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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.\nip_predictors.csv\nREADME.md","is_error":false}]},"uuid":"f0f20a8b-cc70-42a1-b51e-94265240e533","timestamp":"2026-10-30T04:09:13.546Z","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.\nip_predictors.csv\nREADME.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e22627a1-9062-40ed-aab1-e29ab792faac","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0028","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","hash":"7a5a11f2f9dd82143145a4ad8fdf2ae8a768f9968719939be1fa9ae24766f9f4","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01B1Ghy86QYwuPmcs2co6xXw","name":"Bash","input":{"command":"cat TASK.md; ls -R data | head -50","description":"Read task and list data"}}]}}15{"type":"api-request","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","id":"1e954ec8-f863-4b89-bce5-4dae861b5e1a","timestamp":"2026-10-30T04:09:13.550Z","version":"2.1.314","querySource":"sdk","shapeHash":"538eb64e9054ea0ce8e23ce5881faa083ee1c44ea261ea19e2e11dcd30cb99a8","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":"8e876e58-b800-465e-a248-27836b0fbcbe","keep":2,"tail":["7a5a11f2f9dd82143145a4ad8fdf2ae8a768f9968719939be1fa9ae24766f9f4","bd62f78ed7d2d3f93c966befb1983f75e5ba549b241a443614c309d6b03e8152"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"54abbd806e1cc51c2f3c58fa3e3c167ceff045decc6fef1bfeea4ce50b790d50"}16{"parentUuid":"f0f20a8b-cc70-42a1-b51e-94265240e533","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmLyqSW984x3voBnVX1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015mZPczWjAADdrzSggZHLYs","name":"Bash","input":{"command":"cat data/README.md; head -5 data/ip_predictors.csv; tail -3 data/ip_predictors.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;d=pd.read_csv('data/ip_predictors.csv');print(d.isna().sum());print(d[d.isna().any(axis=1)]);print(d.describe().T);m=pd.to_datetime(d.month);print(m.diff().dt.days.value_counts())\"","description":"Inspect data and README"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2317,"cache_read_input_tokens":2234,"output_tokens":232,"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":2317},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmLyfHJZ4voKKbh9SHp","requestRef":"1e954ec8-f863-4b89-bce5-4dae861b5e1a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"38da0500-8ed0-4978-aa31-36be5239cc63","timestamp":"2026-10-30T04:09:15.577Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0028","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"38da0500-8ed0-4978-aa31-36be5239cc63","isSidechain":false,"promptId":"1fb83f78-cd39-42e0-b877-29b0e8b7c0f6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015mZPczWjAADdrzSggZHLYs","type":"tool_result","content":"# 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.\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\nmonth 0\nip_growth 0\noil_chg 0\nfedfunds 0\npermit_chg 0\nneworder_chg 2\ntcu 0\ndtype: int64\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 count mean std ... 50% 75% max\nip_growth 417.0 0.124761 1.039866 ... 0.177862 0.532071 6.347249\noil_chg 417.0 0.385489 9.511083 ... 1.292382 5.653178 54.562104\nfedfunds 417.0 2.664820 2.151342 ... 2.400000 4.830000 6.540000\npermit_chg 417.0 0.066146 4.943466 ... 0.071454 3.083222 17.052698\nneworder_chg 415.0 0.230273 2.955474 ... 0.370381 2.054636 9.096596\ntcu 417.0 78.266985 3.618664 ... 77.803600 80.833400 84.989200\n\n[6 rows x 8 columns]\nmonth\n31.0 243\n30.0 138\n28.0 26\n29.0 9\nName: count, dtype: int64","is_error":false}]},"uuid":"887b944e-46e1-43b6-935b-4e839d6578bb","timestamp":"2026-10-30T04:09:15.816Z","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.\nmonth,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\nmonth 0\nip_growth 0\noil_chg 0\nfedfunds 0\npermit_chg 0\nneworder_chg 2\ntcu 0\ndtype: int64\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 count mean std ... 50% 75% max\nip_growth 417.0 0.124761 1.039866 ... 0.177862 0.532071 6.347249\noil_chg 417.0 0.385489 9.511083 ... 1.292382 5.653178 54.562104\nfedfunds 417.0 2.664820 2.151342 ... 2.400000 4.830000 6.540000\npermit_chg 417.0 0.066146 4.943466 ... 0.071454 3.083222 17.052698\nneworder_chg 415.0 0.230273 2.955474 ... 0.370381 2.054636 9.096596\ntcu 417.0 78.266985 3.618664 ... 77.803600 80.833400 84.989200\n\n[6 rows x 8 columns]\nmonth\n31.0 243\n30.0 138\n28.0 26\n29.0 9\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"38da0500-8ed0-4978-aa31-36be5239cc63","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0028","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","hash":"7ab43ed1a5caece8d49f15df81024ff7bfe1be6515a70ed68e34dc68ebdcfa80","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015mZPczWjAADdrzSggZHLYs","name":"Bash","input":{"command":"cat data/README.md; 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cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding mean, h=1. Run from workspace root.\"\"\"\nimport json, numpy as np, pandas as pd\nDATA = \"data/ip_predictors.csv\"\nLAM = 50.0\nd = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_rows = len(d)\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.to_series().diff().dropna().dt.days.between(28, 31).all() # contiguous monthly\nissues = []\nna = d.isna().sum()\nissues.append({\"issue\": f\"Missing values: {dict(na[na>0].astype(int))} (neworder_chg empty for 1992-01/02). \"\n \"README says the first month of each change series is empty, but ip_growth/oil_chg/permit_chg \"\n \"for 1992-01 are populated in the file.\",\n \"handling\": \"No imputation needed: ridge training starts at s=1992-03 (lags reach 1992-01 for ip_growth only), \"\n \"expanding mean starts 1992-04 as specified; populated 1992-01 values used as given.\"})\nissues.append({\"issue\": \"Large COVID-19 outliers in 2020 (ip_growth min %.2f, max %.2f).\" % (d.ip_growth.min(), d.ip_growth.max()),\n \"handling\": \"Kept as given; no winsorisation (not in specification).\"})\nip = d[\"ip_growth\"]\nF = pd.DataFrame({\"ip0\": ip, \"ip1\": ip.shift(1), \"ip2\": ip.shift(2), \"oil\": d.oil_chg, \"ff\": d.fedfunds,\n \"perm\": d.permit_chg, \"no\": d.neworder_chg, \"tcu\": d.tcu})\nY = ip.shift(-1) # y_{s+1} aligned to s\norigins = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nrows = []; n_train_last = None\nfor t in origins:\n tgt = t + pd.DateOffset(months=1)\n actual = float(ip.loc[tgt])\n rows.append((t, tgt, float(ip.loc[t]), actual, \"naive\"))\n rows.append((t, tgt, float(ip.loc[\"1992-04-01\":t].mean()), actual, \"mean\"))\n idx = F.index[(F.index >= \"1992-03-01\") & (F.index + pd.DateOffset(months=1) <= t)]\n X = F.loc[idx].to_numpy(float); y = Y.loc[idx].to_numpy(float)\n assert not np.isnan(X).any() and not np.isnan(y).any()\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (F.loc[t].to_numpy(float) - mu) / sd\n rows.append((t, tgt, float(ybar + zt @ b), actual, \"ridge\"))\n n_train_last = len(idx)\nfc = pd.DataFrame(rows, columns=[\"origin_date\", \"target_date\", \"forecast\", \"actual\", \"model\"])\nfc.insert(2, \"horizon\", 1)\nfc[\"origin_date\"] = fc.origin_date.dt.strftime(\"%Y-%m-%d\"); fc[\"target_date\"] = fc.target_date.dt.strftime(\"%Y-%m-%d\")\nfc.to_csv(\"out/forecasts.csv\", index=False)\nerr = {}\nfor m in [\"naive\", \"mean\", \"ridge\"]:\n e = fc[fc.model == m].eval(\"forecast-actual\").to_numpy()\n err[m] = e\nres = {\"task_id\": \"F3-conf-03\", \"n_origins\": len(origins),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n \"lambda\": LAM, \"normalisation_window\": \"expanding\", \"n_features\": F.shape[1]}\nfor m, e in err.items():\n res[f\"rmse_{m}\"] = float(np.sqrt(np.mean(e**2))); res[f\"mae_{m}\"] = float(np.mean(np.abs(e)))\nres[\"rel_improvement_vs_naive\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_naive\"]\nres[\"rel_improvement_vs_mean\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_mean\"]\n# Supplementary: Diebold-Mariano (squared loss, h=1, HLN small-sample correction)\nfrom scipy import stats\ndef dm(e1, e2):\n dd = e1**2 - e2**2; n = len(dd); s = dd.mean() / np.sqrt(dd.var(ddof=0) / n)\n s *= np.sqrt((n - 1) / n); return float(s), float(2 * stats.t.sf(abs(s), n - 1))\nsupp = {\"dm_ridge_vs_naive\": dm(err[\"ridge\"], err[\"naive\"]), \"dm_ridge_vs_mean\": dm(err[\"ridge\"], err[\"mean\"])}\n# bootstrap CI (iid over origins) for relative improvements\nrng = np.random.default_rng(20261030); B = 5000; n = len(origins)\nbs = {\"naive\": [], \"mean\": []}\nfor _ in range(B):\n i = rng.integers(0, n, n); r = np.sqrt(np.mean(err[\"ridge\"][i]**2))\n for m in bs: bs[m].append(1 - r / np.sqrt(np.mean(err[m][i]**2)))\nsupp[\"boot95_rel_impr_vs_naive\"] = [float(np.percentile(bs[\"naive\"], q)) for q in (2.5, 97.5)]\nsupp[\"boot95_rel_impr_vs_mean\"] = [float(np.percentile(bs[\"mean\"], q)) for q in (2.5, 97.5)]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n \"n_input_rows\": {\"ip_predictors.csv\": n_rows}, \"n_analysed\": n_train_last, \"random_seed\": 20261030,\n \"supplementary\": supp})\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; 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README says the first month of each change series is empty, but ip_growth/oil_chg/permit_chg for 1992-01 are populated in the file.\",\n \"handling\": \"No imputation needed: ridge training starts at s=1992-03 (lags reach 1992-01 for ip_growth only), expanding mean starts 1992-04 as specified; populated 1992-01 values used as given.\"\n },\n {\n \"issue\": \"Large COVID-19 outliers in 2020 (ip_growth min -14.14, max 6.35).\",\n \"handling\": \"Kept as given; no winsorisation (not in specification).\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": 20261030,\n \"supplementary\": {\n \"dm_ridge_vs_naive\": [\n -3.3649594921847754,\n 0.0013506687241571726\n ],\n \"dm_ridge_vs_mean\": [\n 0.5392198591267081,\n 0.591764258342193\n ],\n \"boot95_rel_impr_vs_naive\": [\n 0.23500539342572274,\n 0.46299549778258603\n ],\n \"boot95_rel_impr_vs_mean\": [\n -0.16820638961201373,\n 0.07227845785548916\n ]\n }\n}\n 181 out/forecasts.csv\norigin_date,target_date,horizon,forecast,actual,model\n2021-09-01,2021-10-01,1,-1.192363,1.334599,naive\n2021-09-01,2021-10-01,1,0.12987187005649717,1.334599,mean\n2021-09-01,2021-10-01,1,0.11247039663896774,1.334599,ridge","is_error":false}]},"uuid":"c16c3ac9-d58d-47db-b3cb-7cda17ab82a1","timestamp":"2026-10-30T04:09:36.522Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-conf-03\",\n \"n_origins\": 60,\n \"first_origin\": \"2021-09-01\",\n \"last_origin\": \"2026-08-01\",\n \"lambda\": 50.0,\n \"normalisation_window\": \"expanding\",\n \"n_features\": 8,\n \"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\": \"Missing values: {'neworder_chg': np.int64(2)} (neworder_chg empty for 1992-01/02). README says the first month of each change series is empty, but ip_growth/oil_chg/permit_chg for 1992-01 are populated in the file.\",\n \"handling\": \"No imputation needed: ridge training starts at s=1992-03 (lags reach 1992-01 for ip_growth only), expanding mean starts 1992-04 as specified; populated 1992-01 values used as given.\"\n },\n {\n \"issue\": \"Large COVID-19 outliers in 2020 (ip_growth min -14.14, max 6.35).\",\n \"handling\": \"Kept as given; no winsorisation (not in specification).\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": 20261030,\n \"supplementary\": {\n \"dm_ridge_vs_naive\": [\n -3.3649594921847754,\n 0.0013506687241571726\n ],\n \"dm_ridge_vs_mean\": [\n 0.5392198591267081,\n 0.591764258342193\n ],\n \"boot95_rel_impr_vs_naive\": [\n 0.23500539342572274,\n 0.46299549778258603\n ],\n \"boot95_rel_impr_vs_mean\": [\n -0.16820638961201373,\n 0.07227845785548916\n ]\n }\n}\n 181 out/forecasts.csv\norigin_date,target_date,horizon,forecast,actual,model\n2021-09-01,2021-10-01,1,-1.192363,1.334599,naive\n2021-09-01,2021-10-01,1,0.12987187005649717,1.334599,mean\n2021-09-01,2021-10-01,1,0.11247039663896774,1.334599,ridge","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"abfcd52e-1e1e-4c94-bef0-f85a99ec806a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0028","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","hash":"fd0de6b2e4d4c0e99f734ab40c5a397bcc7b33c2c128e771d634f96d95216a07","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_018HH1XBFyZviiWHYaCDLwAR","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding mean, h=1. Run from workspace root.\"\"\"\nimport json, numpy as np, pandas as pd\nDATA = \"data/ip_predictors.csv\"\nLAM = 50.0\nd = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_rows = len(d)\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.to_series().diff().dropna().dt.days.between(28, 31).all() # contiguous monthly\nissues = []\nna = d.isna().sum()\nissues.append({\"issue\": f\"Missing values: {dict(na[na>0].astype(int))} (neworder_chg empty for 1992-01/02). \"\n \"README says the first month of each change series is empty, but ip_growth/oil_chg/permit_chg \"\n \"for 1992-01 are populated in the file.\",\n \"handling\": \"No imputation needed: ridge training starts at s=1992-03 (lags reach 1992-01 for ip_growth only), \"\n \"expanding mean starts 1992-04 as specified; populated 1992-01 values used as given.\"})\nissues.append({\"issue\": \"Large COVID-19 outliers in 2020 (ip_growth min %.2f, max %.2f).\" % (d.ip_growth.min(), d.ip_growth.max()),\n \"handling\": \"Kept as given; no winsorisation (not in specification).\"})\nip = d[\"ip_growth\"]\nF = pd.DataFrame({\"ip0\": ip, \"ip1\": ip.shift(1), \"ip2\": ip.shift(2), \"oil\": d.oil_chg, \"ff\": d.fedfunds,\n \"perm\": d.permit_chg, \"no\": d.neworder_chg, \"tcu\": d.tcu})\nY = ip.shift(-1) # y_{s+1} aligned to s\norigins = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nrows = []; n_train_last = None\nfor t in origins:\n tgt = t + pd.DateOffset(months=1)\n actual = float(ip.loc[tgt])\n rows.append((t, tgt, float(ip.loc[t]), actual, \"naive\"))\n rows.append((t, tgt, float(ip.loc[\"1992-04-01\":t].mean()), actual, \"mean\"))\n idx = F.index[(F.index >= \"1992-03-01\") & (F.index + pd.DateOffset(months=1) <= t)]\n X = F.loc[idx].to_numpy(float); y = Y.loc[idx].to_numpy(float)\n assert not np.isnan(X).any() and not np.isnan(y).any()\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (F.loc[t].to_numpy(float) - mu) / sd\n rows.append((t, tgt, float(ybar + zt @ b), actual, \"ridge\"))\n n_train_last = len(idx)\nfc = pd.DataFrame(rows, columns=[\"origin_date\", \"target_date\", \"forecast\", \"actual\", \"model\"])\nfc.insert(2, \"horizon\", 1)\nfc[\"origin_date\"] = fc.origin_date.dt.strftime(\"%Y-%m-%d\"); fc[\"target_date\"] = fc.target_date.dt.strftime(\"%Y-%m-%d\")\nfc.to_csv(\"out/forecasts.csv\", index=False)\nerr = {}\nfor m in [\"naive\", \"mean\", \"ridge\"]:\n e = fc[fc.model == m].eval(\"forecast-actual\").to_numpy()\n err[m] = e\nres = {\"task_id\": \"F3-conf-03\", \"n_origins\": len(origins),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n \"lambda\": LAM, \"normalisation_window\": \"expanding\", \"n_features\": F.shape[1]}\nfor m, e in err.items():\n res[f\"rmse_{m}\"] = float(np.sqrt(np.mean(e**2))); res[f\"mae_{m}\"] = float(np.mean(np.abs(e)))\nres[\"rel_improvement_vs_naive\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_naive\"]\nres[\"rel_improvement_vs_mean\"] = 1 - res[\"rmse_ridge\"] / res[\"rmse_mean\"]\n# Supplementary: Diebold-Mariano (squared loss, h=1, HLN small-sample correction)\nfrom scipy import stats\ndef dm(e1, e2):\n dd = e1**2 - e2**2; n = len(dd); s = dd.mean() / np.sqrt(dd.var(ddof=0) / n)\n s *= np.sqrt((n - 1) / n); return float(s), float(2 * stats.t.sf(abs(s), n - 1))\nsupp = {\"dm_ridge_vs_naive\": dm(err[\"ridge\"], err[\"naive\"]), \"dm_ridge_vs_mean\": dm(err[\"ridge\"], err[\"mean\"])}\n# bootstrap CI (iid over origins) for relative improvements\nrng = np.random.default_rng(20261030); B = 5000; n = len(origins)\nbs = {\"naive\": [], \"mean\": []}\nfor _ in range(B):\n i = rng.integers(0, n, n); r = np.sqrt(np.mean(err[\"ridge\"][i]**2))\n for m in bs: bs[m].append(1 - r / np.sqrt(np.mean(err[m][i]**2)))\nsupp[\"boot95_rel_impr_vs_naive\"] = [float(np.percentile(bs[\"naive\"], q)) for q in (2.5, 97.5)]\nsupp[\"boot95_rel_impr_vs_mean\"] = [float(np.percentile(bs[\"mean\"], q)) for q in (2.5, 97.5)]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": issues,\n \"n_input_rows\": {\"ip_predictors.csv\": n_rows}, \"n_analysed\": n_train_last, \"random_seed\": 20261030,\n \"supplementary\": supp})\njson.dump(res, open(\"out/results.json\", \"w\"), indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; 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README says the first month of each change series is empty, but ip_growth/oil_chg/permit_chg for 1992-01 are populated in the file.\",\n \"handling\": \"No imputation needed: ridge training starts at s=1992-03 (lags reach 1992-01 for ip_growth only), expanding mean starts 1992-04 as specified; populated 1992-01 values used as given.\"\nMD5 (out/results.json) = a5528f0bf01823afd1a1a8e4f94bd46d\nMD5 (out/results.json) = a5528f0bf01823afd1a1a8e4f94bd46d","is_error":false}]},"uuid":"276fbfbd-719f-4b0d-b9ff-0a6c8ce67661","timestamp":"2026-10-30T04:09:41.389Z","toolUseResult":{"stdout":" \"issue\": \"Missing values: {'neworder_chg': 2} (neworder_chg empty for 1992-01/02). 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> out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US IP growth\n\n## Question\nOver 60 rolling origins (2021-09 … 2026-08), at horizon h = 1 month, does a fixed-penalty ridge regression (λ = 50) on 8 standardised predictors forecast US industrial production growth (100·Δln INDPRO) better than (i) the naive last-value forecast and (ii) the expanding historical mean (from 1992-04)?\n\n## Data and exclusions\n`data/ip_predictors.csv`, 417 monthly rows (1992-01 to 2026-09), current FRED vintage. `neworder_chg` is empty for 1992-01/02 (the README also says the 1992-01 values of the other change series are empty, but they are filled in the file; I used them as given). None of these gaps affects the analysis. Ridge training rows start at s = 1992-03, where all 8 features exist. The 2020 COVID swings (ip_growth from −14.1 to +6.3 %) fall inside every training window and were kept, because the specification does not mention outlier treatment. No rows were dropped. At the last origin there are 413 training rows.\n\n## Method\n- Naive forecast = ip_growth_t. Mean forecast = the mean of ip_growth from 1992-04 to t.\n- Ridge setup at each origin t:\n - Training pairs (z_s, y_{s+1}) for 1992-03 ≤ s ≤ t−1.\n - Features z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s).\n - Each feature is standardised with the training-window mean and sample SD (ddof = 1). These are recomputed at every origin (expanding window, no full-sample statistics).\n - The target is centred on its training mean ȳ.\n- Ridge fit: b = (Z′Z + 50·I)⁻¹Z′(y−ȳ), and the forecast is ȳ + z̃_t·b. No value dated after t is used.\n- Errors (forecast − actual) are scored on ip_growth_{t+1}.\n- Supplementary checks:\n - Diebold–Mariano test with squared loss and Harvey–Leybourne–Newbold correction (h = 1, so no autocorrelation adjustment).\n - IID bootstrap over origins (5000 draws, seed 20261030) for the relative improvements.\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.8831 | 0.6990 |\n| expanding mean | 0.5439 | 0.4293 |\n| ridge (λ=50) | 0.5608 | 0.4514 |\n\n- Relative improvement of ridge vs naive (1 − RMSE_ridge/RMSE_naive) = **0.365**.\n- Relative improvement of ridge vs mean = **−0.031**: ridge's RMSE is about 3.1 % *worse* than the mean's, and its MAE is also worse.\n\nRidge clearly beats the naive forecast but does not beat the expanding historical mean.\n\n## Uncertainty\n- Ridge vs naive: DM statistic = −3.36, p = 0.0014; bootstrap 95 % CI for the relative improvement is [0.235, 0.463]. The gain over naive is robust.\n- Ridge vs mean: DM statistic = 0.54, p = 0.59; bootstrap 95 % CI is [−0.168, 0.072]. The two cannot be told apart statistically.\n- With only 60 origins, these intervals are wide. The IID bootstrap ignores any serial dependence in the loss differentials.\n\n## Limitations and open issues\n- The data are current vintage and publication lags are ignored. The results measure how predictable the revised series is under a stylised timing assumption, not real-time forecast performance.\n- λ is fixed at 50 and was not tuned. Results for other penalties, or for robust handling of the COVID outliers, were not evaluated.\n- The evaluation window (2021-09 to 2026-08) is a single post-COVID period, so the conclusions may not carry over to other periods.\n- The README's description of empty cells does not match the file for 1992-01. This does not affect the results.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 over 60 origins (2021-09 to 2026-08), ridge (lambda=50) RMSE is 0.561 vs 0.883 for naive: a 36.5% relative improvement.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, no publication lags; DM p=0.0014.\"},\n {\"id\":\"C2\",\"text\":\"Ridge does not beat the expanding historical mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), MAE 0.451 vs 0.429.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"rel_improvement_vs_mean\",\"mae_ridge\",\"mae_mean\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically significant (DM p=0.59); 60 origins only.\"},\n {\"id\":\"C3\",\"text\":\"All standardisation statistics and the target mean were computed from each origin's own expanding training window.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"413 training rows at the last origin.\"}\n]\nEOF\nls out","description":"Write report and 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> out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US IP growth\n\n## Question\nOver 60 rolling origins (2021-09 … 2026-08), at horizon h = 1 month, does a fixed-penalty ridge regression (λ = 50) on 8 standardised predictors forecast US industrial production growth (100·Δln INDPRO) better than (i) the naive last-value forecast and (ii) the expanding historical mean (from 1992-04)?\n\n## Data and exclusions\n`data/ip_predictors.csv`, 417 monthly rows (1992-01 to 2026-09), current FRED vintage. `neworder_chg` is empty for 1992-01/02 (the README also says the 1992-01 values of the other change series are empty, but they are filled in the file; I used them as given). None of these gaps affects the analysis. Ridge training rows start at s = 1992-03, where all 8 features exist. The 2020 COVID swings (ip_growth from −14.1 to +6.3 %) fall inside every training window and were kept, because the specification does not mention outlier treatment. No rows were dropped. At the last origin there are 413 training rows.\n\n## Method\n- Naive forecast = ip_growth_t. Mean forecast = the mean of ip_growth from 1992-04 to t.\n- Ridge setup at each origin t:\n - Training pairs (z_s, y_{s+1}) for 1992-03 ≤ s ≤ t−1.\n - Features z_s = (ip_s, ip_{s−1}, ip_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s).\n - Each feature is standardised with the training-window mean and sample SD (ddof = 1). These are recomputed at every origin (expanding window, no full-sample statistics).\n - The target is centred on its training mean ȳ.\n- Ridge fit: b = (Z′Z + 50·I)⁻¹Z′(y−ȳ), and the forecast is ȳ + z̃_t·b. No value dated after t is used.\n- Errors (forecast − actual) are scored on ip_growth_{t+1}.\n- Supplementary checks:\n - Diebold–Mariano test with squared loss and Harvey–Leybourne–Newbold correction (h = 1, so no autocorrelation adjustment).\n - IID bootstrap over origins (5000 draws, seed 20261030) for the relative improvements.\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.8831 | 0.6990 |\n| expanding mean | 0.5439 | 0.4293 |\n| ridge (λ=50) | 0.5608 | 0.4514 |\n\n- Relative improvement of ridge vs naive (1 − RMSE_ridge/RMSE_naive) = **0.365**.\n- Relative improvement of ridge vs mean = **−0.031**: ridge's RMSE is about 3.1 % *worse* than the mean's, and its MAE is also worse.\n\nRidge clearly beats the naive forecast but does not beat the expanding historical mean.\n\n## Uncertainty\n- Ridge vs naive: DM statistic = −3.36, p = 0.0014; bootstrap 95 % CI for the relative improvement is [0.235, 0.463]. The gain over naive is robust.\n- Ridge vs mean: DM statistic = 0.54, p = 0.59; bootstrap 95 % CI is [−0.168, 0.072]. The two cannot be told apart statistically.\n- With only 60 origins, these intervals are wide. The IID bootstrap ignores any serial dependence in the loss differentials.\n\n## Limitations and open issues\n- The data are current vintage and publication lags are ignored. The results measure how predictable the revised series is under a stylised timing assumption, not real-time forecast performance.\n- λ is fixed at 50 and was not tuned. Results for other penalties, or for robust handling of the COVID outliers, were not evaluated.\n- The evaluation window (2021-09 to 2026-08) is a single post-COVID period, so the conclusions may not carry over to other periods.\n- The README's description of empty cells does not match the file for 1992-01. This does not affect the results.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 over 60 origins (2021-09 to 2026-08), ridge (lambda=50) RMSE is 0.561 vs 0.883 for naive: a 36.5% relative improvement.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, no publication lags; DM p=0.0014.\"},\n {\"id\":\"C2\",\"text\":\"Ridge does not beat the expanding historical mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), MAE 0.451 vs 0.429.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"rel_improvement_vs_mean\",\"mae_ridge\",\"mae_mean\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically significant (DM p=0.59); 60 origins only.\"},\n {\"id\":\"C3\",\"text\":\"All standardisation statistics and the target mean were computed from each origin's own expanding training window.\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"413 training rows at the last origin.\"}\n]\nEOF\nls out","description":"Write report and 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ridge model beats the naive forecast clearly, but it does not beat the expanding historical mean. Over the 60 origins (2021-09 to 2026-08, one month ahead):\n\n| Forecast | RMSE (% per month) | MAE (% per month) |\n|---|---|---|\n| Naive (last value) | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge (λ = 50) | 0.561 | 0.451 |\n\n- **Against naive:** ridge's RMSE is 36.5% lower. The gain holds up statistically: a Diebold–Mariano test gives p = 0.0014, and a bootstrap 95% interval for the improvement is 0.235 to 0.463.\n- **Against the mean:** ridge's RMSE is 3.1% higher, and its MAE is also worse. The difference is not statistically significant (p = 0.59; bootstrap interval −0.168 to 0.072), so the two can't be told apart on 60 origins.\n\nThe ridge model is implemented exactly as specified. All scaling statistics and the target mean are recomputed from each origin's own training window, and nothing dated after the origin is used. At the last origin there are 413 training rows. Running `out/analysis.py` twice produced identical results.\n\nThings to keep in mind:\n- **Data vintage:** the data are the current revised versions and publication lags are ignored, so this measures how predictable the revised series is, not real-time forecasting performance.\n- **Fixed penalty and outliers:** λ stays at 50 as specified. I did not test other penalties or any outlier treatment. The 2020 COVID swings (−14.1% to +6.3%) are kept in every training window.\n- **Single period:** the evaluation covers one post-COVID period only.\n- **README mismatch:** the README says the January 1992 values of the change columns are empty, but most are filled in the file. Only `neworder_chg` for 1992-01 and 1992-02 is actually missing. I used the values as given; this doesn't affect the results.\n- **Additions beyond the spec:** the significance test and bootstrap intervals are extras I added under a `supplementary` key in `results.json`. The bootstrap treats origins as independent, so it ignores any serial correlation.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv` (180 rows)\n- `report.md`\n- `claims.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2078,"cache_read_input_tokens":10617,"output_tokens":828,"output_tokens_details":{"thinking_tokens":19},"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":2078},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmQMPpbKCjUDCcTeEWv","requestRef":"0a775d23-9ebf-443f-82e4-238555f482a3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"00e505a2-7272-4676-b866-57d5c5c2159f","timestamp":"2026-10-30T04:10:06.870Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0028","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"00e505a2-7272-4676-b866-57d5c5c2159f","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610"}42{"type":"cost-state","sessionId":"410600f9-28fd-4590-9af8-1abfd00fd610","totalCostUSD":0.1848036,"totalAPIDuration":55346,"totalAPIDurationWithoutRetries":55343,"totalToolDuration":2314,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":57799,"startTime":1793333349089,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":6085,"thinkingTokens":398,"cacheReadInputTokens":35463,"cacheCreationInputTokens":11191,"cacheCreationEphemeral5mInputTokens":11191,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1848036}},"hasUnknownModelCost":false}43