a forecast. Current vintage: revisions ignored per README; no real-time claim.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: re-ran analysis.py; results.json and forecasts.csv byte-identical (cmp) — PASS.\n2. forecasts.csv: 180 rows, 60 per model, horizon all 1, target_date = origin_date + 1 month — PASS.\n3. Independent re-implementation (augmented least squares ridge, separate code) with ALL data dated > t replaced by random garbage (×1e3) at each origin: max abs difference vs script forecasts (naive, mean, ridge) 5e-11 (CSV rounding) — PASS; demonstrates no look-ahead leakage incl. scaling statistics.\n4. Ridge intercept ȳ equals expanding mean (both average ip_growth 1992-04..t) — PASS (diff < 1e-10).\n5. RMSE/MAE recomputed from forecasts.csv match results.json (|diff| < 1e-11); rel. improvements identities hold exactly — PASS.\n6. actual column equals ip_growth at target_date — PASS.\n7. RMSE > 0 for all models; RMSE ≥ MAE for all models (0.883≥0.699, 0.544≥0.429, 0.561≥0.451) — PASS.\n8. Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed — PASS.\n9. Chronology: first_origin 2021-09-01 < last_origin 2026-08-01 — PASS.\n10. Magnitudes: ridge RMSE (0.561) between mean (0.544) and naive (0.883); plausible for monthly IP growth sd ≈ 1.04 full sample.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; README discrepancy on 1992-01 empties noted; no exclusions beyond spec window.\n- 2026-10-30 P2: baselines naive/mean computed in analysis.py (same rolling evaluation).\n- 2026-10-30 P3: ridge implemented exactly per spec; added DM (HLN) tests as extra uncertainty keys. Decision: no random seed needed (null).\n- 2026-10-30 P4: verification checks all pass (see checks.md); gate run.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over the 60 origins 2021-09 to 2026-08, the ridge forecast has lower RMSE than the naive forecast (rel. improvement about 36%) and lower MAE.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"mae_ridge\",\"mae_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored; 60 forecasts.\"},\n {\"id\":\"C2\",\"text\":\"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3% higher (rel. improvement about -0.031) and its MAE is also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"rel_improvement_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Small evaluation sample; single fixed lambda.\"},\n {\"id\":\"C3\",\"text\":\"The ridge-vs-naive difference in squared error is statistically significant by a Diebold-Mariano test (HLN-corrected, p about 0.001), while the ridge-vs-mean difference is not (p about 0.59).\",\"results_keys\":[\"dm_stat_naive_vs_ridge\",\"dm_pvalue_naive_vs_ridge\",\"dm_stat_mean_vs_ridge\",\"dm_pvalue_mean_vs_ridge\"],\"status\":\"supported\",\"caveat\":\"DM test asymptotics with n=60; 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rel. improvements 1 − RMSE_ridge/RMSE_baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), fixed model spec; 60 forecasts is a small sample.\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.\n**Information rules.** At origin t use only values dated ≤ t; training rows s ∈ [1992-03, t−1] with target ip_growth_{s+1}; standardisation μ, σ (ddof=1) and ȳ recomputed per origin from training rows only.\n**Invalidity.** Any use of data dated > t (incl. full-sample scaling); wrong origin set; missing target months. Impossible if months non-contiguous or required values missing in the training window.\nEOF\ncat > state/data_audit.md <<'EOF'\n# Data audit\n- data/ip_predictors.csv — SHA-256 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json). data/README.md SHA-256 225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e.\n- 417 rows × 7 columns; month 1992-01-01 … 2026-09-01, contiguous monthly, no duplicates.\n- Schema: month (date), ip_growth, oil_chg, permit_chg, neworder_chg (100×Δln, percent/month), fedfunds (percent, level), tcu (percent of capacity, level). All float.\n- Missing: neworder_chg 1992-01, 1992-02 only. **Discrepancy with README**: README says first month of every change series is empty, but ip_growth, oil_chg, permit_chg are populated for 1992-01. Used as given (only ip_growth_1992-01 enters, as lag-2 for s=1992-03).\n- Anomalies: large ip_growth values 2008-09 (−4.50), 2020-03 (−3.99), 2020-04 (−14.14), 2020-06 (+6.35), 2020-07 (+3.68), 2021-02 (−3.36). Genuine events (crisis, COVID, Texas freeze); kept (spec fixed).\n- Availability: file contains 2026-09 (target of last origin) — it is used only as an `actual`, never as an input to a forecast. Current vintage: revisions ignored per README; no real-time claim.\nEOF\ncat > state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: re-ran analysis.py; results.json and forecasts.csv byte-identical (cmp) — PASS.\n2. forecasts.csv: 180 rows, 60 per model, horizon all 1, target_date = origin_date + 1 month — PASS.\n3. Independent re-implementation (augmented least squares ridge, separate code) with ALL data dated > t replaced by random garbage (×1e3) at each origin: max abs difference vs script forecasts (naive, mean, ridge) 5e-11 (CSV rounding) — PASS; demonstrates no look-ahead leakage incl. scaling statistics.\n4. Ridge intercept ȳ equals expanding mean (both average ip_growth 1992-04..t) — PASS (diff < 1e-10).\n5. RMSE/MAE recomputed from forecasts.csv match results.json (|diff| < 1e-11); rel. improvements identities hold exactly — PASS.\n6. actual column equals ip_growth at target_date — PASS.\n7. RMSE > 0 for all models; RMSE ≥ MAE for all models (0.883≥0.699, 0.544≥0.429, 0.561≥0.451) — PASS.\n8. Exclusion ledger: 417 − 2 − 2 = 413 = n_analysed — PASS.\n9. Chronology: first_origin 2021-09-01 < last_origin 2026-08-01 — PASS.\n10. Magnitudes: ridge RMSE (0.561) between mean (0.544) and naive (0.883); plausible for monthly IP growth sd ≈ 1.04 full sample.\nEOF\ncat > state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0: read TASK.md, wrote contract.\n- 2026-10-30 P1: audited data; README discrepancy on 1992-01 empties noted; no exclusions beyond spec window.\n- 2026-10-30 P2: baselines naive/mean computed in analysis.py (same rolling evaluation).\n- 2026-10-30 P3: ridge implemented exactly per spec; added DM (HLN) tests as extra uncertainty keys. Decision: no random seed needed (null).\n- 2026-10-30 P4: verification checks all pass (see checks.md); gate run.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over the 60 origins 2021-09 to 2026-08, the ridge forecast has lower RMSE than the naive forecast (rel. improvement about 36%) and lower MAE.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"mae_ridge\",\"mae_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored; 60 forecasts.\"},\n {\"id\":\"C2\",\"text\":\"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3% higher (rel. improvement about -0.031) and its MAE is also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"rel_improvement_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Small evaluation sample; single fixed lambda.\"},\n {\"id\":\"C3\",\"text\":\"The ridge-vs-naive difference in squared error is statistically significant by a Diebold-Mariano test (HLN-corrected, p about 0.001), while the ridge-vs-mean difference is not (p about 0.59).\",\"results_keys\":[\"dm_stat_naive_vs_ridge\",\"dm_pvalue_naive_vs_ridge\",\"dm_stat_mean_vs_ridge\",\"dm_pvalue_mean_vs_ridge\"],\"status\":\"supported\",\"caveat\":\"DM test asymptotics with n=60; ridge and mean share the same intercept (nested-like comparison), for which DM can be conservative.\"},\n {\"id\":\"C4\",\"text\":\"All standardisation statistics and the target mean were computed from each origin's training window only (normalisation_window = expanding); an independent re-implementation with future data corrupted reproduced every forecast.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Verification recorded in out/state/checks.md.\"}\n]\nEOF","description":"Write state files and claims ledger"}}]}}51{"type":"api-request","sessionId":"5cd01f1f-3722-4772-bc28-11628f31cc55","id":"1afb68df-d450-45d9-8720-e632c4a73cd6","timestamp":"2026-10-30T04:09:33.547Z","version":"2.1.314","querySource":"sdk","shapeHash":"464bc0125d04a6bc2a1ecbf3e43dc47c9f9bb05c29e3195cf5554b331e25a78d","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":"eb5560d4-f515-422d-bfc3-f388bef5da18","keep":14,"tail":["d4806be27e58efd9eb3117f034fc3060ddd7ce058793ffaf4afbde10e91411d3","a2cc2bdf8665f2121ddbe85cb205b576fb151406916d356a0c2cfd1facf4de0f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e26bd1e93f46d7ae9f277ef8a7489b6ac1e760856b38dac2d88dfeb2fcf4c14c"}52{"parentUuid":"eac5253f-13ef-4602-bb0b-2b229d91b9a9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmNTGyV9YVHDoo3s3eW","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01REqFSmBuPnDyv3R6VKeHXg","name":"Bash","input":{"command":"cd out && cat > 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 eight standardised monthly predictors (`n_features` = 8) forecast next month's US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last-value) forecast and the expanding historical mean? There are `n_origins` = 60 rolling origins, from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, all at horizon h = 1.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 to 2026-09, contiguous months, no duplicates. The SHA-256 matches the README (`sha256_input`).\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. These months come before the training window, so nothing was imputed.\n- Exclusions (`out/exclusions.csv`): two steps take 417 rows down to 413 training rows at the last origin (`n_analysed` = 413). Step 1 drops months before 1992-03-01, as the spec requires (2 rows). Step 2 drops feature months whose target would be dated after the last origin (2 rows).\n- Data issues are listed in `data_issues`:\n - The README says 1992-01 is empty for every change series. In fact `ip_growth`, `oil_chg` and `permit_chg` have values for that month. I used them as given; only `ip_growth` for 1992-01 enters, as the 2-month lag for s = 1992-03.\n - The training windows contain extreme values (2020-04 = −14.1, 2008-09 = −4.5). I kept them because the specification is fixed.\n - The data are current vintage and publication lags are ignored.\n\n## Method\nAll computations are in `out/analysis.py`, which is deterministic and uses no randomness (`random_seed` = null).\n\n**Baselines.**\n- Naive: the forecast is `ip_growth` of month t.\n- Expanding mean: the forecast is the mean of `ip_growth` over 1992-04..t.\n\n**Ridge model.**\n- Training rows: months s with 1992-03 ≤ s ≤ t−1, target `ip_growth`_{s+1}.\n- Features: ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n- At every origin the features are standardised using the training-window mean and sample SD (ddof = 1), and the target is centred on its training mean ȳ (`normalisation_window` = \"expanding\").\n- Coefficients: b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ). Forecast: ȳ + z̃_t·b.\n- ȳ is the same quantity as the expanding-mean forecast. The ridge forecast is therefore the mean forecast plus a shrunken, predictor-based adjustment.\n\n**Evaluation.** RMSE and MAE over the 60 forecasts. Actuals are `ip_growth` at t+1 in the current vintage.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\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- **Ridge vs naive:** ridge improves RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- **Ridge vs expanding mean:** ridge is 3.1% *worse* in RMSE (`rel_improvement_vs_mean` = −0.0310) and also worse in MAE.\n- **Overall:** ridge clearly beats the naive forecast but does not beat the expanding historical mean. The stronger of the two baselines is the mean (`baseline_best_rmse_model`).\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error differential, with the Harvey–Leybourne–Newbold small-sample correction and a t₅₉ reference distribution. For h = 1 no HAC lags are used.\n- Naive vs ridge: DM = 3.36 (`dm_stat_naive_vs_ridge`), p = 0.0014 (`dm_pvalue_naive_vs_ridge`). Ridge is significantly more accurate than naive.\n- Mean vs ridge: DM = −0.54 (`dm_stat_mean_vs_ridge`), p = 0.59 (`dm_pvalue_mean_vs_ridge`). There is no significant difference; the mean's point advantage is within sampling noise.\n\nWith only 60 forecasts, the RMSE differences are imprecise. Ridge and the mean share the same intercept, so the comparison is nested-like, and a standard DM test can be conservative in that setting.\n\n## Limitations and open issues\n- **Data vintage:** the data are the current (revised) vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast performance.\n- **Fixed specification:** λ = 50 and the feature set are fixed by the task and were not tuned. A different λ could change the ranking against the mean; I did not explore this because the task does not ask for it.\n- **Outliers:** the COVID-era outliers (2020) in the training windows are not down-weighted, and they may distort the ridge coefficients.\n- **Sample size:** the evaluation sample is short (60 months) and covers a single regime (2021–2026).\n- **README discrepancy:** the README does not match the data on which 1992-01 cells are empty (see Data and exclusions). The effect is negligible, since only one lag value enters.\n- Verification steps are recorded in `out/state/checks.md`. They include an independent re-implementation run with all future data corrupted, which reproduced every forecast and so rules out look-ahead.\nEOF\ncat > state/phases.json <<'EOF'\n{\n \"P0\": {\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n \"P1\": {\"status\":\"passed\",\"checks\":[\"SHA-256 matches README\",\"417x7, contiguous months, no duplicates\",\"missing values located (neworder_chg 1992-01/02)\",\"exclusion ledger sums 417-4=413\"],\"notes\":\"README empty-cell discrepancy recorded\"},\n \"P2\": {\"status\":\"passed\",\"checks\":[\"naive and expanding mean computed on same 60 origins\"],\"notes\":\"\"},\n \"P3\": {\"status\":\"passed\",\"checks\":[\"analysis.py implements spec; deterministic, no seed\"],\"notes\":\"\"},\n \"P4\": {\"status\":\"running\",\"checks\":[\"rerun byte-identical\",\"independent reimplementation with corrupted future data matches to 5e-11\",\"metric identities hold\",\"see checks.md\"],\"notes\":\"\"},\n \"P5\": {\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; 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identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0025/out/state/gate_receipts/20261030T040957.248107Z.json\n20261030T040957.248107Z.json","is_error":false}]},"uuid":"ed741f27-7b12-4d88-a0a1-f502d1639d93","timestamp":"2026-10-30T04:09:57.259Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: out\nPGAR GATE pgar_gate/1.0 — FAILED (8 of 38 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\n [ok] file:out/forecasts.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:n_origins: ok\n [ok] key:first_origin: ok\n [ok] key:last_origin: ok\n [ok] key:lambda: ok\n [ok] key:normalisation_window: ok\n [ok] key:n_features: ok\n [ok] key:rmse_naive: ok\n [ok] key:mae_naive: ok\n [ok] key:rmse_mean: ok\n [ok] key:mae_mean: ok\n [ok] key:rmse_ridge: ok\n [ok] key:mae_ridge: ok\n [ok] key:rel_improvement_vs_naive: ok\n [ok] key:rel_improvement_vs_mean: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [ok] claims_json: 4 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] chronology:first_origin<=last_origin: 2021-09-01 vs 2026-08-01\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0025/out/state/gate_receipts/20261030T040957.248107Z.json\n20261030T040957.248107Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"80d7991e-3bbe-4acf-a6d8-610117d494ae","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0025","sessionId":"5cd01f1f-3722-4772-bc28-11628f31cc55","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"5cd01f1f-3722-4772-bc28-11628f31cc55","hash":"e770368ddf99d468d12bf6fac8f16e1331fed61c961ff47ccb3d59fc9f16a356","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01REqFSmBuPnDyv3R6VKeHXg","name":"Bash","input":{"command":"cd out && cat > 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 eight standardised monthly predictors (`n_features` = 8) forecast next month's US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last-value) forecast and the expanding historical mean? There are `n_origins` = 60 rolling origins, from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, all at horizon h = 1.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 to 2026-09, contiguous months, no duplicates. The SHA-256 matches the README (`sha256_input`).\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. These months come before the training window, so nothing was imputed.\n- Exclusions (`out/exclusions.csv`): two steps take 417 rows down to 413 training rows at the last origin (`n_analysed` = 413). Step 1 drops months before 1992-03-01, as the spec requires (2 rows). Step 2 drops feature months whose target would be dated after the last origin (2 rows).\n- Data issues are listed in `data_issues`:\n - The README says 1992-01 is empty for every change series. In fact `ip_growth`, `oil_chg` and `permit_chg` have values for that month. I used them as given; only `ip_growth` for 1992-01 enters, as the 2-month lag for s = 1992-03.\n - The training windows contain extreme values (2020-04 = −14.1, 2008-09 = −4.5). I kept them because the specification is fixed.\n - The data are current vintage and publication lags are ignored.\n\n## Method\nAll computations are in `out/analysis.py`, which is deterministic and uses no randomness (`random_seed` = null).\n\n**Baselines.**\n- Naive: the forecast is `ip_growth` of month t.\n- Expanding mean: the forecast is the mean of `ip_growth` over 1992-04..t.\n\n**Ridge model.**\n- Training rows: months s with 1992-03 ≤ s ≤ t−1, target `ip_growth`_{s+1}.\n- Features: ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n- At every origin the features are standardised using the training-window mean and sample SD (ddof = 1), and the target is centred on its training mean ȳ (`normalisation_window` = \"expanding\").\n- Coefficients: b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ). Forecast: ȳ + z̃_t·b.\n- ȳ is the same quantity as the expanding-mean forecast. The ridge forecast is therefore the mean forecast plus a shrunken, predictor-based adjustment.\n\n**Evaluation.** RMSE and MAE over the 60 forecasts. Actuals are `ip_growth` at t+1 in the current vintage.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\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- **Ridge vs naive:** ridge improves RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- **Ridge vs expanding mean:** ridge is 3.1% *worse* in RMSE (`rel_improvement_vs_mean` = −0.0310) and also worse in MAE.\n- **Overall:** ridge clearly beats the naive forecast but does not beat the expanding historical mean. The stronger of the two baselines is the mean (`baseline_best_rmse_model`).\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error differential, with the Harvey–Leybourne–Newbold small-sample correction and a t₅₉ reference distribution. For h = 1 no HAC lags are used.\n- Naive vs ridge: DM = 3.36 (`dm_stat_naive_vs_ridge`), p = 0.0014 (`dm_pvalue_naive_vs_ridge`). Ridge is significantly more accurate than naive.\n- Mean vs ridge: DM = −0.54 (`dm_stat_mean_vs_ridge`), p = 0.59 (`dm_pvalue_mean_vs_ridge`). There is no significant difference; the mean's point advantage is within sampling noise.\n\nWith only 60 forecasts, the RMSE differences are imprecise. Ridge and the mean share the same intercept, so the comparison is nested-like, and a standard DM test can be conservative in that setting.\n\n## Limitations and open issues\n- **Data vintage:** the data are the current (revised) vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast performance.\n- **Fixed specification:** λ = 50 and the feature set are fixed by the task and were not tuned. A different λ could change the ranking against the mean; I did not explore this because the task does not ask for it.\n- **Outliers:** the COVID-era outliers (2020) in the training windows are not down-weighted, and they may distort the ridge coefficients.\n- **Sample size:** the evaluation sample is short (60 months) and covers a single regime (2021–2026).\n- **README discrepancy:** the README does not match the data on which 1992-01 cells are empty (see Data and exclusions). The effect is negligible, since only one lag value enters.\n- Verification steps are recorded in `out/state/checks.md`. 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checks.md\"],\"notes\":\"\"},\n \"P5\": {\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}\n}\nEOF\ncd .. && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write report and phase ledger, run 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> 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 eight standardised monthly predictors (`n_features` = 8) forecast next month's US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last-value) forecast and the expanding historical mean? There are `n_origins` = 60 rolling origins, from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, all at horizon h = 1.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 to 2026-09, contiguous months, no duplicates. The SHA-256 matches the README (`sha256_input`).\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. These months come before the training window, so nothing was imputed.\n- Exclusions (`out/exclusions.csv`): two steps take 417 rows down to 413 training rows at the last origin (`n_analysed` = 413). Step 1 drops months before 1992-03-01, as the spec requires (2 rows). Step 2 drops feature months whose target would be dated after the last origin (2 rows).\n- Data issues are listed in `data_issues`:\n - The README says 1992-01 is empty for every change series. In fact `ip_growth`, `oil_chg` and `permit_chg` have values for that month. I used them as given; only `ip_growth` for 1992-01 enters, as the 2-month lag for s = 1992-03.\n - The training windows contain extreme values (2020-04 = −14.1, 2008-09 = −4.5). I kept them because the specification is fixed.\n - The data are current vintage and publication lags are ignored.\n\n## Method\nAll computations are in `out/analysis.py`, which is deterministic and uses no randomness (`random_seed` = null).\n\n**Baselines.**\n- Naive: the forecast is `ip_growth` of month t.\n- Expanding mean: the forecast is the mean of `ip_growth` over 1992-04..t.\n\n**Ridge model.**\n- Training rows: months s with 1992-03 ≤ s ≤ t−1, target `ip_growth`_{s+1}.\n- Features: ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n- At every origin the features are standardised using the training-window mean and sample SD (ddof = 1), and the target is centred on its training mean ȳ (`normalisation_window` = \"expanding\").\n- Coefficients: b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ). Forecast: ȳ + z̃_t·b.\n- ȳ is the same quantity as the expanding-mean forecast. The ridge forecast is therefore the mean forecast plus a shrunken, predictor-based adjustment.\n\n**Evaluation.** RMSE and MAE over the 60 forecasts. Actuals are `ip_growth` at t+1 in the current vintage.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\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- **Ridge vs naive:** ridge improves RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- **Ridge vs expanding mean:** ridge is 3.1% *worse* in RMSE (`rel_improvement_vs_mean` = −0.0310) and also worse in MAE.\n- **Overall:** ridge clearly beats the naive forecast but does not beat the expanding historical mean. The stronger of the two baselines is the mean (`baseline_best_rmse_model`).\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error differential, with the Harvey–Leybourne–Newbold small-sample correction and a t₅₉ reference distribution. For h = 1 no HAC lags are used.\n- Naive vs ridge: DM = 3.36 (`dm_stat_naive_vs_ridge`), p = 0.0014 (`dm_pvalue_naive_vs_ridge`). Ridge is significantly more accurate than naive.\n- Mean vs ridge: DM = −0.54 (`dm_stat_mean_vs_ridge`), p = 0.59 (`dm_pvalue_mean_vs_ridge`). There is no significant difference; the mean's point advantage is within sampling noise.\n\nWith only 60 forecasts, the RMSE differences are imprecise. Ridge and the mean share the same intercept, so the comparison is nested-like, and a standard DM test can be conservative in that setting.\n\n## Limitations and open issues\n- **Data vintage:** the data are the current (revised) vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast performance.\n- **Fixed specification:** λ = 50 and the feature set are fixed by the task and were not tuned. A different λ could change the ranking against the mean; I did not explore this because the task does not ask for it.\n- **Outliers:** the COVID-era outliers (2020) in the training windows are not down-weighted, and they may distort the ridge coefficients.\n- **Sample size:** the evaluation sample is short (60 months) and covers a single regime (2021–2026).\n- **README discrepancy:** the README does not match the data on which 1992-01 cells are empty (see Data and exclusions). The effect is negligible, since only one lag value enters.\n- Verification steps are recorded in `out/state/checks.md`. 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> 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 eight standardised monthly predictors (`n_features` = 8) forecast next month's US industrial production growth (100 × Δln INDPRO, percent per month) better than the naive (last-value) forecast and the expanding historical mean? There are `n_origins` = 60 rolling origins, from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01, all at horizon h = 1.\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 to 2026-09, contiguous months, no duplicates. The SHA-256 matches the README (`sha256_input`).\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. These months come before the training window, so nothing was imputed.\n- Exclusions (`out/exclusions.csv`): two steps take 417 rows down to 413 training rows at the last origin (`n_analysed` = 413). Step 1 drops months before 1992-03-01, as the spec requires (2 rows). Step 2 drops feature months whose target would be dated after the last origin (2 rows).\n- Data issues are listed in `data_issues`:\n - The README says 1992-01 is empty for every change series. In fact `ip_growth`, `oil_chg` and `permit_chg` have values for that month. I used them as given; only `ip_growth` for 1992-01 enters, as the 2-month lag for s = 1992-03.\n - The training windows contain extreme values (2020-04 = −14.1, 2008-09 = −4.5). I kept them because the specification is fixed.\n - The data are current vintage and publication lags are ignored.\n\n## Method\nAll computations are in `out/analysis.py`, which is deterministic and uses no randomness (`random_seed` = null).\n\n**Baselines.**\n- Naive: the forecast is `ip_growth` of month t.\n- Expanding mean: the forecast is the mean of `ip_growth` over 1992-04..t.\n\n**Ridge model.**\n- Training rows: months s with 1992-03 ≤ s ≤ t−1, target `ip_growth`_{s+1}.\n- Features: ip_growth at s, s−1 and s−2, plus oil_chg, fedfunds, permit_chg, neworder_chg and tcu at s.\n- At every origin the features are standardised using the training-window mean and sample SD (ddof = 1), and the target is centred on its training mean ȳ (`normalisation_window` = \"expanding\").\n- Coefficients: b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ). Forecast: ȳ + z̃_t·b.\n- ȳ is the same quantity as the expanding-mean forecast. The ridge forecast is therefore the mean forecast plus a shrunken, predictor-based adjustment.\n\n**Evaluation.** RMSE and MAE over the 60 forecasts. Actuals are `ip_growth` at t+1 in the current vintage.\n\n## Results\n| model | RMSE (pp/month) | MAE (pp/month) |\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- **Ridge vs naive:** ridge improves RMSE by 36.5% (`rel_improvement_vs_naive` = 0.3649).\n- **Ridge vs expanding mean:** ridge is 3.1% *worse* in RMSE (`rel_improvement_vs_mean` = −0.0310) and also worse in MAE.\n- **Overall:** ridge clearly beats the naive forecast but does not beat the expanding historical mean. The stronger of the two baselines is the mean (`baseline_best_rmse_model`).\n\n## Uncertainty\nI ran Diebold–Mariano tests on the squared-error differential, with the Harvey–Leybourne–Newbold small-sample correction and a t₅₉ reference distribution. For h = 1 no HAC lags are used.\n- Naive vs ridge: DM = 3.36 (`dm_stat_naive_vs_ridge`), p = 0.0014 (`dm_pvalue_naive_vs_ridge`). Ridge is significantly more accurate than naive.\n- Mean vs ridge: DM = −0.54 (`dm_stat_mean_vs_ridge`), p = 0.59 (`dm_pvalue_mean_vs_ridge`). There is no significant difference; the mean's point advantage is within sampling noise.\n\nWith only 60 forecasts, the RMSE differences are imprecise. Ridge and the mean share the same intercept, so the comparison is nested-like, and a standard DM test can be conservative in that setting.\n\n## Limitations and open issues\n- **Data vintage:** the data are the current (revised) vintage and publication lags are ignored. The results describe how predictable the revised series is under stylised timing, not real-time forecast performance.\n- **Fixed specification:** λ = 50 and the feature set are fixed by the task and were not tuned. A different λ could change the ranking against the mean; I did not explore this because the task does not ask for it.\n- **Outliers:** the COVID-era outliers (2020) in the training windows are not down-weighted, and they may distort the ridge coefficients.\n- **Sample size:** the evaluation sample is short (60 months) and covers a single regime (2021–2026).\n- **README discrepancy:** the README does not match the data on which 1992-01 cells are empty (see Data and exclusions). The effect is negligible, since only one lag value enters.\n- Verification steps are recorded in `out/state/checks.md`. 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All phases P0–P5 are complete and the gate passes (0 of 38 checks failed).\n\nResults over the 60 origins (2021-09 to 2026-08, one month ahead), in percentage points per month:\n\n| model | RMSE | MAE |\n|---|---|---|\n| naive | 0.883 | 0.699 |\n| expanding mean | 0.544 | 0.429 |\n| ridge | 0.561 | 0.451 |\n\n- **Ridge vs naive:** ridge has a 36.5% lower RMSE. A Diebold–Mariano test (a standard test for comparing two sets of forecast errors) finds this significant, p = 0.0014.\n- **Ridge vs expanding mean:** ridge's RMSE is 3.1% higher, and its MAE is also higher. The difference is not significant (p = 0.59). The ridge intercept equals the expanding mean, so ridge is the mean forecast plus a shrunken adjustment from the predictors, and that adjustment does not help here. The task didn't ask for the Diebold–Mariano tests; I added them as extra keys in `results.json` to show uncertainty.\n\n**Checks:**\n- **Look-ahead:** at every origin, the scaling statistics and the target mean come only from that origin's training window. A separate re-implementation, run with all data after each origin replaced by random values, reproduced every forecast to within about 5e-11.\n- **Reproducibility:** a re-run gives byte-identical outputs.\n- **Row counts:** 417 input rows minus 4 excluded leaves 413 training rows at the last origin.\n\n**Limitations:**\n- **Revised data:** the data are the current revised vintage and publication delays are ignored, so this is not a real-time result.\n- **Small sample:** 60 forecasts is not many.\n- **Fixed model:** λ = 50 was fixed by the task and not tuned.\n- **Outliers kept:** extreme months such as April 2020 stay in the training data.\n- **README mismatch:** the README says the first month of every change series is empty, but three of them have values for 1992-01. 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