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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","is_error":false}]},"uuid":"853b2a67-553c-4dca-a00b-e7517792b08a","timestamp":"2026-10-30T05:30:56.595Z","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). 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Source: U.S. Bureau of Labor Statistics, Current Population Survey, via FRED (Federal Reserve Bank of St. Louis).\n- **Retrieval URL**: https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE\n- **Retrieved (UTC)**: 2026-10-30T03:20:36Z\n- **SHA-256**: 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\n- **Format**: exactly as served by FRED: header `observation_date,UNRATE`, one row per month dated on the first day of the month (YYYY-MM-01), 945 data rows from 1948-01-01 to 2026-09-01. Missing observations are served as an empty value field.\n- **Units**: percent of the civilian labour force.\n- **Vintage caveat**: this is the current (final) vintage as of the retrieval time. Values may have been revised since their first publication (annual seasonal-factor revisions). Rolling-origin results on this file therefore measure forecastability of the revised series, not historical real-time performance.\n- **License / terms**: FRED terms of use (https://fred.stlouisfed.org/legal/); BLS data are public domain.\n 946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\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,\n1948-03-01,4.0\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\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\n1956-03-01,4.2\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\n1956-11-01,4.3\n1956-12-01,4.2\n1957-01-01,4.2\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\n1967-10-01,4.0\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\n2000-05-01,4.0\n2000-06-01,4.0\n2000-07-01,4.0\n2000-08-01,4.1\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\n2018-06-01,4.0\n2019-01-01,4.0\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\n2022-01-01,4.0\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":"66bf886c-5b92-4e48-908c-b5c8b9e3741b","timestamp":"2026-10-30T05:30:59.372Z","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\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\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,\n1948-03-01,4.0\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\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\n1956-03-01,4.2\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\n1956-11-01,4.3\n1956-12-01,4.2\n1957-01-01,4.2\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\n1967-10-01,4.0\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\n2000-05-01,4.0\n2000-06-01,4.0\n2000-07-01,4.0\n2000-08-01,4.1\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\n2018-06-01,4.0\n2019-01-01,4.0\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\n2022-01-01,4.0\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"40fd9aa4-69c4-4ffe-b4ca-eb59f7fefe55","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","hash":"adf209771a58e2260a80898f4077efd8baa515a5f7966584bbac2293d27672ce","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_018BHYMERzXVSybmsoEX1TVM","name":"Bash","input":{"command":"cat 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8.4","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"174c6945-5115-49c6-8110-eb6c71f98d2f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","hash":"76ad561f5e9dd90b09f49d750476bb8dd68ef2327561485c6b5a40390793bbd9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01J3ivKxMjLjv71YHmBbbMDT","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.UNRATE.isna().sum(),d.observation_date.duplicated().sum())\nprint((d.observation_date.diff().dt.days>31).sum(), d.UNRATE.describe())\nprint(d[d.UNRATE.diff().abs()>1])\n\"","description":"Check data integrity"}}]}}26{"type":"api-request","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","id":"6ebf67f1-7e04-4ef6-a9cf-68c12b5498f7","timestamp":"2026-10-30T05:31:10.262Z","version":"2.1.314","querySource":"sdk","shapeHash":"0eaff4c87636abf767d02d8beccab03884f52c8804ad5b36091a3bce0df1cf52","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":"c34ab00b-ce4b-44da-a39a-8cb933b771af","keep":6,"tail":["76ad561f5e9dd90b09f49d750476bb8dd68ef2327561485c6b5a40390793bbd9","284e841f191e7fba45a7e9fcd66c10c6cb99c3643860c9e43f0ea75fae7be565"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b5dd464434376f6955a1586a8532229f626404dc8c8328e7ca3ad57dcbfe20e7"}27{"parentUuid":"b63cb37d-ba82-4588-9681-6ca702c79a72","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbRXcpD6sRnsd8oULj","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1059,"cache_read_input_tokens":13944,"output_tokens":3871,"output_tokens_details":{"thinking_tokens":156},"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":1059},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1330,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsbRGUjNaYU5MjkC43k","requestRef":"6ebf67f1-7e04-4ef6-a9cf-68c12b5498f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"12a953d2-b8fb-44fc-8698-bd3e8123ac31","timestamp":"2026-10-30T05:31:12.850Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"12a953d2-b8fb-44fc-8698-bd3e8123ac31","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbRXcpD6sRnsd8oULj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012Vof1UKs2gz3stUSBkX6om","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin (expanding-window) forecasts of UNRATE, naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nP = 2 # AR lags on first differences (fixed a priori, not selected)\nCOVID = (pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-09-01\")) # differences dated here are excluded from estimation\nHORIZONS = [1, 3]\nMODEL = \"AR2_diff\"\n\nraw = pd.read_csv(DATA, dtype={\"UNRATE\": \"float64\"})\nn_rows = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns_full = raw.set_index(\"observation_date\")[\"UNRATE\"].asfreq(\"MS\")\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\ndef design(u, exclude_covid=True):\n \"\"\"Regression rows y=du_t on [1, du_{t-1},...,du_{t-P}], using observed (non-interpolated) data only.\"\"\"\n du = u.diff()\n X = pd.concat({f\"l{k}\": du.shift(k) for k in range(1, P + 1)}, axis=1)\n df = pd.concat([du.rename(\"y\"), X], axis=1).dropna()\n if exclude_covid:\n bad = pd.Series(False, index=df.index)\n for k in range(0, P + 1): # drop row if y or any lag is dated inside the COVID window\n d = df.index - pd.DateOffset(months=k)\n bad |= (d >= COVID[0]) & (d <= COVID[1])\n df = df[~bad]\n return df\n\ndef ar_forecast(u_obs, origin, hmax, exclude_covid=True):\n \"\"\"Fit on data <= origin; return dict h->level forecast for h=1..hmax relative to origin.\"\"\"\n u = u_obs[u_obs.index <= origin]\n df = design(u, exclude_covid)\n X = np.column_stack([np.ones(len(df)), df[[f\"l{k}\" for k in range(1, P + 1)]].values])\n beta, *_ = np.linalg.lstsq(X, df[\"y\"].values, rcond=None)\n # state: interpolate interior gaps (uses only data <= origin); trailing gaps are forecast\n last_obs = u.last_valid_index()\n ui = u.loc[:last_obs].interpolate(limit_area=\"inside\")\n gap = (origin.year - last_obs.year) * 12 + origin.month - last_obs.month\n du = list(ui.diff().values[-P:])\n level = ui.iloc[-1]; path = {}\n for step in range(1, gap + hmax + 1):\n d = beta[0] + sum(beta[k] * du[-k] for k in range(1, P + 1))\n du.append(d); level = level + d\n if step > gap: path[step - gap] = level\n return path, len(df), beta\n\ndef run(exclude_covid=True):\n rows = []; n_last = None\n for t in origins:\n u = s_full[s_full.index <= t]\n naive = u.loc[u.last_valid_index()]\n path, n_used, beta = ar_forecast(s_full, t, max(HORIZONS), exclude_covid)\n n_last = n_used\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s_full.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, path[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), n_last\n\ndef stats(fc):\n out = {}\n for h in HORIZONS:\n out[f\"n_evaluated_h{h}\"] = int(fc[(fc.horizon == h) & (fc.model == \"naive\")].actual.notna().sum())\n for m, lab in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n e = fc[(fc.horizon == h) & (fc.model == m)].dropna(subset=[\"actual\"])\n err = e.forecast - e.actual\n out[f\"rmse_{lab}_h{h}\"] = float(np.sqrt(np.mean(err ** 2)))\n out[f\"mae_{lab}_h{h}\"] = float(np.mean(np.abs(err)))\n out[f\"rel_improvement_rmse_h{h}\"] = 1 - out[f\"rmse_model_h{h}\"] / out[f\"rmse_naive_h{h}\"]\n return out\n\ndef dm_test(fc, h):\n \"\"\"Diebold-Mariano (squared loss) with Newey-West HAC var (h-1 lags) and Harvey-Leybourne-Newbold correction.\"\"\"\n from scipy import stats as st\n a = fc[(fc.horizon == h) & (fc.model == \"naive\")].set_index(\"origin_date\")\n b = fc[(fc.horizon == h) & (fc.model == MODEL)].set_index(\"origin_date\")\n ok = a.actual.notna()\n d = ((a.forecast - a.actual) ** 2 - (b.forecast - b.actual) ** 2)[ok].values\n n = len(d); dbar = d.mean(); dc = d - dbar\n v = dc @ dc / n\n for k in range(1, h):\n v += 2 * (1 - k / h) * (dc[k:] @ dc[:-k]) / n\n dmstat = dbar / np.sqrt(v / n)\n hln = dmstat * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n p = 2 * st.t.sf(abs(hln), df=n - 1)\n return float(hln), float(p)\n\ndef boot_rel(fc, h, B=2000, block=6, seed=12345):\n \"\"\"Moving-block bootstrap (over origins) 90% interval for 1 - RMSE_model/RMSE_naive.\"\"\"\n rng = np.random.default_rng(seed)\n a = fc[(fc.horizon == h) & (fc.model == \"naive\")].reset_index(drop=True)\n b = fc[(fc.horizon == h) & (fc.model == MODEL)].reset_index(drop=True)\n ok = a.actual.notna().values\n ea = ((a.forecast - a.actual) ** 2).values[ok]; eb = ((b.forecast - b.actual) ** 2).values[ok]\n n = len(ea); reps = []\n for _ in range(B):\n idx = np.concatenate([np.arange(s, s + block) for s in rng.integers(0, n - block + 1, size=int(np.ceil(n / block)))])[:n]\n reps.append(1 - np.sqrt(eb[idx].mean() / ea[idx].mean()))\n return [float(np.quantile(reps, 0.05)), float(np.quantile(reps, 0.95))]\n\nif __name__ == \"__main__\":\n os.makedirs(OUT, exist_ok=True)\n fc, n_last = run(True)\n fc.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n res = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, iterated to levels for h=1,3; \"\n \"lag order fixed a priori; re-estimated by OLS at each origin on an expanding window from 1948-03 \"\n \"(first available difference rows) to the origin, excluding regression rows whose differences fall in \"\n \"2020-04..2020-09 (COVID outliers) and rows involving the missing 2025-10 value.\"),\n \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\n res.update(stats(fc))\n res.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": \"UNRATE for 2025-10-01 is missing (empty field).\",\n \"handling\": \"Excluded as an evaluation target (origins 2025-09 h=1 and 2025-07 h=3 have empty actual). \"\n \"Naive at origin 2025-10 uses 2025-09. AR model: regression rows involving it are dropped; for the \"\n \"forecast state it is linearly interpolated once 2025-11 is observed (only data <= origin), and at \"\n \"origin 2025-10 the model iterates from 2025-09 (h+1 steps).\"},\n {\"issue\": \"Extreme COVID-19 outliers in 2020 (peak 14.8 in 2020-04; monthly changes up to +10.4 pp).\",\n \"handling\": \"Regression rows whose differences are dated 2020-04..2020-09 excluded from AR estimation (a fixed rule \"\n \"decided from pre-2023 data); sensitivity without exclusion reported in report.md.\"}],\n \"n_input_rows\": {\"UNRATE.csv\": n_rows}, \"n_analysed\": int(n_last), \"random_seed\": 12345})\n json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\n # supplementary diagnostics (not required keys)\n fc2, _ = run(False)\n supp = {\"sensitivity_no_covid_exclusion\": stats(fc2),\n \"dm_hln\": {f\"h{h}\": dict(zip([\"stat\", \"p_value\"], dm_test(fc, h))) for h in HORIZONS},\n \"block_bootstrap_90ci_rel_improvement\": {f\"h{h}\": boot_rel(fc, h) for h in HORIZONS},\n \"last_origin_coefficients\": list(map(float, ar_forecast(s_full, origins[-1], 3)[2]))}\n json.dump(supp, open(f\"{OUT}/supplementary.json\", \"w\"), indent=2)\n print(json.dumps(res, indent=2)); print(json.dumps(supp, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1059,"cache_read_input_tokens":13944,"output_tokens":3871,"output_tokens_details":{"thinking_tokens":156},"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":1059},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbRGUjNaYU5MjkC43k","requestRef":"6ebf67f1-7e04-4ef6-a9cf-68c12b5498f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a2089d0b-6675-467f-b3ba-244d2e37e25a","timestamp":"2026-10-30T05:31:40.950Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"a2089d0b-6675-467f-b3ba-244d2e37e25a","isSidechain":false,"promptId":"cb31a2ad-fe91-4510-a734-c3757fd018df","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012Vof1UKs2gz3stUSBkX6om","type":"tool_result","content":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on first differences of UNRATE, iterated to levels for h=1,3; lag order fixed a priori; re-estimated by OLS at each origin on an expanding window from 1948-03 (first available difference rows) to the origin, excluding regression rows whose differences fall in 2020-04..2020-09 (COVID outliers) and rows involving the missing 2025-10 value.\",\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\": 0.1183164357185546,\n \"mae_model_h1\": 0.09759177142857144,\n \"rel_improvement_rmse_h1\": -0.010318909676543475,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.17424051215760036,\n \"mae_model_h3\": 0.14019328571428577,\n \"rel_improvement_rmse_h3\": -0.09885863445430165,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE for 2025-10-01 is missing (empty field).\",\n \"handling\": \"Excluded as an evaluation target (origins 2025-09 h=1 and 2025-07 h=3 have empty actual). Naive at origin 2025-10 uses 2025-09. AR model: regression rows involving it are dropped; for the forecast state it is linearly interpolated once 2025-11 is observed (only data <= origin), and at origin 2025-10 the model iterates from 2025-09 (h+1 steps).\"\n },\n {\n \"issue\": \"Extreme COVID-19 outliers in 2020 (peak 14.8 in 2020-04; monthly changes up to +10.4 pp).\",\n \"handling\": \"Regression rows whose differences are dated 2020-04..2020-09 excluded from AR estimation (a fixed rule decided from pre-2023 data); sensitivity without exclusion reported in report.md.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 927,\n \"random_seed\": 12345\n}\n{\n \"sensitivity_no_covid_exclusion\": {\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985758509962682,\n \"mae_model_h1\": 0.09565579999999999,\n \"rel_improvement_rmse_h1\": -0.023478977868906847,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.159770241753767,\n \"mae_model_h3\": 0.12128480000000005,\n \"rel_improvement_rmse_h3\": -0.007600973539263922\n },\n \"dm_hln\": {\n \"h1\": {\n \"stat\": -0.28685817220524756,\n \"p_value\": 0.7759618613797988\n },\n \"h3\": {\n \"stat\": -2.1896900782551674,\n \"p_value\": 0.035505930181118264\n }\n },\n \"block_bootstrap_90ci_rel_improvement\": {\n \"h1\": [\n -0.10366514944536562,\n 0.05046231114887323\n ],\n \"h3\": [\n -0.19120544048260774,\n -0.04397339500137136\n ]\n },\n \"last_origin_coefficients\": [\n -0.0008600882553649978,\n 0.07917191725477211,\n 0.27576641014638004\n ]\n}","is_error":false}]},"uuid":"59200bb1-0778-4a6d-9362-bd7cadef1c0a","timestamp":"2026-10-30T05:31:41.542Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on first differences of UNRATE, iterated to levels for h=1,3; lag order fixed a priori; re-estimated by OLS at each origin on an expanding window from 1948-03 (first available difference rows) to the origin, excluding regression rows whose differences fall in 2020-04..2020-09 (COVID outliers) and rows involving the missing 2025-10 value.\",\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\": 0.1183164357185546,\n \"mae_model_h1\": 0.09759177142857144,\n \"rel_improvement_rmse_h1\": -0.010318909676543475,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.17424051215760036,\n \"mae_model_h3\": 0.14019328571428577,\n \"rel_improvement_rmse_h3\": -0.09885863445430165,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE for 2025-10-01 is missing (empty field).\",\n \"handling\": \"Excluded as an evaluation target (origins 2025-09 h=1 and 2025-07 h=3 have empty actual). Naive at origin 2025-10 uses 2025-09. AR model: regression rows involving it are dropped; for the forecast state it is linearly interpolated once 2025-11 is observed (only data <= origin), and at origin 2025-10 the model iterates from 2025-09 (h+1 steps).\"\n },\n {\n \"issue\": \"Extreme COVID-19 outliers in 2020 (peak 14.8 in 2020-04; monthly changes up to +10.4 pp).\",\n \"handling\": \"Regression rows whose differences are dated 2020-04..2020-09 excluded from AR estimation (a fixed rule decided from pre-2023 data); sensitivity without exclusion reported in report.md.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 927,\n \"random_seed\": 12345\n}\n{\n \"sensitivity_no_covid_exclusion\": {\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985758509962682,\n \"mae_model_h1\": 0.09565579999999999,\n \"rel_improvement_rmse_h1\": -0.023478977868906847,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.159770241753767,\n \"mae_model_h3\": 0.12128480000000005,\n \"rel_improvement_rmse_h3\": -0.007600973539263922\n },\n \"dm_hln\": {\n \"h1\": {\n \"stat\": -0.28685817220524756,\n \"p_value\": 0.7759618613797988\n },\n \"h3\": {\n \"stat\": -2.1896900782551674,\n \"p_value\": 0.035505930181118264\n }\n },\n \"block_bootstrap_90ci_rel_improvement\": {\n \"h1\": [\n -0.10366514944536562,\n 0.05046231114887323\n ],\n \"h3\": [\n -0.19120544048260774,\n -0.04397339500137136\n ]\n },\n \"last_origin_coefficients\": [\n -0.0008600882553649978,\n 0.07917191725477211,\n 0.27576641014638004\n ]\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a2089d0b-6675-467f-b3ba-244d2e37e25a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","hash":"83c8e84cae62fc99d8dd01754c4adc97657ca58f0cd87c7b459a7306a6c0b30e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012Vof1UKs2gz3stUSBkX6om","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin (expanding-window) forecasts of UNRATE, naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\n\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nP = 2 # AR lags on first differences (fixed a priori, not selected)\nCOVID = (pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-09-01\")) # differences dated here are excluded from estimation\nHORIZONS = [1, 3]\nMODEL = \"AR2_diff\"\n\nraw = pd.read_csv(DATA, dtype={\"UNRATE\": \"float64\"})\nn_rows = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns_full = raw.set_index(\"observation_date\")[\"UNRATE\"].asfreq(\"MS\")\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\ndef design(u, exclude_covid=True):\n \"\"\"Regression rows y=du_t on [1, du_{t-1},...,du_{t-P}], using observed (non-interpolated) data only.\"\"\"\n du = u.diff()\n X = pd.concat({f\"l{k}\": du.shift(k) for k in range(1, P + 1)}, axis=1)\n df = pd.concat([du.rename(\"y\"), X], axis=1).dropna()\n if exclude_covid:\n bad = pd.Series(False, index=df.index)\n for k in range(0, P + 1): # drop row if y or any lag is dated inside the COVID window\n d = df.index - pd.DateOffset(months=k)\n bad |= (d >= COVID[0]) & (d <= COVID[1])\n df = df[~bad]\n return df\n\ndef ar_forecast(u_obs, origin, hmax, exclude_covid=True):\n \"\"\"Fit on data <= origin; return dict h->level forecast for h=1..hmax relative to origin.\"\"\"\n u = u_obs[u_obs.index <= origin]\n df = design(u, exclude_covid)\n X = np.column_stack([np.ones(len(df)), df[[f\"l{k}\" for k in range(1, P + 1)]].values])\n beta, *_ = np.linalg.lstsq(X, df[\"y\"].values, rcond=None)\n # state: interpolate interior gaps (uses only data <= origin); trailing gaps are forecast\n last_obs = u.last_valid_index()\n ui = u.loc[:last_obs].interpolate(limit_area=\"inside\")\n gap = (origin.year - last_obs.year) * 12 + origin.month - last_obs.month\n du = list(ui.diff().values[-P:])\n level = ui.iloc[-1]; path = {}\n for step in range(1, gap + hmax + 1):\n d = beta[0] + sum(beta[k] * du[-k] for k in range(1, P + 1))\n du.append(d); level = level + d\n if step > gap: path[step - gap] = level\n return path, len(df), beta\n\ndef run(exclude_covid=True):\n rows = []; n_last = None\n for t in origins:\n u = s_full[s_full.index <= t]\n naive = u.loc[u.last_valid_index()]\n path, n_used, beta = ar_forecast(s_full, t, max(HORIZONS), exclude_covid)\n n_last = n_used\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n act = s_full.get(tgt, np.nan)\n for m, f in ((\"naive\", naive), (MODEL, path[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), n_last\n\ndef stats(fc):\n out = {}\n for h in HORIZONS:\n out[f\"n_evaluated_h{h}\"] = int(fc[(fc.horizon == h) & (fc.model == \"naive\")].actual.notna().sum())\n for m, lab in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n e = fc[(fc.horizon == h) & (fc.model == m)].dropna(subset=[\"actual\"])\n err = e.forecast - e.actual\n out[f\"rmse_{lab}_h{h}\"] = float(np.sqrt(np.mean(err ** 2)))\n out[f\"mae_{lab}_h{h}\"] = float(np.mean(np.abs(err)))\n out[f\"rel_improvement_rmse_h{h}\"] = 1 - out[f\"rmse_model_h{h}\"] / out[f\"rmse_naive_h{h}\"]\n return out\n\ndef dm_test(fc, h):\n \"\"\"Diebold-Mariano (squared loss) with Newey-West HAC var (h-1 lags) and Harvey-Leybourne-Newbold correction.\"\"\"\n from scipy import stats as st\n a = fc[(fc.horizon == h) & (fc.model == \"naive\")].set_index(\"origin_date\")\n b = fc[(fc.horizon == h) & (fc.model == MODEL)].set_index(\"origin_date\")\n ok = a.actual.notna()\n d = ((a.forecast - a.actual) ** 2 - (b.forecast - b.actual) ** 2)[ok].values\n n = len(d); dbar = d.mean(); dc = d - dbar\n v = dc @ dc / n\n for k in range(1, h):\n v += 2 * (1 - k / h) * (dc[k:] @ dc[:-k]) / n\n dmstat = dbar / np.sqrt(v / n)\n hln = dmstat * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n p = 2 * st.t.sf(abs(hln), df=n - 1)\n return float(hln), float(p)\n\ndef boot_rel(fc, h, B=2000, block=6, seed=12345):\n \"\"\"Moving-block bootstrap (over origins) 90% interval for 1 - RMSE_model/RMSE_naive.\"\"\"\n rng = np.random.default_rng(seed)\n a = fc[(fc.horizon == h) & (fc.model == \"naive\")].reset_index(drop=True)\n b = fc[(fc.horizon == h) & (fc.model == MODEL)].reset_index(drop=True)\n ok = a.actual.notna().values\n ea = ((a.forecast - a.actual) ** 2).values[ok]; eb = ((b.forecast - b.actual) ** 2).values[ok]\n n = len(ea); reps = []\n for _ in range(B):\n idx = np.concatenate([np.arange(s, s + block) for s in rng.integers(0, n - block + 1, size=int(np.ceil(n / block)))])[:n]\n reps.append(1 - np.sqrt(eb[idx].mean() / ea[idx].mean()))\n return [float(np.quantile(reps, 0.05)), float(np.quantile(reps, 0.95))]\n\nif __name__ == \"__main__\":\n os.makedirs(OUT, exist_ok=True)\n fc, n_last = run(True)\n fc.to_csv(f\"{OUT}/forecasts.csv\", index=False, na_rep=\"\")\n res = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, iterated to levels for h=1,3; \"\n \"lag order fixed a priori; re-estimated by OLS at each origin on an expanding window from 1948-03 \"\n \"(first available difference rows) to the origin, excluding regression rows whose differences fall in \"\n \"2020-04..2020-09 (COVID outliers) and rows involving the missing 2025-10 value.\"),\n \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\n res.update(stats(fc))\n res.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": \"UNRATE for 2025-10-01 is missing (empty field).\",\n \"handling\": \"Excluded as an evaluation target (origins 2025-09 h=1 and 2025-07 h=3 have empty actual). \"\n \"Naive at origin 2025-10 uses 2025-09. AR model: regression rows involving it are dropped; for the \"\n \"forecast state it is linearly interpolated once 2025-11 is observed (only data <= origin), and at \"\n \"origin 2025-10 the model iterates from 2025-09 (h+1 steps).\"},\n {\"issue\": \"Extreme COVID-19 outliers in 2020 (peak 14.8 in 2020-04; monthly changes up to +10.4 pp).\",\n \"handling\": \"Regression rows whose differences are dated 2020-04..2020-09 excluded from AR estimation (a fixed rule \"\n \"decided from pre-2023 data); sensitivity without exclusion reported in report.md.\"}],\n \"n_input_rows\": {\"UNRATE.csv\": n_rows}, \"n_analysed\": int(n_last), \"random_seed\": 12345})\n json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\n # supplementary diagnostics (not required keys)\n fc2, _ = run(False)\n supp = {\"sensitivity_no_covid_exclusion\": stats(fc2),\n \"dm_hln\": {f\"h{h}\": dict(zip([\"stat\", \"p_value\"], dm_test(fc, h))) for h in HORIZONS},\n \"block_bootstrap_90ci_rel_improvement\": 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out; cat > report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR model vs naive\n\n## Question\nUsing 36 expanding-window forecast origins (2023-07 through 2026-06), does a simple autoregressive model forecast the US unemployment rate (FRED UNRATE) better than the naive no-change forecast at horizons of 1 and 3 months? I measure this with out-of-sample RMSE and MAE, and with the relative RMSE improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows (1948-01 to 2026-09), monthly, seasonally adjusted, final vintage. There are no duplicate dates and no gaps in the monthly calendar.\n- **Missing value:** 2025-10-01 has no value. It is the target for (origin 2025-09, h=1) and (origin 2025-07, h=3). Both pairs appear in `forecasts.csv` with an empty `actual` and are left out of the error statistics. That leaves **35 evaluated pairs per horizon**.\n- **COVID outliers:** UNRATE hit 14.8 in 2020-04, with monthly changes of up to +10.4 pp. In the primary model, regression rows whose dependent or lagged difference is dated 2020-04 to 2020-09 are left out of estimation. This rule was fixed using only pre-2023 information, so it is the same at every origin. A sensitivity check without the exclusion is reported below.\n- The 1949-10 value (7.9, a strike-related spike) is kept as published.\n\n## Method\n- **Naive:** the forecast is the latest observation dated ≤ origin. At origin 2025-10, whose own value is missing, that means 2025-09.\n- **Model `AR2_diff`:** an AR(2) with intercept on first differences, Δu_t = c + φ1 Δu_{t−1} + φ2 Δu_{t−2} + e_t. It is estimated by OLS at each origin, using all regression rows from 1948 up to the origin (expanding window), minus the COVID rows and any rows involving the missing 2025-10 value. The fitted differences are iterated recursively and accumulated into levels for h = 1, 3. The lag order of 2 was fixed in advance; there was no data-driven selection and no tuning on the evaluation period.\n- **Missing value inside the model:** once 2025-11 is observed, 2025-10 is linearly interpolated, using only data ≤ origin, so that the forecast has a starting state. It is never used in estimation. At origin 2025-10 the model forecasts from 2025-09, iterating h+1 steps.\n- Every estimation step and transformation uses only data dated ≤ origin. Errors are computed against the final-vintage values in the file. There are 927 regression rows in the last training window (origin 2026-06).\n- Replication: `~/wp29/venv/bin/python out/analysis.py` regenerates `results.json`, `forecasts.csv` and `supplementary.json`, deterministically (bootstrap seed 12345).\n\n## Results\nErrors are in percentage points. n = 35 per horizon.\n\n| Horizon | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h = 1 | 0.1171 | 0.1183 | 0.0914 | 0.0976 | −0.010 |\n| h = 3 | 0.1586 | 0.1742 | 0.1200 | 0.1402 | −0.099 |\n\nThe AR(2)-in-differences model does **not** beat the naive forecast on either metric at either horizon. At h=1 it is about 1% worse in RMSE; at h=3 it is about 10% worse.\n\nSensitivity, from `supplementary.json`: if the 2020 rows are kept in estimation, the relative improvement is −0.023 (h=1) and −0.008 (h=3). So the model is still slightly worse than naive. The h=3 shortfall is smaller here because the estimated coefficients pick up less momentum. The estimated momentum (φ2 ≈ 0.28 at the last origin, intercept ≈ 0) pushed the forecasts upward during the 2024–2025 drift, and they then overshot when unemployment flattened and fell in 2026.\n\n## Uncertainty\n- Diebold–Mariano test (squared loss, HAC variance with h−1 lags, Harvey–Leybourne–Newbold small-sample correction, t(34) reference):\n - h=1: statistic −0.29, p = 0.78. No detectable difference.\n - h=3: statistic −2.19, p = 0.036. The naive forecast is nominally better.\n- Moving-block bootstrap over origins (block 6, 2000 replications), 90% interval for 1 − RMSE ratio:\n - h=1: [−0.104, 0.050]\n - h=3: [−0.191, −0.044]\n- Caveats on these inferences:\n - With only 35 overlapping evaluations in a calm, low-volatility period, they are fragile.\n - At h=3 the overlapping errors make the HAC variance and the bootstrap imprecise.\n - The p = 0.036 at h=3 should not be read as strong evidence. The sensitivity specification, which differs only in the COVID treatment, shrinks the gap to about −1%. That means the h=3 result depends on a specification choice.\n\n## Limitations and open issues\n- Final-vintage data, so the results say nothing about real-time performance. Seasonal-factor revisions are applied retroactively.\n- Errors are reported to sub-0.1 pp precision, but the data are rounded to 0.1 pp. Many naive errors are exactly 0 or ±0.1, so differences between models are small relative to that granularity.\n- Only one model specification was evaluated as primary (plus one sensitivity). Other models (ARMA, levels AR with mean reversion, models with covariates) were not tried, and the conclusion applies only to this simple class.\n- The evaluation window is short (3 years) and covers a single regime, a slow drift up from 3.5% to about 4.4% followed by a plateau. It does not include a turning point such as a recession.\n- The COVID-exclusion window (2020-04..2020-09) is a judgement call. The results are sensitive to it, especially at h=3 (see Results).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(2)-in-differences model does not beat the naive forecast: RMSE 0.118 vs 0.117 pp (relative improvement -0.010).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically distinguishable from zero (DM p=0.78); 35 evaluated pairs.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the AR(2)-in-differences model is worse than naive: RMSE 0.174 vs 0.159 pp (relative improvement -0.099).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"DM p=0.036 but sensitive to COVID-exclusion choice (gap shrinks to -0.8% without exclusion); overlapping errors, short calm window.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranking agrees with RMSE: naive has lower MAE at both horizons.\",\"results_keys\":[\"mae_naive_h1\",\"mae_model_h1\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Data rounded to 0.1 pp.\"},\n {\"id\":\"C4\",\"text\":\"35 of 36 (origin, horizon) pairs per horizon were evaluated; the missing 2025-10 observation excluded one target per horizon.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C5\",\"text\":\"Results apply to the final-vintage series and do not establish real-time forecast performance.\",\"results_keys\":[],\"status\":\"not_evaluated\",\"caveat\":\"No real-time vintages were available.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('claims.json'))\"; ls","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":806,"cache_read_input_tokens":20296,"output_tokens":2937,"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":806},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXse13JQcYCodiZJykXz","requestRef":"6de8f04b-c6e8-48b4-8f01-c05d4e41e487","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"551f1f5d-5e25-4035-bccd-181de9b3b687","timestamp":"2026-10-30T05:32:11.296Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003/out","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"551f1f5d-5e25-4035-bccd-181de9b3b687","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}42{"type":"cost-state","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","totalCostUSD":0.27298639999999996,"totalAPIDuration":77101,"totalAPIDurationWithoutRetries":77095,"totalToolDuration":928,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":78205,"startTime":1793338253148,"modelUsage":{"claude-opus-5-5":{"inputTokens":14,"outputTokens":8175,"thinkingTokens":877,"cacheReadInputTokens":57202,"cacheCreationInputTokens":19598,"cacheCreationEphemeral5mInputTokens":19598,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.27298639999999996}},"hasUnknownModelCost":false}43{"type":"tools-used","tools":["Bash"],"sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}44{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:32:12.621Z","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","content":"Your"}45{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:32:12.622Z","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}46{"parentUuid":"551f1f5d-5e25-4035-bccd-181de9b3b687","isSidechain":false,"promptId":"bfc0e404-896e-4bce-ae21-69627e223b91","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"[Tool call interrupted: the session ended before this call's result was recorded, so its outcome is unknown. 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I measure this with out-of-sample RMSE and MAE, and with the relative RMSE improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 data rows (1948-01 to 2026-09), monthly, seasonally adjusted, final vintage. There are no duplicate dates and no gaps in the monthly calendar.\n- **Missing value:** 2025-10-01 has no value. It is the target for (origin 2025-09, h=1) and (origin 2025-07, h=3). Both pairs appear in `forecasts.csv` with an empty `actual` and are left out of the error statistics. That leaves **35 evaluated pairs per horizon**.\n- **COVID outliers:** UNRATE hit 14.8 in 2020-04, with monthly changes of up to +10.4 pp. In the primary model, regression rows whose dependent or lagged difference is dated 2020-04 to 2020-09 are left out of estimation. This rule was fixed using only pre-2023 information, so it is the same at every origin. A sensitivity check without the exclusion is reported below.\n- The 1949-10 value (7.9, a strike-related spike) is kept as published.\n\n## Method\n- **Naive:** the forecast is the latest observation dated ≤ origin. At origin 2025-10, whose own value is missing, that means 2025-09.\n- **Model `AR2_diff`:** an AR(2) with intercept on first differences, Δu_t = c + φ1 Δu_{t−1} + φ2 Δu_{t−2} + e_t. It is estimated by OLS at each origin, using all regression rows from 1948 up to the origin (expanding window), minus the COVID rows and any rows involving the missing 2025-10 value. The fitted differences are iterated recursively and accumulated into levels for h = 1, 3. The lag order of 2 was fixed in advance; there was no data-driven selection and no tuning on the evaluation period.\n- **Missing value inside the model:** once 2025-11 is observed, 2025-10 is linearly interpolated, using only data ≤ origin, so that the forecast has a starting state. It is never used in estimation. At origin 2025-10 the model forecasts from 2025-09, iterating h+1 steps.\n- Every estimation step and transformation uses only data dated ≤ origin. Errors are computed against the final-vintage values in the file. There are 927 regression rows in the last training window (origin 2026-06).\n- Replication: `~/wp29/venv/bin/python out/analysis.py` regenerates `results.json`, `forecasts.csv` and `supplementary.json`, deterministically (bootstrap seed 12345).\n\n## Results\nErrors are in percentage points. n = 35 per horizon.\n\n| Horizon | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h = 1 | 0.1171 | 0.1183 | 0.0914 | 0.0976 | −0.010 |\n| h = 3 | 0.1586 | 0.1742 | 0.1200 | 0.1402 | −0.099 |\n\nThe AR(2)-in-differences model does **not** beat the naive forecast on either metric at either horizon. At h=1 it is about 1% worse in RMSE; at h=3 it is about 10% worse.\n\nSensitivity, from `supplementary.json`: if the 2020 rows are kept in estimation, the relative improvement is −0.023 (h=1) and −0.008 (h=3). So the model is still slightly worse than naive. The h=3 shortfall is smaller here because the estimated coefficients pick up less momentum. The estimated momentum (φ2 ≈ 0.28 at the last origin, intercept ≈ 0) pushed the forecasts upward during the 2024–2025 drift, and they then overshot when unemployment flattened and fell in 2026.\n\n## Uncertainty\n- Diebold–Mariano test (squared loss, HAC variance with h−1 lags, Harvey–Leybourne–Newbold small-sample correction, t(34) reference):\n - h=1: statistic −0.29, p = 0.78. No detectable difference.\n - h=3: statistic −2.19, p = 0.036. The naive forecast is nominally better.\n- Moving-block bootstrap over origins (block 6, 2000 replications), 90% interval for 1 − RMSE ratio:\n - h=1: [−0.104, 0.050]\n - h=3: [−0.191, −0.044]\n- Caveats on these inferences:\n - With only 35 overlapping evaluations in a calm, low-volatility period, they are fragile.\n - At h=3 the overlapping errors make the HAC variance and the bootstrap imprecise.\n - The p = 0.036 at h=3 should not be read as strong evidence. The sensitivity specification, which differs only in the COVID treatment, shrinks the gap to about −1%. That means the h=3 result depends on a specification choice.\n\n## Limitations and open issues\n- Final-vintage data, so the results say nothing about real-time performance. Seasonal-factor revisions are applied retroactively.\n- Errors are reported to sub-0.1 pp precision, but the data are rounded to 0.1 pp. Many naive errors are exactly 0 or ±0.1, so differences between models are small relative to that granularity.\n- Only one model specification was evaluated as primary (plus one sensitivity). Other models (ARMA, levels AR with mean reversion, models with covariates) were not tried, and the conclusion applies only to this simple class.\n- The evaluation window is short (3 years) and covers a single regime, a slow drift up from 3.5% to about 4.4% followed by a plateau. It does not include a turning point such as a recession.\n- The COVID-exclusion window (2020-04..2020-09) is a judgement call. The results are sensitive to it, especially at h=3 (see Results).\nEOF\ncat > claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR(2)-in-differences model does not beat the naive forecast: RMSE 0.118 vs 0.117 pp (relative improvement -0.010).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"Difference not statistically distinguishable from zero (DM p=0.78); 35 evaluated pairs.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the AR(2)-in-differences model is worse than naive: RMSE 0.174 vs 0.159 pp (relative improvement -0.099).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"DM p=0.036 but sensitive to COVID-exclusion choice (gap shrinks to -0.8% without exclusion); overlapping errors, short calm window.\"},\n {\"id\":\"C3\",\"text\":\"MAE ranking agrees with RMSE: naive has lower MAE at both 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72\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:32 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:31 ..\n-rw-r--r-- 1 simon-pierreboucher staff 7979 oct. 30 01:31 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1403 oct. 30 01:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 6084 oct. 30 01:31 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5424 oct. 30 01:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1936 oct. 30 01:31 results.json\n-rw-r--r-- 1 simon-pierreboucher staff 1044 oct. 30 01:31 supplementary.json\nok\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR model vs naive\n\n## Question\n- The evaluation window is short (3 years) and covers a single regime, a slow drift up from 3.5% to about 4.4% followed by a plateau. It does not include a turning point such as a recession.\n- The COVID-exclusion window (2020-04..2020-09) is a judgement call. The results are sensitive to it, especially at h=3 (see Results).","is_error":false}]},"uuid":"390afb28-b132-4ba1-9f80-18d8fc4325c0","timestamp":"2026-10-30T05:32:15.041Z","toolUseResult":{"stdout":"total 72\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 01:32 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 01:31 ..\n-rw-r--r-- 1 simon-pierreboucher staff 7979 oct. 30 01:31 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 1403 oct. 30 01:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 6084 oct. 30 01:31 forecasts.csv\n-rw-r--r-- 1 simon-pierreboucher staff 5424 oct. 30 01:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 1936 oct. 30 01:31 results.json\n-rw-r--r-- 1 simon-pierreboucher staff 1044 oct. 30 01:31 supplementary.json\nok\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR model vs naive\n\n## Question\n- The evaluation window is short (3 years) and covers a single regime, a slow drift up from 3.5% to about 4.4% followed by a plateau. 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It did: my last step was cut off, but I checked afterwards and every output file was written in full. The simple model does **not** beat the naive (no-change) forecast at either horizon.\n\nI used an AR(2) model on month-to-month changes in the unemployment rate (`AR2_diff`), re-fitted at each of the 36 forecast origins (2023-07 to 2026-06) using only data up to that origin. Errors are in percentage points, with 35 forecasts scored per horizon.\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 month | 0.117 | 0.118 | 0.091 | 0.098 | −0.010 |\n| 3 months | 0.159 | 0.174 | 0.120 | 0.140 | −0.099 |\n\n- **1 month:** the model is about 1% worse, which is no real difference (Diebold–Mariano test p = 0.78).\n- **3 months:** the model is about 10% worse (p = 0.036). That result depends on one choice I made, so treat it as weak evidence:\n - **The choice:** I left the extreme 2020 COVID months (2020-04 to 2020-09) out of the model's fitting data.\n - **Without that exclusion:** the model is still worse than naive at both horizons, but only by about 2% at 1 month and 1% at 3 months.\n\n**Data issue:** the 2025-10 value is missing. The two forecasts that target it are in the ledger with a blank actual and are left out of the scores, which is why each horizon has 35 rather than 36.\n\n**Limitations:**\n- The data are today's revised figures, so this says nothing about how the model would have done in real time.\n- Three years is a short, calm test period with no recession.\n- The data are rounded to 0.1 points, which is coarse next to differences this small.\n- I tested only one model specification, plus the one sensitivity check.\n\nRunning `~/wp29/venv/bin/python out/analysis.py` regenerates `results.json`, `forecasts.csv` and `supplementary.json` identically each time.\n\nFiles are in `out/`:\n- analysis.py\n- forecasts.csv (144 rows)\n- results.json\n- supplementary.json (the sensitivity check and significance tests)\n- report.md\n- claims.json"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":673,"cache_read_input_tokens":24114,"output_tokens":823,"output_tokens_details":{"thinking_tokens":21},"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":673},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsgCD3wuPhrd8vSDTnr","requestRef":"af013582-69a1-44b3-a90b-59052cf05e3e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"264ff6d3-a3b9-407a-8d32-562dea1cbbfc","timestamp":"2026-10-30T05:32:23.195Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0003/out","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","version":"2.1.314","gitBranch":"HEAD"}60{"type":"last-prompt","lastPrompt":"Your","leafUuid":"264ff6d3-a3b9-407a-8d32-562dea1cbbfc","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}61{"type":"mode","mode":"normal","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}62{"type":"atis-latch","atis":"","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb"}63{"type":"cost-state","sessionId":"bcb1b0a0-8a81-4b85-b15d-1c62e5ad7feb","totalCostUSD":0.32025859999999995,"totalAPIDuration":87617,"totalAPIDurationWithoutRetries":87609,"totalToolDuration":979,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":88803,"startTime":1793338253148,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":9164,"thinkingTokens":926,"cacheReadInputTokens":102418,"cacheCreationInputTokens":23283,"cacheCreationEphemeral5mInputTokens":23283,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.32025859999999995}},"hasUnknownModelCost":false}64