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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.\nREADME.md\nUNRATE.csv\n==> data/README.md <==\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\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n1948-03-01,4.0\n1948-04-01,3.9","is_error":false}]},"uuid":"0c6039a1-c7c8-46fe-a2a6-6131e137ccc8","timestamp":"2026-10-30T05:30:56.743Z","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.\nREADME.md\nUNRATE.csv\n==> data/README.md <==\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\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n1948-03-01,4.0\n1948-04-01,3.9","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a60f2a23-b974-4986-858c-10f39890c708","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0002","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","hash":"904f7c787e1e37e36be49e7da60abcd4e3fd6af3cf0c9346dbf7e44a96af3f6b","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01YVp8HTErUaQBCDayomMCTK","name":"Bash","input":{"command":"cat TASK.md; ls -R data; head -5 data/*","description":"Read task and list data"}}]}}15{"type":"api-request","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","id":"237e6a92-ad6f-43a1-b2fe-748a5885581a","timestamp":"2026-10-30T05:30:56.748Z","version":"2.1.314","querySource":"sdk","shapeHash":"cdd52c131a12a09c036ef49469552ce8b5fac140859bccf283e5b4e6b162dcb0","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":"424cd200-2f67-4b6e-a261-288877286082","keep":2,"tail":["904f7c787e1e37e36be49e7da60abcd4e3fd6af3cf0c9346dbf7e44a96af3f6b","d3c7535ed4e6f40a5c3eaed479c21e80e86503ba23b926dfd197fbd0956a5669"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"120333c3b70c593a02ff215f9c47f684e6835cceb82bc6d3dece65db2e750cfa"}16{"parentUuid":"0c6039a1-c7c8-46fe-a2a6-6131e137ccc8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsaQxcAHMRzszQuxgUZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SJs8HcgbUWXi5RVMc3mpnt","name":"Bash","input":{"command":"cat data/README.md; wc -l data/UNRATE.csv; tail -45 data/UNRATE.csv; grep -n ',$' data/UNRATE.csv; awk -F, 'NR>1 && $2!=\"\" && ($2<1||$2>20)' data/UNRATE.csv","description":"Inspect data tail and missing values"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2232,"cache_read_input_tokens":2234,"output_tokens":176,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2232},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsaQhUhVedE2PcAPKhh","requestRef":"237e6a92-ad6f-43a1-b2fe-748a5885581a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"753ec7a5-98df-42e1-84fa-b06ee99c56cf","timestamp":"2026-10-30T05:30:58.904Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0002","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"753ec7a5-98df-42e1-84fa-b06ee99c56cf","isSidechain":false,"promptId":"f20057ca-d2ad-4230-a3ee-44a403016ce1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SJs8HcgbUWXi5RVMc3mpnt","type":"tool_result","content":"# 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.\n 946 data/UNRATE.csv\n2023-01-01,3.5\n2023-02-01,3.6\n2023-03-01,3.5\n2023-04-01,3.4\n2023-05-01,3.6\n2023-06-01,3.6\n2023-07-01,3.5\n2023-08-01,3.7\n2023-09-01,3.7\n2023-10-01,3.9\n2023-11-01,3.7\n2023-12-01,3.8\n2024-01-01,3.7\n2024-02-01,3.9\n2024-03-01,3.9\n2024-04-01,3.9\n2024-05-01,3.9\n2024-06-01,4.1\n2024-07-01,4.2\n2024-08-01,4.2\n2024-09-01,4.1\n2024-10-01,4.1\n2024-11-01,4.2\n2024-12-01,4.1\n2025-01-01,4.0\n2025-02-01,4.2\n2025-03-01,4.2\n2025-04-01,4.2\n2025-05-01,4.3\n2025-06-01,4.1\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\n935:2025-10-01,\n1948-01-01,3.4\n1948-02-01,3.8\n1948-03-01,4.0\n1948-04-01,3.9\n1948-05-01,3.5\n1948-06-01,3.6\n1948-07-01,3.6\n1948-08-01,3.9\n1948-09-01,3.8\n1948-10-01,3.7\n1948-11-01,3.8\n1948-12-01,4.0\n1949-01-01,4.3\n1949-02-01,4.7\n1949-03-01,5.0\n1949-04-01,5.3\n1949-05-01,6.1\n1949-06-01,6.2\n1949-07-01,6.7\n1949-08-01,6.8\n1949-09-01,6.6\n1949-10-01,7.9\n1949-11-01,6.4\n1949-12-01,6.6\n1950-01-01,6.5\n1950-02-01,6.4\n1950-03-01,6.3\n1950-04-01,5.8\n1950-05-01,5.5\n1950-06-01,5.4\n1950-07-01,5.0\n1950-08-01,4.5\n1950-09-01,4.4\n1950-10-01,4.2\n1950-11-01,4.2\n1950-12-01,4.3\n1951-01-01,3.7\n1951-02-01,3.4\n1951-03-01,3.4\n1951-04-01,3.1\n1951-05-01,3.0\n1951-06-01,3.2\n1951-07-01,3.1\n1951-08-01,3.1\n1951-09-01,3.3\n1951-10-01,3.5\n1951-11-01,3.5\n1951-12-01,3.1\n1952-01-01,3.2\n1952-02-01,3.1\n1952-05-01,3.0\n1952-06-01,3.0\n1952-07-01,3.2\n1952-08-01,3.4\n1952-09-01,3.1\n1952-10-01,3.0\n1953-10-01,3.1\n1953-11-01,3.5\n1953-12-01,4.5\n1954-01-01,4.9\n1954-02-01,5.2\n1954-03-01,5.7\n1954-04-01,5.9\n1954-05-01,5.9\n1954-06-01,5.6\n1954-07-01,5.8\n1954-08-01,6.0\n1954-09-01,6.1\n1954-10-01,5.7\n1954-11-01,5.3\n1954-12-01,5.0\n1955-01-01,4.9\n1955-02-01,4.7\n1955-03-01,4.6\n1955-04-01,4.7\n1955-05-01,4.3\n1955-06-01,4.2\n1955-07-01,4.0\n1955-08-01,4.2\n1955-09-01,4.1\n1955-10-01,4.3\n1955-11-01,4.2\n1955-12-01,4.2\n1956-01-01,4.0\n1956-02-01,3.9\n1956-03-01,4.2\n1956-04-01,4.0\n1956-05-01,4.3\n1956-06-01,4.3\n1956-07-01,4.4\n1956-08-01,4.1\n1956-09-01,3.9\n1956-10-01,3.9\n1956-11-01,4.3\n1956-12-01,4.2\n1957-01-01,4.2\n1957-02-01,3.9\n1957-03-01,3.7\n1957-04-01,3.9\n1957-05-01,4.1\n1957-06-01,4.3\n1957-07-01,4.2\n1957-08-01,4.1\n1957-09-01,4.4\n1957-10-01,4.5\n1957-11-01,5.1\n1957-12-01,5.2\n1958-01-01,5.8\n1958-02-01,6.4\n1958-03-01,6.7\n1958-04-01,7.4\n1958-05-01,7.4\n1958-06-01,7.3\n1958-07-01,7.5\n1958-08-01,7.4\n1958-09-01,7.1\n1958-10-01,6.7\n1958-11-01,6.2\n1958-12-01,6.2\n1959-01-01,6.0\n1959-02-01,5.9\n1959-03-01,5.6\n1959-04-01,5.2\n1959-05-01,5.1\n1959-06-01,5.0\n1959-07-01,5.1\n1959-08-01,5.2\n1959-09-01,5.5\n1959-10-01,5.7\n1959-11-01,5.8\n1959-12-01,5.3\n1960-01-01,5.2\n1960-02-01,4.8\n1960-03-01,5.4\n1960-04-01,5.2\n1960-05-01,5.1\n1960-06-01,5.4\n1960-07-01,5.5\n1960-08-01,5.6\n1960-09-01,5.5\n1960-10-01,6.1\n1960-11-01,6.1\n1960-12-01,6.6\n1961-01-01,6.6\n1961-02-01,6.9\n1961-03-01,6.9\n1961-04-01,7.0\n1961-05-01,7.1\n1961-06-01,6.9\n1961-07-01,7.0\n1961-08-01,6.6\n1961-09-01,6.7\n1961-10-01,6.5\n1961-11-01,6.1\n1961-12-01,6.0\n1962-01-01,5.8\n1962-02-01,5.5\n1962-03-01,5.6\n1962-04-01,5.6\n1962-05-01,5.5\n1962-06-01,5.5\n1962-07-01,5.4\n1962-08-01,5.7\n1962-09-01,5.6\n1962-10-01,5.4\n1962-11-01,5.7\n1962-12-01,5.5\n1963-01-01,5.7\n1963-02-01,5.9\n1963-03-01,5.7\n1963-04-01,5.7\n1963-05-01,5.9\n1963-06-01,5.6\n1963-07-01,5.6\n1963-08-01,5.4\n1963-09-01,5.5\n1963-10-01,5.5\n1963-11-01,5.7\n1963-12-01,5.5\n1964-01-01,5.6\n1964-02-01,5.4\n1964-03-01,5.4\n1964-04-01,5.3\n1964-05-01,5.1\n1964-06-01,5.2\n1964-07-01,4.9\n1964-08-01,5.0\n1964-09-01,5.1\n1964-10-01,5.1\n1964-11-01,4.8\n1964-12-01,5.0\n1965-01-01,4.9\n1965-02-01,5.1\n1965-03-01,4.7\n1965-04-01,4.8\n1965-05-01,4.6\n1965-06-01,4.6\n1965-07-01,4.4\n1965-08-01,4.4\n1965-09-01,4.3\n1965-10-01,4.2\n1965-11-01,4.1\n1965-12-01,4.0\n1966-01-01,4.0\n1966-02-01,3.8\n1966-03-01,3.8\n1966-04-01,3.8\n1966-05-01,3.9\n1966-06-01,3.8\n1966-07-01,3.8\n1966-08-01,3.8\n1966-09-01,3.7\n1966-10-01,3.7\n1966-11-01,3.6\n1966-12-01,3.8\n1967-01-01,3.9\n1967-02-01,3.8\n1967-03-01,3.8\n1967-04-01,3.8\n1967-05-01,3.8\n1967-06-01,3.9\n1967-07-01,3.8\n1967-08-01,3.8\n1967-09-01,3.8\n1967-10-01,4.0\n1967-11-01,3.9\n1967-12-01,3.8\n1968-01-01,3.7\n1968-02-01,3.8\n1968-03-01,3.7\n1968-04-01,3.5\n1968-05-01,3.5\n1968-06-01,3.7\n1968-07-01,3.7\n1968-08-01,3.5\n1968-09-01,3.4\n1968-10-01,3.4\n1968-11-01,3.4\n1968-12-01,3.4\n1969-01-01,3.4\n1969-02-01,3.4\n1969-03-01,3.4\n1969-04-01,3.4\n1969-05-01,3.4\n1969-06-01,3.5\n1969-07-01,3.5\n1969-08-01,3.5\n1969-09-01,3.7\n1969-10-01,3.7\n1969-11-01,3.5\n1969-12-01,3.5\n1970-01-01,3.9\n1970-02-01,4.2\n1970-03-01,4.4\n1970-04-01,4.6\n1970-05-01,4.8\n1970-06-01,4.9\n1970-07-01,5.0\n1970-08-01,5.1\n1970-09-01,5.4\n1970-10-01,5.5\n1970-11-01,5.9\n1970-12-01,6.1\n1971-01-01,5.9\n1971-02-01,5.9\n1971-03-01,6.0\n1971-04-01,5.9\n1971-05-01,5.9\n1971-06-01,5.9\n1971-07-01,6.0\n1971-08-01,6.1\n1971-09-01,6.0\n1971-10-01,5.8\n1971-11-01,6.0\n1971-12-01,6.0\n1972-01-01,5.8\n1972-02-01,5.7\n1972-03-01,5.8\n1972-04-01,5.7\n1972-05-01,5.7\n1972-06-01,5.7\n1972-07-01,5.6\n1972-08-01,5.6\n1972-09-01,5.5\n1972-10-01,5.6\n1972-11-01,5.3\n1972-12-01,5.2\n1973-01-01,4.9\n1973-02-01,5.0\n1973-03-01,4.9\n1973-04-01,5.0\n1973-05-01,4.9\n1973-06-01,4.9\n1973-07-01,4.8\n1973-08-01,4.8\n1973-09-01,4.8\n1973-10-01,4.6\n1973-11-01,4.8\n1973-12-01,4.9\n1974-01-01,5.1\n1974-02-01,5.2\n1974-03-01,5.1\n1974-04-01,5.1\n1974-05-01,5.1\n1974-06-01,5.4\n1974-07-01,5.5\n1974-08-01,5.5\n1974-09-01,5.9\n1974-10-01,6.0\n1974-11-01,6.6\n1974-12-01,7.2\n1975-01-01,8.1\n1975-02-01,8.1\n1975-03-01,8.6\n1975-04-01,8.8\n1975-05-01,9.0\n1975-06-01,8.8\n1975-07-01,8.6\n1975-08-01,8.4\n1975-09-01,8.4\n1975-10-01,8.4\n1975-11-01,8.3\n1975-12-01,8.2\n1976-01-01,7.9\n1976-02-01,7.7\n1976-03-01,7.6\n1976-04-01,7.7\n1976-05-01,7.4\n1976-06-01,7.6\n1976-07-01,7.8\n1976-08-01,7.8\n1976-09-01,7.6\n1976-10-01,7.7\n1976-11-01,7.8\n1976-12-01,7.8\n1977-01-01,7.5\n1977-02-01,7.6\n1977-03-01,7.4\n1977-04-01,7.2\n1977-05-01,7.0\n1977-06-01,7.2\n1977-07-01,6.9\n1977-08-01,7.0\n1977-09-01,6.8\n1977-10-01,6.8\n1977-11-01,6.8\n1977-12-01,6.4\n1978-01-01,6.4\n1978-02-01,6.3\n1978-03-01,6.3\n1978-04-01,6.1\n1978-05-01,6.0\n1978-06-01,5.9\n1978-07-01,6.2\n1978-08-01,5.9\n1978-09-01,6.0\n1978-10-01,5.8\n1978-11-01,5.9\n1978-12-01,6.0\n1979-01-01,5.9\n1979-02-01,5.9\n1979-03-01,5.8\n1979-04-01,5.8\n1979-05-01,5.6\n1979-06-01,5.7\n1979-07-01,5.7\n1979-08-01,6.0\n1979-09-01,5.9\n1979-10-01,6.0\n1979-11-01,5.9\n1979-12-01,6.0\n1980-01-01,6.3\n1980-02-01,6.3\n1980-03-01,6.3\n1980-04-01,6.9\n1980-05-01,7.5\n1980-06-01,7.6\n1980-07-01,7.8\n1980-08-01,7.7\n1980-09-01,7.5\n1980-10-01,7.5\n1980-11-01,7.5\n1980-12-01,7.2\n1981-01-01,7.5\n1981-02-01,7.4\n1981-03-01,7.4\n1981-04-01,7.2\n1981-05-01,7.5\n1981-06-01,7.5\n1981-07-01,7.2\n1981-08-01,7.4\n1981-09-01,7.6\n1981-10-01,7.9\n1981-11-01,8.3\n1981-12-01,8.5\n1982-01-01,8.6\n1982-02-01,8.9\n1982-03-01,9.0\n1982-04-01,9.3\n1982-05-01,9.4\n1982-06-01,9.6\n1982-07-01,9.8\n1982-08-01,9.8\n1983-07-01,9.4\n1983-08-01,9.5\n1983-09-01,9.2\n1983-10-01,8.8\n1983-11-01,8.5\n1983-12-01,8.3\n1984-01-01,8.0\n1984-02-01,7.8\n1984-03-01,7.8\n1984-04-01,7.7\n1984-05-01,7.4\n1984-06-01,7.2\n1984-07-01,7.5\n1984-08-01,7.5\n1984-09-01,7.3\n1984-10-01,7.4\n1984-11-01,7.2\n1984-12-01,7.3\n1985-01-01,7.3\n1985-02-01,7.2\n1985-03-01,7.2\n1985-04-01,7.3\n1985-05-01,7.2\n1985-06-01,7.4\n1985-07-01,7.4\n1985-08-01,7.1\n1985-09-01,7.1\n1985-10-01,7.1\n1985-11-01,7.0\n1985-12-01,7.0\n1986-01-01,6.7\n1986-02-01,7.2\n1986-03-01,7.2\n1986-04-01,7.1\n1986-05-01,7.2\n1986-06-01,7.2\n1986-07-01,7.0\n1986-08-01,6.9\n1986-09-01,7.0\n1986-10-01,7.0\n1986-11-01,6.9\n1986-12-01,6.6\n1987-01-01,6.6\n1987-02-01,6.6\n1987-03-01,6.6\n1987-04-01,6.3\n1987-05-01,6.3\n1987-06-01,6.2\n1987-07-01,6.1\n1987-08-01,6.0\n1987-09-01,5.9\n1987-10-01,6.0\n1987-11-01,5.8\n1987-12-01,5.7\n1988-01-01,5.7\n1988-02-01,5.7\n1988-03-01,5.7\n1988-04-01,5.4\n1988-05-01,5.6\n1988-06-01,5.4\n1988-07-01,5.4\n1988-08-01,5.6\n1988-09-01,5.4\n1988-10-01,5.4\n1988-11-01,5.3\n1988-12-01,5.3\n1989-01-01,5.4\n1989-02-01,5.2\n1989-03-01,5.0\n1989-04-01,5.2\n1989-05-01,5.2\n1989-06-01,5.3\n1989-07-01,5.2\n1989-08-01,5.2\n1989-09-01,5.3\n1989-10-01,5.3\n1989-11-01,5.4\n1989-12-01,5.4\n1990-01-01,5.4\n1990-02-01,5.3\n1990-03-01,5.2\n1990-04-01,5.4\n1990-05-01,5.4\n1990-06-01,5.2\n1990-07-01,5.5\n1990-08-01,5.7\n1990-09-01,5.9\n1990-10-01,5.9\n1990-11-01,6.2\n1990-12-01,6.3\n1991-01-01,6.4\n1991-02-01,6.6\n1991-03-01,6.8\n1991-04-01,6.7\n1991-05-01,6.9\n1991-06-01,6.9\n1991-07-01,6.8\n1991-08-01,6.9\n1991-09-01,6.9\n1991-10-01,7.0\n1991-11-01,7.0\n1991-12-01,7.3\n1992-01-01,7.3\n1992-02-01,7.4\n1992-03-01,7.4\n1992-04-01,7.4\n1992-05-01,7.6\n1992-06-01,7.8\n1992-07-01,7.7\n1992-08-01,7.6\n1992-09-01,7.6\n1992-10-01,7.3\n1992-11-01,7.4\n1992-12-01,7.4\n1993-01-01,7.3\n1993-02-01,7.1\n1993-03-01,7.0\n1993-04-01,7.1\n1993-05-01,7.1\n1993-06-01,7.0\n1993-07-01,6.9\n1993-08-01,6.8\n1993-09-01,6.7\n1993-10-01,6.8\n1993-11-01,6.6\n1993-12-01,6.5\n1994-01-01,6.6\n1994-02-01,6.6\n1994-03-01,6.5\n1994-04-01,6.4\n1994-05-01,6.1\n1994-06-01,6.1\n1994-07-01,6.1\n1994-08-01,6.0\n1994-09-01,5.9\n1994-10-01,5.8\n1994-11-01,5.6\n1994-12-01,5.5\n1995-01-01,5.6\n1995-02-01,5.4\n1995-03-01,5.4\n1995-04-01,5.8\n1995-05-01,5.6\n1995-06-01,5.6\n1995-07-01,5.7\n1995-08-01,5.7\n1995-09-01,5.6\n1995-10-01,5.5\n1995-11-01,5.6\n1995-12-01,5.6\n1996-01-01,5.6\n1996-02-01,5.5\n1996-03-01,5.5\n1996-04-01,5.6\n1996-05-01,5.6\n1996-06-01,5.3\n1996-07-01,5.5\n1996-08-01,5.1\n1996-09-01,5.2\n1996-10-01,5.2\n1996-11-01,5.4\n1996-12-01,5.4\n1997-01-01,5.3\n1997-02-01,5.2\n1997-03-01,5.2\n1997-04-01,5.1\n1997-05-01,4.9\n1997-06-01,5.0\n1997-07-01,4.9\n1997-08-01,4.8\n1997-09-01,4.9\n1997-10-01,4.7\n1997-11-01,4.6\n1997-12-01,4.7\n1998-01-01,4.6\n1998-02-01,4.6\n1998-03-01,4.7\n1998-04-01,4.3\n1998-05-01,4.4\n1998-06-01,4.5\n1998-07-01,4.5\n1998-08-01,4.5\n1998-09-01,4.6\n1998-10-01,4.5\n1998-11-01,4.4\n1998-12-01,4.4\n1999-01-01,4.3\n1999-02-01,4.4\n1999-03-01,4.2\n1999-04-01,4.3\n1999-05-01,4.2\n1999-06-01,4.3\n1999-07-01,4.3\n1999-08-01,4.2\n1999-09-01,4.2\n1999-10-01,4.1\n1999-11-01,4.1\n1999-12-01,4.0\n2000-01-01,4.0\n2000-02-01,4.1\n2000-03-01,4.0\n2000-04-01,3.8\n2000-05-01,4.0\n2000-06-01,4.0\n2000-07-01,4.0\n2000-08-01,4.1\n2000-09-01,3.9\n2000-10-01,3.9\n2000-11-01,3.9\n2000-12-01,3.9\n2001-01-01,4.2\n2001-02-01,4.2\n2001-03-01,4.3\n2001-04-01,4.4\n2001-05-01,4.3\n2001-06-01,4.5\n2001-07-01,4.6\n2001-08-01,4.9\n2001-09-01,5.0\n2001-10-01,5.3\n2001-11-01,5.5\n2001-12-01,5.7\n2002-01-01,5.7\n2002-02-01,5.7\n2002-03-01,5.7\n2002-04-01,5.9\n2002-05-01,5.8\n2002-06-01,5.8\n2002-07-01,5.8\n2002-08-01,5.7\n2002-09-01,5.7\n2002-10-01,5.7\n2002-11-01,5.9\n2002-12-01,6.0\n2003-01-01,5.8\n2003-02-01,5.9\n2003-03-01,5.9\n2003-04-01,6.0\n2003-05-01,6.1\n2003-06-01,6.3\n2003-07-01,6.2\n2003-08-01,6.1\n2003-09-01,6.1\n2003-10-01,6.0\n2003-11-01,5.8\n2003-12-01,5.7\n2004-01-01,5.7\n2004-02-01,5.6\n2004-03-01,5.8\n2004-04-01,5.6\n2004-05-01,5.6\n2004-06-01,5.6\n2004-07-01,5.5\n2004-08-01,5.4\n2004-09-01,5.4\n2004-10-01,5.5\n2004-11-01,5.4\n2004-12-01,5.4\n2005-01-01,5.3\n2005-02-01,5.4\n2005-03-01,5.2\n2005-04-01,5.2\n2005-05-01,5.1\n2005-06-01,5.0\n2005-07-01,5.0\n2005-08-01,4.9\n2005-09-01,5.0\n2005-10-01,5.0\n2005-11-01,5.0\n2005-12-01,4.9\n2006-01-01,4.7\n2006-02-01,4.8\n2006-03-01,4.7\n2006-04-01,4.7\n2006-05-01,4.6\n2006-06-01,4.6\n2006-07-01,4.7\n2006-08-01,4.7\n2006-09-01,4.5\n2006-10-01,4.4\n2006-11-01,4.5\n2006-12-01,4.4\n2007-01-01,4.6\n2007-02-01,4.5\n2007-03-01,4.4\n2007-04-01,4.5\n2007-05-01,4.4\n2007-06-01,4.6\n2007-07-01,4.7\n2007-08-01,4.6\n2007-09-01,4.7\n2007-10-01,4.7\n2007-11-01,4.7\n2007-12-01,5.0\n2008-01-01,5.0\n2008-02-01,4.9\n2008-03-01,5.1\n2008-04-01,5.0\n2008-05-01,5.4\n2008-06-01,5.6\n2008-07-01,5.8\n2008-08-01,6.1\n2008-09-01,6.1\n2008-10-01,6.5\n2008-11-01,6.8\n2008-12-01,7.3\n2009-01-01,7.8\n2009-02-01,8.3\n2009-03-01,8.7\n2009-04-01,9.0\n2009-05-01,9.4\n2009-06-01,9.5\n2009-07-01,9.5\n2009-08-01,9.6\n2009-09-01,9.8\n2009-11-01,9.9\n2009-12-01,9.9\n2010-01-01,9.8\n2010-02-01,9.8\n2010-03-01,9.9\n2010-04-01,9.9\n2010-05-01,9.6\n2010-06-01,9.4\n2010-07-01,9.4\n2010-08-01,9.5\n2010-09-01,9.5\n2010-10-01,9.4\n2010-11-01,9.8\n2010-12-01,9.3\n2011-01-01,9.1\n2011-02-01,9.0\n2011-03-01,9.0\n2011-04-01,9.1\n2011-05-01,9.0\n2011-06-01,9.1\n2011-07-01,9.0\n2011-08-01,9.0\n2011-09-01,9.0\n2011-10-01,8.8\n2011-11-01,8.6\n2011-12-01,8.5\n2012-01-01,8.3\n2012-02-01,8.3\n2012-03-01,8.2\n2012-04-01,8.2\n2012-05-01,8.2\n2012-06-01,8.2\n2012-07-01,8.2\n2012-08-01,8.1\n2012-09-01,7.8\n2012-10-01,7.8\n2012-11-01,7.7\n2012-12-01,7.9\n2013-01-01,8.0\n2013-02-01,7.7\n2013-03-01,7.5\n2013-04-01,7.6\n2013-05-01,7.5\n2013-06-01,7.5\n2013-07-01,7.3\n2013-08-01,7.2\n2013-09-01,7.2\n2013-10-01,7.2\n2013-11-01,6.9\n2013-12-01,6.7\n2014-01-01,6.6\n2014-02-01,6.7\n2014-03-01,6.7\n2014-04-01,6.2\n2014-05-01,6.3\n2014-06-01,6.1\n2014-07-01,6.2\n2014-08-01,6.1\n2014-09-01,5.9\n2014-10-01,5.7\n2014-11-01,5.8\n2014-12-01,5.6\n2015-01-01,5.7\n2015-02-01,5.5\n2015-03-01,5.4\n2015-04-01,5.4\n2015-05-01,5.6\n2015-06-01,5.3\n2015-07-01,5.2\n2015-08-01,5.1\n2015-09-01,5.0\n2015-10-01,5.0\n2015-11-01,5.1\n2015-12-01,5.0\n2016-01-01,4.8\n2016-02-01,4.9\n2016-03-01,5.0\n2016-04-01,5.1\n2016-05-01,4.8\n2016-06-01,4.9\n2016-07-01,4.8\n2016-08-01,4.9\n2016-09-01,5.0\n2016-10-01,4.9\n2016-11-01,4.7\n2016-12-01,4.7\n2017-01-01,4.7\n2017-02-01,4.6\n2017-03-01,4.4\n2017-04-01,4.4\n2017-05-01,4.4\n2017-06-01,4.3\n2017-07-01,4.3\n2017-08-01,4.4\n2017-09-01,4.3\n2017-10-01,4.2\n2017-11-01,4.2\n2017-12-01,4.1\n2018-01-01,4.0\n2018-02-01,4.1\n2018-03-01,4.0\n2018-04-01,4.0\n2018-05-01,3.8\n2018-06-01,4.0\n2018-07-01,3.8\n2018-08-01,3.8\n2018-09-01,3.7\n2018-10-01,3.8\n2018-11-01,3.8\n2018-12-01,3.9\n2019-01-01,4.0\n2019-02-01,3.8\n2019-03-01,3.8\n2019-04-01,3.7\n2019-05-01,3.6\n2019-06-01,3.6\n2019-07-01,3.7\n2019-08-01,3.6\n2019-09-01,3.5\n2019-10-01,3.6\n2019-11-01,3.6\n2019-12-01,3.6\n2020-01-01,3.6\n2020-02-01,3.5\n2020-03-01,4.4\n2020-08-01,8.4\n2020-09-01,7.8\n2020-10-01,6.9\n2020-11-01,6.7\n2020-12-01,6.7\n2021-01-01,6.4\n2021-02-01,6.2\n2021-03-01,6.1\n2021-04-01,6.1\n2021-05-01,5.8\n2021-06-01,5.9\n2021-07-01,5.4\n2021-08-01,5.1\n2021-09-01,4.7\n2021-10-01,4.5\n2021-11-01,4.1\n2021-12-01,3.9\n2022-01-01,4.0\n2022-02-01,3.9\n2022-03-01,3.7\n2022-04-01,3.7\n2022-05-01,3.6\n2022-06-01,3.6\n2022-07-01,3.5\n2022-08-01,3.6\n2022-09-01,3.5\n2022-10-01,3.6\n2022-11-01,3.6\n2022-12-01,3.5\n2023-01-01,3.5\n2023-02-01,3.6\n2023-03-01,3.5\n2023-04-01,3.4\n2023-05-01,3.6\n2023-06-01,3.6\n2023-07-01,3.5\n2023-08-01,3.7\n2023-09-01,3.7\n2023-10-01,3.9\n2023-11-01,3.7\n2023-12-01,3.8\n2024-01-01,3.7\n2024-02-01,3.9\n2024-03-01,3.9\n2024-04-01,3.9\n2024-05-01,3.9\n2024-06-01,4.1\n2024-07-01,4.2\n2024-08-01,4.2\n2024-09-01,4.1\n2024-10-01,4.1\n2024-11-01,4.2\n2024-12-01,4.1\n2025-01-01,4.0\n2025-02-01,4.2\n2025-03-01,4.2\n2025-04-01,4.2\n2025-05-01,4.3\n2025-06-01,4.1\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\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":"7bbeb3d4-dacd-457a-8158-931c1393da27","timestamp":"2026-10-30T05:30:58.940Z","toolUseResult":{"stdout":"# 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.\n 946 data/UNRATE.csv\n2023-01-01,3.5\n2023-02-01,3.6\n2023-03-01,3.5\n2023-04-01,3.4\n2023-05-01,3.6\n2023-06-01,3.6\n2023-07-01,3.5\n2023-08-01,3.7\n2023-09-01,3.7\n2023-10-01,3.9\n2023-11-01,3.7\n2023-12-01,3.8\n2024-01-01,3.7\n2024-02-01,3.9\n2024-03-01,3.9\n2024-04-01,3.9\n2024-05-01,3.9\n2024-06-01,4.1\n2024-07-01,4.2\n2024-08-01,4.2\n2024-09-01,4.1\n2024-10-01,4.1\n2024-11-01,4.2\n2024-12-01,4.1\n2025-01-01,4.0\n2025-02-01,4.2\n2025-03-01,4.2\n2025-04-01,4.2\n2025-05-01,4.3\n2025-06-01,4.1\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\n935:2025-10-01,\n1948-01-01,3.4\n1948-02-01,3.8\n1948-03-01,4.0\n1948-04-01,3.9\n1948-05-01,3.5\n1948-06-01,3.6\n1948-07-01,3.6\n1948-08-01,3.9\n1948-09-01,3.8\n1948-10-01,3.7\n1948-11-01,3.8\n1948-12-01,4.0\n1949-01-01,4.3\n1949-02-01,4.7\n1949-03-01,5.0\n1949-04-01,5.3\n1949-05-01,6.1\n1949-06-01,6.2\n1949-07-01,6.7\n1949-08-01,6.8\n1949-09-01,6.6\n1949-10-01,7.9\n1949-11-01,6.4\n1949-12-01,6.6\n1950-01-01,6.5\n1950-02-01,6.4\n1950-03-01,6.3\n1950-04-01,5.8\n1950-05-01,5.5\n1950-06-01,5.4\n1950-07-01,5.0\n1950-08-01,4.5\n1950-09-01,4.4\n1950-10-01,4.2\n1950-11-01,4.2\n1950-12-01,4.3\n1951-01-01,3.7\n1951-02-01,3.4\n1951-03-01,3.4\n1951-04-01,3.1\n1951-05-01,3.0\n1951-06-01,3.2\n1951-07-01,3.1\n1951-08-01,3.1\n1951-09-01,3.3\n1951-10-01,3.5\n1951-11-01,3.5\n1951-12-01,3.1\n1952-01-01,3.2\n1952-02-01,3.1\n1952-05-01,3.0\n1952-06-01,3.0\n1952-07-01,3.2\n1952-08-01,3.4\n1952-09-01,3.1\n1952-10-01,3.0\n1953-10-01,3.1\n1953-11-01,3.5\n1953-12-01,4.5\n1954-01-01,4.9\n1954-02-01,5.2\n1954-03-01,5.7\n1954-04-01,5.9\n1954-05-01,5.9\n1954-06-01,5.6\n1954-07-01,5.8\n1954-08-01,6.0\n1954-09-01,6.1\n1954-10-01,5.7\n1954-11-01,5.3\n1954-12-01,5.0\n1955-01-01,4.9\n1955-02-01,4.7\n1955-03-01,4.6\n1955-04-01,4.7\n1955-05-01,4.3\n1955-06-01,4.2\n1955-07-01,4.0\n1955-08-01,4.2\n1955-09-01,4.1\n1955-10-01,4.3\n1955-11-01,4.2\n1955-12-01,4.2\n1956-01-01,4.0\n1956-02-01,3.9\n1956-03-01,4.2\n1956-04-01,4.0\n1956-05-01,4.3\n1956-06-01,4.3\n1956-07-01,4.4\n1956-08-01,4.1\n1956-09-01,3.9\n1956-10-01,3.9\n1956-11-01,4.3\n1956-12-01,4.2\n1957-01-01,4.2\n1957-02-01,3.9\n1957-03-01,3.7\n1957-04-01,3.9\n1957-05-01,4.1\n1957-06-01,4.3\n1957-07-01,4.2\n1957-08-01,4.1\n1957-09-01,4.4\n1957-10-01,4.5\n1957-11-01,5.1\n1957-12-01,5.2\n1958-01-01,5.8\n1958-02-01,6.4\n1958-03-01,6.7\n1958-04-01,7.4\n1958-05-01,7.4\n1958-06-01,7.3\n1958-07-01,7.5\n1958-08-01,7.4\n1958-09-01,7.1\n1958-10-01,6.7\n1958-11-01,6.2\n1958-12-01,6.2\n1959-01-01,6.0\n1959-02-01,5.9\n1959-03-01,5.6\n1959-04-01,5.2\n1959-05-01,5.1\n1959-06-01,5.0\n1959-07-01,5.1\n1959-08-01,5.2\n1959-09-01,5.5\n1959-10-01,5.7\n1959-11-01,5.8\n1959-12-01,5.3\n1960-01-01,5.2\n1960-02-01,4.8\n1960-03-01,5.4\n1960-04-01,5.2\n1960-05-01,5.1\n1960-06-01,5.4\n1960-07-01,5.5\n1960-08-01,5.6\n1960-09-01,5.5\n1960-10-01,6.1\n1960-11-01,6.1\n1960-12-01,6.6\n1961-01-01,6.6\n1961-02-01,6.9\n1961-03-01,6.9\n1961-04-01,7.0\n1961-05-01,7.1\n1961-06-01,6.9\n1961-07-01,7.0\n1961-08-01,6.6\n1961-09-01,6.7\n1961-10-01,6.5\n1961-11-01,6.1\n1961-12-01,6.0\n1962-01-01,5.8\n1962-02-01,5.5\n1962-03-01,5.6\n1962-04-01,5.6\n1962-05-01,5.5\n1962-06-01,5.5\n1962-07-01,5.4\n1962-08-01,5.7\n1962-09-01,5.6\n1962-10-01,5.4\n1962-11-01,5.7\n1962-12-01,5.5\n1963-01-01,5.7\n1963-02-01,5.9\n1963-03-01,5.7\n1963-04-01,5.7\n1963-05-01,5.9\n1963-06-01,5.6\n1963-07-01,5.6\n1963-08-01,5.4\n1963-09-01,5.5\n1963-10-01,5.5\n1963-11-01,5.7\n1963-12-01,5.5\n1964-01-01,5.6\n1964-02-01,5.4\n1964-03-01,5.4\n1964-04-01,5.3\n1964-05-01,5.1\n1964-06-01,5.2\n1964-07-01,4.9\n1964-08-01,5.0\n1964-09-01,5.1\n1964-10-01,5.1\n1964-11-01,4.8\n1964-12-01,5.0\n1965-01-01,4.9\n1965-02-01,5.1\n1965-03-01,4.7\n1965-04-01,4.8\n1965-05-01,4.6\n1965-06-01,4.6\n1965-07-01,4.4\n1965-08-01,4.4\n1965-09-01,4.3\n1965-10-01,4.2\n1965-11-01,4.1\n1965-12-01,4.0\n1966-01-01,4.0\n1966-02-01,3.8\n1966-03-01,3.8\n1966-04-01,3.8\n1966-05-01,3.9\n1966-06-01,3.8\n1966-07-01,3.8\n1966-08-01,3.8\n1966-09-01,3.7\n1966-10-01,3.7\n1966-11-01,3.6\n1966-12-01,3.8\n1967-01-01,3.9\n1967-02-01,3.8\n1967-03-01,3.8\n1967-04-01,3.8\n1967-05-01,3.8\n1967-06-01,3.9\n1967-07-01,3.8\n1967-08-01,3.8\n1967-09-01,3.8\n1967-10-01,4.0\n1967-11-01,3.9\n1967-12-01,3.8\n1968-01-01,3.7\n1968-02-01,3.8\n1968-03-01,3.7\n1968-04-01,3.5\n1968-05-01,3.5\n1968-06-01,3.7\n1968-07-01,3.7\n1968-08-01,3.5\n1968-09-01,3.4\n1968-10-01,3.4\n1968-11-01,3.4\n1968-12-01,3.4\n1969-01-01,3.4\n1969-02-01,3.4\n1969-03-01,3.4\n1969-04-01,3.4\n1969-05-01,3.4\n1969-06-01,3.5\n1969-07-01,3.5\n1969-08-01,3.5\n1969-09-01,3.7\n1969-10-01,3.7\n1969-11-01,3.5\n1969-12-01,3.5\n1970-01-01,3.9\n1970-02-01,4.2\n1970-03-01,4.4\n1970-04-01,4.6\n1970-05-01,4.8\n1970-06-01,4.9\n1970-07-01,5.0\n1970-08-01,5.1\n1970-09-01,5.4\n1970-10-01,5.5\n1970-11-01,5.9\n1970-12-01,6.1\n1971-01-01,5.9\n1971-02-01,5.9\n1971-03-01,6.0\n1971-04-01,5.9\n1971-05-01,5.9\n1971-06-01,5.9\n1971-07-01,6.0\n1971-08-01,6.1\n1971-09-01,6.0\n1971-10-01,5.8\n1971-11-01,6.0\n1971-12-01,6.0\n1972-01-01,5.8\n1972-02-01,5.7\n1972-03-01,5.8\n1972-04-01,5.7\n1972-05-01,5.7\n1972-06-01,5.7\n1972-07-01,5.6\n1972-08-01,5.6\n1972-09-01,5.5\n1972-10-01,5.6\n1972-11-01,5.3\n1972-12-01,5.2\n1973-01-01,4.9\n1973-02-01,5.0\n1973-03-01,4.9\n1973-04-01,5.0\n1973-05-01,4.9\n1973-06-01,4.9\n1973-07-01,4.8\n1973-08-01,4.8\n1973-09-01,4.8\n1973-10-01,4.6\n1973-11-01,4.8\n1973-12-01,4.9\n1974-01-01,5.1\n1974-02-01,5.2\n1974-03-01,5.1\n1974-04-01,5.1\n1974-05-01,5.1\n1974-06-01,5.4\n1974-07-01,5.5\n1974-08-01,5.5\n1974-09-01,5.9\n1974-10-01,6.0\n1974-11-01,6.6\n1974-12-01,7.2\n1975-01-01,8.1\n1975-02-01,8.1\n1975-03-01,8.6\n1975-04-01,8.8\n1975-05-01,9.0\n1975-06-01,8.8\n1975-07-01,8.6\n1975-08-01,8.4\n1975-09-01,8.4\n1975-10-01,8.4\n1975-11-01,8.3\n1975-12-01,8.2\n1976-01-01,7.9\n1976-02-01,7.7\n1976-03-01,7.6\n1976-04-01,7.7\n1976-05-01,7.4\n1976-06-01,7.6\n1976-07-01,7.8\n1976-08-01,7.8\n1976-09-01,7.6\n1976-10-01,7.7\n1976-11-01,7.8\n1976-12-01,7.8\n1977-01-01,7.5\n1977-02-01,7.6\n1977-03-01,7.4\n1977-04-01,7.2\n1977-05-01,7.0\n1977-06-01,7.2\n1977-07-01,6.9\n1977-08-01,7.0\n1977-09-01,6.8\n1977-10-01,6.8\n1977-11-01,6.8\n1977-12-01,6.4\n1978-01-01,6.4\n1978-02-01,6.3\n1978-03-01,6.3\n1978-04-01,6.1\n1978-05-01,6.0\n1978-06-01,5.9\n1978-07-01,6.2\n1978-08-01,5.9\n1978-09-01,6.0\n1978-10-01,5.8\n1978-11-01,5.9\n1978-12-01,6.0\n1979-01-01,5.9\n1979-02-01,5.9\n1979-03-01,5.8\n1979-04-01,5.8\n1979-05-01,5.6\n1979-06-01,5.7\n1979-07-01,5.7\n1979-08-01,6.0\n1979-09-01,5.9\n1979-10-01,6.0\n1979-11-01,5.9\n1979-12-01,6.0\n1980-01-01,6.3\n1980-02-01,6.3\n1980-03-01,6.3\n1980-04-01,6.9\n1980-05-01,7.5\n1980-06-01,7.6\n1980-07-01,7.8\n1980-08-01,7.7\n1980-09-01,7.5\n1980-10-01,7.5\n1980-11-01,7.5\n1980-12-01,7.2\n1981-01-01,7.5\n1981-02-01,7.4\n1981-03-01,7.4\n1981-04-01,7.2\n1981-05-01,7.5\n1981-06-01,7.5\n1981-07-01,7.2\n1981-08-01,7.4\n1981-09-01,7.6\n1981-10-01,7.9\n1981-11-01,8.3\n1981-12-01,8.5\n1982-01-01,8.6\n1982-02-01,8.9\n1982-03-01,9.0\n1982-04-01,9.3\n1982-05-01,9.4\n1982-06-01,9.6\n1982-07-01,9.8\n1982-08-01,9.8\n1983-07-01,9.4\n1983-08-01,9.5\n1983-09-01,9.2\n1983-10-01,8.8\n1983-11-01,8.5\n1983-12-01,8.3\n1984-01-01,8.0\n1984-02-01,7.8\n1984-03-01,7.8\n1984-04-01,7.7\n1984-05-01,7.4\n1984-06-01,7.2\n1984-07-01,7.5\n1984-08-01,7.5\n1984-09-01,7.3\n1984-10-01,7.4\n1984-11-01,7.2\n1984-12-01,7.3\n1985-01-01,7.3\n1985-02-01,7.2\n1985-03-01,7.2\n1985-04-01,7.3\n1985-05-01,7.2\n1985-06-01,7.4\n1985-07-01,7.4\n1985-08-01,7.1\n1985-09-01,7.1\n1985-10-01,7.1\n1985-11-01,7.0\n1985-12-01,7.0\n1986-01-01,6.7\n1986-02-01,7.2\n1986-03-01,7.2\n1986-04-01,7.1\n1986-05-01,7.2\n1986-06-01,7.2\n1986-07-01,7.0\n1986-08-01,6.9\n1986-09-01,7.0\n1986-10-01,7.0\n1986-11-01,6.9\n1986-12-01,6.6\n1987-01-01,6.6\n1987-02-01,6.6\n1987-03-01,6.6\n1987-04-01,6.3\n1987-05-01,6.3\n1987-06-01,6.2\n1987-07-01,6.1\n1987-08-01,6.0\n1987-09-01,5.9\n1987-10-01,6.0\n1987-11-01,5.8\n1987-12-01,5.7\n1988-01-01,5.7\n1988-02-01,5.7\n1988-03-01,5.7\n1988-04-01,5.4\n1988-05-01,5.6\n1988-06-01,5.4\n1988-07-01,5.4\n1988-08-01,5.6\n1988-09-01,5.4\n1988-10-01,5.4\n1988-11-01,5.3\n1988-12-01,5.3\n1989-01-01,5.4\n1989-02-01,5.2\n1989-03-01,5.0\n1989-04-01,5.2\n1989-05-01,5.2\n1989-06-01,5.3\n1989-07-01,5.2\n1989-08-01,5.2\n1989-09-01,5.3\n1989-10-01,5.3\n1989-11-01,5.4\n1989-12-01,5.4\n1990-01-01,5.4\n1990-02-01,5.3\n1990-03-01,5.2\n1990-04-01,5.4\n1990-05-01,5.4\n1990-06-01,5.2\n1990-07-01,5.5\n1990-08-01,5.7\n1990-09-01,5.9\n1990-10-01,5.9\n1990-11-01,6.2\n1990-12-01,6.3\n1991-01-01,6.4\n1991-02-01,6.6\n1991-03-01,6.8\n1991-04-01,6.7\n1991-05-01,6.9\n1991-06-01,6.9\n1991-07-01,6.8\n1991-08-01,6.9\n1991-09-01,6.9\n1991-10-01,7.0\n1991-11-01,7.0\n1991-12-01,7.3\n1992-01-01,7.3\n1992-02-01,7.4\n1992-03-01,7.4\n1992-04-01,7.4\n1992-05-01,7.6\n1992-06-01,7.8\n1992-07-01,7.7\n1992-08-01,7.6\n1992-09-01,7.6\n1992-10-01,7.3\n1992-11-01,7.4\n1992-12-01,7.4\n1993-01-01,7.3\n1993-02-01,7.1\n1993-03-01,7.0\n1993-04-01,7.1\n1993-05-01,7.1\n1993-06-01,7.0\n1993-07-01,6.9\n1993-08-01,6.8\n1993-09-01,6.7\n1993-10-01,6.8\n1993-11-01,6.6\n1993-12-01,6.5\n1994-01-01,6.6\n1994-02-01,6.6\n1994-03-01,6.5\n1994-04-01,6.4\n1994-05-01,6.1\n1994-06-01,6.1\n1994-07-01,6.1\n1994-08-01,6.0\n1994-09-01,5.9\n1994-10-01,5.8\n1994-11-01,5.6\n1994-12-01,5.5\n1995-01-01,5.6\n1995-02-01,5.4\n1995-03-01,5.4\n1995-04-01,5.8\n1995-05-01,5.6\n1995-06-01,5.6\n1995-07-01,5.7\n1995-08-01,5.7\n1995-09-01,5.6\n1995-10-01,5.5\n1995-11-01,5.6\n1995-12-01,5.6\n1996-01-01,5.6\n1996-02-01,5.5\n1996-03-01,5.5\n1996-04-01,5.6\n1996-05-01,5.6\n1996-06-01,5.3\n1996-07-01,5.5\n1996-08-01,5.1\n1996-09-01,5.2\n1996-10-01,5.2\n1996-11-01,5.4\n1996-12-01,5.4\n1997-01-01,5.3\n1997-02-01,5.2\n1997-03-01,5.2\n1997-04-01,5.1\n1997-05-01,4.9\n1997-06-01,5.0\n1997-07-01,4.9\n1997-08-01,4.8\n1997-09-01,4.9\n1997-10-01,4.7\n1997-11-01,4.6\n1997-12-01,4.7\n1998-01-01,4.6\n1998-02-01,4.6\n1998-03-01,4.7\n1998-04-01,4.3\n1998-05-01,4.4\n1998-06-01,4.5\n1998-07-01,4.5\n1998-08-01,4.5\n1998-09-01,4.6\n1998-10-01,4.5\n1998-11-01,4.4\n1998-12-01,4.4\n1999-01-01,4.3\n1999-02-01,4.4\n1999-03-01,4.2\n1999-04-01,4.3\n1999-05-01,4.2\n1999-06-01,4.3\n1999-07-01,4.3\n1999-08-01,4.2\n1999-09-01,4.2\n1999-10-01,4.1\n1999-11-01,4.1\n1999-12-01,4.0\n2000-01-01,4.0\n2000-02-01,4.1\n2000-03-01,4.0\n2000-04-01,3.8\n2000-05-01,4.0\n2000-06-01,4.0\n2000-07-01,4.0\n2000-08-01,4.1\n2000-09-01,3.9\n2000-10-01,3.9\n2000-11-01,3.9\n2000-12-01,3.9\n2001-01-01,4.2\n2001-02-01,4.2\n2001-03-01,4.3\n2001-04-01,4.4\n2001-05-01,4.3\n2001-06-01,4.5\n2001-07-01,4.6\n2001-08-01,4.9\n2001-09-01,5.0\n2001-10-01,5.3\n2001-11-01,5.5\n2001-12-01,5.7\n2002-01-01,5.7\n2002-02-01,5.7\n2002-03-01,5.7\n2002-04-01,5.9\n2002-05-01,5.8\n2002-06-01,5.8\n2002-07-01,5.8\n2002-08-01,5.7\n2002-09-01,5.7\n2002-10-01,5.7\n2002-11-01,5.9\n2002-12-01,6.0\n2003-01-01,5.8\n2003-02-01,5.9\n2003-03-01,5.9\n2003-04-01,6.0\n2003-05-01,6.1\n2003-06-01,6.3\n2003-07-01,6.2\n2003-08-01,6.1\n2003-09-01,6.1\n2003-10-01,6.0\n2003-11-01,5.8\n2003-12-01,5.7\n2004-01-01,5.7\n2004-02-01,5.6\n2004-03-01,5.8\n2004-04-01,5.6\n2004-05-01,5.6\n2004-06-01,5.6\n2004-07-01,5.5\n2004-08-01,5.4\n2004-09-01,5.4\n2004-10-01,5.5\n2004-11-01,5.4\n2004-12-01,5.4\n2005-01-01,5.3\n2005-02-01,5.4\n2005-03-01,5.2\n2005-04-01,5.2\n2005-05-01,5.1\n2005-06-01,5.0\n2005-07-01,5.0\n2005-08-01,4.9\n2005-09-01,5.0\n2005-10-01,5.0\n2005-11-01,5.0\n2005-12-01,4.9\n2006-01-01,4.7\n2006-02-01,4.8\n2006-03-01,4.7\n2006-04-01,4.7\n2006-05-01,4.6\n2006-06-01,4.6\n2006-07-01,4.7\n2006-08-01,4.7\n2006-09-01,4.5\n2006-10-01,4.4\n2006-11-01,4.5\n2006-12-01,4.4\n2007-01-01,4.6\n2007-02-01,4.5\n2007-03-01,4.4\n2007-04-01,4.5\n2007-05-01,4.4\n2007-06-01,4.6\n2007-07-01,4.7\n2007-08-01,4.6\n2007-09-01,4.7\n2007-10-01,4.7\n2007-11-01,4.7\n2007-12-01,5.0\n2008-01-01,5.0\n2008-02-01,4.9\n2008-03-01,5.1\n2008-04-01,5.0\n2008-05-01,5.4\n2008-06-01,5.6\n2008-07-01,5.8\n2008-08-01,6.1\n2008-09-01,6.1\n2008-10-01,6.5\n2008-11-01,6.8\n2008-12-01,7.3\n2009-01-01,7.8\n2009-02-01,8.3\n2009-03-01,8.7\n2009-04-01,9.0\n2009-05-01,9.4\n2009-06-01,9.5\n2009-07-01,9.5\n2009-08-01,9.6\n2009-09-01,9.8\n2009-11-01,9.9\n2009-12-01,9.9\n2010-01-01,9.8\n2010-02-01,9.8\n2010-03-01,9.9\n2010-04-01,9.9\n2010-05-01,9.6\n2010-06-01,9.4\n2010-07-01,9.4\n2010-08-01,9.5\n2010-09-01,9.5\n2010-10-01,9.4\n2010-11-01,9.8\n2010-12-01,9.3\n2011-01-01,9.1\n2011-02-01,9.0\n2011-03-01,9.0\n2011-04-01,9.1\n2011-05-01,9.0\n2011-06-01,9.1\n2011-07-01,9.0\n2011-08-01,9.0\n2011-09-01,9.0\n2011-10-01,8.8\n2011-11-01,8.6\n2011-12-01,8.5\n2012-01-01,8.3\n2012-02-01,8.3\n2012-03-01,8.2\n2012-04-01,8.2\n2012-05-01,8.2\n2012-06-01,8.2\n2012-07-01,8.2\n2012-08-01,8.1\n2012-09-01,7.8\n2012-10-01,7.8\n2012-11-01,7.7\n2012-12-01,7.9\n2013-01-01,8.0\n2013-02-01,7.7\n2013-03-01,7.5\n2013-04-01,7.6\n2013-05-01,7.5\n2013-06-01,7.5\n2013-07-01,7.3\n2013-08-01,7.2\n2013-09-01,7.2\n2013-10-01,7.2\n2013-11-01,6.9\n2013-12-01,6.7\n2014-01-01,6.6\n2014-02-01,6.7\n2014-03-01,6.7\n2014-04-01,6.2\n2014-05-01,6.3\n2014-06-01,6.1\n2014-07-01,6.2\n2014-08-01,6.1\n2014-09-01,5.9\n2014-10-01,5.7\n2014-11-01,5.8\n2014-12-01,5.6\n2015-01-01,5.7\n2015-02-01,5.5\n2015-03-01,5.4\n2015-04-01,5.4\n2015-05-01,5.6\n2015-06-01,5.3\n2015-07-01,5.2\n2015-08-01,5.1\n2015-09-01,5.0\n2015-10-01,5.0\n2015-11-01,5.1\n2015-12-01,5.0\n2016-01-01,4.8\n2016-02-01,4.9\n2016-03-01,5.0\n2016-04-01,5.1\n2016-05-01,4.8\n2016-06-01,4.9\n2016-07-01,4.8\n2016-08-01,4.9\n2016-09-01,5.0\n2016-10-01,4.9\n2016-11-01,4.7\n2016-12-01,4.7\n2017-01-01,4.7\n2017-02-01,4.6\n2017-03-01,4.4\n2017-04-01,4.4\n2017-05-01,4.4\n2017-06-01,4.3\n2017-07-01,4.3\n2017-08-01,4.4\n2017-09-01,4.3\n2017-10-01,4.2\n2017-11-01,4.2\n2017-12-01,4.1\n2018-01-01,4.0\n2018-02-01,4.1\n2018-03-01,4.0\n2018-04-01,4.0\n2018-05-01,3.8\n2018-06-01,4.0\n2018-07-01,3.8\n2018-08-01,3.8\n2018-09-01,3.7\n2018-10-01,3.8\n2018-11-01,3.8\n2018-12-01,3.9\n2019-01-01,4.0\n2019-02-01,3.8\n2019-03-01,3.8\n2019-04-01,3.7\n2019-05-01,3.6\n2019-06-01,3.6\n2019-07-01,3.7\n2019-08-01,3.6\n2019-09-01,3.5\n2019-10-01,3.6\n2019-11-01,3.6\n2019-12-01,3.6\n2020-01-01,3.6\n2020-02-01,3.5\n2020-03-01,4.4\n2020-08-01,8.4\n2020-09-01,7.8\n2020-10-01,6.9\n2020-11-01,6.7\n2020-12-01,6.7\n2021-01-01,6.4\n2021-02-01,6.2\n2021-03-01,6.1\n2021-04-01,6.1\n2021-05-01,5.8\n2021-06-01,5.9\n2021-07-01,5.4\n2021-08-01,5.1\n2021-09-01,4.7\n2021-10-01,4.5\n2021-11-01,4.1\n2021-12-01,3.9\n2022-01-01,4.0\n2022-02-01,3.9\n2022-03-01,3.7\n2022-04-01,3.7\n2022-05-01,3.6\n2022-06-01,3.6\n2022-07-01,3.5\n2022-08-01,3.6\n2022-09-01,3.5\n2022-10-01,3.6\n2022-11-01,3.6\n2022-12-01,3.5\n2023-01-01,3.5\n2023-02-01,3.6\n2023-03-01,3.5\n2023-04-01,3.4\n2023-05-01,3.6\n2023-06-01,3.6\n2023-07-01,3.5\n2023-08-01,3.7\n2023-09-01,3.7\n2023-10-01,3.9\n2023-11-01,3.7\n2023-12-01,3.8\n2024-01-01,3.7\n2024-02-01,3.9\n2024-03-01,3.9\n2024-04-01,3.9\n2024-05-01,3.9\n2024-06-01,4.1\n2024-07-0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Deterministic; no randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nMODEL = \"AR_BIC_diff\"\nPMAX = 6\nDUMMY_DATES = [pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-05-01\")]\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(DATA)\nn_rows = len(raw)\ndf = raw.copy()\ndf[\"observation_date\"] = pd.to_datetime(df[\"observation_date\"])\ndf[\"UNRATE\"] = pd.to_numeric(df[\"UNRATE\"], errors=\"coerce\")\ndf = df.set_index(\"observation_date\").asfreq(\"MS\") # full monthly grid\ny_all = df[\"UNRATE\"]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\n\ndef fit_predict(y, origin, hmax, use_dummies=True):\n \"\"\"y: level series on monthly grid, already truncated to dates <= origin.\"\"\"\n dy = y.diff() # NaN where either month missing -> those rows dropped in estimation\n best = None\n for p in range(PMAX + 1):\n X = [np.ones(len(dy))]\n for k in range(1, p + 1):\n X.append(dy.shift(k).values)\n cols = len(X)\n if use_dummies:\n for d in DUMMY_DATES:\n if d <= origin:\n X.append((dy.index == d).astype(float))\n X = np.column_stack(X); Y = dy.values\n ok = ~np.isnan(Y) & ~np.isnan(X).any(1)\n # common estimation sample across p for comparable BIC: start after PMAX lags\n ok &= np.arange(len(Y)) > PMAX\n Xo, Yo = X[ok], Y[ok]\n b, *_ = np.linalg.lstsq(Xo, Yo, rcond=None)\n e = Yo - Xo @ b; n = len(Yo); kpar = Xo.shape[1]\n bic = n * np.log(e @ e / n) + kpar * np.log(n)\n if best is None or bic < best[0]:\n best = (bic, p, b[:cols], n)\n _, p, b, nreg = best\n # state for forecasting: interior missing levels linearly interpolated (uses only data <= origin);\n yy = y.copy()\n last_obs = yy.last_valid_index()\n yy.loc[:last_obs] = yy.loc[:last_obs].interpolate()\n dyy = list(yy.loc[:last_obs].diff().values)\n level = yy.loc[last_obs]\n steps = (origin.year - last_obs.year) * 12 + origin.month - last_obs.month + hmax\n fc = []\n for _ in range(steps):\n d = b[0] + sum(b[k] * dyy[-k] for k in range(1, p + 1))\n dyy.append(d); level = level + d; fc.append(level)\n # fc[i] is forecast for last_obs + i+1 months; return by horizon relative to origin\n off = steps - hmax\n return {h: fc[off + h - 1] for h in range(1, hmax + 1)}, p, nreg\n\n\ndef run(use_dummies=True):\n rows, ps = [], []\n for t in origins:\n y = y_all.loc[:t]\n naive = y.loc[y.last_valid_index()]\n fc, p, nreg = fit_predict(y, t, max(HORIZONS), use_dummies)\n ps.append(p)\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = y_all.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, fc[h])):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=h, forecast=round(float(f), 6),\n actual=None if pd.isna(act) else float(act), model=m))\n return pd.DataFrame(rows), ps, nreg\n\n\nled, ps, nreg_last = run(True)\nled.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\nled_nd, _, _ = run(False) # sensitivity only (no COVID dummies)\n\n\ndef errs(L, m, h):\n s = L[(L.model == m) & (L.horizon == h) & L.actual.notna()]\n e = s.actual - s.forecast\n return e.values, s.origin_date.values\n\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(p) on monthly first differences of UNRATE with intercept, p in 0..6 re-selected by BIC at \"\n \"each origin, OLS on an expanding window starting 1948 (first usable change 1948-02, common \"\n \"sample after 6 lags) with impulse dummies for the 2020-04 and 2020-05 changes; iterated \"\n \"multi-step forecasts cumulated to levels.\"),\n \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nextra = {}\nfor h in HORIZONS:\n en, _ = errs(led, \"naive\", h); em, _ = errs(led, MODEL, h)\n res[f\"n_evaluated_h{h}\"] = int(len(en))\n for nm, e in ((\"naive\", en), (\"model\", em)):\n res[f\"rmse_{nm}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{nm}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n\n# Uncertainty: Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free rectangular lags\n# (Harvey-Leybourne-Newbold small-sample correction), t(n-1) reference. Note the evaluation set has one gap.\nfor h in HORIZONS:\n en, _ = errs(led, \"naive\", h); em, _ = errs(led, MODEL, h)\n d = em ** 2 - en ** 2; n = len(d); dbar = d.mean(); dc = d - dbar\n g = [dc @ dc / n] + [dc[k:] @ dc[:-k] / n for k in range(1, h)]\n var = (g[0] + 2 * sum(g[1:])) / n\n dm = dbar / np.sqrt(var) if var > 0 else np.nan\n hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n extra[f\"dm_stat_h{h}\"] = float(hln)\n extra[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(hln), n - 1))\n e2, _ = errs(led_nd, MODEL, h)\n extra[f\"sensitivity_no_covid_dummies_rmse_model_h{h}\"] = float(np.sqrt(np.mean(e2 ** 2)))\n extra[f\"sensitivity_no_covid_dummies_rel_improvement_h{h}\"] = float(1 - np.sqrt(np.mean(e2 ** 2)) / res[f\"rmse_naive_h{h}\"])\nextra[\"selected_p_counts\"] = {str(k): int(v) for k, v in zip(*np.unique(ps, return_counts=True))}\nextra[\"n_regression_rows_last_window\"] = int(nreg_last)\n\nn_missing = int(y_all.isna().sum())\nres.update({\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} missing observation(s): \" + \", \".join(d.strftime('%Y-%m-%d') for d in y_all.index[y_all.isna()]) + \" (empty value field)\",\n \"handling\": \"Target pairs with missing actual (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and excluded from error statistics; differences touching the missing month dropped from estimation; for forecasting at later origins the interior gap is linearly interpolated from neighbours dated <= origin; at origin 2025-10 the naive forecast uses 2025-09 and the model forecasts 2025-10 as an intermediate step.\"},\n {\"issue\": \"Extreme COVID-19 jump (2020-04 +10.4 pp, 2020-05 -1.6 pp) inside every training window\",\n \"handling\": \"Impulse dummies on the 2020-04 and 2020-05 monthly changes (dates known before all origins); sensitivity without dummies reported in report.md.\"},\n {\"issue\": \"Final-vintage data (revised seasonal factors), 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_rows)},\n \"n_analysed\": int(y_all.loc[:origins[-1]].notna().sum()),\n \"random_seed\": None,\n})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nwith open(os.path.join(OUT, \"supplementary.json\"), \"w\") as f:\n json.dump(extra, f, indent=2)\nprint(json.dumps(res, indent=2)); print(json.dumps(extra, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1487,"cache_read_input_tokens":15731,"output_tokens":4158,"output_tokens_details":{"thinking_tokens":501},"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":1487},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbfra71kRwpLA5FbkN","requestRef":"ab0113ca-9eab-4574-acba-00fba3c13d98","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"de6089ef-9571-4397-a76a-524ea092b1ec","timestamp":"2026-10-30T05:31:49.029Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0002","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"de6089ef-9571-4397-a76a-524ea092b1ec","isSidechain":false,"promptId":"f20057ca-d2ad-4230-a3ee-44a403016ce1","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014ahxjUVXwLLmLcs8iackcj","type":"tool_result","content":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR_BIC_diff\",\n \"model_description\": \"AR(p) on monthly first differences of UNRATE with intercept, p in 0..6 re-selected by BIC at each origin, OLS on an expanding window starting 1948 (first usable change 1948-02, common sample after 6 lags) with impulse dummies for the 2020-04 and 2020-05 changes; iterated multi-step forecasts cumulated to levels.\",\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\": 0.13126670452109548,\n \"mae_model_h1\": 0.1062675428571429,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.1813477002590013,\n \"mae_model_h3\": 0.14058517142857146,\n \"rel_improvement_rmse_h1\": -0.1209028820311926,\n \"rel_improvement_rmse_h3\": -0.1436805585591352,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"1 missing observation(s): 2025-10-01 (empty value field)\",\n \"handling\": \"Target pairs with missing actual (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and excluded from error statistics; differences touching the missing month dropped from estimation; for forecasting at later origins the interior gap is linearly interpolated from neighbours dated <= origin; at origin 2025-10 the naive forecast uses 2025-09 and the model forecasts 2025-10 as an intermediate step.\"\n },\n {\n \"issue\": \"Extreme COVID-19 jump (2020-04 +10.4 pp, 2020-05 -1.6 pp) inside every training window\",\n \"handling\": \"Impulse dummies on the 2020-04 and 2020-05 monthly changes (dates known before all origins); sensitivity without dummies reported in report.md.\"\n },\n {\n \"issue\": \"Final-vintage data (revised seasonal factors), 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{\n \"dm_stat_h1\": 2.858696312787161,\n \"dm_pvalue_two_sided_h1\": 0.00721513911436424,\n \"sensitivity_no_covid_dummies_rmse_model_h1\": 0.11710933229654619,\n \"sensitivity_no_covid_dummies_rel_improvement_h1\": -1.1301897592241872e-05,\n \"dm_stat_h3\": 2.457613784280108,\n \"dm_pvalue_two_sided_h3\": 0.01924100668580027,\n \"sensitivity_no_covid_dummies_rmse_model_h3\": 0.15859834979505394,\n \"sensitivity_no_covid_dummies_rel_improvement_h3\": -0.0002103639644099875,\n \"selected_p_counts\": {\n \"1\": 36\n },\n \"n_regression_rows_last_window\": 932\n}","is_error":false}]},"uuid":"919ddb21-7fb9-4267-9081-2c08621f6f20","timestamp":"2026-10-30T05:31:49.806Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR_BIC_diff\",\n \"model_description\": \"AR(p) on monthly first differences of UNRATE with intercept, p in 0..6 re-selected by BIC at each origin, OLS on an expanding window starting 1948 (first usable change 1948-02, common sample after 6 lags) with impulse dummies for the 2020-04 and 2020-05 changes; iterated multi-step forecasts cumulated to levels.\",\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\": 0.13126670452109548,\n \"mae_model_h1\": 0.1062675428571429,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.1813477002590013,\n \"mae_model_h3\": 0.14058517142857146,\n \"rel_improvement_rmse_h1\": -0.1209028820311926,\n \"rel_improvement_rmse_h3\": -0.1436805585591352,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"1 missing observation(s): 2025-10-01 (empty value field)\",\n \"handling\": \"Target pairs with missing actual (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and excluded from error statistics; differences touching the missing month dropped from estimation; for forecasting at later origins the interior gap is linearly interpolated from neighbours dated <= origin; at origin 2025-10 the naive forecast uses 2025-09 and the model forecasts 2025-10 as an intermediate step.\"\n },\n {\n \"issue\": \"Extreme COVID-19 jump (2020-04 +10.4 pp, 2020-05 -1.6 pp) inside every training window\",\n \"handling\": \"Impulse dummies on the 2020-04 and 2020-05 monthly changes (dates known before all origins); sensitivity without dummies reported in report.md.\"\n },\n {\n \"issue\": \"Final-vintage data (revised seasonal factors), 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{\n \"dm_stat_h1\": 2.858696312787161,\n \"dm_pvalue_two_sided_h1\": 0.00721513911436424,\n \"sensitivity_no_covid_dummies_rmse_model_h1\": 0.11710933229654619,\n \"sensitivity_no_covid_dummies_rel_improvement_h1\": -1.1301897592241872e-05,\n \"dm_stat_h3\": 2.457613784280108,\n \"dm_pvalue_two_sided_h3\": 0.01924100668580027,\n \"sensitivity_no_covid_dummies_rmse_model_h3\": 0.15859834979505394,\n \"sensitivity_no_covid_dummies_rel_improvement_h3\": -0.0002103639644099875,\n \"selected_p_counts\": {\n \"1\": 36\n },\n \"n_regression_rows_last_window\": 932\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"de6089ef-9571-4397-a76a-524ea092b1ec","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0002","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","hash":"71ffcafb609314f6d88e0c3c75110a38c950076e43fdee1bad9d1fb90e264248","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014ahxjUVXwLLmLcs8iackcj","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin (expanding window) forecasts of UNRATE.\nModel: AR(p) on monthly first differences, intercept, OLS, p chosen by BIC in 0..6\nat every origin, estimation sample from 1948, with impulse dummies for the\n2020-04 and 2020-05 COVID changes. Deterministic; no randomness.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nMODEL = \"AR_BIC_diff\"\nPMAX = 6\nDUMMY_DATES = [pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-05-01\")]\nHORIZONS = [1, 3]\n\nraw = pd.read_csv(DATA)\nn_rows = len(raw)\ndf = raw.copy()\ndf[\"observation_date\"] = pd.to_datetime(df[\"observation_date\"])\ndf[\"UNRATE\"] = pd.to_numeric(df[\"UNRATE\"], errors=\"coerce\")\ndf = df.set_index(\"observation_date\").asfreq(\"MS\") # full monthly grid\ny_all = df[\"UNRATE\"]\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\n\ndef fit_predict(y, origin, hmax, use_dummies=True):\n \"\"\"y: level series on monthly grid, already truncated to dates <= origin.\"\"\"\n dy = y.diff() # NaN where either month missing -> those rows dropped in estimation\n best = None\n for p in range(PMAX + 1):\n X = [np.ones(len(dy))]\n for k in range(1, p + 1):\n X.append(dy.shift(k).values)\n cols = len(X)\n if use_dummies:\n for d in DUMMY_DATES:\n if d <= origin:\n X.append((dy.index == d).astype(float))\n X = np.column_stack(X); Y = dy.values\n ok = ~np.isnan(Y) & ~np.isnan(X).any(1)\n # common estimation sample across p for comparable BIC: start after PMAX lags\n ok &= np.arange(len(Y)) > PMAX\n Xo, Yo = X[ok], Y[ok]\n b, *_ = np.linalg.lstsq(Xo, Yo, rcond=None)\n e = Yo - Xo @ b; n = len(Yo); kpar = Xo.shape[1]\n bic = n * np.log(e @ e / n) + kpar * np.log(n)\n if best is None or bic < best[0]:\n best = (bic, p, b[:cols], n)\n _, p, b, nreg = best\n # state for forecasting: interior missing levels linearly interpolated (uses only data <= origin);\n yy = y.copy()\n last_obs = yy.last_valid_index()\n yy.loc[:last_obs] = yy.loc[:last_obs].interpolate()\n dyy = list(yy.loc[:last_obs].diff().values)\n level = yy.loc[last_obs]\n steps = (origin.year - last_obs.year) * 12 + origin.month - last_obs.month + hmax\n fc = []\n for _ in range(steps):\n d = b[0] + sum(b[k] * dyy[-k] for k in range(1, p + 1))\n dyy.append(d); level = level + d; fc.append(level)\n # fc[i] is forecast for last_obs + i+1 months; return by horizon relative to origin\n off = steps - hmax\n return {h: fc[off + h - 1] for h in range(1, hmax + 1)}, p, nreg\n\n\ndef run(use_dummies=True):\n rows, ps = [], []\n for t in origins:\n y = y_all.loc[:t]\n naive = y.loc[y.last_valid_index()]\n fc, p, nreg = fit_predict(y, t, max(HORIZONS), use_dummies)\n ps.append(p)\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = y_all.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, fc[h])):\n rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n horizon=h, forecast=round(float(f), 6),\n actual=None if pd.isna(act) else float(act), model=m))\n return pd.DataFrame(rows), ps, nreg\n\n\nled, ps, nreg_last = run(True)\nled.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\nled_nd, _, _ = run(False) # sensitivity only (no COVID dummies)\n\n\ndef errs(L, m, h):\n s = L[(L.model == m) & (L.horizon == h) & L.actual.notna()]\n e = s.actual - s.forecast\n return e.values, s.origin_date.values\n\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(p) on monthly first differences of UNRATE with intercept, p in 0..6 re-selected by BIC at \"\n \"each origin, OLS on an expanding window starting 1948 (first usable change 1948-02, common \"\n \"sample after 6 lags) with impulse dummies for the 2020-04 and 2020-05 changes; iterated \"\n \"multi-step forecasts cumulated to levels.\"),\n \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nextra = {}\nfor h in HORIZONS:\n en, _ = errs(led, \"naive\", h); em, _ = errs(led, MODEL, h)\n res[f\"n_evaluated_h{h}\"] = int(len(en))\n for nm, e in ((\"naive\", en), (\"model\", em)):\n res[f\"rmse_{nm}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{nm}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n\n# Uncertainty: Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free rectangular lags\n# (Harvey-Leybourne-Newbold small-sample correction), t(n-1) reference. Note the evaluation set has one gap.\nfor h in HORIZONS:\n en, _ = errs(led, \"naive\", h); em, _ = errs(led, MODEL, h)\n d = em ** 2 - en ** 2; n = len(d); dbar = d.mean(); dc = d - dbar\n g = [dc @ dc / n] + [dc[k:] @ dc[:-k] / n for k in range(1, h)]\n var = (g[0] + 2 * sum(g[1:])) / n\n dm = dbar / np.sqrt(var) if var > 0 else np.nan\n hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n extra[f\"dm_stat_h{h}\"] = float(hln)\n extra[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(hln), n - 1))\n e2, _ = errs(led_nd, MODEL, h)\n extra[f\"sensitivity_no_covid_dummies_rmse_model_h{h}\"] = float(np.sqrt(np.mean(e2 ** 2)))\n extra[f\"sensitivity_no_covid_dummies_rel_improvement_h{h}\"] = float(1 - np.sqrt(np.mean(e2 ** 2)) / res[f\"rmse_naive_h{h}\"])\nextra[\"selected_p_counts\"] = {str(k): int(v) for k, v in zip(*np.unique(ps, return_counts=True))}\nextra[\"n_regression_rows_last_window\"] = int(nreg_last)\n\nn_missing = int(y_all.isna().sum())\nres.update({\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{n_missing} missing observation(s): \" + \", \".join(d.strftime('%Y-%m-%d') for d in y_all.index[y_all.isna()]) + \" (empty value field)\",\n \"handling\": \"Target pairs with missing actual (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and excluded from error statistics; differences touching the missing month dropped from estimation; for forecasting at later origins the interior gap is linearly interpolated from neighbours dated <= origin; at origin 2025-10 the naive forecast uses 2025-09 and the model forecasts 2025-10 as an intermediate step.\"},\n {\"issue\": \"Extreme COVID-19 jump (2020-04 +10.4 pp, 2020-05 -1.6 pp) inside every training window\",\n \"handling\": \"Impulse dummies on the 2020-04 and 2020-05 monthly changes (dates known before all origins); sensitivity without dummies reported in report.md.\"},\n {\"issue\": \"Final-vintage data (revised seasonal factors), 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_rows)},\n \"n_analysed\": int(y_all.loc[:origins[-1]].notna().sum()),\n \"random_seed\": None,\n})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nwith open(os.path.join(OUT, \"supplementary.json\"), \"w\") as f:\n json.dump(extra, f, indent=2)\nprint(json.dumps(res, indent=2)); print(json.dumps(extra, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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4.2 4.093924 4.1\n 3 4.0 4.0 4.078281 4.1\n2024-11-01 1 4.1 4.1 4.219534 4.2\n 3 4.2 4.2 4.212346 4.2\n2024-12-01 1 4.0 4.0 4.068440 4.1\n 3 4.2 4.2 4.044618 4.1\n2025-01-01 1 4.2 4.2 3.968355 4.0\n 3 4.2 4.2 3.944334 4.0\n2025-02-01 1 4.2 4.2 4.244959 4.2\n 3 4.3 4.3 4.245962 4.2\n2025-03-01 1 4.2 4.2 4.194030 4.2\n 3 4.1 4.1 4.178671 4.2\n2025-04-01 1 4.3 4.3 4.194037 4.2\n 3 4.3 4.3 4.178694 4.2\n2025-05-01 1 4.1 4.1 4.319575 4.3\n 3 4.3 4.3 4.312634 4.3\n2025-06-01 1 4.3 4.3 4.043160 4.1\n 3 4.4 4.4 4.011349 4.1\n2025-07-01 1 4.3 4.3 4.344734 4.3\n 3 NaN NaN 4.345780 4.3\n2025-08-01 1 4.4 4.4 4.294125 4.3\n 3 4.5 4.5 4.279033 4.3\n2025-09-01 1 NaN NaN 4.419503 4.4\n 3 4.4 4.4 4.412700 4.4\n2025-10-01 1 4.5 4.5 4.418670 4.4\n 3 4.3 4.3 4.405431 4.4\n2025-11-01 1 4.4 4.4 4.506871 4.5\n 3 4.4 4.4 4.496071 4.5\n2025-12-01 1 4.3 4.3 4.368977 4.4\n 3 4.3 4.3 4.346185 4.4\n2026-01-01 1 4.4 4.4 4.268892 4.3\n 3 4.3 4.3 4.245902 4.3\n2026-02-01 1 4.3 4.3 4.419558 4.4\n 3 4.3 4.3 4.412917 4.4\n2026-03-01 1 4.3 4.3 4.268948 4.3\n 3 4.2 4.2 4.246017 4.3\n2026-04-01 1 4.3 4.3 4.294207 4.3\n 3 4.1 4.1 4.279329 4.3\n2026-05-01 1 4.2 4.2 4.294213 4.3\n 3 4.1 4.1 4.279352 4.3\n2026-06-01 1 4.1 4.1 4.168893 4.2\n 3 4.2 4.2 4.145808 4.2","is_error":false}]},"uuid":"78d34239-f103-4740-b086-20d0df6527f4","timestamp":"2026-10-30T05:31:53.798Z","toolUseResult":{"stdout":"144\n actual forecast \nmodel AR_BIC_diff naive AR_BIC_diff naive\norigin_date horizon \n2023-07-01 1 3.7 3.7 3.467557 3.5\n 3 3.9 3.9 3.441986 3.5\n2023-08-01 1 3.7 3.7 3.745056 3.7\n 3 3.7 3.7 3.745157 3.7\n2023-09-01 1 3.9 3.9 3.693534 3.7\n 3 3.8 3.8 3.676847 3.7\n2023-10-01 1 3.7 3.7 3.945206 3.9\n 3 3.7 3.7 3.945747 3.9\n2023-11-01 1 3.8 3.8 3.642242 3.7\n 3 3.9 3.9 3.608942 3.7\n2023-12-01 1 3.7 3.7 3.819206 3.8\n 3 3.9 3.9 3.811039 3.8\n2024-01-01 1 3.9 3.9 3.667978 3.7\n 3 3.9 3.9 3.643080 3.7\n2024-02-01 1 3.9 3.9 3.944749 3.9\n 3 3.9 3.9 3.945001 3.9\n2024-03-01 1 3.9 3.9 3.893711 3.9\n 3 4.1 4.1 3.877521 3.9\n2024-04-01 1 3.9 3.9 3.893718 3.9\n 3 4.2 4.2 3.877546 3.9\n2024-05-01 1 4.1 4.1 3.893725 3.9\n 3 4.2 4.2 3.877570 3.9\n2024-06-01 1 4.2 4.2 4.144912 4.1\n 3 4.1 4.1 4.145635 4.1\n2024-07-01 1 4.2 4.2 4.219518 4.2\n 3 4.1 4.1 4.212271 4.2\n2024-08-01 1 4.1 4.1 4.193994 4.2\n 3 4.2 4.2 4.178528 4.2\n2024-09-01 1 4.1 4.1 4.068394 4.1\n 3 4.1 4.1 4.044505 4.1\n2024-10-01 1 4.2 4.2 4.093924 4.1\n 3 4.0 4.0 4.078281 4.1\n2024-11-01 1 4.1 4.1 4.219534 4.2\n 3 4.2 4.2 4.212346 4.2\n2024-12-01 1 4.0 4.0 4.068440 4.1\n 3 4.2 4.2 4.044618 4.1\n2025-01-01 1 4.2 4.2 3.968355 4.0\n 3 4.2 4.2 3.944334 4.0\n2025-02-01 1 4.2 4.2 4.244959 4.2\n 3 4.3 4.3 4.245962 4.2\n2025-03-01 1 4.2 4.2 4.194030 4.2\n 3 4.1 4.1 4.178671 4.2\n2025-04-01 1 4.3 4.3 4.194037 4.2\n 3 4.3 4.3 4.178694 4.2\n2025-05-01 1 4.1 4.1 4.319575 4.3\n 3 4.3 4.3 4.312634 4.3\n2025-06-01 1 4.3 4.3 4.043160 4.1\n 3 4.4 4.4 4.011349 4.1\n2025-07-01 1 4.3 4.3 4.344734 4.3\n 3 NaN NaN 4.345780 4.3\n2025-08-01 1 4.4 4.4 4.294125 4.3\n 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out; cat > report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver 36 monthly forecast origins (2023-07-01 … 2026-06-01), does a simple autoregressive model beat the naive (no-change) forecast of the US civilian unemployment rate (FRED UNRATE, SA, %) at horizons h = 1 and h = 3 months? Measured by out-of-sample RMSE and MAE, plus the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows, 1948-01-01 to 2026-09-01, a full monthly grid (no duplicates, no skipped dates, all values numeric).\n- **One missing value**: 2025-10-01 (empty field). Two (origin, horizon) pairs have that month as their target (origin 2025-09 at h=1, origin 2025-07 at h=3). They stay in `forecasts.csv` with an empty `actual` but are left out of all error statistics. So **35 pairs are evaluated at each horizon**.\n- How the gap is handled in the model: monthly changes that involve 2025-10 are dropped from estimation. At origin 2025-10 the naive forecast is the 2025-09 value, and the model treats 2025-10 as an extra forecast step. At later origins, the lag values that need 2025-10 use a linear interpolation between 2025-09 and 2025-11, both of which are dated ≤ origin.\n- Every training window contains the COVID-19 jump (2020-04: +10.4 pp; 2020-05: −1.6 pp). It is handled with impulse dummies (see Method).\n- The data are the final vintage (revised seasonal factors). The errors measure how forecastable the revised series is. They are **not** real-time forecast performance.\n\n## Method\n- **Naive**: for every horizon, the forecast is the latest non-missing observation dated ≤ origin.\n- **Model `AR_BIC_diff`**: an AR(p) model on monthly first differences, with an intercept and impulse dummies for the 2020-04 and 2020-05 changes, estimated by OLS.\n - At each origin, p ∈ {0,…,6} is re-selected by BIC on a common sample (starting after 6 lags).\n - The window expands from 1948 to the origin.\n - Multi-step forecasts are iterated and then summed back to levels.\n - Selection, estimation and differencing use only observations dated ≤ origin.\n - BIC chose p = 1 at all 36 origins.\n- I fixed this specification before looking at any evaluation-period errors. I did not search across alternative models for a better score.\n- `out/analysis.py` regenerates `results.json`, `forecasts.csv` and `supplementary.json` (the extra statistics below). It is deterministic and uses no random numbers.\n\n## Results\n| horizon | n evaluated | RMSE naive | RMSE model | MAE naive | MAE model | 1 − RMSE_m/RMSE_n |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 | 0.1313 | 0.0914 | 0.1063 | −0.121 |\n| 3 | 35 | 0.1586 | 0.1813 | 0.1200 | 0.1406 | −0.144 |\n\n(Units are percentage points.) The AR model on differences does **not** beat the naive forecast in this period. Its RMSE is about 12% higher at h=1 and 14% higher at h=3, and its MAE is also higher at both horizons. The estimated AR(1) coefficient on monthly changes is positive (momentum). In 2023–2026, though, monthly changes were small, rounded to 0.1 pp, and tended to reverse, so extrapolating the last change hurt accuracy.\n\n## Uncertainty\n- **Diebold–Mariano test** (squared-error loss, Harvey–Leybourne–Newbold small-sample correction, rectangular HAC with h−1 lags, t(n−1) reference). A positive statistic means the model is worse.\n - h=1: statistic 2.86, two-sided p = 0.007.\n - h=3: statistic 2.46, p = 0.019.\n- How much to trust these tests:\n - The sample is only 35 evaluated pairs from one stretch of the labour market.\n - The h=3 errors overlap, and the missing target breaks the error sequence, so the HAC estimate is rough.\n - Errors from forecast origins close together are correlated.\n - The p-values should therefore be read as indicative only.\n- **Sensitivity check** (not the main specification): the same model without the COVID dummies gives RMSE 0.11711 at h=1 and 0.15860 at h=3. That is a relative improvement of about −0.00001 and −0.0002, so it is essentially identical to naive. The outlier pushes the AR coefficient almost to zero. The result therefore depends on how the COVID outlier is handled: with the dummies the model is worse than naive, and without them it is tied with naive. In neither case does it beat naive.\n\n## Limitations and open issues\n- I evaluated a single model specification. Other simple models (levels AR, a short estimation window, AR models with mean reversion) were not tested, so this result does not show that no simple model can beat naive.\n- The data are the final vintage, so this is not a real-time evaluation.\n- The evaluation is short (36 origins in a calm period) and has low power. The conclusion may not carry over to recessions, when momentum in unemployment changes is strong.\n- Imputing the 2025-10 gap by linear interpolation for the lag values is a choice I made. It affects only forecasts made at origins 2025-11 onward, and only through the lagged change.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(p)-on-differences model (BIC chose p=1) has higher RMSE than the naive forecast (0.131 vs 0.117 pp; relative improvement -0.12).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data, one model specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model has higher RMSE than naive (0.181 vs 0.159 pp; relative improvement -0.14).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping errors; 35 evaluated pairs.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranks the forecasts the same way: naive beats the model at both horizons.\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Diebold-Mariano tests indicate the model's loss is significantly larger than naive at the 5% level at both horizons (p=0.007, 0.019).\",\"results_keys\":[],\"status\":\"supported\",\"caveat\":\"Statistics are in out/supplementary.json, not results.json; small sample and approximate HAC make p-values indicative only.\"},\n {\"id\":\"C5\",\"text\":\"Over 2023-07 to 2026-06, the simple AR model did not beat the naive forecast at either horizon; without COVID dummies it is essentially tied with naive.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"The no-dummy sensitivity is in supplementary.json. 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Measured by out-of-sample RMSE and MAE, plus the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows, 1948-01-01 to 2026-09-01, a full monthly grid (no duplicates, no skipped dates, all values numeric).\n- **One missing value**: 2025-10-01 (empty field). Two (origin, horizon) pairs have that month as their target (origin 2025-09 at h=1, origin 2025-07 at h=3). They stay in `forecasts.csv` with an empty `actual` but are left out of all error statistics. So **35 pairs are evaluated at each horizon**.\n- How the gap is handled in the model: monthly changes that involve 2025-10 are dropped from estimation. At origin 2025-10 the naive forecast is the 2025-09 value, and the model treats 2025-10 as an extra forecast step. At later origins, the lag values that need 2025-10 use a linear interpolation between 2025-09 and 2025-11, both of which are dated ≤ origin.\n- Every training window contains the COVID-19 jump (2020-04: +10.4 pp; 2020-05: −1.6 pp). It is handled with impulse dummies (see Method).\n- The data are the final vintage (revised seasonal factors). The errors measure how forecastable the revised series is. They are **not** real-time forecast performance.\n\n## Method\n- **Naive**: for every horizon, the forecast is the latest non-missing observation dated ≤ origin.\n- **Model `AR_BIC_diff`**: an AR(p) model on monthly first differences, with an intercept and impulse dummies for the 2020-04 and 2020-05 changes, estimated by OLS.\n - At each origin, p ∈ {0,…,6} is re-selected by BIC on a common sample (starting after 6 lags).\n - The window expands from 1948 to the origin.\n - Multi-step forecasts are iterated and then summed back to levels.\n - Selection, estimation and differencing use only observations dated ≤ origin.\n - BIC chose p = 1 at all 36 origins.\n- I fixed this specification before looking at any evaluation-period errors. I did not search across alternative models for a better score.\n- `out/analysis.py` regenerates `results.json`, `forecasts.csv` and `supplementary.json` (the extra statistics below). It is deterministic and uses no random numbers.\n\n## Results\n| horizon | n evaluated | RMSE naive | RMSE model | MAE naive | MAE model | 1 − RMSE_m/RMSE_n |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 | 0.1313 | 0.0914 | 0.1063 | −0.121 |\n| 3 | 35 | 0.1586 | 0.1813 | 0.1200 | 0.1406 | −0.144 |\n\n(Units are percentage points.) The AR model on differences does **not** beat the naive forecast in this period. Its RMSE is about 12% higher at h=1 and 14% higher at h=3, and its MAE is also higher at both horizons. The estimated AR(1) coefficient on monthly changes is positive (momentum). In 2023–2026, though, monthly changes were small, rounded to 0.1 pp, and tended to reverse, so extrapolating the last change hurt accuracy.\n\n## Uncertainty\n- **Diebold–Mariano test** (squared-error loss, Harvey–Leybourne–Newbold small-sample correction, rectangular HAC with h−1 lags, t(n−1) reference). A positive statistic means the model is worse.\n - h=1: statistic 2.86, two-sided p = 0.007.\n - h=3: statistic 2.46, p = 0.019.\n- How much to trust these tests:\n - The sample is only 35 evaluated pairs from one stretch of the labour market.\n - The h=3 errors overlap, and the missing target breaks the error sequence, so the HAC estimate is rough.\n - Errors from forecast origins close together are correlated.\n - The p-values should therefore be read as indicative only.\n- **Sensitivity check** (not the main specification): the same model without the COVID dummies gives RMSE 0.11711 at h=1 and 0.15860 at h=3. That is a relative improvement of about −0.00001 and −0.0002, so it is essentially identical to naive. The outlier pushes the AR coefficient almost to zero. The result therefore depends on how the COVID outlier is handled: with the dummies the model is worse than naive, and without them it is tied with naive. In neither case does it beat naive.\n\n## Limitations and open issues\n- I evaluated a single model specification. Other simple models (levels AR, a short estimation window, AR models with mean reversion) were not tested, so this result does not show that no simple model can beat naive.\n- The data are the final vintage, so this is not a real-time evaluation.\n- The evaluation is short (36 origins in a calm period) and has low power. The conclusion may not carry over to recessions, when momentum in unemployment changes is strong.\n- Imputing the 2025-10 gap by linear interpolation for the lag values is a choice I made. It affects only forecasts made at origins 2025-11 onward, and only through the lagged change.\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(p)-on-differences model (BIC chose p=1) has higher RMSE than the naive forecast (0.131 vs 0.117 pp; relative improvement -0.12).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs, final-vintage data, one model specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model has higher RMSE than naive (0.181 vs 0.159 pp; relative improvement -0.14).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping errors; 35 evaluated pairs.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranks the forecasts the same way: naive beats the model at both horizons.\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Diebold-Mariano tests indicate the model's loss is significantly larger than naive at the 5% level at both horizons (p=0.007, 0.019).\",\"results_keys\":[],\"status\":\"supported\",\"caveat\":\"Statistics are in out/supplementary.json, not results.json; small sample and approximate HAC make p-values indicative only.\"},\n {\"id\":\"C5\",\"text\":\"Over 2023-07 to 2026-06, the simple AR model did not beat the naive forecast at either horizon; without COVID dummies it is essentially tied with naive.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"The no-dummy sensitivity is in supplementary.json. 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It was worse at both the 1-month and 3-month horizons. All required files are written, and running `out/analysis.py` twice produced identical `results.json` and `forecasts.csv`. The last step had been cut off by the interruption, so I re-ran it.\n\n**Model (`AR_BIC_diff`):** an autoregressive model on monthly changes in the unemployment rate. At each forecast origin it picks up to 6 lags by BIC and is fitted by least squares on all data from 1948 up to that origin. It includes one-off adjustment terms (\"dummies\") for the 2020-04 and 2020-05 COVID jumps. BIC picked one lag at all 36 origins. I fixed this specification before looking at any errors from the evaluation period.\n\n**Results** (percentage points; 35 forecasts scored per horizon):\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | 1 − RMSE_model/RMSE_naive |\n|---|---|---|---|---|---|\n| 1 month | 0.117 | 0.131 | 0.091 | 0.106 | −0.12 |\n| 3 months | 0.159 | 0.181 | 0.120 | 0.141 | −0.14 |\n\n- **Significance:** a Diebold–Mariano test says the model is significantly worse (p = 0.007 at 1 month, p = 0.019 at 3 months). With only 35 forecasts these p-values are only a rough guide.\n- **Why it loses:** the model extrapolates the last monthly change. Over 2023–26, monthly changes were small, rounded to 0.1 pp and tended to reverse, so that extrapolation hurt.\n- **COVID handling matters:** without the COVID dummies, the model's error is essentially the same as the naive forecast's (difference below 0.1%). Either way, it never beats naive.\n\n**Data issues:**\n- **Missing month:** 2025-10 is blank in the file. The two forecasts aimed at that month stay in the forecast file with an empty actual value but are not scored. The gap is handled using only data available at each origin; details are in the report.\n- **COVID outlier:** the 2020 jump is in every training window and is handled by the dummies above.\n\n**Limitations:**\n- Only one model was tested, so this doesn't show that no simple model can beat naive.\n- The data are today's revised figures, not what was published at the time, so this is not a real-time test.\n- The test period is short and calm, so it has little power to detect differences.\n\nThe significance tests and the no-dummy check are not among the required keys, so they are in an extra file, `out/supplementary.json`.\n\nFiles are in `out/`:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv` (144 rows)\n- `report.md`\n- `claims.json`\n- `supplementary.json`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":266,"cache_read_input_tokens":28477,"output_tokens":969,"output_tokens_details":{"thinking_tokens":14},"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":266},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXshMxDpHKSKVdRJKXhq","requestRef":"c144ad0d-1753-4fca-860f-4e3f7a81cda2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"298a7737-c06e-497f-82fe-f8104e948e82","timestamp":"2026-10-30T05:32:40.800Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0002","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","version":"2.1.314","gitBranch":"HEAD"}65{"type":"last-prompt","lastPrompt":"Your","leafUuid":"298a7737-c06e-497f-82fe-f8104e948e82","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675"}66{"type":"mode","mode":"normal","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675"}67{"type":"atis-latch","atis":"","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675"}68{"type":"cost-state","sessionId":"5bdf8284-e042-46da-99aa-2a24d1637675","totalCostUSD":0.37515679999999996,"totalAPIDuration":102560,"totalAPIDurationWithoutRetries":102553,"totalToolDuration":3165,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":106001,"startTime":1793338253483,"modelUsage":{"claude-opus-5-5":{"inputTokens":22,"outputTokens":10496,"thinkingTokens":2055,"cacheReadInputTokens":144769,"cacheCreationInputTokens":27239,"cacheCreationEphemeral5mInputTokens":27239,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.37515679999999996}},"hasUnknownModelCost":false}69