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In the current working directory, `TASK.md` describes a bounded scientific task and the directory `out/` contains the deliverables submitted by another assistant (you have not seen how they were produced and you must not look for their working transcript). There is no network access. Use `~/wp29/venv/bin/python` if you need to re-run or test code. Do not modify any file under `out/` except that you must create `out/review/review.json`.\n\nReview the deliverables against `TASK.md` only. Specifically check:\n1. Does `out/results.json` contain every required key with legitimately computed values? Re-run `out/analysis.py` in a scratch copy if needed and compare.\n2. Are the information-availability rules of `TASK.md` respected by the code (for example no use of future information, no use of forbidden inputs)? Read the code to verify; do not trust the report.\n3. Are the central claims in `out/report.md` and `out/claims.json` supported by the numbers in `out/results.json`, with appropriate uncertainty and caveats? Flag any claim that is stronger than the evidence.\n4. Are counts, exclusions, units, signs and magnitudes internally consistent? Are baseline comparisons present when the task requires them?\n5. Are limitations stated honestly? Is anything declared complete that is not?\n\nYou have no reference answers; do not guess what the \"right\" number is. Report only issues you can justify from the files. Write `out/review/review.json` as {\"verdict\": \"accept\"|\"revise\", \"issues\": [{\"id\": \"R1\", \"severity\": \"critical\"|\"major\"|\"minor\", \"location\": \"<file or key>\", \"issue\": \"...\", \"suggested_fix\": \"...\"}], \"summary\": \"...\"}. Use \"revise\" if there is at least one critical or major issue. Be concise and specific. 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The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\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 | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\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 72\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 01:33 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:31 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6641 oct. 30 01:33 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1835 oct. 30 01:33 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 329 oct. 30 01:33 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 6222 oct. 30 01:33 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5448 oct. 30 01:33 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1980 oct. 30 01:33 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:33 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:32 state\n{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"ARIMA210\",\n \"model_description\": \"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, re-estimated at every origin; h-step forecasts by recursive iteration.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.11979041413031663,\n \"mae_model_h1\": 0.09561928226076515,\n \"rmse_model_h3\": 0.1593741901249531,\n \"mae_model_h3\": 0.12090394601678622,\n \"rel_improvement_rmse_h1\": -0.022905396522720833,\n \"rel_improvement_rmse_h3\": -0.005103249292343737,\n \"ar1_coef_last_window\": 0.036569699661563586,\n \"ar2_coef_last_window\": -0.04222744929256375,\n \"dm_stat_h1\": 2.4559640158118414,\n \"dm_pvalue_h1\": 0.019315858142969447,\n \"dm_stat_h3\": 1.0949160137919336,\n \"dm_pvalue_h3\": 0.2812494464012166,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing UNRATE value(s) at 2025-10-01\",\n \"handling\": \"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; (origin,horizon) pairs with a missing target (2025-07-01 h=3; 2025-09-01 h=1) are in the ledger with empty actual and excluded from error statistics.\"\n },\n {\n \"issue\": \"Final (revised) vintage data, not real-time vintages\",\n \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"random_seed\": null\n}[\n {\n \"id\": \"C1\",\n \"text\": \"The pre-specified ARIMA(2,1,0) model does not improve on the naive no-change forecast at h=1: its RMSE is about 2.3% higher.\",\n \"results_keys\": [\n \"rmse_model_h1\",\n \"rmse_naive_h1\",\n \"rel_improvement_rmse_h1\",\n \"mae_model_h1\",\n \"mae_naive_h1\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"35 evaluated pairs; final-vintage data; only one model specification.\"\n },\n {\n \"id\": \"C2\",\n \"text\": \"At h=3 the ARIMA(2,1,0) model is also slightly worse than naive: RMSE about 0.5% higher and MAE also higher.\",\n \"results_keys\": [\n \"rmse_model_h3\",\n \"rmse_naive_h3\",\n \"rel_improvement_rmse_h3\",\n \"mae_model_h3\",\n \"mae_naive_h3\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"Overlapping 3-step errors; the difference is very small.\"\n },\n {\n \"id\": \"C3\",\n \"text\": \"The h=1 loss difference is statistically significant by the Diebold-Mariano test (p about 0.02); the h=3 difference is not (p about 0.28).\",\n \"results_keys\": [\n \"dm_stat_h1\",\n \"dm_pvalue_h1\",\n \"dm_stat_h3\",\n \"dm_pvalue_h3\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"The DM test is not designed for nested models and the forecasts are nearly identical, so the p-values are approximate.\"\n },\n {\n \"id\": \"C4\",\n \"text\": \"35 of the 36 origin pairs per horizon were evaluated, because the 2025-10-01 observation is missing.\",\n \"results_keys\": [\n \"n_evaluated_h1\",\n \"n_evaluated_h3\",\n \"n_origins\",\n \"data_issues\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"\"\n },\n {\n \"id\": \"C5\",\n \"text\": \"The estimated AR coefficients on monthly changes in the last window are close to zero, so the model's forecasts are nearly identical to naive.\",\n \"results_keys\": [\n \"ar1_coef_last_window\",\n \"ar2_coef_last_window\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"Probably influenced by the 2020 COVID outlier kept in the sample.\"\n }\n]","is_error":false}]},"uuid":"79949a36-7bd3-4604-bdb8-77970063339b","timestamp":"2026-10-30T05:33:40.791Z","toolUseResult":{"stdout":"# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: can a simple time-series model beat the naive forecast?\n\n## Scientific question\nOver the last three years, does a simple autoregressive-type model forecast the US civilian unemployment rate better than the naive (no-change) forecast at one- and three-month horizons? The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\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 | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\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 72\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 01:33 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:31 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6641 oct. 30 01:33 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1835 oct. 30 01:33 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 329 oct. 30 01:33 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 6222 oct. 30 01:33 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5448 oct. 30 01:33 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1980 oct. 30 01:33 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 01:33 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:32 state\n{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"ARIMA210\",\n \"model_description\": \"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, re-estimated at every origin; h-step forecasts by recursive iteration.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h1\": 0.11979041413031663,\n \"mae_model_h1\": 0.09561928226076515,\n \"rmse_model_h3\": 0.1593741901249531,\n \"mae_model_h3\": 0.12090394601678622,\n \"rel_improvement_rmse_h1\": -0.022905396522720833,\n \"rel_improvement_rmse_h3\": -0.005103249292343737,\n \"ar1_coef_last_window\": 0.036569699661563586,\n \"ar2_coef_last_window\": -0.04222744929256375,\n \"dm_stat_h1\": 2.4559640158118414,\n \"dm_pvalue_h1\": 0.019315858142969447,\n \"dm_stat_h3\": 1.0949160137919336,\n \"dm_pvalue_h3\": 0.2812494464012166,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing UNRATE value(s) at 2025-10-01\",\n \"handling\": \"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; (origin,horizon) pairs with a missing target (2025-07-01 h=3; 2025-09-01 h=1) are in the ledger with empty actual and excluded from error statistics.\"\n },\n {\n \"issue\": \"Final (revised) vintage data, not real-time vintages\",\n \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"random_seed\": null\n}[\n {\n \"id\": \"C1\",\n \"text\": \"The pre-specified ARIMA(2,1,0) model does not improve on the naive no-change forecast at h=1: its RMSE is about 2.3% higher.\",\n \"results_keys\": [\n \"rmse_model_h1\",\n \"rmse_naive_h1\",\n \"rel_improvement_rmse_h1\",\n \"mae_model_h1\",\n \"mae_naive_h1\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"35 evaluated pairs; final-vintage data; only one model specification.\"\n },\n {\n \"id\": \"C2\",\n \"text\": \"At h=3 the ARIMA(2,1,0) model is also slightly worse than naive: RMSE about 0.5% higher and MAE also higher.\",\n \"results_keys\": [\n \"rmse_model_h3\",\n \"rmse_naive_h3\",\n \"rel_improvement_rmse_h3\",\n \"mae_model_h3\",\n \"mae_naive_h3\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"Overlapping 3-step errors; the difference is very small.\"\n },\n {\n \"id\": \"C3\",\n \"text\": \"The h=1 loss difference is statistically significant by the Diebold-Mariano test (p about 0.02); the h=3 difference is not (p about 0.28).\",\n \"results_keys\": [\n \"dm_stat_h1\",\n \"dm_pvalue_h1\",\n \"dm_stat_h3\",\n \"dm_pvalue_h3\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"The DM test is not designed for nested models and the forecasts are nearly identical, so the p-values are approximate.\"\n },\n {\n \"id\": \"C4\",\n \"text\": \"35 of the 36 origin pairs per horizon were evaluated, because the 2025-10-01 observation is missing.\",\n \"results_keys\": [\n \"n_evaluated_h1\",\n \"n_evaluated_h3\",\n \"n_origins\",\n \"data_issues\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"\"\n },\n {\n \"id\": \"C5\",\n \"text\": \"The estimated AR coefficients on monthly changes in the last window are close to zero, so the model's forecasts are nearly identical to naive.\",\n \"results_keys\": [\n \"ar1_coef_last_window\",\n \"ar2_coef_last_window\"\n ],\n \"status\": \"supported\",\n \"caveat\": \"Probably influenced by the 2020 COVID outlier kept in the sample.\"\n }\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"69917b47-5bbf-4532-b085-cb8c38ecbcdc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","hash":"beb6a7de5c51298e3d1d9d5799f62f6228b4416851bfde50724f4facb4c650dc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0133qKyPVDH3RJ23CKs9EfJB","name":"Bash","input":{"command":"cat TASK.md; 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writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nMODEL_NAME = \"ARIMA210\"\nHORIZONS = [1, 3]\nSEED = None # no randomness used\n\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"))\nn_raw = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns = raw.set_index(\"observation_date\")[\"UNRATE\"].astype(float)\ns.index = pd.DatetimeIndex(s.index, freq=\"MS\")\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nmissing = s[s.isna()].index\n\nrows = []\nfor t in origins:\n train = s.loc[:t] # only observations dated <= t\n naive = train.dropna().iloc[-1]\n mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n res = mod.fit(disp=False, maxiter=200)\n fc = res.forecast(steps=max(HORIZONS))\n last_params = res.params # forecasts for t+1..t+3 (Kalman state handles gaps)\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s.get(tgt, np.nan)\n rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(naive), actual=act, model=\"naive\"))\n rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(fc.loc[tgt]), actual=act, model=MODEL_NAME))\nled = pd.DataFrame(rows)\nassert (led.target_date > led.origin_date).all()\nout = led.copy()\nout[\"origin_date\"] = out.origin_date.dt.strftime(\"%Y-%m-%d\")\nout[\"target_date\"] = out.target_date.dt.strftime(\"%Y-%m-%d\")\nout[\"forecast\"] = out.forecast.round(10)\nout.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\ndef err(model, h):\n d = led[(led.model == model) & (led.horizon == h)].dropna(subset=[\"actual\"])\n e = d.actual - d.forecast\n return len(d), float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n \"model_description\": (\"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), \"\n \"estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months \"\n \"treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, \"\n \"re-estimated at every origin; h-step forecasts by recursive iteration.\"),\n \"n_origins\": int(len(origins)),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n n, rn, mn = err(\"naive\", h)\n n2, rm, mm = err(MODEL_NAME, h)\n assert n == n2\n R[f\"n_evaluated_h{h}\"] = n\n R[f\"rmse_naive_h{h}\"], R[f\"mae_naive_h{h}\"] = rn, mn\n R[f\"rmse_model_h{h}\"], R[f\"mae_model_h{h}\"] = rm, mm\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - rm / rn\norder = [\"task_id\", \"model_name\", \"model_description\", \"n_origins\", \"first_origin\", \"last_origin\",\n \"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]\n# Diebold-Mariano test (squared-error loss, HAC variance with h-1 Newey-West lags,\n# Harvey-Leybourne-Newbold small-sample correction, t(n-1) reference). Positive stat = model worse.\nfrom scipy import stats\nfor h in HORIZONS:\n a = led[(led.model == \"naive\") & (led.horizon == h)].reset_index(drop=True)\n b = led[(led.model == MODEL_NAME) & (led.horizon == h)].reset_index(drop=True)\n ok = a.actual.notna().values\n d = ((b.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2).values[ok]\n n = len(d); dbar = d.mean(); dc = d - dbar\n lrv = np.sum(dc ** 2) / n\n for k in range(1, h):\n lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n dm = dbar / np.sqrt(lrv / n)\n hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n R[f\"dm_stat_h{h}\"] = float(dm * hln)\n R[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm * hln), df=n - 1))\nR[\"ar1_coef_last_window\"] = float(last_params[\"ar.L1\"])\nR[\"ar2_coef_last_window\"] = float(last_params[\"ar.L2\"])\norder += [\"ar1_coef_last_window\", \"ar2_coef_last_window\"]\norder += [\"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"]\nR = {k: R[k] for k in order}\nmiss_pairs = led[(led.model == \"naive\") & led.actual.isna()]\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [{\"issue\": f\"Missing UNRATE value(s) at {', '.join(d.strftime('%Y-%m-%d') for d in missing)}\",\n \"handling\": (\"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier \"\n \"observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; \"\n \"(origin,horizon) pairs with a missing target (\"\n + \"; \".join(f\"{o.strftime('%Y-%m-%d')} h={h}\" for o, h in zip(miss_pairs.origin_date, miss_pairs.horizon))\n + \") are in the ledger with empty actual and excluded from error statistics.\")},\n {\"issue\": \"Final (revised) vintage data, not real-time vintages\",\n \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"}]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(R, f, indent=2)\n\n# exclusion ledger (observations in the last training window)\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n rationale=\"empty value field in source (2025-10-01); treated as missing, not imputed\"),\n dict(step=2, rule=\"keep observations dated <= last origin (2026-06-01) for the last training window\",\n n_before=n1, n_after=R[\"n_analysed\"], n_removed=n1 - R[\"n_analysed\"],\n rationale=\"information-availability rule; later months used only as actuals\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nfor k in order[6:]:\n print(f\"{k}: {R[k]}\")\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver the 36 expanding-window forecast origins 2023-07-01 … 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, %, SA, final vintage) more accurately than the naive no-change forecast at horizons of 1 and 3 months? Accuracy is measured by out-of-sample RMSE and MAE, and the relative improvement is 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 data rows (`n_input_rows`), covering 1948-01 to 2026-09. Its SHA-256 matches the README. There are no duplicate dates and no calendar gaps. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). It was not imputed. Observation ledger (`out/exclusions.csv`): 945 rows → drop 1 missing → 944 → keep only observations dated ≤ the last origin → 941 observations in the last training window (`n_analysed`).\n- Forecast pairs: the ledger `out/forecasts.csv` holds all 144 rows. The missing month is the target of two pairs: (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3). Both stay in the ledger with an empty `actual` and are left out of the error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- At origin 2025-10-01 (whose own value is missing), the naive forecast uses the 2025-09-01 value. The ARIMA model forecasts from its Kalman-filter state given the data through that origin.\n\n## Method\n- **Naive baseline:** the latest non-missing observation dated ≤ t, used for every horizon.\n- **Model (`model_name` = ARIMA210):** see `model_description`. It is an ARIMA(2,1,0) without a constant, i.e. an AR(2) on monthly changes. It is fitted by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter, with missing months treated as missing). The sample starts 1948-01-01 and ends at each origin t, and the model is re-fitted at every origin. 3-step forecasts are built recursively.\n- I fixed this specification before computing any evaluation error (`out/state/log.md`). No other model was tried on the evaluation window. All estimation uses only observations dated ≤ t. No randomness is involved (`random_seed` = null).\n- **Uncertainty:** a Diebold–Mariano test of equal mean squared error (keys `dm_stat_h*`, `dm_pvalue_h*`). It uses a Newey–West variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and a t(n−1) reference. A positive statistic means the model's squared errors are larger than the naive ones.\n- Replication: `~/wp29/venv/bin/python out/analysis.py`.\n\n## Results\n| horizon | n | RMSE naive | MAE naive | RMSE ARIMA210 | MAE ARIMA210 | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\n(Units: percentage points.) The ARIMA(2,1,0) model did **not** beat the naive forecast at either horizon:\n- At h=1 its RMSE is about 2.3% higher than naive (`rel_improvement_rmse_h1`).\n- At h=3 its RMSE is about 0.5% higher (`rel_improvement_rmse_h3`).\n- Its MAE is also higher at both horizons (`mae_model_h1`, `mae_model_h3`).\n\nThe estimated AR coefficients on monthly changes are close to zero. In the last window they are 0.037 and −0.042 (`ar1_coef_last_window`, `ar2_coef_last_window`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** the Diebold–Mariano statistic is 2.46 with p = 0.019 (`dm_stat_h1`, `dm_pvalue_h1`). Taken at face value, the model's loss is significantly higher than naive at the 5% level.\n- **h=3:** the statistic is 1.09 with p = 0.28 (`dm_stat_h3`, `dm_pvalue_h3`), so there is no significant difference.\n- Caveats on these tests:\n - The model nests the random walk, and its forecasts are almost the same as naive. The standard DM test is not designed for nested models (Clark–West would be the usual alternative, and it was not computed). So these p-values are approximate.\n - With 35 evaluation points, and overlapping errors at h=3, all accuracy estimates are imprecise.\n - The absolute differences in RMSE are below 0.003 pp.\n\n## Limitations and open issues\n- **Final vintage:** the evaluation uses revised data, so it is not a real-time assessment.\n- **One specification only:** a single model was fixed in advance. The result does not show that no AR-type model can beat naive. The 2020 COVID spike (14.8% in 2020-04) is kept in every estimation window. It probably pulls the AR coefficients toward zero, which makes the model nearly a random walk. Alternatives such as a 2020 dummy, a later sample start or robust estimation were deliberately not explored, because choosing among them on this evaluation window would be selection on test data.\n- **Missing month:** the 2025-10 observation is missing. It removes one evaluated pair per horizon, and the training windows after 2025-10 contain a gap, which the Kalman filter handles.\n- **Short window:** 36 origins in a relatively calm period (the rate stayed between about 3.5% and 4.6%). The results may not carry over to recessions.\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,\"empty value field in source (2025-10-01); treated as missing, not imputed\"\n2,keep observations dated <= last origin (2026-06-01) for the last training window,944,941,3,information-availability rule; later months used only as actuals\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\norigin_date,target_date,horizon,forecast,actual,model\n2023-07-01,2023-08-01,1,3.5,3.7,naive\n2023-07-01,2023-08-01,1,3.496160281,3.7,ARIMA210\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.500630386,3.9,ARIMA210\n 145 out/forecasts.csv\n100:2025-07-01,2025-10-01,3,4.3,,naive\n101:2025-07-01,2025-10-01,3,4.307063775,,ARIMA210\n106:2025-09-01,2025-10-01,1,4.4,,naive\n107:2025-09-01,2025-10-01,1,4.403716752,,ARIMA210\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\n2025-10-01,\n2025-11-01,4.5\n2025-12-01,4.4\n2026-01-01,4.3\n2026-02-01,4.4\n2026-03-01,4.3\n2026-04-01,4.3\n2026-05-01,4.3\n2026-06-01,4.2\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2","is_error":false}]},"uuid":"40308f57-4a24-4c3c-91f3-40b7795996dd","timestamp":"2026-10-30T05:33:43.796Z","toolUseResult":{"stdout":"\"\"\"F3-dev-01: rolling-origin forecasts of UNRATE, naive vs ARIMA(2,1,0).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nMODEL_NAME = \"ARIMA210\"\nHORIZONS = [1, 3]\nSEED = None # no randomness used\n\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"))\nn_raw = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns = raw.set_index(\"observation_date\")[\"UNRATE\"].astype(float)\ns.index = pd.DatetimeIndex(s.index, freq=\"MS\")\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nmissing = s[s.isna()].index\n\nrows = []\nfor t in origins:\n train = s.loc[:t] # only observations dated <= t\n naive = train.dropna().iloc[-1]\n mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n res = mod.fit(disp=False, maxiter=200)\n fc = res.forecast(steps=max(HORIZONS))\n last_params = res.params # forecasts for t+1..t+3 (Kalman state handles gaps)\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s.get(tgt, np.nan)\n rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(naive), actual=act, model=\"naive\"))\n rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(fc.loc[tgt]), actual=act, model=MODEL_NAME))\nled = pd.DataFrame(rows)\nassert (led.target_date > led.origin_date).all()\nout = led.copy()\nout[\"origin_date\"] = out.origin_date.dt.strftime(\"%Y-%m-%d\")\nout[\"target_date\"] = out.target_date.dt.strftime(\"%Y-%m-%d\")\nout[\"forecast\"] = out.forecast.round(10)\nout.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\ndef err(model, h):\n d = led[(led.model == model) & (led.horizon == h)].dropna(subset=[\"actual\"])\n e = d.actual - d.forecast\n return len(d), float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n \"model_description\": (\"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), \"\n \"estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months \"\n \"treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, \"\n \"re-estimated at every origin; h-step forecasts by recursive iteration.\"),\n \"n_origins\": int(len(origins)),\n \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n n, rn, mn = err(\"naive\", h)\n n2, rm, mm = err(MODEL_NAME, h)\n assert n == n2\n R[f\"n_evaluated_h{h}\"] = n\n R[f\"rmse_naive_h{h}\"], R[f\"mae_naive_h{h}\"] = rn, mn\n R[f\"rmse_model_h{h}\"], R[f\"mae_model_h{h}\"] = rm, mm\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - rm / rn\norder = [\"task_id\", \"model_name\", \"model_description\", \"n_origins\", \"first_origin\", \"last_origin\",\n \"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]\n# Diebold-Mariano test (squared-error loss, HAC variance with h-1 Newey-West lags,\n# Harvey-Leybourne-Newbold small-sample correction, t(n-1) reference). Positive stat = model worse.\nfrom scipy import stats\nfor h in HORIZONS:\n a = led[(led.model == \"naive\") & (led.horizon == h)].reset_index(drop=True)\n b = led[(led.model == MODEL_NAME) & (led.horizon == h)].reset_index(drop=True)\n ok = a.actual.notna().values\n d = ((b.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2).values[ok]\n n = len(d); dbar = d.mean(); dc = d - dbar\n lrv = np.sum(dc ** 2) / n\n for k in range(1, h):\n lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n dm = dbar / np.sqrt(lrv / n)\n hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n R[f\"dm_stat_h{h}\"] = float(dm * hln)\n R[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm * hln), df=n - 1))\nR[\"ar1_coef_last_window\"] = float(last_params[\"ar.L1\"])\nR[\"ar2_coef_last_window\"] = float(last_params[\"ar.L2\"])\norder += [\"ar1_coef_last_window\", \"ar2_coef_last_window\"]\norder += [\"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"]\nR = {k: R[k] for k in order}\nmiss_pairs = led[(led.model == \"naive\") & led.actual.isna()]\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [{\"issue\": f\"Missing UNRATE value(s) at {', '.join(d.strftime('%Y-%m-%d') for d in missing)}\",\n \"handling\": (\"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier \"\n \"observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; \"\n \"(origin,horizon) pairs with a missing target (\"\n + \"; \".join(f\"{o.strftime('%Y-%m-%d')} h={h}\" for o, h in zip(miss_pairs.origin_date, miss_pairs.horizon))\n + \") are in the ledger with empty actual and excluded from error statistics.\")},\n {\"issue\": \"Final (revised) vintage data, not real-time vintages\",\n \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"}]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(R, f, indent=2)\n\n# exclusion ledger (observations in the last training window)\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n rationale=\"empty value field in source (2025-10-01); treated as missing, not imputed\"),\n dict(step=2, rule=\"keep observations dated <= last origin (2026-06-01) for the last training window\",\n n_before=n1, n_after=R[\"n_analysed\"], n_removed=n1 - R[\"n_analysed\"],\n rationale=\"information-availability rule; later months used only as actuals\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nfor k in order[6:]:\n print(f\"{k}: {R[k]}\")\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver the 36 expanding-window forecast origins 2023-07-01 … 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, %, SA, final vintage) more accurately than the naive no-change forecast at horizons of 1 and 3 months? Accuracy is measured by out-of-sample RMSE and MAE, and the relative improvement is 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 data rows (`n_input_rows`), covering 1948-01 to 2026-09. Its SHA-256 matches the README. There are no duplicate dates and no calendar gaps. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). It was not imputed. Observation ledger (`out/exclusions.csv`): 945 rows → drop 1 missing → 944 → keep only observations dated ≤ the last origin → 941 observations in the last training window (`n_analysed`).\n- Forecast pairs: the ledger `out/forecasts.csv` holds all 144 rows. The missing month is the target of two pairs: (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3). Both stay in the ledger with an empty `actual` and are left out of the error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- At origin 2025-10-01 (whose own value is missing), the naive forecast uses the 2025-09-01 value. The ARIMA model forecasts from its Kalman-filter state given the data through that origin.\n\n## Method\n- **Naive baseline:** the latest non-missing observation dated ≤ t, used for every horizon.\n- **Model (`model_name` = ARIMA210):** see `model_description`. It is an ARIMA(2,1,0) without a constant, i.e. an AR(2) on monthly changes. It is fitted by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter, with missing months treated as missing). The sample starts 1948-01-01 and ends at each origin t, and the model is re-fitted at every origin. 3-step forecasts are built recursively.\n- I fixed this specification before computing any evaluation error (`out/state/log.md`). No other model was tried on the evaluation window. All estimation uses only observations dated ≤ t. No randomness is involved (`random_seed` = null).\n- **Uncertainty:** a Diebold–Mariano test of equal mean squared error (keys `dm_stat_h*`, `dm_pvalue_h*`). It uses a Newey–West variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and a t(n−1) reference. A positive statistic means the model's squared errors are larger than the naive ones.\n- Replication: `~/wp29/venv/bin/python out/analysis.py`.\n\n## Results\n| horizon | n | RMSE naive | MAE naive | RMSE ARIMA210 | MAE ARIMA210 | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\n(Units: percentage points.) The ARIMA(2,1,0) model did **not** beat the naive forecast at either horizon:\n- At h=1 its RMSE is about 2.3% higher than naive (`rel_improvement_rmse_h1`).\n- At h=3 its RMSE is about 0.5% higher (`rel_improvement_rmse_h3`).\n- Its MAE is also higher at both horizons (`mae_model_h1`, `mae_model_h3`).\n\nThe estimated AR coefficients on monthly changes are close to zero. In the last window they are 0.037 and −0.042 (`ar1_coef_last_window`, `ar2_coef_last_window`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** the Diebold–Mariano statistic is 2.46 with p = 0.019 (`dm_stat_h1`, `dm_pvalue_h1`). Taken at face value, the model's loss is significantly higher than naive at the 5% level.\n- **h=3:** the statistic is 1.09 with p = 0.28 (`dm_stat_h3`, `dm_pvalue_h3`), so there is no significant difference.\n- Caveats on these tests:\n - The model nests the random walk, and its forecasts are almost the same as naive. The standard DM test is not designed for nested models (Clark–West would be the usual alternative, and it was not computed). So these p-values are approximate.\n - With 35 evaluation points, and overlapping errors at h=3, all accuracy estimates are imprecise.\n - The absolute differences in RMSE are below 0.003 pp.\n\n## Limitations and open issues\n- **Final vintage:** the evaluation uses revised data, so it is not a real-time assessment.\n- **One specification only:** a single model was fixed in advance. The result does not show that no AR-type model can beat naive. The 2020 COVID spike (14.8% in 2020-04) is kept in every estimation window. It probably pulls the AR coefficients toward zero, which makes the model nearly a random walk. Alternatives such as a 2020 dummy, a later sample start or robust estimation were deliberately not explored, because choosing among them on this evaluation window would be selection on test data.\n- **Missing month:** the 2025-10 observation is missing. It removes one evaluated pair per horizon, and the training windows after 2025-10 contain a gap, which the Kalman filter handles.\n- **Short window:** 36 origins in a relatively calm period (the rate stayed between about 3.5% and 4.6%). The results may not carry over to recessions.\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,\"empty value field in source (2025-10-01); treated as missing, not imputed\"\n2,keep observations dated <= last origin (2026-06-01) for the last training window,944,941,3,information-availability rule; later months used only as actuals\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json\norigin_date,target_date,horizon,forecast,actual,model\n2023-07-01,2023-08-01,1,3.5,3.7,naive\n2023-07-01,2023-08-01,1,3.496160281,3.7,ARIMA210\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.500630386,3.9,ARIMA210\n 145 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horizon\nARIMA210 1 36\n 3 36\nnaive 1 36\n 3 36\ndtype: int64\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: audit done; 1 missing value (2025-10-01).\n- 2026-10-30 Decision (made before computing any evaluation errors): model = ARIMA(2,1,0) without constant, Gaussian MLE via statsmodels SARIMAX (Kalman filter handles the missing month), estimation sample from 1948-01-01 to origin, re-estimated at every origin. 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Source: U.S. Bureau of Labor Statistics, Current Population Survey, via FRED (Federal Reserve Bank of St. Louis).\n- **Retrieval URL**: https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE\n- **Retrieved (UTC)**: 2026-10-30T03:20:36Z\n- **SHA-256**: 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\n- **Format**: exactly as served by FRED: header `observation_date,UNRATE`, one row per month dated on the first day of the month (YYYY-MM-01), 945 data rows from 1948-01-01 to 2026-09-01. Missing observations are served as an empty value field.\n- **Units**: percent of the civilian labour force.\n- **Vintage caveat**: this is the current (final) vintage as of the retrieval time. Values may have been revised since their first publication (annual seasonal-factor revisions). Rolling-origin results on this file therefore measure forecastability of the revised series, not historical real-time performance.\n- **License / terms**: FRED terms of use (https://fred.stlouisfed.org/legal/); BLS data are public domain.","is_error":false}]},"uuid":"1b7ee58e-aacb-41c6-9751-e541c6921488","timestamp":"2026-10-30T05:33:54.373Z","toolUseResult":{"stdout":"SAME\n0.016127461000000398\nmodel horizon\nARIMA210 1 36\n 3 36\nnaive 1 36\n 3 36\ndtype: int64\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: audit done; 1 missing value (2025-10-01).\n- 2026-10-30 Decision (made before computing any evaluation errors): model = ARIMA(2,1,0) without constant, Gaussian MLE via statsmodels SARIMAX (Kalman filter handles the missing month), estimation sample from 1948-01-01 to origin, re-estimated at every origin. No alternative models will be compared on the evaluation window (avoid selection on test data). 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The models are nested and the forecasts are nearly identical (max |ARIMA - naive| = 0.016 pp), so the DM null distribution is non-standard. The caveat acknowledges this, but the claim text still states significance without qualification. The RMSE gap is only 0.0027 pp.\",\n \"suggested_fix\": \"Rephrase as 'nominally significant under a standard DM test (approximate for nested models)', or compute Clark-West, and stress that the gap is too small to matter in practice.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md Results\",\n \"issue\": \"The report says model and naive forecasts differ 'by less than 0.2 pp in every case'. On re-run the largest absolute difference is about 0.016 pp, an order of magnitude smaller. The statement is true but loose, and it understates how close the two forecasts are.\",\n \"suggested_fix\": \"Report the actual maximum difference (~0.016 pp).\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C5 caveat; out/report.md Limitations\",\n \"issue\": \"The idea that the 2020 COVID spike pulls the AR coefficients toward zero is plausible, but it was not tested.\",\n \"suggested_fix\": \"Label it explicitly as an untested conjecture.\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"out/exclusions.csv step 1\",\n \"issue\": \"The ledger says the missing row is 'dropped'. For the ARIMA it is actually kept as a missing value inside the Kalman filter, not removed from the series. 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The models are nested and the forecasts are nearly identical (max |ARIMA - naive| = 0.016 pp), so the DM null distribution is non-standard. The caveat acknowledges this, but the claim text still states significance without qualification. The RMSE gap is only 0.0027 pp.\",\n \"suggested_fix\": \"Rephrase as 'nominally significant under a standard DM test (approximate for nested models)', or compute Clark-West, and stress that the gap is too small to matter in practice.\"\n },\n {\n \"id\": \"R2\",\n \"severity\": \"minor\",\n \"location\": \"out/report.md Results\",\n \"issue\": \"The report says model and naive forecasts differ 'by less than 0.2 pp in every case'. On re-run the largest absolute difference is about 0.016 pp, an order of magnitude smaller. The statement is true but loose, and it understates how close the two forecasts are.\",\n \"suggested_fix\": \"Report the actual maximum difference (~0.016 pp).\"\n },\n {\n \"id\": \"R3\",\n \"severity\": \"minor\",\n \"location\": \"out/claims.json C5 caveat; out/report.md Limitations\",\n \"issue\": \"The idea that the 2020 COVID spike pulls the AR coefficients toward zero is plausible, but it was not tested.\",\n \"suggested_fix\": \"Label it explicitly as an untested conjecture.\"\n },\n {\n \"id\": \"R4\",\n \"severity\": \"minor\",\n \"location\": \"out/exclusions.csv step 1\",\n \"issue\": \"The ledger says the missing row is 'dropped'. For the ARIMA it is actually kept as a missing value inside the Kalman filter, not removed from the series. This wording affects only the observation count, not the results.\",\n \"suggested_fix\": \"Word it as 'missing value excluded from observation count (kept as NaN for the state-space filter)'.\"\n }\n ],\n \"summary\": \"A scratch re-run reproduces results.json and forecasts.csv exactly. All required keys are present, and the ledger has 144 rows with the two missing-target pairs left blank. Counts check out: 35 evaluated pairs per horizon and n_analysed = 941. The code respects the information-availability rules: it fits on s.loc[:t] only and re-estimates at each origin. The naive forecast correctly falls back to an earlier value at the missing 2025-10 origin, and the baseline comparison is present. The model was fixed before any evaluation and limitations are stated honestly. Only minor wording and over-statement issues remain.\"\n}\nEOF\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON"}}]}}33{"type":"api-request","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","id":"a22bb70b-edf7-490e-94ff-2166403e29c3","timestamp":"2026-10-30T05:34:06.896Z","version":"2.1.314","querySource":"sdk","shapeHash":"7d218f5e4413e751251cf1075b08a89cc4f1d0a39f546590daf07b19db176a7f","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":"cea9a28b-9e6d-4415-98ee-328c99221f2c","keep":8,"tail":["1501a27e25c56f6b6ed843d957639ce0862b422eaecdc1d114536cae05072ee9","78afcca1bb1288a02ff616fe0fbf3f45cf2493c6aab0b8664eafa51dcf72276f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a34215ed25b7c242fead9501849110555efd7c2340aeb1a1a5e0e6e1936655c6"}34{"parentUuid":"5b8f809f-ae7b-4cfd-b87d-d434a17b52cb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspS5DgnCQTnrn2Df96","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1330,"cache_read_input_tokens":14344,"output_tokens":628,"output_tokens_details":{"thinking_tokens":5},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1330},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5355,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXspRt3kb648LFRCeoF1","requestRef":"a22bb70b-edf7-490e-94ff-2166403e29c3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8aa2307b-feb9-43c5-bfa3-7ea08287af59","timestamp":"2026-10-30T05:34:13.307Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"8aa2307b-feb9-43c5-bfa3-7ea08287af59","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXspS5DgnCQTnrn2Df96","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**, with four minor issues and no critical or major ones. I wrote the review to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy and it reproduced `results.json` and `forecasts.csv` exactly. All required keys are present. The forecast ledger has the expected 144 rows, and the two pairs whose target month (2025-10, missing in the data) has no value stay in the ledger with an empty actual and are left out of the error statistics. That gives 35 evaluated pairs per horizon, and the count of 941 observations in the last training window checks out.\n\nThe code follows the task's rules on what a forecast may use. It fits only on data up to each origin and re-estimates the model at every origin. The naive forecast correctly falls back to the September value at the 2025-10 origin. The model (an autoregressive model on monthly changes) was fixed before any evaluation errors were computed, the comparison with the naive forecast is there, and the limitations are stated honestly.\n\nThe four minor issues:\n1. **Significance claim worded too strongly:** claim C3 says the h=1 difference is \"statistically significant\" (p = 0.019) without qualification. The two models are nested and their forecasts are nearly identical, so that p-value is only approximate. The RMSE gap is just 0.0027 percentage points.\n2. **Loose figure in the report:** it says model and naive forecasts differ by \"less than 0.2 pp\". The actual largest difference is about 0.016 pp, ten times smaller.\n3. **Untested explanation:** the claim that the 2020 COVID spike pulls the model's coefficients toward zero is plausible but was never checked, and should be labelled as a guess.\n4. **Wording in `out/exclusions.csv`:** it says the missing month is \"dropped\", but the model keeps it in the series as a missing value. This affects only the observation count, not the results."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1330,"cache_read_input_tokens":14344,"output_tokens":628,"output_tokens_details":{"thinking_tokens":5},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1330},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXspRt3kb648LFRCeoF1","requestRef":"a22bb70b-edf7-490e-94ff-2166403e29c3","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a7f530d-4844-4130-bbb6-75ff07bdc6e2","timestamp":"2026-10-30T05:34:13.319Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","version":"2.1.314","gitBranch":"HEAD"}36{"type":"last-prompt","lastPrompt":"Review","leafUuid":"6a7f530d-4844-4130-bbb6-75ff07bdc6e2","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d"}37{"type":"cost-state","sessionId":"9a5014ce-1393-43f9-a1b7-054156dabe0d","totalCostUSD":0.1405284,"totalAPIDuration":33866,"totalAPIDurationWithoutRetries":33864,"totalToolDuration":1643,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":35624,"startTime":1793338417715,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":3111,"thinkingTokens":699,"cacheReadInputTokens":37052,"cacheCreationInputTokens":14170,"cacheCreationEphemeral5mInputTokens":14170,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1405284}},"hasUnknownModelCost":false}38