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The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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{"parentUuid":"478b7286-cd43-440d-aa49-a89c95f970a1","isSidechain":false,"promptId":"09035934-c66a-47d9-8e8f-2df5321d4d7f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01L9HahfGpHpUHV4DKUMVwkE","type":"tool_result","content":"# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: can a simple time-series model beat the naive forecast?\n\n## Scientific question\nOver the last three years, does a simple autoregressive-type model forecast the US civilian unemployment rate better than the naive (no-change) forecast at one- and three-month horizons? The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:36 .\ndrwxr-xr-x  17 simon-pierreboucher  staff   544 oct.  29 23:59 ..\n-rw-r--r--   1 simon-pierreboucher  staff   508 oct.  29 23:36 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:33 data\n-rw-r--r--   1 simon-pierreboucher  staff  2093 oct.  29 23:34 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4893 oct.  29 23:34 TASK.md\ndata:\nREADME.md\nUNRATE.csv","is_error":false}]},"uuid":"9dc034c2-3e4d-4a58-b8b3-f6a190860b59","timestamp":"2026-10-30T03:59:07.068Z","toolUseResult":{"stdout":"# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: can a simple time-series model beat the naive forecast?\n\n## Scientific question\nOver the last three years, does a simple autoregressive-type model forecast the US civilian unemployment rate better than the naive (no-change) forecast at one- and three-month horizons? The estimands are the out-of-sample root-mean-squared error (RMSE) and mean absolute error (MAE) of (a) the naive forecast and (b) one model of your choice, computed over a fixed set of rolling (expanding-window) forecast origins, and the relative improvement of the model over the naive forecast, 1 − RMSE_model / RMSE_naive, at each horizon.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: US unemployment rate (percent, seasonally adjusted, monthly), FRED series UNRATE (source BLS), retrieved 2026-10-30 (UTC) from https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE, exactly as served (header `observation_date,UNRATE`, one row per month dated YYYY-MM-01, 1948-01 to 2026-09). Missing observations appear as an empty value field. This is the current (final) vintage; see `data/README.md` for provenance, SHA-256 and terms.\n\n## Information-availability rules\n- Forecast origins: the 36 months t = 2023-07-01, 2023-08-01, …, 2026-06-01 (the `observation_date` of the origin month). Horizons h ∈ {1, 3} months; the target of origin t at horizon h is the month t + h.\n- A forecast made at origin t may use only observations dated ≤ t (expanding window: the training sample grows with t). Model selection, parameter estimation and any transformation (e.g. differencing, scaling) must use only observations dated ≤ t.\n- Naive baseline: the forecast for every horizon is the most recent available observation dated ≤ t (if the origin month itself is missing, use the latest earlier observation).\n- If the actual value at a target month is missing in the data, that (origin, horizon) pair still appears in the forecast ledger with an empty `actual`, but is excluded from all error statistics; report the number of evaluated pairs per horizon.\n- The evaluation errors are computed on the data as given (final vintage); do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), with columns `origin_date` (YYYY-MM-DD), `target_date` (YYYY-MM-DD), `horizon` (integer), `forecast` (number), `actual` (number, empty if missing), `model` (string: `naive` for the baseline, and the value of `model_name` for your model). Expected: 36 origins × 2 horizons × 2 models = 144 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-dev-01\" |\n| `model_name` | string | — | short name of your model (e.g. \"AR2_diff\"), used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, transformation, estimation sample start, estimation method) |\n| `n_origins` | integer | — | number of forecast origins (36) |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `n_evaluated_h1`, `n_evaluated_h3` | integer | — | (origin, horizon) pairs with a non-missing actual, per horizon |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive-forecast errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive (negative if the model is worse) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"UNRATE.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations used in the last training window |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:36 .\ndrwxr-xr-x  17 simon-pierreboucher  staff   544 oct.  29 23:59 ..\n-rw-r--r--   1 simon-pierreboucher  staff   508 oct.  29 23:36 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:33 data\n-rw-r--r--   1 simon-pierreboucher  staff  2093 oct.  29 23:34 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  4893 oct.  29 23:34 TASK.md\ndata:\nREADME.md\nUNRATE.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"478b7286-cd43-440d-aa49-a89c95f970a1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"581d9150-c1b9-4ad2-912f-b62cecc30d2c","isSidechain":false,"promptId":"09035934-c66a-47d9-8e8f-2df5321d4d7f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TwLT7aVYjYZPTwtWwKTYT9","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.\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\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,","is_error":false}]},"uuid":"e69d3363-ebb3-4c2d-80d0-c13e7da26139","timestamp":"2026-10-30T03:59:09.366Z","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.\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\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,","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"581d9150-c1b9-4ad2-912f-b62cecc30d2c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
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With 36 (overlapping for h=3) errors, sampling uncertainty is large.\n\n**Outputs.** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json (keys task_id, model_name, model_description, n_origins, first_origin, last_origin, n_evaluated_h1, n_evaluated_h3, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json.\n\n**Information rules.** Forecast at origin t uses only observations dated ≤ t, for estimation, model selection and transformations. Naive = latest non-missing observation ≤ t. Target = t+h; missing actual → row kept with empty actual, excluded from statistics; report evaluated counts.\n\n**Invalidity / impossibility.** Using data > t; choosing the model by looking at out-of-sample errors; missing data making targets unevaluable (handled by exclusion, not imputation of actuals). Task is infeasible only if data unreadable; not the case.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README & bundle.json) | 945 | 2 |\n\n- Schema: `observation_date` (YYYY-MM-01 string), `UNRATE` (float, percent of civilian labour force, SA).\n- Date range 1948-01-01 to 2026-09-01; consecutive months, no gaps in dates, no duplicate dates.\n- Values: 944 non-missing, min 2.5, max 14.8 (2020-04, COVID), mean 5.65. No implausible values.\n- Missing: 1 value, **2025-10-01** (empty field; consistent with no October 2025 CPS household survey release during the 2025 US federal government shutdown — stated as context, not verified from the data).\n- Consequences: (a) origin 2025-10-01: naive uses 2025-09-01 value; model must forecast from 2025-09; (b) targets 2025-10-01 are missing for (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3) → kept in ledger, excluded from error statistics; (c) later training windows contain a gap; differences involving 2025-10 are NaN.\n- Information-availability: the file is the final vintage, so all values are revised data; no input violates the date-≤-t rule as long as we slice by date. 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Decision: model = AR(2) on first differences with intercept, OLS, full sample from 1948, fixed a priori (no out-of-sample selection); missing in-window level filled by the model's own forecast from earlier data for forecasting only; OLS uses complete cases.\nEOF","description":"Write contract, data audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2390,"cache_read_input_tokens":6969,"output_tokens":1599,"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":2390},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkbNunL4MmUn61ebxgt","requestRef":"68bff005-c901-4793-86da-d599ece3490a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2339870e-76aa-44d0-b242-4e9f60713ddd","timestamp":"2026-10-30T03:59:37.907Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
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{"type":"api-request-blob","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","hash":"19769c64adc2b6911bc2e2298dcdffd0c8cfc2f0c152a1f64ac49bbaa31d997d","message":{"role":"assistant","content":[{"type":"text","text":"Now P0 contract, P1 audit, and the state files."},{"type":"tool_use","id":"toolu_011AKBu62xDD9qoS3xBpoCut","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F3-dev-01\n**Question.** Over 36 expanding-window forecast origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast the US unemployment rate (UNRATE, %, SA, monthly, final vintage) better than the naive no-change forecast at horizons h=1 and h=3 months?\n\n**Estimands.** Per horizon: out-of-sample RMSE and MAE (percentage points) of naive and of one model; relative improvement 1 − RMSE_model/RMSE_naive. Meaningful as pseudo-out-of-sample accuracy on the revised (final-vintage) series, over this specific 36-origin window; not real-time accuracy. With 36 (overlapping for h=3) errors, sampling uncertainty is large.\n\n**Outputs.** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json (keys task_id, model_name, model_description, n_origins, first_origin, last_origin, n_evaluated_h1, n_evaluated_h3, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json.\n\n**Information rules.** Forecast at origin t uses only observations dated ≤ t, for estimation, model selection and transformations. Naive = latest non-missing observation ≤ t. Target = t+h; missing actual → row kept with empty actual, excluded from statistics; report evaluated counts.\n\n**Invalidity / impossibility.** Using data > t; choosing the model by looking at out-of-sample errors; missing data making targets unevaluable (handled by exclusion, not imputation of actuals). Task is infeasible only if data unreadable; not the case.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README & bundle.json) | 945 | 2 |\n\n- Schema: `observation_date` (YYYY-MM-01 string), `UNRATE` (float, percent of civilian labour force, SA).\n- Date range 1948-01-01 to 2026-09-01; consecutive months, no gaps in dates, no duplicate dates.\n- Values: 944 non-missing, min 2.5, max 14.8 (2020-04, COVID), mean 5.65. No implausible values.\n- Missing: 1 value, **2025-10-01** (empty field; consistent with no October 2025 CPS household survey release during the 2025 US federal government shutdown — stated as context, not verified from the data).\n- Consequences: (a) origin 2025-10-01: naive uses 2025-09-01 value; model must forecast from 2025-09; (b) targets 2025-10-01 are missing for (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3) → kept in ledger, excluded from error statistics; (c) later training windows contain a gap; differences involving 2025-10 are NaN.\n- Information-availability: the file is the final vintage, so all values are revised data; no input violates the date-≤-t rule as long as we slice by date. Handling: evaluation reported as pseudo-out-of-sample on revised data, no real-time claim.\n- Anomaly: COVID spike 2020-04 (14.8, +10.4 pp month-on-month) is a genuine observation; kept (no outlier removal), noted as a possible influence on AR coefficients.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-29 P0 passed: contract written.\n- 2026-10-29 P1 passed: data audited; one missing value (2025-10-01). Decision: model = AR(2) on first differences with intercept, OLS, full sample from 1948, fixed a priori (no out-of-sample selection); missing in-window level filled by the model's own forecast from earlier data for forecasting only; OLS uses complete cases.\nEOF","description":"Write contract, data audit, log"}}]}}
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{"parentUuid":"44fdfee3-2f18-4ebb-b322-fa2aa2b71ee5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkcZ3m4biycbjaWaL13","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017jVBrJNCHRDzS2bHh6heqi","name":"Bash","input":{"command":"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\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nP = 2                      # AR lag order on first differences (fixed a priori)\nHORIZONS = [1, 3]\nMODEL_NAME = \"AR2_diff\"\nORIGINS = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str})\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\ns = raw.set_index(\"date\")[\"UNRATE\"].astype(float)\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nassert (s.index == pd.date_range(s.index[0], s.index[-1], freq=\"MS\")).all()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\n\ndef fit_ar_diff(y):\n    \"\"\"OLS of dy_t on const + dy_{t-1..t-P}, complete cases only. y: level series (may contain NaN).\"\"\"\n    dy = y.diff()\n    X = pd.concat([dy.shift(k) for k in range(1, P + 1)], axis=1)\n    df = pd.concat([dy, X], axis=1).dropna()\n    Y = df.iloc[:, 0].to_numpy()\n    Xm = np.column_stack([np.ones(len(df)), df.iloc[:, 1:].to_numpy()])\n    beta, *_ = np.linalg.lstsq(Xm, Y, rcond=None)\n    return beta, len(df)\n\n\ndef forecast_ar_diff(y, origin, hmax):\n    \"\"\"Forecast levels at origin+1..origin+hmax using only y dated <= origin.\n    Missing levels inside the window (and the origin month if missing) are filled sequentially\n    with the model's own one-step forecast from earlier data (no information after the origin).\"\"\"\n    y = y.loc[:origin]\n    beta, nobs = fit_ar_diff(y)\n    lv = y.copy()\n    # fill internal missing values (needed only if they are among the last P+1 levels used)\n    for i in np.where(lv.isna().to_numpy())[0]:\n        if i >= len(lv) - (P + 1) - hmax:   # only recent gaps matter for the recursion\n            d = lv.diff().iloc[i - P:i].to_numpy()[::-1]   # dy_{i-1},...,dy_{i-P}\n            lv.iloc[i] = lv.iloc[i - 1] + beta[0] + beta[1:] @ d\n    assert not lv.iloc[-(P + 1):].isna().any()\n    hist_d = list(lv.diff().iloc[-P:].to_numpy()[::-1])   # most recent first\n    level = lv.iloc[-1]\n    out = []\n    for _ in range(hmax):\n        d = beta[0] + beta[1:] @ np.array(hist_d[:P])\n        level = level + d\n        hist_d.insert(0, d)\n        out.append(level)\n    return out, beta, nobs\n\n\nrows = []\nlast_window_n = None\nfor t in ORIGINS:\n    window = s.loc[:t]\n    naive = window.dropna().iloc[-1]\n    fc, beta, nobs = forecast_ar_diff(s, t, max(HORIZONS))\n    last_window_n = int(window.notna().sum())\n    for h in HORIZONS:\n        target = t + pd.DateOffset(months=h)\n        actual = s.get(target, np.nan)\n        for model, f in ((\"naive\", naive), (MODEL_NAME, fc[h - 1])):\n            rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=target.strftime(\"%Y-%m-%d\"),\n                             horizon=h, forecast=round(float(f), 10),\n                             actual=(None if pd.isna(actual) else float(actual)), model=model))\n\nfc_df = pd.DataFrame(rows)\nassert len(fc_df) == len(ORIGINS) * len(HORIZONS) * 2\nassert (pd.to_datetime(fc_df.target_date) > pd.to_datetime(fc_df.origin_date)).all()\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n       \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, levels forecast by iterating the \"\n                             \"difference equation; estimated by OLS on complete cases over an expanding window from \"\n                             \"1948 (first usable dependent obs 1948-04) to the origin; lag order fixed a priori; a \"\n                             \"missing in-window level is replaced for forecasting by the model's own forecast from \"\n                             \"earlier data.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n       \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    sub = fc_df[(fc_df.horizon == h) & fc_df.actual.notna()]\n    n_ev = sub[sub.model == \"naive\"].shape[0]\n    res[f\"n_evaluated_h{h}\"] = int(n_ev)\nfor h in HORIZONS:\n    for model, key in ((\"naive\", \"naive\"), (MODEL_NAME, \"model\")):\n        sub = fc_df[(fc_df.horizon == h) & (fc_df.model == model) & fc_df.actual.notna()]\n        e = sub.actual.to_numpy() - sub.forecast.to_numpy()\n        res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n        res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1.0 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n\nexcluded_pairs = fc_df[fc_df.actual.isna() & (fc_df.model == \"naive\")][[\"origin_date\", \"horizon\"]]\nres.update({\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [{\"issue\": f\"Missing UNRATE value at {d} (empty field).\",\n                     \"handling\": (\"Kept as missing. Naive forecast at origin 2025-10-01 uses 2025-09-01. AR estimation \"\n                                  \"uses complete cases only; for forecasting, the missing level is replaced by the \"\n                                  \"model's own forecast from data before it. (origin, horizon) pairs whose target is \"\n                                  \"this month are kept in forecasts.csv with empty actual and excluded from error \"\n                                  \"statistics: \" + \"; \".join(f\"{r.origin_date} h={r.horizon}\"\n                                                             for r in excluded_pairs.itertuples()) + \".\")}\n                    for d in missing_dates] + [\n        {\"issue\": \"Data are the final (revised) vintage, not real-time vintages.\",\n         \"handling\": \"Errors are pseudo-out-of-sample on revised data; no real-time validity is claimed.\"}],\n    \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n    \"n_analysed\": last_window_n,\n    \"random_seed\": None,\n})\n# diagnostic (not a required key): coefficients at last origin\nres[\"diag_ar_coef_last_origin\"] = [float(b) for b in beta]\nres[\"diag_ar_nobs_last_origin\"] = int(nobs)\n\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger (observation-level)\nn1 = int(s.notna().sum())\nn2 = int(s.loc[:ORIGINS[-1]].notna().sum())\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"empty value field (\" + \", \".join(missing_dates) + \")\"),\n    dict(step=2, rule=\"drop observations dated after last origin 2026-06-01 from training\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"information rule: training uses only data dated <= origin (used only as targets)\"),\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nfor h in HORIZONS:\n    print(f\"h={h}: n={res[f'n_evaluated_h{h}']} RMSE naive={res[f'rmse_naive_h{h}']:.4f} \"\n          f\"model={res[f'rmse_model_h{h}']:.4f} rel.impr={res[f'rel_improvement_rmse_h{h}']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.csv && grep -E '2025-(0[7-9]|1[0-2])' out/forecasts.csv","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":1614,"cache_read_input_tokens":9359,"output_tokens":3683,"output_tokens_details":{"thinking_tokens":256},"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":1614},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkcYmPX7PV1RgcVBmLc","requestRef":"f8b72e0b-67f7-426a-af9b-ce1e9b4dfa9c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"068b2ce6-f26b-44c3-a957-58740708394a","timestamp":"2026-10-30T04:00:08.111Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"068b2ce6-f26b-44c3-a957-58740708394a","isSidechain":false,"promptId":"09035934-c66a-47d9-8e8f-2df5321d4d7f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017jVBrJNCHRDzS2bHh6heqi","type":"tool_result","content":"h=1: n=35 RMSE naive=0.1171 model=0.1199 rel.impr=-0.0239\nh=3: n=35 RMSE naive=0.1586 model=0.1598 rel.impr=-0.0076\n{\n  \"task_id\": \"F3-dev-01\",\n  \"model_name\": \"AR2_diff\",\n  \"model_description\": \"AR(2) with intercept on first differences of UNRATE, levels forecast by iterating the difference equation; estimated by OLS on complete cases over an expanding window from 1948 (first usable dependent obs 1948-04) to the origin; lag order fixed a priori; a missing in-window level is replaced for forecasting by the model's own forecast from earlier data.\",\n  \"n_origins\": 36,\n  \"first_origin\": \"2023-07-01\",\n  \"last_origin\": \"2026-06-01\",\n  \"n_evaluated_h1\": 35,\n  \"n_evaluated_h3\": 35,\n  \"rmse_naive_h1\": 0.11710800875382403,\n  \"mae_naive_h1\": 0.09142857142857148,\n  \"rmse_model_h1\": 0.11990235383143451,\n  \"mae_model_h1\": 0.09570395610285726,\n  \"rmse_naive_h3\": 0.15856499343441843,\n  \"mae_naive_h3\": 0.12000000000000005,\n  \"rmse_model_h3\": 0.1597646592179836,\n  \"mae_model_h3\": 0.1212753795742857,\n  \"rel_improvement_rmse_h1\": -0.02386126369448016,\n  \"rel_improvement_rmse_h3\": -0.00756576692989519,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"Missing UNRATE value at 2025-10-01 (empty field).\",\n      \"handling\": \"Kept as missing. Naive forecast at origin 2025-10-01 uses 2025-09-01. AR estimation uses complete cases only; for forecasting, the missing level is replaced by the model's own forecast from data before it. (origin, horizon) pairs whose target is this month are kept in forecasts.csv with empty actual and excluded from error statistics: 2025-07-01 h=3; 2025-09-01 h=1.\"\n    },\n    {\n      \"issue\": \"Data are the final (revised) vintage, not real-time vintages.\",\n      \"handling\": \"Errors are pseudo-out-of-sample on revised data; no real-time validity is claimed.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 941,\n  \"random_seed\": null,\n  \"diag_ar_coef_last_origin\": [\n    0.0003368050557942884,\n    0.036584777952152876,\n    -0.04220948335502803\n  ],\n  \"diag_ar_nobs_last_origin\": 935\n}step,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE,945,944,1,empty value field (2025-10-01)\n2,drop observations dated after last origin 2026-06-01 from training,944,941,3,information rule: training uses only data dated <= origin (used only as targets)\n2025-04-01,2025-07-01,3,4.2,4.3,naive\n2025-04-01,2025-07-01,3,4.200718661,4.3,AR2_diff\n2025-05-01,2025-08-01,3,4.3,4.3,naive\n2025-05-01,2025-08-01,3,4.300333815,4.3,AR2_diff\n2025-06-01,2025-07-01,1,4.1,4.3,naive\n2025-06-01,2025-07-01,1,4.088469214,4.3,AR2_diff\n2025-06-01,2025-09-01,3,4.1,4.4,naive\n2025-06-01,2025-09-01,3,4.097590373,4.4,AR2_diff\n2025-07-01,2025-08-01,1,4.3,4.3,naive\n2025-07-01,2025-08-01,1,4.316178278,4.3,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.308038663,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.291862566,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.292579942,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.404123155,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.400647159,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.400502971,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.401256559,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.503797643,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.500503508,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.392730606,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.398057759,4.3,AR2_diff","is_error":false}]},"uuid":"0ef7b1ea-0535-4503-aa1a-d2d830bf08c2","timestamp":"2026-10-30T04:00:08.419Z","toolUseResult":{"stdout":"h=1: n=35 RMSE naive=0.1171 model=0.1199 rel.impr=-0.0239\nh=3: n=35 RMSE naive=0.1586 model=0.1598 rel.impr=-0.0076\n{\n  \"task_id\": \"F3-dev-01\",\n  \"model_name\": \"AR2_diff\",\n  \"model_description\": \"AR(2) with intercept on first differences of UNRATE, levels forecast by iterating the difference equation; estimated by OLS on complete cases over an expanding window from 1948 (first usable dependent obs 1948-04) to the origin; lag order fixed a priori; a missing in-window level is replaced for forecasting by the model's own forecast from earlier data.\",\n  \"n_origins\": 36,\n  \"first_origin\": \"2023-07-01\",\n  \"last_origin\": \"2026-06-01\",\n  \"n_evaluated_h1\": 35,\n  \"n_evaluated_h3\": 35,\n  \"rmse_naive_h1\": 0.11710800875382403,\n  \"mae_naive_h1\": 0.09142857142857148,\n  \"rmse_model_h1\": 0.11990235383143451,\n  \"mae_model_h1\": 0.09570395610285726,\n  \"rmse_naive_h3\": 0.15856499343441843,\n  \"mae_naive_h3\": 0.12000000000000005,\n  \"rmse_model_h3\": 0.1597646592179836,\n  \"mae_model_h3\": 0.1212753795742857,\n  \"rel_improvement_rmse_h1\": -0.02386126369448016,\n  \"rel_improvement_rmse_h3\": -0.00756576692989519,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"Missing UNRATE value at 2025-10-01 (empty field).\",\n      \"handling\": \"Kept as missing. Naive forecast at origin 2025-10-01 uses 2025-09-01. AR estimation uses complete cases only; for forecasting, the missing level is replaced by the model's own forecast from data before it. (origin, horizon) pairs whose target is this month are kept in forecasts.csv with empty actual and excluded from error statistics: 2025-07-01 h=3; 2025-09-01 h=1.\"\n    },\n    {\n      \"issue\": \"Data are the final (revised) vintage, not real-time vintages.\",\n      \"handling\": \"Errors are pseudo-out-of-sample on revised data; no real-time validity is claimed.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 941,\n  \"random_seed\": null,\n  \"diag_ar_coef_last_origin\": [\n    0.0003368050557942884,\n    0.036584777952152876,\n    -0.04220948335502803\n  ],\n  \"diag_ar_nobs_last_origin\": 935\n}step,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE,945,944,1,empty value field (2025-10-01)\n2,drop observations dated after last origin 2026-06-01 from training,944,941,3,information rule: training uses only data dated <= origin (used only as targets)\n2025-04-01,2025-07-01,3,4.2,4.3,naive\n2025-04-01,2025-07-01,3,4.200718661,4.3,AR2_diff\n2025-05-01,2025-08-01,3,4.3,4.3,naive\n2025-05-01,2025-08-01,3,4.300333815,4.3,AR2_diff\n2025-06-01,2025-07-01,1,4.1,4.3,naive\n2025-06-01,2025-07-01,1,4.088469214,4.3,AR2_diff\n2025-06-01,2025-09-01,3,4.1,4.4,naive\n2025-06-01,2025-09-01,3,4.097590373,4.4,AR2_diff\n2025-07-01,2025-08-01,1,4.3,4.3,naive\n2025-07-01,2025-08-01,1,4.316178278,4.3,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.308038663,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.291862566,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.292579942,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.404123155,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.400647159,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.400502971,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.401256559,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.503797643,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.500503508,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.392730606,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.398057759,4.3,AR2_diff","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"068b2ce6-f26b-44c3-a957-58740708394a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
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Deterministic (no randomness).\"\"\"\nimport json\nimport os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nP = 2                      # AR lag order on first differences (fixed a priori)\nHORIZONS = [1, 3]\nMODEL_NAME = \"AR2_diff\"\nORIGINS = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str})\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\ns = raw.set_index(\"date\")[\"UNRATE\"].astype(float)\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nassert (s.index == pd.date_range(s.index[0], s.index[-1], freq=\"MS\")).all()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\n\n\ndef fit_ar_diff(y):\n    \"\"\"OLS of dy_t on const + dy_{t-1..t-P}, complete cases only. y: level series (may contain NaN).\"\"\"\n    dy = y.diff()\n    X = pd.concat([dy.shift(k) for k in range(1, P + 1)], axis=1)\n    df = pd.concat([dy, X], axis=1).dropna()\n    Y = df.iloc[:, 0].to_numpy()\n    Xm = np.column_stack([np.ones(len(df)), df.iloc[:, 1:].to_numpy()])\n    beta, *_ = np.linalg.lstsq(Xm, Y, rcond=None)\n    return beta, len(df)\n\n\ndef forecast_ar_diff(y, origin, hmax):\n    \"\"\"Forecast levels at origin+1..origin+hmax using only y dated <= origin.\n    Missing levels inside the window (and the origin month if missing) are filled sequentially\n    with the model's own one-step forecast from earlier data (no information after the origin).\"\"\"\n    y = y.loc[:origin]\n    beta, nobs = fit_ar_diff(y)\n    lv = y.copy()\n    # fill internal missing values (needed only if they are among the last P+1 levels used)\n    for i in np.where(lv.isna().to_numpy())[0]:\n        if i >= len(lv) - (P + 1) - hmax:   # only recent gaps matter for the recursion\n            d = lv.diff().iloc[i - P:i].to_numpy()[::-1]   # dy_{i-1},...,dy_{i-P}\n            lv.iloc[i] = lv.iloc[i - 1] + beta[0] + beta[1:] @ d\n    assert not lv.iloc[-(P + 1):].isna().any()\n    hist_d = list(lv.diff().iloc[-P:].to_numpy()[::-1])   # most recent first\n    level = lv.iloc[-1]\n    out = []\n    for _ in range(hmax):\n        d = beta[0] + beta[1:] @ np.array(hist_d[:P])\n        level = level + d\n        hist_d.insert(0, d)\n        out.append(level)\n    return out, beta, nobs\n\n\nrows = []\nlast_window_n = None\nfor t in ORIGINS:\n    window = s.loc[:t]\n    naive = window.dropna().iloc[-1]\n    fc, beta, nobs = forecast_ar_diff(s, t, max(HORIZONS))\n    last_window_n = int(window.notna().sum())\n    for h in HORIZONS:\n        target = t + pd.DateOffset(months=h)\n        actual = s.get(target, np.nan)\n        for model, f in ((\"naive\", naive), (MODEL_NAME, fc[h - 1])):\n            rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=target.strftime(\"%Y-%m-%d\"),\n                             horizon=h, forecast=round(float(f), 10),\n                             actual=(None if pd.isna(actual) else float(actual)), model=model))\n\nfc_df = pd.DataFrame(rows)\nassert len(fc_df) == len(ORIGINS) * len(HORIZONS) * 2\nassert (pd.to_datetime(fc_df.target_date) > pd.to_datetime(fc_df.origin_date)).all()\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n       \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, levels forecast by iterating the \"\n                             \"difference equation; estimated by OLS on complete cases over an expanding window from \"\n                             \"1948 (first usable dependent obs 1948-04) to the origin; lag order fixed a priori; a \"\n                             \"missing in-window level is replaced for forecasting by the model's own forecast from \"\n                             \"earlier data.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n       \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    sub = fc_df[(fc_df.horizon == h) & fc_df.actual.notna()]\n    n_ev = sub[sub.model == \"naive\"].shape[0]\n    res[f\"n_evaluated_h{h}\"] = int(n_ev)\nfor h in HORIZONS:\n    for model, key in ((\"naive\", \"naive\"), (MODEL_NAME, \"model\")):\n        sub = fc_df[(fc_df.horizon == h) & (fc_df.model == model) & fc_df.actual.notna()]\n        e = sub.actual.to_numpy() - sub.forecast.to_numpy()\n        res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n        res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1.0 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n\nexcluded_pairs = fc_df[fc_df.actual.isna() & (fc_df.model == \"naive\")][[\"origin_date\", \"horizon\"]]\nres.update({\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [{\"issue\": f\"Missing UNRATE value at {d} (empty field).\",\n                     \"handling\": (\"Kept as missing. Naive forecast at origin 2025-10-01 uses 2025-09-01. AR estimation \"\n                                  \"uses complete cases only; for forecasting, the missing level is replaced by the \"\n                                  \"model's own forecast from data before it. (origin, horizon) pairs whose target is \"\n                                  \"this month are kept in forecasts.csv with empty actual and excluded from error \"\n                                  \"statistics: \" + \"; \".join(f\"{r.origin_date} h={r.horizon}\"\n                                                             for r in excluded_pairs.itertuples()) + \".\")}\n                    for d in missing_dates] + [\n        {\"issue\": \"Data are the final (revised) vintage, not real-time vintages.\",\n         \"handling\": \"Errors are pseudo-out-of-sample on revised data; no real-time validity is claimed.\"}],\n    \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n    \"n_analysed\": last_window_n,\n    \"random_seed\": None,\n})\n# diagnostic (not a required key): coefficients at last origin\nres[\"diag_ar_coef_last_origin\"] = [float(b) for b in beta]\nres[\"diag_ar_nobs_last_origin\"] = int(nobs)\n\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger (observation-level)\nn1 = int(s.notna().sum())\nn2 = int(s.loc[:ORIGINS[-1]].notna().sum())\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"empty value field (\" + \", \".join(missing_dates) + \")\"),\n    dict(step=2, rule=\"drop observations dated after last origin 2026-06-01 from training\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"information rule: training uses only data dated <= origin (used only as targets)\"),\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nfor h in HORIZONS:\n    print(f\"h={h}: n={res[f'n_evaluated_h{h}']} RMSE naive={res[f'rmse_naive_h{h}']:.4f} \"\n          f\"model={res[f'rmse_model_h{h}']:.4f} rel.impr={res[f'rel_improvement_rmse_h{h}']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.csv && grep -E '2025-(0[7-9]|1[0-2])' out/forecasts.csv","description":"Write and run analysis script"}}]}}
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{"parentUuid":"397646cb-aa70-4f19-a5da-1474d2eafe32","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkhVTqnnmDNWWMwFuDa","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fxeb7WYZoV7H41huTRaazA","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR(2) on differences vs naive\n\n## Question\nOver the 36 monthly forecast origins from 2023-07-01 to 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive model forecast the US civilian unemployment rate (FRED UNRATE, %, SA) better than the naive no-change forecast at horizons of 1 and 3 months? Forecasts use expanding windows; accuracy is measured by out-of-sample RMSE and MAE and by the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- Input: `data/UNRATE.csv`, 945 data rows, 1948-01 to 2026-09 (`n_input_rows`); SHA-256 matches the README. Audit details in `out/state/data_audit.md`.\n- One missing value: 2025-10-01 (empty field) (`data_issues`). Handling: the naive forecast at origin 2025-10-01 uses the 2025-09-01 value; AR estimation drops regressions involving the gap (complete cases); for forecasting, the missing level is replaced by the model's own forecast from earlier data. Two (origin, horizon) pairs target 2025-10-01 (origin 2025-09-01 at h=1; origin 2025-07-01 at h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. As a result, 35 pairs are evaluated at each horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Observation-level exclusion ledger (`out/exclusions.csv`): 945 raw rows → −1 missing → −3 observations after the last origin, which are used only as targets → 941 observations in the last training window (`n_analysed`).\n- The file is the final (revised) vintage, so this exercise is pseudo-out-of-sample and has no real-time validity.\n\n## Method\n- **Naive:** the forecast at every horizon is the latest non-missing observation dated ≤ origin.\n- **Model `AR2_diff`** (`model_name`, `model_description`): Δy_t = c + φ1 Δy_{t−1} + φ2 Δy_{t−2} + e_t, estimated by OLS on all complete cases from 1948 up to the origin and re-estimated at each origin. Forecasts for h = 1..3 come from iterating the difference equation and cumulating onto the last level. I fixed the lag order (2) and the transformation (first differences) before looking at any out-of-sample result. No model selection was done on the evaluation window. Every estimation step, transformation and gap fill uses only data dated ≤ origin; a perturbation test confirmed there is no look-ahead (`out/state/checks.md`, check 11).\n- The procedure is deterministic, with no random numbers (`random_seed` = null). `out/analysis.py` regenerates every output.\n- Uncertainty: a Diebold–Mariano test on squared-error loss differences, using a HAC variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction, and a t(n−1) reference (`diag_dm_stat_h*`, `diag_dm_pvalue_two_sided_h*`).\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0239 |\n| 3 | 35 | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.0076 |\n\nKeys: `rmse_naive_h1`, `rmse_model_h1`, `mae_naive_h1`, `mae_model_h1`, `rel_improvement_rmse_h1`, and likewise for `_h3`. Values are in percentage points, rounded from results.json.\n\n- At h=1 the AR model is about 2.4% worse than the naive forecast in RMSE (`rel_improvement_rmse_h1` = −0.024). At h=3 it is about 0.8% worse (`rel_improvement_rmse_h3` = −0.008). MAE points the same way at both horizons.\n- **The answer is no: over this window, the simple AR model did not beat the naive forecast at either horizon.**\n- The two forecasts are almost the same. The estimated AR coefficients at the last origin are small (`diag_ar_coef_last_origin` ≈ [0.0003, 0.037, −0.042]), so the model behaves almost like a random walk and its forecasts stay within 0.1 pp of the naive forecasts.\n\n## Uncertainty\n- Diebold–Mariano, h=1: statistic 2.53, two-sided p = 0.016 (`diag_dm_stat_h1`, `diag_dm_pvalue_two_sided_h1`). Taken at face value, the AR model's h=1 loss is significantly larger than the naive loss. Treat this with caution. The forecasts differ by only hundredths of a point, the actuals are rounded to 0.1 pp, and there are only 35 errors, so the test statistic rests on very small loss differences.\n- Diebold–Mariano, h=3: statistic 1.36, p = 0.18 (`diag_dm_stat_h3`, `diag_dm_pvalue_two_sided_h3`). There is no significant difference at h=3. The overlapping 3-month errors are handled through the HAC variance.\n- The main finding is robust in direction (the model does not beat naive). The size of the differences (≤ 2.4% of RMSE) is small compared with sampling variability over a 3-year window.\n\n## Limitations and open issues\n- Only one model specification was evaluated, and its specification was fixed in advance. Other AR variants might do better or worse; I did not explore them, to avoid choosing a model on the evaluation window.\n- The estimation sample includes the 2020 COVID spike (+10.4 pp in one month). This probably pulls the OLS AR coefficients towards zero, which makes the model nearly a random walk. A later estimation start or outlier dummies might change the results. I did not try these, because choosing them after seeing the results would be data snooping.\n- The evaluation uses the final-vintage data, so it says nothing about real-time performance.\n- The 2025-10 observation is missing. Each horizon therefore has one unevaluated pair, and the forecasts at nearby origins depend on the gap-filling rule.\n- The evaluation window is short (36 origins) and fairly calm (unemployment between about 3.4% and 4.5%), so the results may not hold in other periods.\n- The `diag_*` keys are extra diagnostics beyond the required schema.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At the 1-month horizon the AR(2)-on-differences model has higher RMSE than the naive forecast (relative improvement about -0.024) and higher MAE.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; one fixed model specification.\"},\n {\"id\":\"C2\",\"text\":\"At the 3-month horizon the AR model has slightly higher RMSE than naive (relative improvement about -0.008) and higher MAE.\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping errors; difference not statistically significant (DM p=0.18).\"},\n {\"id\":\"C3\",\"text\":\"The simple AR model did not beat the naive forecast at either horizon over the 36 origins 2023-07 to 2026-06.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\",\"n_origins\",\"first_origin\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Pseudo-out-of-sample on revised data; short, calm window.\"},\n {\"id\":\"C4\",\"text\":\"The h=1 loss difference is nominally significant (HLN-corrected DM p about 0.016) while h=3 is not (p about 0.18).\",\"results_keys\":[\"diag_dm_stat_h1\",\"diag_dm_pvalue_two_sided_h1\",\"diag_dm_stat_h3\",\"diag_dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Forecast differences are hundredths of a point vs 0.1 pp data rounding; small sample; interpret cautiously.\"},\n {\"id\":\"C5\",\"text\":\"The estimated AR coefficients are small, so the model forecasts are close to a random walk.\",\"results_keys\":[\"diag_ar_coef_last_origin\",\"diag_ar_nobs_last_origin\"],\"status\":\"supported\",\"caveat\":\"Likely influenced by the 2020 COVID outliers in the estimation sample (not tested).\"},\n {\"id\":\"C6\",\"text\":\"One observation (2025-10-01) is missing, leaving 35 evaluated pairs per horizon out of 36 origins; 941 observations are in the last training window.\",\"results_keys\":[\"data_issues\",\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written with question, estimands, outputs, info rules, invalidity conditions\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, dates consecutive, no duplicates\",\"1 missing value (2025-10-01) identified and handling decided\",\"exclusions.csv sums: 945-4=941=n_analysed\"],\"notes\":\"final vintage -> no real-time claims\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed per info rule with same evaluation rule (rmse/mae_naive_h1/h3 in results.json)\",\"naive == last obs <= origin verified\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"deterministic, no RNG (random_seed null)\"],\"notes\":\"AR2_diff fixed a priori\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and forecasts.csv\",\"12 consistency checks PASS (out/state/checks.md) incl. chronology, identities, look-ahead perturbation test\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, quantities cite results keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'));print(all(k in r for x in c for k in x['results_keys']))\"\necho \"- 2026-10-29 P2/P3 passed: baseline + AR2_diff computed; DM diagnostics added. P4 passed: reproducibility and 12 checks. P5 passed: report, claims written.\" >> out/state/log.md; ls out","description":"Write report, claims, phases; validate claim keys"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":837,"cache_read_input_tokens":20134,"output_tokens":4032,"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":837},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXkhV8kiks2C5DDzRzeY","requestRef":"f744f3b4-2535-4704-984b-6f0bec1cc26a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"27162e3c-27ac-43b2-b24d-e4211a384422","timestamp":"2026-10-30T04:01:18.889Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0022","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","version":"2.1.314","gitBranch":"HEAD"}
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{"type":"api-request-blob","sessionId":"d18d1f47-f7dc-49d7-b7b0-229ed6d72a98","hash":"85abc84ed7bd14ba2fd9d2447d37035ec26b5f1c00322d11212d98ddd62676d2","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Fxeb7WYZoV7H41huTRaazA","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR(2) on differences vs naive\n\n## Question\nOver the 36 monthly forecast origins from 2023-07-01 to 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive model forecast the US civilian unemployment rate (FRED UNRATE, %, SA) better than the naive no-change forecast at horizons of 1 and 3 months? Forecasts use expanding windows; accuracy is measured by out-of-sample RMSE and MAE and by the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- Input: `data/UNRATE.csv`, 945 data rows, 1948-01 to 2026-09 (`n_input_rows`); SHA-256 matches the README. Audit details in `out/state/data_audit.md`.\n- One missing value: 2025-10-01 (empty field) (`data_issues`). Handling: the naive forecast at origin 2025-10-01 uses the 2025-09-01 value; AR estimation drops regressions involving the gap (complete cases); for forecasting, the missing level is replaced by the model's own forecast from earlier data. Two (origin, horizon) pairs target 2025-10-01 (origin 2025-09-01 at h=1; origin 2025-07-01 at h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. As a result, 35 pairs are evaluated at each horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Observation-level exclusion ledger (`out/exclusions.csv`): 945 raw rows → −1 missing → −3 observations after the last origin, which are used only as targets → 941 observations in the last training window (`n_analysed`).\n- The file is the final (revised) vintage, so this exercise is pseudo-out-of-sample and has no real-time validity.\n\n## Method\n- **Naive:** the forecast at every horizon is the latest non-missing observation dated ≤ origin.\n- **Model `AR2_diff`** (`model_name`, `model_description`): Δy_t = c + φ1 Δy_{t−1} + φ2 Δy_{t−2} + e_t, estimated by OLS on all complete cases from 1948 up to the origin and re-estimated at each origin. Forecasts for h = 1..3 come from iterating the difference equation and cumulating onto the last level. I fixed the lag order (2) and the transformation (first differences) before looking at any out-of-sample result. No model selection was done on the evaluation window. Every estimation step, transformation and gap fill uses only data dated ≤ origin; a perturbation test confirmed there is no look-ahead (`out/state/checks.md`, check 11).\n- The procedure is deterministic, with no random numbers (`random_seed` = null). `out/analysis.py` regenerates every output.\n- Uncertainty: a Diebold–Mariano test on squared-error loss differences, using a HAC variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction, and a t(n−1) reference (`diag_dm_stat_h*`, `diag_dm_pvalue_two_sided_h*`).\n\n## Results\n| horizon | n | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.0239 |\n| 3 | 35 | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.0076 |\n\nKeys: `rmse_naive_h1`, `rmse_model_h1`, `mae_naive_h1`, `mae_model_h1`, `rel_improvement_rmse_h1`, and likewise for `_h3`. Values are in percentage points, rounded from results.json.\n\n- At h=1 the AR model is about 2.4% worse than the naive forecast in RMSE (`rel_improvement_rmse_h1` = −0.024). At h=3 it is about 0.8% worse (`rel_improvement_rmse_h3` = −0.008). MAE points the same way at both horizons.\n- **The answer is no: over this window, the simple AR model did not beat the naive forecast at either horizon.**\n- The two forecasts are almost the same. The estimated AR coefficients at the last origin are small (`diag_ar_coef_last_origin` ≈ [0.0003, 0.037, −0.042]), so the model behaves almost like a random walk and its forecasts stay within 0.1 pp of the naive forecasts.\n\n## Uncertainty\n- Diebold–Mariano, h=1: statistic 2.53, two-sided p = 0.016 (`diag_dm_stat_h1`, `diag_dm_pvalue_two_sided_h1`). Taken at face value, the AR model's h=1 loss is significantly larger than the naive loss. Treat this with caution. The forecasts differ by only hundredths of a point, the actuals are rounded to 0.1 pp, and there are only 35 errors, so the test statistic rests on very small loss differences.\n- Diebold–Mariano, h=3: statistic 1.36, p = 0.18 (`diag_dm_stat_h3`, `diag_dm_pvalue_two_sided_h3`). There is no significant difference at h=3. The overlapping 3-month errors are handled through the HAC variance.\n- The main finding is robust in direction (the model does not beat naive). The size of the differences (≤ 2.4% of RMSE) is small compared with sampling variability over a 3-year window.\n\n## Limitations and open issues\n- Only one model specification was evaluated, and its specification was fixed in advance. Other AR variants might do better or worse; I did not explore them, to avoid choosing a model on the evaluation window.\n- The estimation sample includes the 2020 COVID spike (+10.4 pp in one month). This probably pulls the OLS AR coefficients towards zero, which makes the model nearly a random walk. A later estimation start or outlier dummies might change the results. I did not try these, because choosing them after seeing the results would be data snooping.\n- The evaluation uses the final-vintage data, so it says nothing about real-time performance.\n- The 2025-10 observation is missing. Each horizon therefore has one unevaluated pair, and the forecasts at nearby origins depend on the gap-filling rule.\n- The evaluation window is short (36 origins) and fairly calm (unemployment between about 3.4% and 4.5%), so the results may not hold in other periods.\n- The `diag_*` keys are extra diagnostics beyond the required schema.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At the 1-month horizon the AR(2)-on-differences model has higher RMSE than the naive forecast (relative improvement about -0.024) and higher MAE.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; one fixed model specification.\"},\n {\"id\":\"C2\",\"text\":\"At the 3-month horizon the AR model has slightly higher RMSE than naive (relative improvement about -0.008) and higher MAE.\",\"results_keys\":[\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping errors; difference not statistically significant (DM p=0.18).\"},\n {\"id\":\"C3\",\"text\":\"The simple AR model did not beat the naive forecast at either horizon over the 36 origins 2023-07 to 2026-06.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\",\"n_origins\",\"first_origin\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Pseudo-out-of-sample on revised data; short, calm window.\"},\n {\"id\":\"C4\",\"text\":\"The h=1 loss difference is nominally significant (HLN-corrected DM p about 0.016) while h=3 is not (p about 0.18).\",\"results_keys\":[\"diag_dm_stat_h1\",\"diag_dm_pvalue_two_sided_h1\",\"diag_dm_stat_h3\",\"diag_dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Forecast differences are hundredths of a point vs 0.1 pp data rounding; small sample; interpret cautiously.\"},\n {\"id\":\"C5\",\"text\":\"The estimated AR coefficients are small, so the model forecasts are close to a random walk.\",\"results_keys\":[\"diag_ar_coef_last_origin\",\"diag_ar_nobs_last_origin\"],\"status\":\"supported\",\"caveat\":\"Likely influenced by the 2020 COVID outliers in the estimation sample (not tested).\"},\n {\"id\":\"C6\",\"text\":\"One observation (2025-10-01) is missing, leaving 35 evaluated pairs per horizon out of 36 origins; 941 observations are in the last training window.\",\"results_keys\":[\"data_issues\",\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_analysed\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written with question, estimands, outputs, info rules, invalidity conditions\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, dates consecutive, no duplicates\",\"1 missing value (2025-10-01) identified and handling decided\",\"exclusions.csv sums: 945-4=941=n_analysed\"],\"notes\":\"final vintage -> no real-time claims\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed per info rule with same evaluation rule (rmse/mae_naive_h1/h3 in results.json)\",\"naive == last obs <= origin verified\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"deterministic, no RNG (random_seed null)\"],\"notes\":\"AR2_diff fixed a priori\"},\n \"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json and forecasts.csv\",\"12 consistency checks PASS (out/state/checks.md) incl. chronology, identities, look-ahead perturbation test\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, quantities cite results keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\n}\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));c=json.load(open('out/claims.json'));print(all(k in r for x in c for k in x['results_keys']))\"\necho \"- 2026-10-29 P2/P3 passed: baseline + AR2_diff computed; DM diagnostics added. P4 passed: reproducibility and 12 checks. P5 passed: report, claims written.\" >> out/state/log.md; ls out","description":"Write report, claims, phases; validate claim keys"}}]}}
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