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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 5442 oct. 30 01:32 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1535 oct. 30 01:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 437 oct. 30 01:32 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 6280 oct. 30 01:32 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4609 oct. 30 01:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1759 oct. 30 01:32 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\": \"AR2_diff\",\n \"model_description\": \"AR(2) on first differences of UNRATE with intercept, estimated by OLS on the expanding window from 1948-01 (first usable difference regression row 1948-04) to the origin; levels forecast by iterating the difference recursion and cumulating; lag order fixed a priori, no selection.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985529982763829,\n \"mae_model_h1\": 0.0956535147914287,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.15974133959765854,\n \"mae_model_h3\": 0.12123991414571429,\n \"dm_stat_h1\": 2.5019139072700796,\n \"dm_pvalue_h1\": 0.017327515364319198,\n \"dm_stat_h3\": 1.5733474095224582,\n \"dm_pvalue_h3\": 0.12489906317464519,\n \"rel_improvement_rmse_h1\": -0.023459463644279088,\n \"rel_improvement_rmse_h3\": -0.0074187002929284596,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing UNRATE value at 2025-10-01 (empty field)\",\n \"handling\": \"Pairs targeting this month kept in ledger with empty actual and excluded from error stats; naive at origin 2025-10 uses 2025-09; model forecasts from 2025-09 at that origin and linearly interpolates the gap (using only data <= origin) at later origins.\"\n },\n {\n \"issue\": \"Final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"Errors computed on data as given; 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{\"id\":\"C1\",\"text\":\"Over 35 evaluated origins at h=1, the AR(2)-on-differences model has higher RMSE than the naive forecast (relative improvement about -2.3%).\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"Final-vintage data; small sample.\"},\n{\"id\":\"C2\",\"text\":\"At h=3 the model's RMSE is also slightly higher than naive (relative improvement about -0.7%).\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically significant (DM p=0.125).\"},\n{\"id\":\"C3\",\"text\":\"MAE gives the same ranking as RMSE at both horizons (naive better).\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n{\"id\":\"C4\",\"text\":\"The h=1 loss difference is statistically significant (DM p=0.017); the h=3 difference is not (p=0.125).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"Asymptotic test, n=35, HLN-corrected; economically negligible magnitudes.\"},\n{\"id\":\"C5\",\"text\":\"One target month (2025-10-01) is missing, leaving 35 evaluated pairs per horizon out of 36.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n{\"id\":\"C6\",\"text\":\"Results do not establish real-time forecasting performance.\",\"results_keys\":[\"data_issues\"],\"status\":\"not_evaluated\",\"caveat\":\"Only the final vintage is available.\"}\n]","is_error":false}]},"uuid":"bb519cc2-ea72-4a97-bf71-77f1429b03ee","timestamp":"2026-10-30T05:33:12.109Z","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 5442 oct. 30 01:32 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1535 oct. 30 01:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 437 oct. 30 01:32 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 6280 oct. 30 01:32 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4609 oct. 30 01:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1759 oct. 30 01:32 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\": \"AR2_diff\",\n \"model_description\": \"AR(2) on first differences of UNRATE with intercept, estimated by OLS on the expanding window from 1948-01 (first usable difference regression row 1948-04) to the origin; levels forecast by iterating the difference recursion and cumulating; lag order fixed a priori, no selection.\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985529982763829,\n \"mae_model_h1\": 0.0956535147914287,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.15974133959765854,\n \"mae_model_h3\": 0.12123991414571429,\n \"dm_stat_h1\": 2.5019139072700796,\n \"dm_pvalue_h1\": 0.017327515364319198,\n \"dm_stat_h3\": 1.5733474095224582,\n \"dm_pvalue_h3\": 0.12489906317464519,\n \"rel_improvement_rmse_h1\": -0.023459463644279088,\n \"rel_improvement_rmse_h3\": -0.0074187002929284596,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"Missing UNRATE value at 2025-10-01 (empty field)\",\n \"handling\": \"Pairs targeting this month kept in ledger with empty actual and excluded from error stats; naive at origin 2025-10 uses 2025-09; model forecasts from 2025-09 at that origin and linearly interpolates the gap (using only data <= origin) at later origins.\"\n },\n {\n \"issue\": \"Final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"Errors computed on data as given; 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{\"id\":\"C1\",\"text\":\"Over 35 evaluated origins at h=1, the AR(2)-on-differences model has higher RMSE than the naive forecast (relative improvement about -2.3%).\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"Final-vintage data; small sample.\"},\n{\"id\":\"C2\",\"text\":\"At h=3 the model's RMSE is also slightly higher than naive (relative improvement about -0.7%).\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically significant (DM p=0.125).\"},\n{\"id\":\"C3\",\"text\":\"MAE gives the same ranking as RMSE at both horizons (naive better).\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n{\"id\":\"C4\",\"text\":\"The h=1 loss difference is statistically significant (DM p=0.017); the h=3 difference is not (p=0.125).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"Asymptotic test, n=35, HLN-corrected; economically negligible magnitudes.\"},\n{\"id\":\"C5\",\"text\":\"One target month (2025-10-01) is missing, leaving 35 evaluated pairs per horizon out of 36.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n{\"id\":\"C6\",\"text\":\"Results do not establish real-time forecasting performance.\",\"results_keys\":[\"data_issues\"],\"status\":\"not_evaluated\",\"caveat\":\"Only the final vintage is available.\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f0e18a25-eca8-492a-b92c-f25bd6ee6225","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0001","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","hash":"8f7b81614534f17d675add15bf1ed48f60fc3fb17012dbfec3d989752d638cfb","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Jxwh5dWvaowDreGeobgDj8","name":"Bash","input":{"command":"cat TASK.md; 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writes out/results.json and out/forecasts.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"), dtype={\"observation_date\": str})\nn_input = len(raw)\ns = pd.Series(raw[\"UNRATE\"].values, index=pd.PeriodIndex(raw[\"observation_date\"], freq=\"M\"))\nmissing = [str(p.to_timestamp().date()) for p in s.index[s.isna()]]\n\nP = 2 # AR order on first differences, fixed a priori\nMODEL = \"AR2_diff\"\norigins = pd.period_range(\"2023-07\", \"2026-06\", freq=\"M\")\nH = [1, 3]\n\ndef fit_ar_diff(y):\n \"\"\"OLS of dy_t on const, dy_{t-1..t-P}. y: pandas Series (no NaN, contiguous).\"\"\"\n dy = np.diff(y.values)\n X = np.column_stack([np.ones(len(dy) - P)] + [dy[P - k:len(dy) - k] for k in range(1, P + 1)])\n beta, *_ = np.linalg.lstsq(X, dy[P:], rcond=None)\n return beta, dy\n\ndef forecast_ar_diff(beta, y_last, dy_hist, steps):\n hist = list(dy_hist[-P:]); lvl = y_last; path = []\n for _ in range(steps):\n d = beta[0] + sum(beta[k] * hist[-k] for k in range(1, P + 1))\n hist.append(d); lvl = lvl + d; path.append(lvl)\n return path\n\nrows = []; last_n = None\nfor t in origins:\n w = s[s.index <= t] # information set: dated <= t\n obs = w.dropna()\n last_obs_p = obs.index[-1] # latest observed month <= t\n naive = float(obs.iloc[-1])\n # model input: trim trailing missing, interpolate interior gaps using only data <= t\n wm = w.loc[:last_obs_p].interpolate(method=\"linear\")\n beta, dy = fit_ar_diff(wm)\n maxsteps = max(H) + (t - last_obs_p).n\n path = forecast_ar_diff(beta, float(wm.iloc[-1]), dy, maxsteps)\n last_n = int(obs.shape[0])\n for h in H:\n tgt = t + h\n act = s.get(tgt, np.nan)\n steps = (tgt - last_obs_p).n\n for m, f in ((\"naive\", naive), (MODEL, path[steps - 1])):\n rows.append({\"origin_date\": str(t.to_timestamp().date()), \"target_date\": str(tgt.to_timestamp().date()),\n \"horizon\": h, \"forecast\": round(float(f), 10),\n \"actual\": (None if pd.isna(act) else float(act)), \"model\": m})\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(2) on first differences of UNRATE with intercept, estimated by OLS on the expanding window \"\n \"from 1948-01 (first usable difference regression row 1948-04) to the origin; levels forecast by \"\n \"iterating the difference recursion and cumulating; lag order fixed a priori, no selection.\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].to_timestamp().date()),\n \"last_origin\": str(origins[-1].to_timestamp().date())}\nfor h in H:\n ev = fc[(fc.horizon == h) & fc.actual.notna()]\n res[f\"n_evaluated_h{h}\"] = int((ev.model == \"naive\").sum())\n for m, key in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n e = ev[ev.model == m]; err = e.actual - e.forecast\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(err ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(err)))\nfrom scipy import stats\nfor h in H:\n ev = fc[(fc.horizon == h) & fc.actual.notna()]\n en = (ev[ev.model == \"naive\"].set_index(\"origin_date\").eval(\"actual - forecast\"))\n em = (ev[ev.model == MODEL].set_index(\"origin_date\").eval(\"actual - forecast\")).loc[en.index]\n d = (em ** 2 - en ** 2).values; n = len(d); dc = d - d.mean()\n lrv = np.mean(dc ** 2) + 2 * sum((1 - k / h) * np.mean(dc[k:] * dc[:-k]) for k in range(1, h)) # Bartlett, h-1 lags\n dm = d.mean() / np.sqrt(lrv / n)\n dm *= np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n) # Harvey-Leybourne-Newbold correction\n res[f\"dm_stat_h{h}\"] = float(dm)\n res[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm), df=n - 1))\nfor h in H:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": f\"Missing UNRATE value at {d} (empty field)\",\n \"handling\": (\"Pairs targeting this month kept in ledger with empty actual and excluded from error stats; \"\n \"naive at origin 2025-10 uses 2025-09; model forecasts from 2025-09 at that origin and \"\n \"linearly interpolates the gap (using only data <= origin) at later origins.\")} for d in missing]\n + [{\"issue\": \"Final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"Errors computed on data as given; no real-time validity claimed.\"}],\n \"n_input_rows\": {\"UNRATE.csv\": int(n_input)}, \"n_analysed\": last_n, \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nfor k in (\"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"rmse_model_h1\", \"rmse_naive_h3\", \"rmse_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\", \"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"):\n print(k, res[k])\n# F3-dev-01 — Rolling-origin forecasts of US unemployment: AR(2) on differences vs naive\n\n## Question\nOver 36 expanding-window forecast origins from 2023-07-01 to 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive model forecast the US unemployment rate (UNRATE, SA, percent) better than the naive no-change forecast at horizons of 1 and 3 months? The measures are RMSE, MAE and 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` (FRED, final vintage), 945 data rows, 1948-01 to 2026-09 (`n_input_rows`). The SHA-256 matches the README. Dates are contiguous monthly with no duplicates. See `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). Two pairs target that month (origin 2025-09 at h=1 and origin 2025-07 at h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of all error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 rows − 1 missing − 3 months after the last origin (2026-07 to 2026-09, used only as targets) = 941 observed months in the last training window (`n_analysed`).\n\n## Method\n- **Naive:** the forecast is the latest non-missing observation dated ≤ origin. At origin 2025-10 this is the 2025-09 value.\n- **Model `AR2_diff`** (`model_name`, `model_description`): an AR(2) on first differences with an intercept. It is fitted by OLS on the expanding window from 1948-01 to the origin and re-estimated at every origin. Level forecasts come from iterating the difference recursion and adding up the steps. The lag order and sample start were fixed before looking at any results, with no selection step, so the evaluation window was never used for tuning.\n- **Information rules:** at each origin the series is cut to dates ≤ origin before any transformation or estimation. An interior gap in the training window (2025-10, at origins ≥ 2025-11) is filled by linear interpolation that uses only data ≤ origin. At origin 2025-10, where the origin month itself is missing, the model forecasts from 2025-09 using 2 steps (h=1) and 4 steps (h=3).\n- Nothing is random (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0235 |\n| h=3 | 35 | 0.1586 | 0.1597 | 0.1200 | 0.1212 | −0.0074 |\n\nKeys: `rmse_naive_h1`, `rmse_model_h1`, `mae_naive_h1`, `mae_model_h1`, `rel_improvement_rmse_h1`, and the matching `_h3` keys. All values are in percentage points except the last column, which is a fraction.\n\nThe AR(2)-on-differences model did **not** beat the naive forecast. Its RMSE was about 2.3% higher at h=1 (`rel_improvement_rmse_h1` = −0.023) and about 0.7% higher at h=3 (`rel_improvement_rmse_h3` = −0.007). MAE gives the same ranking. The model's forecasts never differ from the naive ones by more than 0.017 pp (`out/state/checks.md`), so in practice it acts as a slightly perturbed random walk.\n\n## Uncertainty\nA Diebold–Mariano test was run on the squared-error loss differential (model minus naive). It uses a Bartlett HAC variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and t(n−1) reference values.\n- At h=1, DM = 2.50 and p = 0.017 (`dm_stat_h1`, `dm_pvalue_h1`): the model is slightly but consistently worse.\n- At h=3, DM = 1.57 and p = 0.125 (`dm_stat_h3`, `dm_pvalue_h3`): no significant difference.\n\nEach horizon has only 35 evaluated pairs. The DM test with HAC variance is only approximate in samples this small, and the overlapping h=3 errors are autocorrelated. The size of the RMSE differences (a few thousandths of a point) is negligible compared with UNRATE's 0.1-pp reporting precision.\n\n## Limitations and open issues\n- The data are the final vintage, so the errors measure forecastability of the revised series. They do **not** show how the model would have performed in real time.\n- Only one pre-specified model was tested. Other AR-type choices could rank differently, for example a shorter estimation window, excluding 2020 (the COVID spike strongly affects OLS on differences), or an AR in levels. They were not explored, to avoid tuning on the evaluation window.\n- The 2025-10 gap is handled by linear interpolation for the model's lag inputs. This touches only a few origins.\n- The window is short (3 years) and quiet (unemployment between about 3.4 and 4.6%), so the results may not carry over to recessions.\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE from the observation set (2025-10-01),945,944,1,empty value field; month kept in forecast ledger as target with empty actual; interpolated only as a model lag input at origins >= 2025-11\n2,keep only observations dated <= last origin 2026-06-01 (last training window),944,941,3,2026-07..2026-09 are evaluation targets only and never enter any training window\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\n2023-07-01,2023-08-01,1,3.495669711,3.7,AR2_diff\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.499055711,3.9,AR2_diff\n 145 out/forecasts.csv\n935:2025-10-01,\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":"1280ba84-45c3-4b46-a072-845c6bc055a5","timestamp":"2026-10-30T05:33:13.790Z","toolUseResult":{"stdout":"\"\"\"Rolling-origin forecasts of UNRATE: naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json and out/forecasts.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"), dtype={\"observation_date\": str})\nn_input = len(raw)\ns = pd.Series(raw[\"UNRATE\"].values, index=pd.PeriodIndex(raw[\"observation_date\"], freq=\"M\"))\nmissing = [str(p.to_timestamp().date()) for p in s.index[s.isna()]]\n\nP = 2 # AR order on first differences, fixed a priori\nMODEL = \"AR2_diff\"\norigins = pd.period_range(\"2023-07\", \"2026-06\", freq=\"M\")\nH = [1, 3]\n\ndef fit_ar_diff(y):\n \"\"\"OLS of dy_t on const, dy_{t-1..t-P}. y: pandas Series (no NaN, contiguous).\"\"\"\n dy = np.diff(y.values)\n X = np.column_stack([np.ones(len(dy) - P)] + [dy[P - k:len(dy) - k] for k in range(1, P + 1)])\n beta, *_ = np.linalg.lstsq(X, dy[P:], rcond=None)\n return beta, dy\n\ndef forecast_ar_diff(beta, y_last, dy_hist, steps):\n hist = list(dy_hist[-P:]); lvl = y_last; path = []\n for _ in range(steps):\n d = beta[0] + sum(beta[k] * hist[-k] for k in range(1, P + 1))\n hist.append(d); lvl = lvl + d; path.append(lvl)\n return path\n\nrows = []; last_n = None\nfor t in origins:\n w = s[s.index <= t] # information set: dated <= t\n obs = w.dropna()\n last_obs_p = obs.index[-1] # latest observed month <= t\n naive = float(obs.iloc[-1])\n # model input: trim trailing missing, interpolate interior gaps using only data <= t\n wm = w.loc[:last_obs_p].interpolate(method=\"linear\")\n beta, dy = fit_ar_diff(wm)\n maxsteps = max(H) + (t - last_obs_p).n\n path = forecast_ar_diff(beta, float(wm.iloc[-1]), dy, maxsteps)\n last_n = int(obs.shape[0])\n for h in H:\n tgt = t + h\n act = s.get(tgt, np.nan)\n steps = (tgt - last_obs_p).n\n for m, f in ((\"naive\", naive), (MODEL, path[steps - 1])):\n rows.append({\"origin_date\": str(t.to_timestamp().date()), \"target_date\": str(tgt.to_timestamp().date()),\n \"horizon\": h, \"forecast\": round(float(f), 10),\n \"actual\": (None if pd.isna(act) else float(act)), \"model\": m})\n\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(2) on first differences of UNRATE with intercept, estimated by OLS on the expanding window \"\n \"from 1948-01 (first usable difference regression row 1948-04) to the origin; levels forecast by \"\n \"iterating the difference recursion and cumulating; lag order fixed a priori, no selection.\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].to_timestamp().date()),\n \"last_origin\": str(origins[-1].to_timestamp().date())}\nfor h in H:\n ev = fc[(fc.horizon == h) & fc.actual.notna()]\n res[f\"n_evaluated_h{h}\"] = int((ev.model == \"naive\").sum())\n for m, key in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n e = ev[ev.model == m]; err = e.actual - e.forecast\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(err ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(err)))\nfrom scipy import stats\nfor h in H:\n ev = fc[(fc.horizon == h) & fc.actual.notna()]\n en = (ev[ev.model == \"naive\"].set_index(\"origin_date\").eval(\"actual - forecast\"))\n em = (ev[ev.model == MODEL].set_index(\"origin_date\").eval(\"actual - forecast\")).loc[en.index]\n d = (em ** 2 - en ** 2).values; n = len(d); dc = d - d.mean()\n lrv = np.mean(dc ** 2) + 2 * sum((1 - k / h) * np.mean(dc[k:] * dc[:-k]) for k in range(1, h)) # Bartlett, h-1 lags\n dm = d.mean() / np.sqrt(lrv / n)\n dm *= np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n) # Harvey-Leybourne-Newbold correction\n res[f\"dm_stat_h{h}\"] = float(dm)\n res[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm), df=n - 1))\nfor h in H:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": f\"Missing UNRATE value at {d} (empty field)\",\n \"handling\": (\"Pairs targeting this month kept in ledger with empty actual and excluded from error stats; \"\n \"naive at origin 2025-10 uses 2025-09; model forecasts from 2025-09 at that origin and \"\n \"linearly interpolates the gap (using only data <= origin) at later origins.\")} for d in missing]\n + [{\"issue\": \"Final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"Errors computed on data as given; no real-time validity claimed.\"}],\n \"n_input_rows\": {\"UNRATE.csv\": int(n_input)}, \"n_analysed\": last_n, \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nfor k in (\"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"rmse_model_h1\", \"rmse_naive_h3\", \"rmse_model_h3\",\n \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\", \"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"):\n print(k, res[k])\n# F3-dev-01 — Rolling-origin forecasts of US unemployment: AR(2) on differences vs naive\n\n## Question\nOver 36 expanding-window forecast origins from 2023-07-01 to 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive model forecast the US unemployment rate (UNRATE, SA, percent) better than the naive no-change forecast at horizons of 1 and 3 months? The measures are RMSE, MAE and 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` (FRED, final vintage), 945 data rows, 1948-01 to 2026-09 (`n_input_rows`). The SHA-256 matches the README. Dates are contiguous monthly with no duplicates. See `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). Two pairs target that month (origin 2025-09 at h=1 and origin 2025-07 at h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of all error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 rows − 1 missing − 3 months after the last origin (2026-07 to 2026-09, used only as targets) = 941 observed months in the last training window (`n_analysed`).\n\n## Method\n- **Naive:** the forecast is the latest non-missing observation dated ≤ origin. At origin 2025-10 this is the 2025-09 value.\n- **Model `AR2_diff`** (`model_name`, `model_description`): an AR(2) on first differences with an intercept. It is fitted by OLS on the expanding window from 1948-01 to the origin and re-estimated at every origin. Level forecasts come from iterating the difference recursion and adding up the steps. The lag order and sample start were fixed before looking at any results, with no selection step, so the evaluation window was never used for tuning.\n- **Information rules:** at each origin the series is cut to dates ≤ origin before any transformation or estimation. An interior gap in the training window (2025-10, at origins ≥ 2025-11) is filled by linear interpolation that uses only data ≤ origin. At origin 2025-10, where the origin month itself is missing, the model forecasts from 2025-09 using 2 steps (h=1) and 4 steps (h=3).\n- Nothing is random (`random_seed` = null). `out/analysis.py` regenerates every output.\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0235 |\n| h=3 | 35 | 0.1586 | 0.1597 | 0.1200 | 0.1212 | −0.0074 |\n\nKeys: `rmse_naive_h1`, `rmse_model_h1`, `mae_naive_h1`, `mae_model_h1`, `rel_improvement_rmse_h1`, and the matching `_h3` keys. All values are in percentage points except the last column, which is a fraction.\n\nThe AR(2)-on-differences model did **not** beat the naive forecast. Its RMSE was about 2.3% higher at h=1 (`rel_improvement_rmse_h1` = −0.023) and about 0.7% higher at h=3 (`rel_improvement_rmse_h3` = −0.007). MAE gives the same ranking. The model's forecasts never differ from the naive ones by more than 0.017 pp (`out/state/checks.md`), so in practice it acts as a slightly perturbed random walk.\n\n## Uncertainty\nA Diebold–Mariano test was run on the squared-error loss differential (model minus naive). It uses a Bartlett HAC variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and t(n−1) reference values.\n- At h=1, DM = 2.50 and p = 0.017 (`dm_stat_h1`, `dm_pvalue_h1`): the model is slightly but consistently worse.\n- At h=3, DM = 1.57 and p = 0.125 (`dm_stat_h3`, `dm_pvalue_h3`): no significant difference.\n\nEach horizon has only 35 evaluated pairs. The DM test with HAC variance is only approximate in samples this small, and the overlapping h=3 errors are autocorrelated. The size of the RMSE differences (a few thousandths of a point) is negligible compared with UNRATE's 0.1-pp reporting precision.\n\n## Limitations and open issues\n- The data are the final vintage, so the errors measure forecastability of the revised series. They do **not** show how the model would have performed in real time.\n- Only one pre-specified model was tested. Other AR-type choices could rank differently, for example a shorter estimation window, excluding 2020 (the COVID spike strongly affects OLS on differences), or an AR in levels. They were not explored, to avoid tuning on the evaluation window.\n- The 2025-10 gap is handled by linear interpolation for the model's lag inputs. This touches only a few origins.\n- The window is short (3 years) and quiet (unemployment between about 3.4 and 4.6%), so the results may not carry over to recessions.\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE from the observation set (2025-10-01),945,944,1,empty value field; month kept in forecast ledger as target with empty actual; interpolated only as a model lag input at origins >= 2025-11\n2,keep only observations dated <= last origin 2026-06-01 (last training window),944,941,3,2026-07..2026-09 are evaluation targets only and never enter any training window\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\n2023-07-01,2023-08-01,1,3.495669711,3.7,AR2_diff\n2023-07-01,2023-10-01,3,3.5,3.9,naive\n2023-07-01,2023-10-01,3,3.499055711,3.9,AR2_diff\n 145 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25 10 0\n3 25 10 0\n origin_date target_date horizon forecast actual model\n98 2025-07-01 2025-10-01 3 4.300000 NaN naive\n99 2025-07-01 2025-10-01 3 4.308039 NaN AR2_diff\n104 2025-09-01 2025-10-01 1 4.400000 NaN naive\n105 2025-09-01 2025-10-01 1 4.404123 NaN AR2_diff\nmodel AR2_diff naive\nhorizon \n1 4.400503 4.4\n3 4.401257 4.4\n3.5 4.5\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (results.json, forecasts.csv deleted, regenerated twice) byte-identical | PASS |\n| forecasts.csv has 144 rows: 36 per (model, horizon) | PASS |\n| target_date − origin_date = horizon months for every row | PASS |\n| Naive forecasts independently recomputed as last non-missing obs <= origin | PASS |\n| Exactly 1 missing actual per (model,horizon) (target 2025-10-01) -> n_evaluated = 35 each, 36−1 | PASS |\n| MAE <= RMSE for every model/horizon; RMSEs > 0 | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive (computed directly in code) | PASS |\n| Chronology: first_origin 2023-07-01 < last_origin 2026-06-01; training data always truncated to <= origin (s.index <= t in code) | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| Magnitude vs baseline: model forecasts differ from naive by at most 0.016 pp; model RMSE within 3% of naive | PASS (plausible: AR(2)-diff coefficients small, intercept ~0) |\n# data/README.md — F3-dev-01\n\n## UNRATE.csv\n- **Series**: UNRATE — Unemployment Rate, United States, percent, seasonally adjusted, monthly. 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":"54e4a936-d03d-4f06-8874-e24432bbe939","timestamp":"2026-10-30T05:33:24.833Z","toolUseResult":{"stdout":"SAME\n0.016367719000000225\n1 25 10 0\n3 25 10 0\n origin_date target_date horizon forecast actual model\n98 2025-07-01 2025-10-01 3 4.300000 NaN naive\n99 2025-07-01 2025-10-01 3 4.308039 NaN AR2_diff\n104 2025-09-01 2025-10-01 1 4.400000 NaN naive\n105 2025-09-01 2025-10-01 1 4.404123 NaN AR2_diff\nmodel AR2_diff naive\nhorizon \n1 4.400503 4.4\n3 4.401257 4.4\n3.5 4.5\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (results.json, forecasts.csv deleted, regenerated twice) byte-identical | PASS |\n| forecasts.csv has 144 rows: 36 per (model, horizon) | PASS |\n| target_date − origin_date = horizon months for every row | PASS |\n| Naive forecasts independently recomputed as last non-missing obs <= origin | PASS |\n| Exactly 1 missing actual per (model,horizon) (target 2025-10-01) -> n_evaluated = 35 each, 36−1 | PASS |\n| MAE <= RMSE for every model/horizon; RMSEs > 0 | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive (computed directly in code) | PASS |\n| Chronology: first_origin 2023-07-01 < last_origin 2026-06-01; training data always truncated to <= origin (s.index <= t in code) | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| Magnitude vs baseline: model forecasts differ from naive by at most 0.016 pp; model RMSE within 3% of naive | PASS (plausible: AR(2)-diff coefficients small, intercept ~0) |\n# data/README.md — F3-dev-01\n\n## UNRATE.csv\n- **Series**: UNRATE — Unemployment Rate, United States, percent, seasonally adjusted, monthly. 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> out/review/review.json <<'E'\n{\n \"verdict\": \"accept\",\n \"issues\": [\n {\"id\": \"R1\", \"severity\": \"minor\", \"location\": \"out/report.md (Limitations)\",\n \"issue\": \"The report says unemployment was 'between about 3.4 and 4.6%' over the window. In data/UNRATE.csv, values from 2023-07 to 2026-09 range from 3.5 to 4.5.\",\n \"suggested_fix\": \"Change the range to 3.5–4.5%.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"out/claims.json C4; out/report.md (Uncertainty)\",\n \"issue\": \"The naive random walk is nested in AR(2)-on-differences with intercept (it is the case where all coefficients are 0). For nested models the standard DM/HLN distribution is not exact (Clark–McCracken). The h=1 p=0.017 tests equal finite-sample accuracy, not population accuracy, and it is one of two horizon tests. The wording 'slightly but consistently worse' is also somewhat strong: the model had the larger squared error at 25 of 35 h=1 origins, not all of them.\",\n \"suggested_fix\": \"Add the nested-model caveat and the 25/35 count to C4. Describe the h=1 result as evidence that estimation noise costs a little accuracy, not as a robust significant ranking.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"out/results.json n_analysed\",\n \"issue\": \"n_analysed = 941 counts the observed months in the last training window. The final fit actually uses 942 level values, because the 2025-10 value is interpolated. This is disclosed in exclusions.csv but not in the key's description.\",\n \"suggested_fix\": \"State in the report that 941 excludes the one interpolated value used as a model input.\"}\n ],\n \"summary\": \"The deliverables are complete and reproducible. Re-running out/analysis.py in a scratch copy reproduced results.json and forecasts.csv byte for byte. The ledger has 144 rows. The missing target month 2025-10 is handled correctly: 2 pairs have an empty actual, leaving n=35 per horizon. 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Only minor wording and caveat issues remain.\"\n}\nE\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and 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In data/UNRATE.csv, values from 2023-07 to 2026-09 range from 3.5 to 4.5.\",\n \"suggested_fix\": \"Change the range to 3.5–4.5%.\"},\n {\"id\": \"R2\", \"severity\": \"minor\", \"location\": \"out/claims.json C4; out/report.md (Uncertainty)\",\n \"issue\": \"The naive random walk is nested in AR(2)-on-differences with intercept (it is the case where all coefficients are 0). For nested models the standard DM/HLN distribution is not exact (Clark–McCracken). The h=1 p=0.017 tests equal finite-sample accuracy, not population accuracy, and it is one of two horizon tests. The wording 'slightly but consistently worse' is also somewhat strong: the model had the larger squared error at 25 of 35 h=1 origins, not all of them.\",\n \"suggested_fix\": \"Add the nested-model caveat and the 25/35 count to C4. Describe the h=1 result as evidence that estimation noise costs a little accuracy, not as a robust significant ranking.\"},\n {\"id\": \"R3\", \"severity\": \"minor\", \"location\": \"out/results.json n_analysed\",\n \"issue\": \"n_analysed = 941 counts the observed months in the last training window. The final fit actually uses 942 level values, because the 2025-10 value is interpolated. This is disclosed in exclusions.csv but not in the key's description.\",\n \"suggested_fix\": \"State in the report that 941 excludes the one interpolated value used as a model input.\"}\n ],\n \"summary\": \"The deliverables are complete and reproducible. Re-running out/analysis.py in a scratch copy reproduced results.json and forecasts.csv byte for byte. The ledger has 144 rows. The missing target month 2025-10 is handled correctly: 2 pairs have an empty actual, leaving n=35 per horizon. Every model/transform step uses only data dated ≤ origin, including the interpolation and the origin-month-missing case. The naive baseline follows the rule. rel_improvement keys are computed correctly and negative at both horizons, which matches the claims. Only minor wording and caveat issues remain.\"\n}\nE\n~/wp29/venv/bin/python -m json.tool out/review/review.json >/dev/null && echo ok","description":"Write review JSON and validate"}}]}}33{"type":"api-request","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","id":"25a7a3b6-5502-444e-89b2-559364097051","timestamp":"2026-10-30T05:33:40.666Z","version":"2.1.314","querySource":"sdk","shapeHash":"418c28dca1ebdab01ddf60c3c2bab49522c8f3e0b11f3f20aef95dbb423b12ba","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":"7c1dbf59-aa77-4655-ab15-45b662851807","keep":8,"tail":["cdcaeba1a97a12b4567b13a89e48a138bdd526e5730403f6bc45bd46b07db02a","7925149236426c265c0d2cb5bfeb0bc94ff4220aef2c32ef79ad97289ea52c4f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"74a2a1ba3b88e9864fe58860de0c7bf65ac4234fc5f69c03f762f64384583eb1"}34{"parentUuid":"78010a5d-c03e-463b-9417-8505217d3259","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsnVs7QNZMQsWsNKDYY","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":1590,"cache_read_input_tokens":13590,"output_tokens":678,"output_tokens_details":{"thinking_tokens":15},"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":1590},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5904,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsnVfT9rkBCRjk2CD2h","requestRef":"25a7a3b6-5502-444e-89b2-559364097051","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b6d89226-b377-4676-9c0e-ec1d9aa4546d","timestamp":"2026-10-30T05:33:47.478Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0001","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"b6d89226-b377-4676-9c0e-ec1d9aa4546d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsnVs7QNZMQsWsNKDYY","type":"message","role":"assistant","content":[{"type":"text","text":"My verdict is **accept**: the deliverables meet the task, and I found only three minor issues. I re-ran `out/analysis.py` on a scratch copy of the data and it reproduced `results.json` and `forecasts.csv` byte for byte. The review is written to `out/review/review.json`.\n\n**What checks out:**\n- **Required outputs:** every required key is present, and the ledger has the expected 144 rows (36 origins × 2 horizons × 2 models).\n- **Missing month:** the empty value at 2025-10 is handled as the task requires. The two pairs that target it stay in the ledger with an empty actual and are left out of the statistics, leaving 35 evaluated pairs per horizon.\n- **No future information:** I read the code rather than trusting the report. At each origin the data are cut to dates on or before that origin before anything else happens. The fill for the 2025-10 gap at later origins uses only data available by then. At origin 2025-10 itself, both the naive and the model forecasts correctly start from 2025-09.\n- **Central claims:** the model does not beat the naive forecast. Its RMSE is about 2.3% higher at 1 month and about 0.7% higher at 3 months. MAE ranks them the same way, and the report states the final-vintage limitation honestly.\n\n**Minor issues:**\n- **R1:** the report says unemployment ranged from about 3.4% to 4.6% over the window. The data show 3.5% to 4.5%.\n- **R2:** the \"significant at 1 month\" claim (p = 0.017) needs more caveats.\n - The naive forecast is a special case of the AR model, and in that setting the standard test's p-values are not exact.\n - It is one of two horizon tests.\n - \"Consistently worse\" overstates it: the model had the larger error at 25 of 35 one-month origins, not all of them.\n- **R3:** `n_analysed` = 941 counts observed months only. The final fit actually uses 942 values, including the filled-in 2025-10, which the report could state."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1590,"cache_read_input_tokens":13590,"output_tokens":678,"output_tokens_details":{"thinking_tokens":15},"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":1590},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsnVfT9rkBCRjk2CD2h","requestRef":"25a7a3b6-5502-444e-89b2-559364097051","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8a267ae8-74f5-4ee6-8789-9a3718b1088a","timestamp":"2026-10-30T05:33:47.480Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0001","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","version":"2.1.314","gitBranch":"HEAD"}36{"type":"last-prompt","lastPrompt":"Review","leafUuid":"8a267ae8-74f5-4ee6-8789-9a3718b1088a","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac"}37{"type":"atis-latch","atis":"","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac"}38{"type":"cost-state","sessionId":"7ada7b32-1f1c-43f5-a4a3-9a53ddae9dac","totalCostUSD":0.1494052,"totalAPIDuration":36281,"totalAPIDurationWithoutRetries":36276,"totalToolDuration":1167,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":37636,"startTime":1793338389853,"modelUsage":{"claude-opus-5-5":{"inputTokens":12,"outputTokens":3702,"thinkingTokens":1160,"cacheReadInputTokens":34686,"cacheCreationInputTokens":13676,"cacheCreationEphemeral5mInputTokens":13676,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1494052}},"hasUnknownModelCost":false}39