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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 80\ndrwxr-xr-x@ 10 simon-pierreboucher staff 320 oct. 30 00:12 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:10 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 7233 oct. 30 00:11 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1606 oct. 30 00:12 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 364 oct. 30 00:11 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 10204 oct. 30 00:11 forecasts.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 5347 oct. 30 00:12 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 2384 oct. 30 00:11 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:12 review\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 00:11 state\n{\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": 60,\n \"first_origin\": \"2021-09-01\",\n \"last_origin\": \"2026-08-01\",\n \"lambda\": 50.0,\n \"normalisation_window\": \"expanding\",\n \"n_features\": 8,\n \"rmse_naive\": 0.8830699166753823,\n \"mae_naive\": 0.6989594000000001,\n \"rmse_mean\": 0.5439355781141625,\n \"mae_mean\": 0.42929777256194207,\n \"rmse_ridge\": 0.5607989303521244,\n \"mae_ridge\": 0.45141289074453916,\n \"rel_improvement_vs_naive\": 0.3649439078805412,\n \"rel_improvement_vs_mean\": -0.031002480654837017,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n \"handling\": \"Not needed: training rows start at s=1992-03 as specified; no imputation.\"\n },\n {\n \"issue\": \"README states the 1992-01 value of every change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as the 2nd lag of the first training row); documentation discrepancy noted.\"\n },\n {\n \"issue\": \"Extreme COVID-era observations (ip_growth 2020-04 = -14.1%, 2020-06; oil_chg 2020-03..05) lie in every training window\",\n \"handling\": \"Retained: the model is fully specified by the task; no outlier treatment. Discussed as a limitation.\"\n },\n {\n \"issue\": \"Current-vintage (revised) data and ignored publication lags\",\n \"handling\": \"Results interpreted as pseudo-out-of-sample on revised data, not real-time accuracy.\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": null,\n \"n_train_first_origin\": 354,\n \"n_train_last_origin\": 413,\n \"first_target\": \"2021-10-01\",\n \"last_target\": \"2026-09-01\",\n \"rel_improvement_mae_vs_naive\": 0.3541643609850027,\n \"rel_improvement_mae_vs_mean\": -0.05151463528594524,\n \"dm_stat_ridge_vs_naive\": -3.3649594921847754,\n \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571726,\n \"dm_stat_ridge_vs_mean\": 0.5392198591267084,\n \"dm_pvalue_ridge_vs_mean\": 0.5917642583421929,\n \"ridge_coef_last_origin\": {\n \"ip_l0\": 0.06478836171179567,\n \"ip_l1\": -0.10788765902756307,\n \"ip_l2\": 0.02651437472844772,\n \"oil_chg\": 0.34029324128529637,\n \"fedfunds\": 0.053871751020018094,\n \"permit_chg\": 0.050665850231511385,\n \"neworder_chg\": 0.022836054568240408,\n \"tcu\": 0.000994313695189975\n }\n}[\n {\"id\": \"C1\", \"text\": \"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has RMSE 0.561 vs 0.883 for naive, a 36.5% RMSE improvement.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data; publication lags ignored.\"},\n {\"id\": \"C2\", \"text\": \"The ridge forecast does not beat the expanding mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), and MAE is also higher.\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"rel_improvement_vs_mean\", \"mae_ridge\", \"mae_mean\", \"rel_improvement_mae_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Difference is small and not statistically significant (C4).\"},\n {\"id\": \"C3\", \"text\": \"Ridge's advantage over the naive forecast is statistically significant (DM-HLN p = 0.0014).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\"], \"status\": \"supported\", \"caveat\": \"Only 60 errors; DM assumptions (stationary loss differential) assumed.\"},\n {\"id\": \"C4\", \"text\": \"Ridge and expanding-mean forecast accuracy cannot be distinguished statistically (DM-HLN p = 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Low power with 60 evaluation points.\"},\n {\"id\": \"C5\", \"text\": \"All standardisation statistics and the target mean are computed from each origin's training window only; forecasts are invariant to corruption of post-origin data.\", \"results_keys\": [\"normalisation_window\", \"n_analysed\"], \"status\": \"supported\", \"caveat\": \"Verified by leakage test recorded in out/state/checks.md.\"}\n]\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next-month US industrial production growth (100 × Δln INDPRO, % per month) better than the naive (last value) and expanding-mean forecasts, over `n_origins` = 60 rolling origins from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01 (targets 2021-10 … 2026-09, keys `first_target`, `last_target`), at horizon h = 1?\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 … 2026-09, SHA-256 matches the README. No duplicate or missing months; only missing cells are `neworder_chg` for 1992-01/02 (full audit in `out/state/data_audit.md`).\n- Exclusions (`out/exclusions.csv`): (1) months before 1992-03 are not training rows (417 → 415); (2) at the last origin, rows s with s + 1 > 2026-08 have no available label (415 → 413). Training rows at the last origin: `n_analysed` = 413; at the first origin: `n_train_first_origin` = 354.\n- Data issues and handling are listed in `data_issues`: the README claims all change series are empty in 1992-01, but `ip_growth`, `oil_chg`, `permit_chg` are populated (used as given; only `ip_growth` 1992-01 enters, as a second lag); COVID-era extremes (ip_growth 2020-04 ≈ −14.1 %) are retained because the model is fully specified; data are current vintage.\n\n## Method\n- Naive: forecast of ip_growth(t+1) = ip_growth(t). Expanding mean: mean of ip_growth over 1992-04 … t.\n- Ridge, exactly as specified: training rows s ∈ [1992-03, t−1] with label ip_growth(s+1) and features (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s). Each origin recomputes the feature means and sample SDs (ddof = 1) and the target mean from its own training rows (`normalisation_window` = \"expanding\"); b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ); forecast = ȳ + z̃_t · b. No intercept penalty. Nothing random (`random_seed` = null).\n- Evaluation: RMSE and MAE over the 60 forecast errors; relative improvement 1 − RMSE_ridge / RMSE_baseline. As a supplementary step, a Diebold–Mariano test (squared loss, h = 1, Harvey–Leybourne–Newbold correction, t₅₉ reference) compares the ridge and each baseline.\n- Verification (`out/state/checks.md`): an independent re-implementation matches all forecasts to within 5e-11. Corrupting every value dated after 2023-06 leaves all forecasts for origins ≤ 2023-06 unchanged (no look-ahead). A clean re-run reproduces the outputs byte for byte.\n\n## Results\n| model | RMSE (% / month) | MAE (% / 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 (λ=50) | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge improves RMSE by 36.5 % (`rel_improvement_vs_naive` = 0.365) and MAE by 35.4 % (`rel_improvement_mae_vs_naive`).\n- Compared with the expanding mean, ridge is **worse**: RMSE is 3.1 % higher (`rel_improvement_vs_mean` = −0.031) and MAE is 5.2 % higher (`rel_improvement_mae_vs_mean` = −0.052).\n- Answer: the ridge model beats the naive forecast but not the expanding historical mean over 2021-09 … 2026-08. Of the three forecasts, the unconditional expanding mean has the lowest error.\n- At the last origin, oil_chg has by far the largest ridge coefficient (+0.34 per SD, `ridge_coef_last_origin`). This most likely reflects the joint COVID-2020 collapse/rebound in oil prices and IP inside the training window. It is descriptive only.\n\n## Uncertainty\n- Ridge vs naive: DM statistic −3.36, p = 0.0014 (`dm_stat_ridge_vs_naive`, `dm_pvalue_ridge_vs_naive`), so ridge's advantage over naive is statistically clear.\n- Ridge vs expanding mean: DM statistic 0.54, p = 0.59 (`dm_stat_ridge_vs_mean`, `dm_pvalue_ridge_vs_mean`). The two cannot be distinguished. The negative relative improvement is small and not statistically significant. The supported conclusion is \"no evidence that ridge beats the mean\", not \"the mean is significantly better\".\n- Only 60 evaluation points are available. The DM test assumes squared-loss differentials that are covariance-stationary and roughly serially uncorrelated (h = 1). No confidence intervals for RMSE ratios were computed.\n\n## Limitations and open issues\n- Current-vintage (revised) data and ignored publication lags: these are pseudo-out-of-sample results on revised series under a stylised timing assumption. They do not show real-time forecast accuracy (INDPRO, TCU and NEWORDER are revised substantially, and the IP release lags by about two weeks).\n- λ = 50 is fixed by the task and was not tuned. Results may differ for other penalties, but exploring them was out of scope.\n- The training windows include extreme COVID-era observations. These can dominate the ridge coefficients (e.g. on oil_chg). No robust treatment was applied, as required by the exact model specification.\n- The evaluation period 2021-10 … 2026-09 is a single, relatively calm post-COVID regime. Conclusions may not generalise to other periods.\n- There is a documentation discrepancy in `data/README.md` about empty 1992-01 cells (see `data_issues`). It does not affect the results.","is_error":false}]},"uuid":"2d391503-af10-4835-bb8c-fe91dda3a7a0","timestamp":"2026-10-30T04:12:32.447Z","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). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 80\ndrwxr-xr-x@ 10 simon-pierreboucher staff 320 oct. 30 00:12 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:10 ..\n-rw-r--r--@ 1 simon-pierreboucher staff 7233 oct. 30 00:11 analysis.py\n-rw-r--r--@ 1 simon-pierreboucher staff 1606 oct. 30 00:12 claims.json\n-rw-r--r--@ 1 simon-pierreboucher staff 364 oct. 30 00:11 exclusions.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 10204 oct. 30 00:11 forecasts.csv\n-rw-r--r--@ 1 simon-pierreboucher staff 5347 oct. 30 00:12 report.md\n-rw-r--r--@ 1 simon-pierreboucher staff 2384 oct. 30 00:11 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:12 review\ndrwxr-xr-x@ 9 simon-pierreboucher staff 288 oct. 30 00:11 state\n{\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": 60,\n \"first_origin\": \"2021-09-01\",\n \"last_origin\": \"2026-08-01\",\n \"lambda\": 50.0,\n \"normalisation_window\": \"expanding\",\n \"n_features\": 8,\n \"rmse_naive\": 0.8830699166753823,\n \"mae_naive\": 0.6989594000000001,\n \"rmse_mean\": 0.5439355781141625,\n \"mae_mean\": 0.42929777256194207,\n \"rmse_ridge\": 0.5607989303521244,\n \"mae_ridge\": 0.45141289074453916,\n \"rel_improvement_vs_naive\": 0.3649439078805412,\n \"rel_improvement_vs_mean\": -0.031002480654837017,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n \"handling\": \"Not needed: training rows start at s=1992-03 as specified; no imputation.\"\n },\n {\n \"issue\": \"README states the 1992-01 value of every change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as the 2nd lag of the first training row); documentation discrepancy noted.\"\n },\n {\n \"issue\": \"Extreme COVID-era observations (ip_growth 2020-04 = -14.1%, 2020-06; oil_chg 2020-03..05) lie in every training window\",\n \"handling\": \"Retained: the model is fully specified by the task; no outlier treatment. Discussed as a limitation.\"\n },\n {\n \"issue\": \"Current-vintage (revised) data and ignored publication lags\",\n \"handling\": \"Results interpreted as pseudo-out-of-sample on revised data, not real-time accuracy.\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": null,\n \"n_train_first_origin\": 354,\n \"n_train_last_origin\": 413,\n \"first_target\": \"2021-10-01\",\n \"last_target\": \"2026-09-01\",\n \"rel_improvement_mae_vs_naive\": 0.3541643609850027,\n \"rel_improvement_mae_vs_mean\": -0.05151463528594524,\n \"dm_stat_ridge_vs_naive\": -3.3649594921847754,\n \"dm_pvalue_ridge_vs_naive\": 0.0013506687241571726,\n \"dm_stat_ridge_vs_mean\": 0.5392198591267084,\n \"dm_pvalue_ridge_vs_mean\": 0.5917642583421929,\n \"ridge_coef_last_origin\": {\n \"ip_l0\": 0.06478836171179567,\n \"ip_l1\": -0.10788765902756307,\n \"ip_l2\": 0.02651437472844772,\n \"oil_chg\": 0.34029324128529637,\n \"fedfunds\": 0.053871751020018094,\n \"permit_chg\": 0.050665850231511385,\n \"neworder_chg\": 0.022836054568240408,\n \"tcu\": 0.000994313695189975\n }\n}[\n {\"id\": \"C1\", \"text\": \"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has RMSE 0.561 vs 0.883 for naive, a 36.5% RMSE improvement.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data; publication lags ignored.\"},\n {\"id\": \"C2\", \"text\": \"The ridge forecast does not beat the expanding mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), and MAE is also higher.\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"rel_improvement_vs_mean\", \"mae_ridge\", \"mae_mean\", \"rel_improvement_mae_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Difference is small and not statistically significant (C4).\"},\n {\"id\": \"C3\", \"text\": \"Ridge's advantage over the naive forecast is statistically significant (DM-HLN p = 0.0014).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\"], \"status\": \"supported\", \"caveat\": \"Only 60 errors; DM assumptions (stationary loss differential) assumed.\"},\n {\"id\": \"C4\", \"text\": \"Ridge and expanding-mean forecast accuracy cannot be distinguished statistically (DM-HLN p = 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Low power with 60 evaluation points.\"},\n {\"id\": \"C5\", \"text\": \"All standardisation statistics and the target mean are computed from each origin's training window only; forecasts are invariant to corruption of post-origin data.\", \"results_keys\": [\"normalisation_window\", \"n_analysed\"], \"status\": \"supported\", \"caveat\": \"Verified by leakage test recorded in out/state/checks.md.\"}\n]\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (`n_features`) forecast next-month US industrial production growth (100 × Δln INDPRO, % per month) better than the naive (last value) and expanding-mean forecasts, over `n_origins` = 60 rolling origins from `first_origin` = 2021-09-01 to `last_origin` = 2026-08-01 (targets 2021-10 … 2026-09, keys `first_target`, `last_target`), at horizon h = 1?\n\n## Data and exclusions\n- Input: `data/ip_predictors.csv`, 417 data rows (`n_input_rows`), 1992-01 … 2026-09, SHA-256 matches the README. No duplicate or missing months; only missing cells are `neworder_chg` for 1992-01/02 (full audit in `out/state/data_audit.md`).\n- Exclusions (`out/exclusions.csv`): (1) months before 1992-03 are not training rows (417 → 415); (2) at the last origin, rows s with s + 1 > 2026-08 have no available label (415 → 413). Training rows at the last origin: `n_analysed` = 413; at the first origin: `n_train_first_origin` = 354.\n- Data issues and handling are listed in `data_issues`: the README claims all change series are empty in 1992-01, but `ip_growth`, `oil_chg`, `permit_chg` are populated (used as given; only `ip_growth` 1992-01 enters, as a second lag); COVID-era extremes (ip_growth 2020-04 ≈ −14.1 %) are retained because the model is fully specified; data are current vintage.\n\n## Method\n- Naive: forecast of ip_growth(t+1) = ip_growth(t). Expanding mean: mean of ip_growth over 1992-04 … t.\n- Ridge, exactly as specified: training rows s ∈ [1992-03, t−1] with label ip_growth(s+1) and features (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s). Each origin recomputes the feature means and sample SDs (ddof = 1) and the target mean from its own training rows (`normalisation_window` = \"expanding\"); b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ); forecast = ȳ + z̃_t · b. No intercept penalty. Nothing random (`random_seed` = null).\n- Evaluation: RMSE and MAE over the 60 forecast errors; relative improvement 1 − RMSE_ridge / RMSE_baseline. As a supplementary step, a Diebold–Mariano test (squared loss, h = 1, Harvey–Leybourne–Newbold correction, t₅₉ reference) compares the ridge and each baseline.\n- Verification (`out/state/checks.md`): an independent re-implementation matches all forecasts to within 5e-11. Corrupting every value dated after 2023-06 leaves all forecasts for origins ≤ 2023-06 unchanged (no look-ahead). A clean re-run reproduces the outputs byte for byte.\n\n## Results\n| model | RMSE (% / month) | MAE (% / 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 (λ=50) | 0.5608 (`rmse_ridge`) | 0.4514 (`mae_ridge`) |\n\n- Compared with the naive forecast, ridge improves RMSE by 36.5 % (`rel_improvement_vs_naive` = 0.365) and MAE by 35.4 % (`rel_improvement_mae_vs_naive`).\n- Compared with the expanding mean, ridge is **worse**: RMSE is 3.1 % higher (`rel_improvement_vs_mean` = −0.031) and MAE is 5.2 % higher (`rel_improvement_mae_vs_mean` = −0.052).\n- Answer: the ridge model beats the naive forecast but not the expanding historical mean over 2021-09 … 2026-08. Of the three forecasts, the unconditional expanding mean has the lowest error.\n- At the last origin, oil_chg has by far the largest ridge coefficient (+0.34 per SD, `ridge_coef_last_origin`). This most likely reflects the joint COVID-2020 collapse/rebound in oil prices and IP inside the training window. It is descriptive only.\n\n## Uncertainty\n- Ridge vs naive: DM statistic −3.36, p = 0.0014 (`dm_stat_ridge_vs_naive`, `dm_pvalue_ridge_vs_naive`), so ridge's advantage over naive is statistically clear.\n- Ridge vs expanding mean: DM statistic 0.54, p = 0.59 (`dm_stat_ridge_vs_mean`, `dm_pvalue_ridge_vs_mean`). The two cannot be distinguished. The negative relative improvement is small and not statistically significant. The supported conclusion is \"no evidence that ridge beats the mean\", not \"the mean is significantly better\".\n- Only 60 evaluation points are available. The DM test assumes squared-loss differentials that are covariance-stationary and roughly serially uncorrelated (h = 1). No confidence intervals for RMSE ratios were computed.\n\n## Limitations and open issues\n- Current-vintage (revised) data and ignored publication lags: these are pseudo-out-of-sample results on revised series under a stylised timing assumption. They do not show real-time forecast accuracy (INDPRO, TCU and NEWORDER are revised substantially, and the IP release lags by about two weeks).\n- λ = 50 is fixed by the task and was not tuned. Results may differ for other penalties, but exploring them was out of scope.\n- The training windows include extreme COVID-era observations. These can dominate the ridge coefficients (e.g. on oil_chg). No robust treatment was applied, as required by the exact model specification.\n- The evaluation period 2021-10 … 2026-09 is a single, relatively calm post-COVID regime. Conclusions may not generalise to other periods.\n- There is a documentation discrepancy in `data/README.md` about empty 1992-01 cells (see `data_issues`). 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Deterministic (no randomness).\"\"\"\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAMBDA = 50.0\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nORIGINS = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nFEATURES = [\"ip_l0\", \"ip_l1\", \"ip_l2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\ndf = raw.sort_values(\"month\").set_index(\"month\")\nassert df.index.is_unique\nassert (df.index == pd.date_range(df.index[0], df.index[-1], freq=\"MS\")).all(), \"non-contiguous months\"\n\n# Feature vector z_s (all values dated <= s) and the h=1 target y_{s+1}\nfeat = pd.DataFrame(index=df.index)\nfeat[\"ip_l0\"] = df[\"ip_growth\"]\nfeat[\"ip_l1\"] = df[\"ip_growth\"].shift(1)\nfeat[\"ip_l2\"] = df[\"ip_growth\"].shift(2)\nfor c in [\"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]:\n feat[c] = df[c]\ntarget_next = df[\"ip_growth\"].shift(-1) # y_{s+1}, used only as a training label for s+1 <= t\n\n\ndef ridge_forecast(t):\n \"\"\"Forecast of ip_growth(t+1) using only data dated <= t.\"\"\"\n train_idx = feat.index[(feat.index >= TRAIN_START) & (feat.index + pd.DateOffset(months=1) <= t)]\n X = feat.loc[train_idx, FEATURES].to_numpy(float)\n y = target_next.loc[train_idx].to_numpy(float)\n # sanity: every label date is <= t\n assert (train_idx + pd.DateOffset(months=1)).max() <= t\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 + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (feat.loc[t, FEATURES].to_numpy(float) - mu) / sd\n return float(ybar + zt @ b), len(train_idx), b\n\n\nrows = []\nn_train = {}\ncoefs = {}\nfor t in ORIGINS:\n tgt = t + pd.DateOffset(months=1)\n actual = float(df.loc[tgt, \"ip_growth\"])\n naive = float(df.loc[t, \"ip_growth\"])\n mean_fc = float(df.loc[MEAN_START:t, \"ip_growth\"].mean())\n ridge_fc, ntr, b = ridge_forecast(t)\n n_train[t] = ntr\n coefs[t] = b\n for m, f in [(\"naive\", naive), (\"mean\", mean_fc), (\"ridge\", ridge_fc)]:\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\nerr = {m: (g[\"forecast\"] - g[\"actual\"]).to_numpy() for m, g in fc.groupby(\"model\", sort=False)}\nrmse = {m: float(np.sqrt(np.mean(e ** 2))) for m, e in err.items()}\nmae = {m: float(np.mean(np.abs(e))) for m, e in err.items()}\n\n\ndef dm_test(e_model, e_base):\n \"\"\"Diebold-Mariano (h=1, squared loss) with Harvey-Leybourne-Newbold small-sample correction.\n d = e_model^2 - e_base^2; negative mean favours the model.\"\"\"\n d = e_model ** 2 - e_base ** 2\n n = len(d)\n stat = d.mean() / np.sqrt(d.var(ddof=0) / n)\n stat *= np.sqrt((n + 1 - 2 + 0) / n) # HLN factor 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\n\ndm_naive = dm_test(err[\"ridge\"], err[\"naive\"])\ndm_mean = dm_test(err[\"ridge\"], err[\"mean\"])\n\nlast = ORIGINS[-1]\nn_analysed = int(n_train[last])\n\n# Exclusion ledger (rows of the input file -> training rows at the last origin)\nn_after_start = int((feat.index >= TRAIN_START).sum())\nexcl = pd.DataFrame([\n dict(step=1, rule=\"month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete)\",\n n_before=n_raw, n_after=n_after_start, n_removed=n_raw - n_after_start,\n rationale=\"Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\"),\n dict(step=2, rule=\"s + 1 > last origin 2026-08-01 (target not available at last origin)\",\n n_before=n_after_start, n_after=n_analysed, n_removed=n_after_start - n_analysed,\n rationale=\"Information rule: label y_{s+1} must be dated <= origin t\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n_raw - excl[\"n_removed\"].sum() == n_analysed\n\nresults = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": int(len(ORIGINS)),\n \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": last.strftime(\"%Y-%m-%d\"),\n \"lambda\": LAMBDA,\n \"normalisation_window\": \"expanding\",\n \"n_features\": len(FEATURES),\n \"rmse_naive\": rmse[\"naive\"], \"mae_naive\": mae[\"naive\"],\n \"rmse_mean\": rmse[\"mean\"], \"mae_mean\": mae[\"mean\"],\n \"rmse_ridge\": rmse[\"ridge\"], \"mae_ridge\": mae[\"ridge\"],\n \"rel_improvement_vs_naive\": 1 - rmse[\"ridge\"] / rmse[\"naive\"],\n \"rel_improvement_vs_mean\": 1 - rmse[\"ridge\"] / rmse[\"mean\"],\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n \"handling\": \"Not needed: training rows start at s=1992-03 as specified; no imputation.\"},\n {\"issue\": \"README states the 1992-01 value of every change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as the 2nd lag of the first training row); documentation discrepancy noted.\"},\n {\"issue\": \"Extreme COVID-era observations (ip_growth 2020-04 = -14.1%, 2020-06; oil_chg 2020-03..05) lie in every training window\",\n \"handling\": \"Retained: the model is fully specified by the task; no outlier treatment. Discussed as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data and ignored publication lags\",\n \"handling\": \"Results interpreted as pseudo-out-of-sample on revised data, not real-time accuracy.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n \"n_analysed\": n_analysed,\n \"random_seed\": None,\n # supplementary keys\n \"n_train_first_origin\": int(n_train[ORIGINS[0]]),\n \"n_train_last_origin\": n_analysed,\n \"first_target\": (ORIGINS[0] + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"last_target\": (last + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"rel_improvement_mae_vs_naive\": 1 - mae[\"ridge\"] / mae[\"naive\"],\n \"rel_improvement_mae_vs_mean\": 1 - mae[\"ridge\"] / mae[\"mean\"],\n \"dm_stat_ridge_vs_naive\": dm_naive[0], \"dm_pvalue_ridge_vs_naive\": dm_naive[1],\n \"dm_stat_ridge_vs_mean\": dm_mean[0], \"dm_pvalue_ridge_vs_mean\": dm_mean[1],\n \"ridge_coef_last_origin\": {k: float(v) for k, v in zip(FEATURES, coefs[last])},\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(results, f, indent=2)\nprint(json.dumps({k: results[k] for k in [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\", \"mae_naive\", \"mae_mean\",\n \"mae_ridge\", \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\",\n \"dm_pvalue_ridge_vs_naive\", \"dm_pvalue_ridge_vs_mean\", \"n_analysed\"]}, indent=1))\nstep,rule,n_before,n_after,n_removed,rationale\n1,month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete),417,415,2,Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\n2,s + 1 > last origin 2026-08-01 (target not available at last origin),415,413,2,Information rule: label y_{s+1} must be dated <= origin t\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\norigin_date,target_date,horizon,forecast,actual,model\n2021-09-01,2021-10-01,1,-1.1923630000,1.3345990000,naive\n2021-09-01,2021-10-01,1,0.1298718701,1.3345990000,mean\n2021-09-01,2021-10-01,1,0.1124703966,1.3345990000,ridge\n 181 out/forecasts.csv","is_error":false}]},"uuid":"e2571a1b-9e63-45ef-ad15-529096447fbd","timestamp":"2026-10-30T04:12:34.601Z","toolUseResult":{"stdout":"\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAMBDA = 50.0\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nORIGINS = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nFEATURES = [\"ip_l0\", \"ip_l1\", \"ip_l2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\ndf = raw.sort_values(\"month\").set_index(\"month\")\nassert df.index.is_unique\nassert (df.index == pd.date_range(df.index[0], df.index[-1], freq=\"MS\")).all(), \"non-contiguous months\"\n\n# Feature vector z_s (all values dated <= s) and the h=1 target y_{s+1}\nfeat = pd.DataFrame(index=df.index)\nfeat[\"ip_l0\"] = df[\"ip_growth\"]\nfeat[\"ip_l1\"] = df[\"ip_growth\"].shift(1)\nfeat[\"ip_l2\"] = df[\"ip_growth\"].shift(2)\nfor c in [\"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]:\n feat[c] = df[c]\ntarget_next = df[\"ip_growth\"].shift(-1) # y_{s+1}, used only as a training label for s+1 <= t\n\n\ndef ridge_forecast(t):\n \"\"\"Forecast of ip_growth(t+1) using only data dated <= t.\"\"\"\n train_idx = feat.index[(feat.index >= TRAIN_START) & (feat.index + pd.DateOffset(months=1) <= t)]\n X = feat.loc[train_idx, FEATURES].to_numpy(float)\n y = target_next.loc[train_idx].to_numpy(float)\n # sanity: every label date is <= t\n assert (train_idx + pd.DateOffset(months=1)).max() <= t\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 + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (feat.loc[t, FEATURES].to_numpy(float) - mu) / sd\n return float(ybar + zt @ b), len(train_idx), b\n\n\nrows = []\nn_train = {}\ncoefs = {}\nfor t in ORIGINS:\n tgt = t + pd.DateOffset(months=1)\n actual = float(df.loc[tgt, \"ip_growth\"])\n naive = float(df.loc[t, \"ip_growth\"])\n mean_fc = float(df.loc[MEAN_START:t, \"ip_growth\"].mean())\n ridge_fc, ntr, b = ridge_forecast(t)\n n_train[t] = ntr\n coefs[t] = b\n for m, f in [(\"naive\", naive), (\"mean\", mean_fc), (\"ridge\", ridge_fc)]:\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\nerr = {m: (g[\"forecast\"] - g[\"actual\"]).to_numpy() for m, g in fc.groupby(\"model\", sort=False)}\nrmse = {m: float(np.sqrt(np.mean(e ** 2))) for m, e in err.items()}\nmae = {m: float(np.mean(np.abs(e))) for m, e in err.items()}\n\n\ndef dm_test(e_model, e_base):\n \"\"\"Diebold-Mariano (h=1, squared loss) with Harvey-Leybourne-Newbold small-sample correction.\n d = e_model^2 - e_base^2; negative mean favours the model.\"\"\"\n d = e_model ** 2 - e_base ** 2\n n = len(d)\n stat = d.mean() / np.sqrt(d.var(ddof=0) / n)\n stat *= np.sqrt((n + 1 - 2 + 0) / n) # HLN factor 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\n\ndm_naive = dm_test(err[\"ridge\"], err[\"naive\"])\ndm_mean = dm_test(err[\"ridge\"], err[\"mean\"])\n\nlast = ORIGINS[-1]\nn_analysed = int(n_train[last])\n\n# Exclusion ledger (rows of the input file -> training rows at the last origin)\nn_after_start = int((feat.index >= TRAIN_START).sum())\nexcl = pd.DataFrame([\n dict(step=1, rule=\"month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete)\",\n n_before=n_raw, n_after=n_after_start, n_removed=n_raw - n_after_start,\n rationale=\"Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\"),\n dict(step=2, rule=\"s + 1 > last origin 2026-08-01 (target not available at last origin)\",\n n_before=n_after_start, n_after=n_analysed, n_removed=n_after_start - n_analysed,\n rationale=\"Information rule: label y_{s+1} must be dated <= origin t\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n_raw - excl[\"n_removed\"].sum() == n_analysed\n\nresults = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": int(len(ORIGINS)),\n \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": last.strftime(\"%Y-%m-%d\"),\n \"lambda\": LAMBDA,\n \"normalisation_window\": \"expanding\",\n \"n_features\": len(FEATURES),\n \"rmse_naive\": rmse[\"naive\"], \"mae_naive\": mae[\"naive\"],\n \"rmse_mean\": rmse[\"mean\"], \"mae_mean\": mae[\"mean\"],\n \"rmse_ridge\": rmse[\"ridge\"], \"mae_ridge\": mae[\"ridge\"],\n \"rel_improvement_vs_naive\": 1 - rmse[\"ridge\"] / rmse[\"naive\"],\n \"rel_improvement_vs_mean\": 1 - rmse[\"ridge\"] / rmse[\"mean\"],\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n \"handling\": \"Not needed: training rows start at s=1992-03 as specified; no imputation.\"},\n {\"issue\": \"README states the 1992-01 value of every change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as the 2nd lag of the first training row); documentation discrepancy noted.\"},\n {\"issue\": \"Extreme COVID-era observations (ip_growth 2020-04 = -14.1%, 2020-06; oil_chg 2020-03..05) lie in every training window\",\n \"handling\": \"Retained: the model is fully specified by the task; no outlier treatment. Discussed as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data and ignored publication lags\",\n \"handling\": \"Results interpreted as pseudo-out-of-sample on revised data, not real-time accuracy.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n \"n_analysed\": n_analysed,\n \"random_seed\": None,\n # supplementary keys\n \"n_train_first_origin\": int(n_train[ORIGINS[0]]),\n \"n_train_last_origin\": n_analysed,\n \"first_target\": (ORIGINS[0] + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"last_target\": (last + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"rel_improvement_mae_vs_naive\": 1 - mae[\"ridge\"] / mae[\"naive\"],\n \"rel_improvement_mae_vs_mean\": 1 - mae[\"ridge\"] / mae[\"mean\"],\n \"dm_stat_ridge_vs_naive\": dm_naive[0], \"dm_pvalue_ridge_vs_naive\": dm_naive[1],\n \"dm_stat_ridge_vs_mean\": dm_mean[0], \"dm_pvalue_ridge_vs_mean\": dm_mean[1],\n \"ridge_coef_last_origin\": {k: float(v) for k, v in zip(FEATURES, coefs[last])},\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(results, f, indent=2)\nprint(json.dumps({k: results[k] for k in [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\", \"mae_naive\", \"mae_mean\",\n \"mae_ridge\", \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\",\n \"dm_pvalue_ridge_vs_naive\", \"dm_pvalue_ridge_vs_mean\", \"n_analysed\"]}, indent=1))\nstep,rule,n_before,n_after,n_removed,rationale\n1,month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete),417,415,2,Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\n2,s + 1 > last origin 2026-08-01 (target not available at last origin),415,413,2,Information rule: label y_{s+1} must be dated <= origin t\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\norigin_date,target_date,horizon,forecast,actual,model\n2021-09-01,2021-10-01,1,-1.1923630000,1.3345990000,naive\n2021-09-01,2021-10-01,1,0.1298718701,1.3345990000,mean\n2021-09-01,2021-10-01,1,0.1124703966,1.3345990000,ridge\n 181 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1a625141-0cdf-4305-b0ea-6d574f4d15e1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0030","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"e2571a1b-9e63-45ef-ad15-529096447fbd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmbeDeWnnPrg1kEgPvY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XZ39cUXmLrmJqc8txfLgrC","name":"Bash","input":{"command":"rm -rf /tmp/rc30 && mkdir /tmp/rc30 && cp -r data out /tmp/rc30/ && cd /tmp/rc30 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json ~/wp29/runs/RC0030/out/results.json && cmp out/forecasts.csv ~/wp29/runs/RC0030/out/forecasts.csv && echo SAME\ncd /tmp/rc30 && ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month']).set_index('month')\nip=d.ip_growth\nF=pd.DataFrame({'a':ip,'b':ip.shift(1),'c':ip.shift(2),'o':d.oil_chg,'f':d.fedfunds,'p':d.permit_chg,'n':d.neworder_chg,'t':d.tcu})\ny=ip.shift(-1)\ne={'n':[],'m':[],'r':[]}\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n tr=F[(F.index>='1992-03-01')&(F.index<t)]; yt=y[tr.index]\n mu=tr.mean();sd=tr.std(ddof=1);Z=((tr-mu)/sd).values;yb=yt.mean()\n b=np.linalg.solve(Z.T@Z+50*np.eye(8),Z.T@(yt.values-yb))\n fr=yb+((F.loc[t]-mu)/sd).values@b\n a=ip[t+pd.DateOffset(months=1)]\n e['r'].append(a-fr);e['n'].append(a-ip[t]);e['m'].append(a-ip['1992-04-01':t].mean())\nfor k,v in e.items(): v=np.array(v);print(k,np.sqrt((v**2).mean()),np.abs(v).mean(),len(tr))\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6818,"cache_read_input_tokens":2220,"output_tokens":855,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":6818},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmbe2zGyC7MyU7MAUbD","requestRef":"5379b5ab-5024-4b58-a7f8-b2dfa9b8dc99","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ccb55f73-ffaa-44ff-9b05-b07fde6dcdd5","timestamp":"2026-10-30T04:12:39.849Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0030","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"ccb55f73-ffaa-44ff-9b05-b07fde6dcdd5","isSidechain":false,"promptId":"4b4d4995-156d-4d30-96da-4b2a113b431a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XZ39cUXmLrmJqc8txfLgrC","type":"tool_result","content":"SAME\nn 0.8830699166753823 0.6989594000000001 413\nm 0.5439355781141625 0.42929777256194207 413\nr 0.5607989303521244 0.45141289074453916 413\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0030","is_error":false}]},"uuid":"0bf9da32-7f97-482e-963b-47982c782339","timestamp":"2026-10-30T04:12:40.874Z","toolUseResult":{"stdout":"SAME\nn 0.8830699166753823 0.6989594000000001 413\nm 0.5439355781141625 0.42929777256194207 413\nr 0.5607989303521244 0.45141289074453916 413","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0030","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ccb55f73-ffaa-44ff-9b05-b07fde6dcdd5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0030","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","version":"2.1.314","gitBranch":"HEAD"}22{"type":"api-request-blob","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","hash":"2a8ad0340cea5d73c2e164947f788e30bd0ddaf29fb61f7a3c71a898a0aac12b","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01SYRVvdNvj1Pf8D86xUte1n","name":"Bash","input":{"command":"cat out/analysis.py; cat out/exclusions.csv; head -3 data/ip_predictors.csv; head -4 out/forecasts.csv; wc -l out/forecasts.csv"}},{"type":"tool_use","id":"toolu_01XZ39cUXmLrmJqc8txfLgrC","name":"Bash","input":{"command":"rm -rf /tmp/rc30 && mkdir /tmp/rc30 && cp -r data out /tmp/rc30/ && cd /tmp/rc30 && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json ~/wp29/runs/RC0030/out/results.json && cmp out/forecasts.csv ~/wp29/runs/RC0030/out/forecasts.csv && echo SAME\ncd /tmp/rc30 && ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/ip_predictors.csv',parse_dates=['month']).set_index('month')\nip=d.ip_growth\nF=pd.DataFrame({'a':ip,'b':ip.shift(1),'c':ip.shift(2),'o':d.oil_chg,'f':d.fedfunds,'p':d.permit_chg,'n':d.neworder_chg,'t':d.tcu})\ny=ip.shift(-1)\ne={'n':[],'m':[],'r':[]}\nfor t in pd.date_range('2021-09-01','2026-08-01',freq='MS'):\n tr=F[(F.index>='1992-03-01')&(F.index<t)]; yt=y[tr.index]\n mu=tr.mean();sd=tr.std(ddof=1);Z=((tr-mu)/sd).values;yb=yt.mean()\n b=np.linalg.solve(Z.T@Z+50*np.eye(8),Z.T@(yt.values-yb))\n fr=yb+((F.loc[t]-mu)/sd).values@b\n a=ip[t+pd.DateOffset(months=1)]\n e['r'].append(a-fr);e['n'].append(a-ip[t]);e['m'].append(a-ip['1992-04-01':t].mean())\nfor k,v in e.items(): v=np.array(v);print(k,np.sqrt((v**2).mean()),np.abs(v).mean(),len(tr))\n\""}}]}}23{"type":"api-request-blob","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","hash":"f06fbdae1d491c41430ef3db5d22d905f248baa3b00b64a0c52bfbc613a948c9","message":{"role":"user","content":[{"tool_use_id":"toolu_01SYRVvdNvj1Pf8D86xUte1n","type":"tool_result","content":"\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAMBDA = 50.0\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nORIGINS = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nFEATURES = [\"ip_l0\", \"ip_l1\", \"ip_l2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\ndf = raw.sort_values(\"month\").set_index(\"month\")\nassert df.index.is_unique\nassert (df.index == pd.date_range(df.index[0], df.index[-1], freq=\"MS\")).all(), \"non-contiguous months\"\n\n# Feature vector z_s (all values dated <= s) and the h=1 target y_{s+1}\nfeat = pd.DataFrame(index=df.index)\nfeat[\"ip_l0\"] = df[\"ip_growth\"]\nfeat[\"ip_l1\"] = df[\"ip_growth\"].shift(1)\nfeat[\"ip_l2\"] = df[\"ip_growth\"].shift(2)\nfor c in [\"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]:\n feat[c] = df[c]\ntarget_next = df[\"ip_growth\"].shift(-1) # y_{s+1}, used only as a training label for s+1 <= t\n\n\ndef ridge_forecast(t):\n \"\"\"Forecast of ip_growth(t+1) using only data dated <= t.\"\"\"\n train_idx = feat.index[(feat.index >= TRAIN_START) & (feat.index + pd.DateOffset(months=1) <= t)]\n X = feat.loc[train_idx, FEATURES].to_numpy(float)\n y = target_next.loc[train_idx].to_numpy(float)\n # sanity: every label date is <= t\n assert (train_idx + pd.DateOffset(months=1)).max() <= t\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 + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (feat.loc[t, FEATURES].to_numpy(float) - mu) / sd\n return float(ybar + zt @ b), len(train_idx), b\n\n\nrows = []\nn_train = {}\ncoefs = {}\nfor t in ORIGINS:\n tgt = t + pd.DateOffset(months=1)\n actual = float(df.loc[tgt, \"ip_growth\"])\n naive = float(df.loc[t, \"ip_growth\"])\n mean_fc = float(df.loc[MEAN_START:t, \"ip_growth\"].mean())\n ridge_fc, ntr, b = ridge_forecast(t)\n n_train[t] = ntr\n coefs[t] = b\n for m, f in [(\"naive\", naive), (\"mean\", mean_fc), (\"ridge\", ridge_fc)]:\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\nerr = {m: (g[\"forecast\"] - g[\"actual\"]).to_numpy() for m, g in fc.groupby(\"model\", sort=False)}\nrmse = {m: float(np.sqrt(np.mean(e ** 2))) for m, e in err.items()}\nmae = {m: float(np.mean(np.abs(e))) for m, e in err.items()}\n\n\ndef dm_test(e_model, e_base):\n \"\"\"Diebold-Mariano (h=1, squared loss) with Harvey-Leybourne-Newbold small-sample correction.\n d = e_model^2 - e_base^2; negative mean favours the model.\"\"\"\n d = e_model ** 2 - e_base ** 2\n n = len(d)\n stat = d.mean() / np.sqrt(d.var(ddof=0) / n)\n stat *= np.sqrt((n + 1 - 2 + 0) / n) # HLN factor 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\n\ndm_naive = dm_test(err[\"ridge\"], err[\"naive\"])\ndm_mean = dm_test(err[\"ridge\"], err[\"mean\"])\n\nlast = ORIGINS[-1]\nn_analysed = int(n_train[last])\n\n# Exclusion ledger (rows of the input file -> training rows at the last origin)\nn_after_start = int((feat.index >= TRAIN_START).sum())\nexcl = pd.DataFrame([\n dict(step=1, rule=\"month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete)\",\n n_before=n_raw, n_after=n_after_start, n_removed=n_raw - n_after_start,\n rationale=\"Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\"),\n dict(step=2, rule=\"s + 1 > last origin 2026-08-01 (target not available at last origin)\",\n n_before=n_after_start, n_after=n_analysed, n_removed=n_after_start - n_analysed,\n rationale=\"Information rule: label y_{s+1} must be dated <= origin t\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n_raw - excl[\"n_removed\"].sum() == n_analysed\n\nresults = {\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": int(len(ORIGINS)),\n \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": last.strftime(\"%Y-%m-%d\"),\n \"lambda\": LAMBDA,\n \"normalisation_window\": \"expanding\",\n \"n_features\": len(FEATURES),\n \"rmse_naive\": rmse[\"naive\"], \"mae_naive\": mae[\"naive\"],\n \"rmse_mean\": rmse[\"mean\"], \"mae_mean\": mae[\"mean\"],\n \"rmse_ridge\": rmse[\"ridge\"], \"mae_ridge\": mae[\"ridge\"],\n \"rel_improvement_vs_naive\": 1 - rmse[\"ridge\"] / rmse[\"naive\"],\n \"rel_improvement_vs_mean\": 1 - rmse[\"ridge\"] / rmse[\"mean\"],\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n \"handling\": \"Not needed: training rows start at s=1992-03 as specified; no imputation.\"},\n {\"issue\": \"README states the 1992-01 value of every change series is empty, but ip_growth, oil_chg and permit_chg are populated for 1992-01\",\n \"handling\": \"Values used as given (ip_growth 1992-01 enters only as the 2nd lag of the first training row); documentation discrepancy noted.\"},\n {\"issue\": \"Extreme COVID-era observations (ip_growth 2020-04 = -14.1%, 2020-06; oil_chg 2020-03..05) lie in every training window\",\n \"handling\": \"Retained: the model is fully specified by the task; no outlier treatment. Discussed as a limitation.\"},\n {\"issue\": \"Current-vintage (revised) data and ignored publication lags\",\n \"handling\": \"Results interpreted as pseudo-out-of-sample on revised data, not real-time accuracy.\"},\n ],\n \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n \"n_analysed\": n_analysed,\n \"random_seed\": None,\n # supplementary keys\n \"n_train_first_origin\": int(n_train[ORIGINS[0]]),\n \"n_train_last_origin\": n_analysed,\n \"first_target\": (ORIGINS[0] + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"last_target\": (last + pd.DateOffset(months=1)).strftime(\"%Y-%m-%d\"),\n \"rel_improvement_mae_vs_naive\": 1 - mae[\"ridge\"] / mae[\"naive\"],\n \"rel_improvement_mae_vs_mean\": 1 - mae[\"ridge\"] / mae[\"mean\"],\n \"dm_stat_ridge_vs_naive\": dm_naive[0], \"dm_pvalue_ridge_vs_naive\": dm_naive[1],\n \"dm_stat_ridge_vs_mean\": dm_mean[0], \"dm_pvalue_ridge_vs_mean\": dm_mean[1],\n \"ridge_coef_last_origin\": {k: float(v) for k, v in zip(FEATURES, coefs[last])},\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(results, f, indent=2)\nprint(json.dumps({k: results[k] for k in [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\", \"mae_naive\", \"mae_mean\",\n \"mae_ridge\", \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\",\n \"dm_pvalue_ridge_vs_naive\", \"dm_pvalue_ridge_vs_mean\", \"n_analysed\"]}, indent=1))\nstep,rule,n_before,n_after,n_removed,rationale\n1,month < 1992-03-01 (before training start; ip lags / neworder_chg incomplete),417,415,2,Task specifies training rows start at s=1992-03-01; neworder_chg missing for 1992-01/02\n2,s + 1 > last origin 2026-08-01 (target not available at last origin),415,413,2,Information rule: label y_{s+1} must be dated <= origin t\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\norigin_date,target_date,horizon,forecast,actual,model\n2021-09-01,2021-10-01,1,-1.1923630000,1.3345990000,naive\n2021-09-01,2021-10-01,1,0.1298718701,1.3345990000,mean\n2021-09-01,2021-10-01,1,0.1124703966,1.3345990000,ridge\n 181 out/forecasts.csv","is_error":false},{"tool_use_id":"toolu_01XZ39cUXmLrmJqc8txfLgrC","type":"tool_result","content":"SAME\nn 0.8830699166753823 0.6989594000000001 413\nm 0.5439355781141625 0.42929777256194207 413\nr 0.5607989303521244 0.45141289074453916 413\nShell cwd was reset to 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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.\n7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b data/ip_predictors.csv","is_error":false}]},"uuid":"be25949a-f712-40fb-90a9-aeaa9db5d2b9","timestamp":"2026-10-30T04:12:45.473Z","toolUseResult":{"stdout":"checks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\nverify.py\n# 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). 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Reading the code does confirm the claim: training rows satisfy s+1<=t, and mu, sd and ybar are recomputed from those rows at every origin. The cited evidence, though, sits outside the reproducible pipeline.\", \"suggested_fix\": \"Cite the code-level guarantee (train_idx filter and per-origin statistics), or move the leakage test into a reproducible script.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"out/report.md Limitations\", \"issue\": \"The report calls 2021-10…2026-09 a 'relatively calm post-COVID regime' without giving any statistic to support it.\", \"suggested_fix\": \"Report the evaluation-period SD of ip_growth, or soften the wording.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"out/report.md Uncertainty\", \"issue\": \"The DM tests use a plain variance with no HAC term, which is acceptable for h=1. However, no interval is given for the relative improvements, which are the task's main estimands. This omission is acknowledged.\", \"suggested_fix\": \"Optionally add a block-bootstrap CI for 1 - RMSE_ridge/RMSE_baseline.\"}\n ],\n \"summary\": \"All required keys are present. A scratch re-run of analysis.py reproduces results.json and forecasts.csv byte for byte, and an independent re-implementation of all three forecasts gives identical RMSE/MAE (naive 0.883/0.699, mean 0.544/0.429, ridge 0.561/0.451) with 413 training rows at the last origin. The code follows the information rules: per-origin expanding standardisation with ddof=1, an unpenalised intercept ybar, lambda=50, and labels dated <=t. Counts (417 rows, 180 forecast rows, 354/413 training rows), the exclusion ledger, the SHA-256 and the README discrepancy all check out. Claims are appropriately hedged: ridge beats naive, and ridge is not significantly different from the expanding mean (it is nominally worse). Only minor presentation issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok"}}]}}36{"type":"api-request","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","id":"96a61948-00b2-411a-a52d-a8bcfcac332b","timestamp":"2026-10-30T04:12:55.136Z","version":"2.1.314","querySource":"sdk","shapeHash":"ea2dbce45bb05c779431d09fded7291805216c1c318aab140e69e8b51c6c5fcc","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":"ca0bbbe2-2fbc-4592-b541-b511116f10a3","keep":8,"tail":["778c25eef194dffae17db30575f9a77684c4bc2c10a1bfe6a0bbab403f44da35","197d418ba7d1d0e5b51cc33aeed64e1aea84197dcec26985640a903ad2148390"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7af14fc6e2a9eda5382d1d25765e7a1aedbbdf58b9258f660ade577d51a85fda"}37{"parentUuid":"6a57535b-b2a7-45a6-a76e-2253df1b9332","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmdKGguoLHTkcLZVnjT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":992,"cache_read_input_tokens":15470,"output_tokens":764,"output_tokens_details":{"thinking_tokens":5},"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":992},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6244,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmdJzonWXktC7tgcgu7","requestRef":"96a61948-00b2-411a-a52d-a8bcfcac332b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"61a9514b-c40c-4030-a09d-20df2fb374b4","timestamp":"2026-10-30T04:13:02.698Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0030","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"61a9514b-c40c-4030-a09d-20df2fb374b4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmdKGguoLHTkcLZVnjT","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**, with three minor issues. The review is saved to `out/review/review.json`.\n\nI re-ran `out/analysis.py` on a scratch copy and its output matched `results.json` and `forecasts.csv` byte for byte. I also wrote my own version of all three forecasts, and it gives the same errors:\n\n| Forecast | RMSE | MAE |\n|---|---|---|\n| Naive | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge | 0.561 | 0.451 |\n\nAll values are percent per month. My version also uses 413 training rows at the last origin, the same as `n_analysed`.\n\n- **Information rules:** I read the code and it follows them. Training rows only include months whose target is dated on or before the origin. The feature means, standard deviations and target mean are recomputed from each origin's training window, using the n − 1 denominator. The intercept is not penalised, λ = 50, and nothing dated after the origin is used.\n- **Counts and data:** 417 input rows, 180 forecast rows, and 354 / 413 training rows at the first / last origin are all consistent. The exclusion table adds up, the data file's SHA-256 matches the README, and the README's wrong statement about empty 1992-01 cells is real.\n- **Claims:** they match the numbers and are suitably cautious. Ridge beats the naive forecast (36.5% lower RMSE, Diebold–Mariano p = 0.0014). Ridge is slightly worse than the expanding mean (−3.1% relative improvement), but the difference is not significant (p = 0.59). The report correctly says this means \"no evidence ridge beats the mean\", not \"the mean is significantly better\".\n\nThe three minor issues:\n1. **No-look-ahead claim (C5):** it cites a test recorded in `out/state/checks.md`, which `analysis.py` doesn't regenerate. My reading of the code supports the claim, but the cited evidence can't be reproduced from the deliverables.\n2. **\"Calm regime\":** the report calls the 2021–2026 evaluation period a \"relatively calm\" regime without giving any figure to back it up.\n3. **No intervals on the main estimands:** the report gives no confidence interval for the relative improvements. The report admits this."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":992,"cache_read_input_tokens":15470,"output_tokens":764,"output_tokens_details":{"thinking_tokens":5},"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":992},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmdJzonWXktC7tgcgu7","requestRef":"96a61948-00b2-411a-a52d-a8bcfcac332b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b97b839d-dff2-4a3c-8742-b4c0a018004f","timestamp":"2026-10-30T04:13:02.923Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0030","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","version":"2.1.314","gitBranch":"HEAD"}39{"type":"last-prompt","lastPrompt":"Review","leafUuid":"b97b839d-dff2-4a3c-8742-b4c0a018004f","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d"}40{"type":"cost-state","sessionId":"5bd619f5-b1db-461c-a3b2-4af0cf77760d","totalCostUSD":0.1439696,"totalAPIDuration":31316,"totalAPIDurationWithoutRetries":31312,"totalToolDuration":1213,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":32708,"startTime":1793333550222,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":3034,"thinkingTokens":354,"cacheReadInputTokens":42258,"cacheCreationInputTokens":14958,"cacheCreationEphemeral5mInputTokens":14958,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1439696}},"hasUnknownModelCost":false}41