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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 64\ndrwxr-xr-x 4 simon-pierreboucher staff 128 oct. 29 23:46 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:52 ..\n-rw-r--r-- 1 simon-pierreboucher staff 28378 oct. 29 23:50 ip_predictors.csv\n-rw-r--r-- 1 simon-pierreboucher staff 2224 oct. 29 23:50 README.md","is_error":true,"tool_use_id":"toolu_01DvyeJUgMEaSYTMS6b7TksS"}]},"uuid":"56638f86-f1ce-4075-8164-b13f8e7c66b2","timestamp":"2026-10-30T04:11:56.246Z","toolUseResult":"Error: Exit code 1\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth with standardised predictors\n\n## Scientific question\nDoes a fixed-penalty ridge regression on eight standardised monthly predictors forecast next month's US industrial production growth better than two simple baselines — the naive (last value) forecast and the expanding historical mean — over 60 rolling origins from 2021-09 to 2026-08? The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\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\n2026-05-01,0.142621,1.788143,3.630000,-0.917761,1.893103,76.222500\n2026-06-01,0.231228,-18.583305,3.630000,-2.586351,1.642436,76.332100\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\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 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\nTrue month\n31.0 243\n30.0 138\n28.0 26\n29.0 9\nName: count, dtype: int64","is_error":false}]},"uuid":"9eabf404-66f3-4a86-aecd-caaddeb727af","timestamp":"2026-10-30T04:11:58.671Z","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\n2026-05-01,0.142621,1.788143,3.630000,-0.917761,1.893103,76.222500\n2026-06-01,0.231228,-18.583305,3.630000,-2.586351,1.642436,76.332100\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\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 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\nTrue month\n31.0 243\n30.0 138\n28.0 26\n29.0 9\nName: count, dtype: 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nLAM = 50.0\nd = pd.read_csv(\"data/ip_predictors.csv\", parse_dates=[\"month\"])\nn_rows = len(d)\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique\nmonths = d.index\nissues = []\n# data checks\ngaps = pd.Series(months).diff().dt.days.dropna()\nif not gaps.between(28, 31).all():\n issues.append({\"issue\": \"non-consecutive months\", \"handling\": \"none needed check failed\"})\nna = d.isna().sum()\nissues.append({\"issue\": f\"missing values: neworder_chg {int(na['neworder_chg'])} (1992-01/02); other columns {int(na.drop('neworder_chg').sum())}\",\n \"handling\": \"outside every training window (training starts 1992-03, lags reach back to 1992-01 only for ip_growth); no imputation\"})\nif not np.isnan(d[\"ip_growth\"].iloc[0]):\n issues.append({\"issue\": \"README says the first month of each change series is empty, but ip_growth (and other change columns) for 1992-01 are populated\",\n \"handling\": \"values kept as given; ip_growth 1992-01 is used only as the second lag of the 1992-03 training row, as the task specification implies\"})\ncovid = d.loc[\"2020-03-01\":\"2020-07-01\", \"ip_growth\"]\nissues.append({\"issue\": f\"extreme COVID-19 observations in 2020 (ip_growth range {covid.min():.2f} to {covid.max():.2f})\",\n \"handling\": \"kept in all training windows as specified (no outlier treatment); they influence standardisation statistics and coefficients\"})\n\nip = d[\"ip_growth\"]\nfeat = pd.DataFrame({\n \"ip0\": ip, \"ip1\": ip.shift(1), \"ip2\": ip.shift(2),\n \"oil\": d[\"oil_chg\"], \"ff\": d[\"fedfunds\"], \"perm\": d[\"permit_chg\"],\n \"nord\": d[\"neworder_chg\"], \"tcu\": d[\"tcu\"]}, index=months)\ntarget_next = ip.shift(-1) # y_{s+1} indexed by s\n\norigins = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nrows = []\nn_train_last = None\nfor t in origins:\n tgt = t + pd.offsets.MonthBegin(1)\n actual = float(ip.loc[tgt])\n # naive\n f_naive = float(ip.loc[t])\n # expanding mean 1992-04..t\n f_mean = float(ip.loc[\"1992-04-01\":t].mean())\n # ridge: training s in [1992-03, t-1]\n s_idx = months[(months >= \"1992-03-01\") & (months <= t - pd.offsets.MonthBegin(1))]\n X = feat.loc[s_idx].to_numpy(float)\n y = target_next.loc[s_idx].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n assert (s_idx + pd.offsets.MonthBegin(1)).max() <= t # no info after t\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (feat.loc[t].to_numpy(float) - mu) / sd\n f_ridge = float(ybar + zt @ b)\n n_train_last = len(s_idx)\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append({\"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(\"out/forecasts.csv\", index=False)\n\ndef err(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\nr = {k: err(k) for k in (\"naive\", \"mean\", \"ridge\")}\nres = {\n \"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\": 8,\n \"rmse_naive\": r[\"naive\"][0], \"mae_naive\": r[\"naive\"][1],\n \"rmse_mean\": r[\"mean\"][0], \"mae_mean\": r[\"mean\"][1],\n \"rmse_ridge\": r[\"ridge\"][0], \"mae_ridge\": r[\"ridge\"][1],\n \"rel_improvement_vs_naive\": 1 - r[\"ridge\"][0] / r[\"naive\"][0],\n \"rel_improvement_vs_mean\": 1 - r[\"ridge\"][0] / r[\"mean\"][0],\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"ip_predictors.csv\": n_rows},\n \"n_analysed\": n_train_last, \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2053,"cache_read_input_tokens":4703,"output_tokens":2259,"output_tokens_details":{"thinking_tokens":126},"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":2053},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmZAyzNrp1ZEr7nJYfT","requestRef":"169c722f-d486-441b-b047-6b1fc03e1b4c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"03c5df1c-a7dc-4978-8998-bb20dabf19bf","timestamp":"2026-10-30T04:12:16.466Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"03c5df1c-a7dc-4978-8998-bb20dabf19bf","isSidechain":false,"promptId":"5c88b580-bfad-4fb4-ac29-3fde9cc5f512","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GA9exs47c9Dyi9cQw6FSE1","type":"tool_result","content":"{\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 2 (1992-01/02); other columns 0\",\n \"handling\": \"outside every training window (training starts 1992-03, lags reach back to 1992-01 only for ip_growth); no imputation\"\n },\n {\n \"issue\": \"README says the first month of each change series is empty, but ip_growth (and other change columns) for 1992-01 are populated\",\n \"handling\": \"values kept as given; ip_growth 1992-01 is used only as the second lag of the 1992-03 training row, as the task specification implies\"\n },\n {\n \"issue\": \"extreme COVID-19 observations in 2020 (ip_growth range -14.14 to 6.35)\",\n \"handling\": \"kept in all training windows as specified (no outlier treatment); they influence standardisation statistics and coefficients\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": null\n}","is_error":false}]},"uuid":"97b88879-8a35-402a-8d08-57d1d27caff9","timestamp":"2026-10-30T04:12:16.771Z","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 2 (1992-01/02); other columns 0\",\n \"handling\": \"outside every training window (training starts 1992-03, lags reach back to 1992-01 only for ip_growth); no imputation\"\n },\n {\n \"issue\": \"README says the first month of each change series is empty, but ip_growth (and other change columns) for 1992-01 are populated\",\n \"handling\": \"values kept as given; ip_growth 1992-01 is used only as the second lag of the 1992-03 training row, as the task specification implies\"\n },\n {\n \"issue\": \"extreme COVID-19 observations in 2020 (ip_growth range -14.14 to 6.35)\",\n \"handling\": \"kept in all training windows as specified (no outlier treatment); they influence standardisation statistics and coefficients\"\n }\n ],\n \"n_input_rows\": {\n \"ip_predictors.csv\": 417\n },\n \"n_analysed\": 413,\n \"random_seed\": 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-p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nLAM = 50.0\nd = pd.read_csv(\"data/ip_predictors.csv\", parse_dates=[\"month\"])\nn_rows = len(d)\nd = d.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique\nmonths = d.index\nissues = []\n# data checks\ngaps = pd.Series(months).diff().dt.days.dropna()\nif not gaps.between(28, 31).all():\n issues.append({\"issue\": \"non-consecutive months\", \"handling\": \"none needed check failed\"})\nna = d.isna().sum()\nissues.append({\"issue\": f\"missing values: neworder_chg {int(na['neworder_chg'])} (1992-01/02); other columns {int(na.drop('neworder_chg').sum())}\",\n \"handling\": \"outside every training window (training starts 1992-03, lags reach back to 1992-01 only for ip_growth); no imputation\"})\nif not np.isnan(d[\"ip_growth\"].iloc[0]):\n issues.append({\"issue\": \"README says the first month of each change series is empty, but ip_growth (and other change columns) for 1992-01 are populated\",\n \"handling\": \"values kept as given; ip_growth 1992-01 is used only as the second lag of the 1992-03 training row, as the task specification implies\"})\ncovid = d.loc[\"2020-03-01\":\"2020-07-01\", \"ip_growth\"]\nissues.append({\"issue\": f\"extreme COVID-19 observations in 2020 (ip_growth range {covid.min():.2f} to {covid.max():.2f})\",\n \"handling\": \"kept in all training windows as specified (no outlier treatment); they influence standardisation statistics and coefficients\"})\n\nip = d[\"ip_growth\"]\nfeat = pd.DataFrame({\n \"ip0\": ip, \"ip1\": ip.shift(1), \"ip2\": ip.shift(2),\n \"oil\": d[\"oil_chg\"], \"ff\": d[\"fedfunds\"], \"perm\": d[\"permit_chg\"],\n \"nord\": d[\"neworder_chg\"], \"tcu\": d[\"tcu\"]}, index=months)\ntarget_next = ip.shift(-1) # y_{s+1} indexed by s\n\norigins = pd.date_range(\"2021-09-01\", \"2026-08-01\", freq=\"MS\")\nrows = []\nn_train_last = None\nfor t in origins:\n tgt = t + pd.offsets.MonthBegin(1)\n actual = float(ip.loc[tgt])\n # naive\n f_naive = float(ip.loc[t])\n # expanding mean 1992-04..t\n f_mean = float(ip.loc[\"1992-04-01\":t].mean())\n # ridge: training s in [1992-03, t-1]\n s_idx = months[(months >= \"1992-03-01\") & (months <= t - pd.offsets.MonthBegin(1))]\n X = feat.loc[s_idx].to_numpy(float)\n y = target_next.loc[s_idx].to_numpy(float)\n assert np.isfinite(X).all() and np.isfinite(y).all()\n assert (s_idx + pd.offsets.MonthBegin(1)).max() <= t # no info after t\n mu = X.mean(0); sd = X.std(0, ddof=1); ybar = y.mean()\n Z = (X - mu) / sd\n b = np.linalg.solve(Z.T @ Z + LAM * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n zt = (feat.loc[t].to_numpy(float) - mu) / sd\n f_ridge = float(ybar + zt @ b)\n n_train_last = len(s_idx)\n for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n rows.append({\"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(\"out/forecasts.csv\", index=False)\n\ndef err(m):\n e = fc.loc[fc.model == m, \"forecast\"] - fc.loc[fc.model == m, \"actual\"]\n return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\nr = {k: err(k) for k in (\"naive\", \"mean\", \"ridge\")}\nres = {\n \"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\": 8,\n \"rmse_naive\": r[\"naive\"][0], \"mae_naive\": r[\"naive\"][1],\n \"rmse_mean\": r[\"mean\"][0], \"mae_mean\": r[\"mean\"][1],\n \"rmse_ridge\": r[\"ridge\"][0], \"mae_ridge\": r[\"ridge\"][1],\n \"rel_improvement_vs_naive\": 1 - r[\"ridge\"][0] / r[\"naive\"][0],\n \"rel_improvement_vs_mean\": 1 - r[\"ridge\"][0] / r[\"mean\"][0],\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": {\"ip_predictors.csv\": n_rows},\n \"n_analysed\": n_train_last, \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py"}}]}}26{"type":"api-request","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","id":"964caef0-d89e-44d4-993e-3893a88ef99d","timestamp":"2026-10-30T04:12:16.774Z","version":"2.1.314","querySource":"sdk","shapeHash":"2a2de96c2a71b7542fc288e449a0d6ada25f645271def522452c22c6e24699cd","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":"169c722f-d486-441b-b047-6b1fc03e1b4c","keep":6,"tail":["9bb2ff1e5259043b0c82fbddac1ca504584680ca842c540bfdefeb495f2239a2","ed47324c1038cac50d8afe87cefdba958934f971098eae873e850e3a53d0f108"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6efc05f96c67bd424a3788e9969b21ec19d14fd029fa2a6dd8a0da57fbcb5eb1"}27{"parentUuid":"97b88879-8a35-402a-8d08-57d1d27caff9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmaWdxY4jcBDkbWwKSV","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2900,"cache_read_input_tokens":6756,"output_tokens":1219,"output_tokens_details":{"thinking_tokens":172},"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":2900},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1470,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmaWMrLyRsmYDuy5Mhu","requestRef":"964caef0-d89e-44d4-993e-3893a88ef99d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f7830b27-9049-404b-8053-338b46c632c5","timestamp":"2026-10-30T04:12:20.223Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"f7830b27-9049-404b-8053-338b46c632c5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmaWdxY4jcBDkbWwKSV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LJYRs4emyEyKCd9Ltoe4Y2","name":"Bash","input":{"command":"cat >> out/analysis.py <<'EOF'\n\n# ---- Uncertainty diagnostics (written to out/uncertainty.json; not part of results.json) ----\nfrom scipy import stats\ndef dm(base, model=\"ridge\"):\n e1 = (fc.loc[fc.model == model, \"forecast\"].values - fc.loc[fc.model == model, \"actual\"].values)\n e0 = (fc.loc[fc.model == base, \"forecast\"].values - fc.loc[fc.model == base, \"actual\"].values)\n dl = e1 ** 2 - e0 ** 2\n n = len(dl)\n stat = dl.mean() / np.sqrt(dl.var(ddof=0) / n)\n stat_hln = stat * np.sqrt((n + 1 - 2 + 0) / n) # Harvey-Leybourne-Newbold correction, h=1\n p = 2 * stats.t.sf(abs(stat_hln), df=n - 1)\n return {\"mean_loss_diff_sq\": float(dl.mean()), \"dm_stat_hln\": float(stat_hln), \"p_two_sided\": float(p)}\nrng = np.random.default_rng(12345)\ndef boot_rel(base, B=5000, block=6):\n e1 = (fc.loc[fc.model == \"ridge\", \"forecast\"].values - fc.loc[fc.model == \"ridge\", \"actual\"].values)\n e0 = (fc.loc[fc.model == base, \"forecast\"].values - fc.loc[fc.model == base, \"actual\"].values)\n n = len(e1); out = []\n for _ in range(B):\n starts = rng.integers(0, n - block + 1, size=int(np.ceil(n / block)))\n idx = np.concatenate([np.arange(s, s + block) for s in starts])[:n]\n out.append(1 - np.sqrt((e1[idx] ** 2).mean()) / np.sqrt((e0[idx] ** 2).mean()))\n return [float(np.quantile(out, 0.025)), float(np.quantile(out, 0.975))]\nunc = {\"dm_ridge_vs_naive\": dm(\"naive\"), \"dm_ridge_vs_mean\": dm(\"mean\"),\n \"boot95_rel_impr_vs_naive\": boot_rel(\"naive\"), \"boot95_rel_impr_vs_mean\": boot_rel(\"mean\"),\n \"bootstrap\": \"moving block, block=6, B=5000, seed=12345\"}\nwith open(\"out/uncertainty.json\", \"w\") as f:\n json.dump(unc, f, indent=2)\nprint(json.dumps(unc, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py | tail -25; ~/wp29/venv/bin/python -c \"\nimport pandas as pd;f=pd.read_csv('out/forecasts.csv');print(len(f));print(f.pivot(index='origin_date',columns='model',values='forecast').describe())\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2900,"cache_read_input_tokens":6756,"output_tokens":1219,"output_tokens_details":{"thinking_tokens":172},"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":2900},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmaWMrLyRsmYDuy5Mhu","requestRef":"964caef0-d89e-44d4-993e-3893a88ef99d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"862fbf33-2a69-4521-bdc4-b3f45f2dd822","timestamp":"2026-10-30T04:12:27.388Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"862fbf33-2a69-4521-bdc4-b3f45f2dd822","isSidechain":false,"promptId":"5c88b580-bfad-4fb4-ac29-3fde9cc5f512","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LJYRs4emyEyKCd9Ltoe4Y2","type":"tool_result","content":" },\n \"n_analysed\": 413,\n \"random_seed\": null\n}\n{\n \"dm_ridge_vs_naive\": {\n \"mean_loss_diff_sq\": -0.4653170374529798,\n \"dm_stat_hln\": -3.3649594921847754,\n \"p_two_sided\": 0.0013506687241571726\n },\n \"dm_ridge_vs_mean\": {\n \"mean_loss_diff_sq\": 0.018629527145698717,\n \"dm_stat_hln\": 0.5392198591267081,\n \"p_two_sided\": 0.591764258342193\n },\n \"boot95_rel_impr_vs_naive\": [\n 0.2484528889532762,\n 0.4256535787875549\n ],\n \"boot95_rel_impr_vs_mean\": [\n -0.15820533296870948,\n 0.05644458857719433\n ],\n \"bootstrap\": \"moving block, block=6, B=5000, seed=12345\"\n}\n180\nmodel mean naive ridge\ncount 60.000000 60.000000 60.000000\nmean 0.125533 0.052842 0.157557\nstd 0.005115 0.565006 0.327908\nmin 0.118401 -1.382028 -0.539406\n25% 0.121425 -0.271161 -0.090793\n50% 0.123986 0.045606 0.123726\n75% 0.129724 0.419948 0.318947\nmax 0.134789 1.334599 1.210173","is_error":false}]},"uuid":"ab17b09a-d0c1-4847-9302-1a6ca460c485","timestamp":"2026-10-30T04:12:28.486Z","toolUseResult":{"stdout":" },\n \"n_analysed\": 413,\n \"random_seed\": null\n}\n{\n \"dm_ridge_vs_naive\": {\n \"mean_loss_diff_sq\": -0.4653170374529798,\n \"dm_stat_hln\": -3.3649594921847754,\n \"p_two_sided\": 0.0013506687241571726\n },\n \"dm_ridge_vs_mean\": {\n \"mean_loss_diff_sq\": 0.018629527145698717,\n \"dm_stat_hln\": 0.5392198591267081,\n \"p_two_sided\": 0.591764258342193\n },\n \"boot95_rel_impr_vs_naive\": [\n 0.2484528889532762,\n 0.4256535787875549\n ],\n \"boot95_rel_impr_vs_mean\": [\n -0.15820533296870948,\n 0.05644458857719433\n ],\n \"bootstrap\": \"moving block, block=6, B=5000, seed=12345\"\n}\n180\nmodel mean naive ridge\ncount 60.000000 60.000000 60.000000\nmean 0.125533 0.052842 0.157557\nstd 0.005115 0.565006 0.327908\nmin 0.118401 -1.382028 -0.539406\n25% 0.121425 -0.271161 -0.090793\n50% 0.123986 0.045606 0.123726\n75% 0.129724 0.419948 0.318947\nmax 0.134789 1.334599 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>> out/analysis.py <<'EOF'\n\n# ---- Uncertainty diagnostics (written to out/uncertainty.json; not part of results.json) ----\nfrom scipy import stats\ndef dm(base, model=\"ridge\"):\n e1 = (fc.loc[fc.model == model, \"forecast\"].values - fc.loc[fc.model == model, \"actual\"].values)\n e0 = (fc.loc[fc.model == base, \"forecast\"].values - fc.loc[fc.model == base, \"actual\"].values)\n dl = e1 ** 2 - e0 ** 2\n n = len(dl)\n stat = dl.mean() / np.sqrt(dl.var(ddof=0) / n)\n stat_hln = stat * np.sqrt((n + 1 - 2 + 0) / n) # Harvey-Leybourne-Newbold correction, h=1\n p = 2 * stats.t.sf(abs(stat_hln), df=n - 1)\n return {\"mean_loss_diff_sq\": float(dl.mean()), \"dm_stat_hln\": float(stat_hln), \"p_two_sided\": float(p)}\nrng = np.random.default_rng(12345)\ndef boot_rel(base, B=5000, block=6):\n e1 = (fc.loc[fc.model == \"ridge\", \"forecast\"].values - fc.loc[fc.model == \"ridge\", \"actual\"].values)\n e0 = (fc.loc[fc.model == base, \"forecast\"].values - fc.loc[fc.model == base, \"actual\"].values)\n n = len(e1); out = []\n for _ in range(B):\n starts = rng.integers(0, n - block + 1, size=int(np.ceil(n / block)))\n idx = np.concatenate([np.arange(s, s + block) for s in starts])[:n]\n out.append(1 - np.sqrt((e1[idx] ** 2).mean()) / np.sqrt((e0[idx] ** 2).mean()))\n return [float(np.quantile(out, 0.025)), float(np.quantile(out, 0.975))]\nunc = {\"dm_ridge_vs_naive\": dm(\"naive\"), \"dm_ridge_vs_mean\": dm(\"mean\"),\n \"boot95_rel_impr_vs_naive\": boot_rel(\"naive\"), \"boot95_rel_impr_vs_mean\": boot_rel(\"mean\"),\n \"bootstrap\": \"moving block, block=6, B=5000, seed=12345\"}\nwith open(\"out/uncertainty.json\", \"w\") as f:\n json.dump(unc, f, indent=2)\nprint(json.dumps(unc, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py | tail -25; ~/wp29/venv/bin/python -c \"\nimport pandas as 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> out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50) on eight standardised monthly predictors forecast next month's US industrial production growth (100 × Δln INDPRO, % per month) better than (a) the naive last-value forecast and (b) the expanding historical mean, over 60 rolling origins from 2021-09 to 2026-08 (horizon h = 1)?\n\n## Data and exclusions\n- `data/ip_predictors.csv`: 417 monthly rows, 1992-01 to 2026-09, current-vintage FRED data (see `data/README.md`). No rows were dropped.\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. Neither falls in any training window (training rows start at 1992-03), so no imputation was needed.\n- Minor discrepancy: the README says the first month of every change series is empty, but `ip_growth`, `oil_chg` and `permit_chg` are populated for 1992-01. I kept the values as given. `ip_growth` 1992-01 enters only as the second lag in the 1992-03 training row, which is what the task specification implies.\n- The extreme COVID-19 months of 2020 (ip_growth from −14.14 to +6.35 %) stay in every training window, as the specification requires. They inflate the standardisation statistics and affect the coefficients.\n\n## Method\n- Origins t = 2021-09-01 … 2026-08-01 (60). Each origin's target is `ip_growth` at t+1.\n- Naive forecast: `ip_growth` at t.\n- Expanding-mean forecast: the mean of `ip_growth` from 1992-04 to t.\n- Ridge: the training rows are s = 1992-03 … t−1, with target y_{s+1}. The features are ip_growth_s, ip_growth_{s−1} and ip_growth_{s−2}, plus oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s.\n - At every origin I recompute the feature means and sample SDs (ddof = 1) and the target mean from that origin's training rows only. `normalisation_window = \"expanding\"`.\n - b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), with no penalised intercept. Forecast = ȳ + ((z_t − μ)/σ)·b.\n - The code asserts that no training target is dated after t.\n- I scored errors against current-vintage actuals and computed RMSE and MAE over the 60 origins.\n- `out/analysis.py` regenerates `results.json`, `forecasts.csv` (180 rows) and `uncertainty.json`.\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 RMSE improvement of ridge over naive: 1 − 0.5608/0.8831 = **+0.365** (36.5 % lower RMSE).\n- Relative RMSE improvement of ridge over the expanding mean: **−0.031**. Ridge's RMSE is 3.1 % *higher* than the mean's, and its MAE is also higher.\n\nSo the ridge model clearly beats the naive forecast but does **not** beat the expanding historical mean over this evaluation window. At the one-month horizon, the eight predictors add no out-of-sample value over the unconditional mean.\n\n## Uncertainty\nThese diagnostics are in `out/uncertainty.json`, produced by the same script.\n- **Diebold–Mariano tests** (squared loss, with the Harvey–Leybourne–Newbold small-sample correction and a t(59) reference):\n - ridge vs naive: statistic −3.36, p = 0.0014. Ridge is significantly better.\n - ridge vs mean: statistic +0.54, p = 0.59. There is no significant difference.\n- **Moving-block bootstrap** (block 6, B = 5000, seed 12345), 95 % intervals for the relative RMSE improvement:\n - vs naive: [0.248, 0.426].\n - vs mean: [−0.158, 0.056].\n- With only 60 evaluation months, a small loss to the mean cannot be told apart from a small gain.\n\n## Limitations and open issues\n- **Data vintage.** I used current-vintage, revised data and ignored publication lags (INDPRO, TCU and NEWORDER are revised substantially). These results are **not** real-time forecast performance.\n- **Model choices.** λ = 50 is fixed by the task, not tuned. The level variables (fedfunds, tcu) enter untransformed. The COVID outliers are untreated. Each of these could change the ridge results.\n- **Evaluation window.** It is one short window (2021-09 to 2026-08) and includes the post-pandemic recovery. The relative ranking may not generalise to other periods.\n- **Uncertainty method.** The DM test and the bootstrap both assume approximate stationarity of loss differentials. The bootstrap seed does not affect any value in `results.json`.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 60 origins (2021-09 to 2026-08), the ridge forecast has RMSE 0.561 vs 0.883 for the naive forecast, a 36.5% relative improvement.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored; DM p=0.0014 (uncertainty.json).\"},\n {\"id\":\"C2\",\"text\":\"The ridge forecast does not beat the expanding historical mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), and MAE is also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"rel_improvement_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Difference is not statistically significant (DM p=0.59; block-bootstrap 95% CI [-0.158, 0.056]).\"},\n {\"id\":\"C3\",\"text\":\"All standardisation statistics and the target mean were recomputed from each origin's training window only (expanding).\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Enforced in code by construction and an assertion on training target dates.\"}\n]\nEOF\nls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":445,"cache_read_input_tokens":11359,"output_tokens":2299,"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":445},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmbiKxRAWVPmb9NNsDT","requestRef":"85e4a58d-ff93-41b2-a3f8-911994e37a67","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"251d40a2-5f2a-4e6c-b7b4-c4b0fc9014f8","timestamp":"2026-10-30T04:12:52.705Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"251d40a2-5f2a-4e6c-b7b4-c4b0fc9014f8","isSidechain":false,"promptId":"5c88b580-bfad-4fb4-ac29-3fde9cc5f512","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017FUcv29Tc8xDpXP8KThopW","type":"tool_result","content":"analysis.py\nclaims.json\nforecasts.csv\nreport.md\nresults.json\nuncertainty.json","is_error":false}]},"uuid":"a1a48ad6-0651-463e-87b5-708b5bf9fe83","timestamp":"2026-10-30T04:12:52.733Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nforecasts.csv\nreport.md\nresults.json\nuncertainty.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"251d40a2-5f2a-4e6c-b7b4-c4b0fc9014f8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","hash":"0c2d20a90842ee0cdce7268abbd3a32fbdf5ea63b56c576b56d44b0a92efce8d","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017FUcv29Tc8xDpXP8KThopW","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50) on eight standardised monthly predictors forecast next month's US industrial production growth (100 × Δln INDPRO, % per month) better than (a) the naive last-value forecast and (b) the expanding historical mean, over 60 rolling origins from 2021-09 to 2026-08 (horizon h = 1)?\n\n## Data and exclusions\n- `data/ip_predictors.csv`: 417 monthly rows, 1992-01 to 2026-09, current-vintage FRED data (see `data/README.md`). No rows were dropped.\n- Missing values: only `neworder_chg` for 1992-01 and 1992-02. Neither falls in any training window (training rows start at 1992-03), so no imputation was needed.\n- Minor discrepancy: the README says the first month of every change series is empty, but `ip_growth`, `oil_chg` and `permit_chg` are populated for 1992-01. I kept the values as given. `ip_growth` 1992-01 enters only as the second lag in the 1992-03 training row, which is what the task specification implies.\n- The extreme COVID-19 months of 2020 (ip_growth from −14.14 to +6.35 %) stay in every training window, as the specification requires. They inflate the standardisation statistics and affect the coefficients.\n\n## Method\n- Origins t = 2021-09-01 … 2026-08-01 (60). Each origin's target is `ip_growth` at t+1.\n- Naive forecast: `ip_growth` at t.\n- Expanding-mean forecast: the mean of `ip_growth` from 1992-04 to t.\n- Ridge: the training rows are s = 1992-03 … t−1, with target y_{s+1}. The features are ip_growth_s, ip_growth_{s−1} and ip_growth_{s−2}, plus oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s.\n - At every origin I recompute the feature means and sample SDs (ddof = 1) and the target mean from that origin's training rows only. `normalisation_window = \"expanding\"`.\n - b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), with no penalised intercept. Forecast = ȳ + ((z_t − μ)/σ)·b.\n - The code asserts that no training target is dated after t.\n- I scored errors against current-vintage actuals and computed RMSE and MAE over the 60 origins.\n- `out/analysis.py` regenerates `results.json`, `forecasts.csv` (180 rows) and `uncertainty.json`.\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 RMSE improvement of ridge over naive: 1 − 0.5608/0.8831 = **+0.365** (36.5 % lower RMSE).\n- Relative RMSE improvement of ridge over the expanding mean: **−0.031**. Ridge's RMSE is 3.1 % *higher* than the mean's, and its MAE is also higher.\n\nSo the ridge model clearly beats the naive forecast but does **not** beat the expanding historical mean over this evaluation window. At the one-month horizon, the eight predictors add no out-of-sample value over the unconditional mean.\n\n## Uncertainty\nThese diagnostics are in `out/uncertainty.json`, produced by the same script.\n- **Diebold–Mariano tests** (squared loss, with the Harvey–Leybourne–Newbold small-sample correction and a t(59) reference):\n - ridge vs naive: statistic −3.36, p = 0.0014. Ridge is significantly better.\n - ridge vs mean: statistic +0.54, p = 0.59. There is no significant difference.\n- **Moving-block bootstrap** (block 6, B = 5000, seed 12345), 95 % intervals for the relative RMSE improvement:\n - vs naive: [0.248, 0.426].\n - vs mean: [−0.158, 0.056].\n- With only 60 evaluation months, a small loss to the mean cannot be told apart from a small gain.\n\n## Limitations and open issues\n- **Data vintage.** I used current-vintage, revised data and ignored publication lags (INDPRO, TCU and NEWORDER are revised substantially). These results are **not** real-time forecast performance.\n- **Model choices.** λ = 50 is fixed by the task, not tuned. The level variables (fedfunds, tcu) enter untransformed. The COVID outliers are untreated. Each of these could change the ridge results.\n- **Evaluation window.** It is one short window (2021-09 to 2026-08) and includes the post-pandemic recovery. The relative ranking may not generalise to other periods.\n- **Uncertainty method.** The DM test and the bootstrap both assume approximate stationarity of loss differentials. The bootstrap seed does not affect any value in `results.json`.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 60 origins (2021-09 to 2026-08), the ridge forecast has RMSE 0.561 vs 0.883 for the naive forecast, a 36.5% relative improvement.\",\"results_keys\":[\"rmse_ridge\",\"rmse_naive\",\"rel_improvement_vs_naive\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, publication lags ignored; DM p=0.0014 (uncertainty.json).\"},\n {\"id\":\"C2\",\"text\":\"The ridge forecast does not beat the expanding historical mean: RMSE 0.561 vs 0.544 (relative improvement -3.1%), and MAE is also higher.\",\"results_keys\":[\"rmse_ridge\",\"rmse_mean\",\"mae_ridge\",\"mae_mean\",\"rel_improvement_vs_mean\"],\"status\":\"supported\",\"caveat\":\"Difference is not statistically significant (DM p=0.59; block-bootstrap 95% CI [-0.158, 0.056]).\"},\n {\"id\":\"C3\",\"text\":\"All standardisation statistics and the target mean were recomputed from each origin's training window only (expanding).\",\"results_keys\":[\"normalisation_window\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Enforced in code by construction and an assertion on training target dates.\"}\n]\nEOF\nls 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ridge model clearly beats the naive forecast, but it does not beat the expanding historical mean. Over the 60 origins (2021-09 to 2026-08, one month ahead), the eight predictors add no out-of-sample value over the plain average.\n\n| Forecast | RMSE (% per month) | MAE (% per month) |\n|---|---|---|\n| Naive (last value) | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge (λ = 50) | 0.561 | 0.451 |\n\n- **Ridge vs naive:** ridge's RMSE is 36.5% lower (relative improvement +0.365). A Diebold–Mariano test gives p = 0.0014, so the gain is statistically significant.\n- **Ridge vs expanding mean:** ridge's RMSE is 3.1% higher (relative improvement −0.031). The difference is not significant (p = 0.59), and the 95% bootstrap interval for the improvement runs from −0.158 to 0.056. With only 60 test months, a small loss and a small gain can't be told apart.\n\nAs the task requires, all scaling statistics and the target mean are recomputed at each origin from that origin's training window only. The last origin trains on 413 rows. Running the script twice gave identical results.\n\n**Data issues:**\n- The only missing values are `neworder_chg` for 1992-01 and 1992-02, and neither falls in any training window.\n- The data README says the first month of each change series is empty, but `ip_growth`, `oil_chg` and `permit_chg` are filled in for 1992-01. I kept the values as given.\n- The extreme 2020 COVID months stay in every training window, as specified, and they affect the scaling and coefficients.\n\n**Limitations:**\n- The data are revised (current-vintage) figures, and publication delays are ignored. These numbers are not real-time forecast performance.\n- The penalty λ = 50 is fixed by the task rather than tuned.\n- The test window is a single, short, post-pandemic period, so the ranking may not hold elsewhere.\n\n**Addition beyond the spec:** I wrote a supplementary `out/uncertainty.json` with the significance tests and bootstrap intervals. The bootstrap seed (12345) is recorded as `random_seed` in `results.json`, but none of the `results.json` values depend on it.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv`\n- `report.md`\n- `claims.json`\n- `uncertainty.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2338,"cache_read_input_tokens":11804,"output_tokens":927,"output_tokens_details":{"thinking_tokens":81},"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":2338},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmdA8Wg8z8br2e68TAV","requestRef":"4366c64a-749c-45bb-ac63-b5a12b56ea72","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"810ecd3d-7c55-4d21-95fd-f4ee2d793099","timestamp":"2026-10-30T04:13:00.515Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0035","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","version":"2.1.314","gitBranch":"HEAD"}45{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"810ecd3d-7c55-4d21-95fd-f4ee2d793099","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef"}46{"type":"cost-state","sessionId":"dc45a84e-953b-48b3-bf18-4e4777b5cdef","totalCostUSD":0.2190972,"totalAPIDuration":63600,"totalAPIDurationWithoutRetries":63596,"totalToolDuration":3265,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":67032,"startTime":1793333513490,"modelUsage":{"claude-opus-5-5":{"inputTokens":16,"outputTokens":7312,"thinkingTokens":507,"cacheReadInputTokens":48016,"cacheCreationInputTokens":12638,"cacheCreationEphemeral5mInputTokens":12638,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2190972}},"hasUnknownModelCost":false}47