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Work until the deliverables are complete, then stop."},"uuid":"fa4e18de-0cae-4fef-8e3c-8679ed78ae19","timestamp":"2026-10-30T06:00:50.918Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","shapeHash":"4e1d5bc1569d5f6ee7b21c934e6ec40908a758b852e5440cebf7a05bc2ef1968","timestamp":"2026-10-30T06:00:50.936Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RR0016\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. 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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\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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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 205 simon-pierreboucher staff 6560 oct. 30 02:00 ..\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":"ec76130e-d0d4-48c6-a2c9-355c348d3bda","timestamp":"2026-10-30T06:00:53.418Z","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{\n \"task_id\": \"F3-dev-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"n_evaluated_h1\": {\"type\": \"integer\", \"nullable\": false},\n \"n_evaluated_h3\": {\"type\": \"integer\", \"nullable\": false},\n \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"F3-dev-01\",\n \"family\": \"F3\",\n \"split\": \"dev\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:36:32.922288+00:00\",\n \"files\": {\n \"TASK.md\": \"833ca858b6ed80242f4f98d27c4d0171d9f2e96debc31a4662de6460fcd18351\",\n \"data/README.md\": \"62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1\",\n \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n \"gate_spec.json\": \"0c33840434296984e05d8ee43f34155234e15336eb7d7232262070a866c5f012\"\n }\n}62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1 data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c data/UNRATE.csv\n 946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n2023-01-01,3.5\n2023-02-01,3.6\n2023-03-01,3.5\n2023-04-01,3.4\n2023-05-01,3.6\n2023-06-01,3.6\n2023-07-01,3.5\n2023-08-01,3.7\n2023-09-01,3.7\n2023-10-01,3.9\n2023-11-01,3.7\n2023-12-01,3.8\n2024-01-01,3.7\n2024-02-01,3.9\n2024-03-01,3.9\n2024-04-01,3.9\n2024-05-01,3.9\n2024-06-01,4.1\n2024-07-01,4.2\n2024-08-01,4.2\n2024-09-01,4.1\n2024-10-01,4.1\n2024-11-01,4.2\n2024-12-01,4.1\n2025-01-01,4.0\n2025-02-01,4.2\n2025-03-01,4.2\n2025-04-01,4.2\n2025-05-01,4.3\n2025-06-01,4.1\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\n2025-10-01,\n2025-11-01,4.5\n2025-12-01,4.4\n2026-01-01,4.3\n2026-02-01,4.4\n2026-03-01,4.3\n2026-04-01,4.3\n2026-05-01,4.3\n2026-06-01,4.2\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n935:2025-10-01,","is_error":false}]},"uuid":"f074a135-2375-42a6-840f-6fdc4ea66546","timestamp":"2026-10-30T06:00:56.148Z","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{\n \"task_id\": \"F3-dev-01\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"n_evaluated_h1\": {\"type\": \"integer\", \"nullable\": false},\n \"n_evaluated_h3\": {\"type\": \"integer\", \"nullable\": false},\n \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n \"random_seed\": {\"type\": \"integer\", 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python3\n\"\"\"PGAR runtime gate (oracle-free). Standalone: standard library only, so that it runs with any Python ≥ 3.9.\n\nUsage: python pgar_gate.py <workspace_dir> [--python <interpreter>] [--no-repro] [--json]\nReads <workspace_dir>/gate_spec.json and checks the OUTPUT CONTRACT and REPRODUCIBILITY of the deliverables\nunder <workspace_dir>/out. It contains no reference answers and never judges scientific correctness.\nWrites a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n\"\"\"\nimport csv, datetime, json, math, os, re, shutil, subprocess, sys, tempfile, hashlib\n\nGATE_VERSION = \"pgar_gate/1.0\"\nNUMERIC_RE = r\"^#+\\s*\"\n\ndef _utc():\n return datetime.datetime.now(datetime.timezone.utc).strftime(\"%Y%m%dT%H%M%S.%fZ\")\n\ndef _load_json(path):\n try:\n with open(path) as fh:\n return json.load(fh), None\n except FileNotFoundError:\n return None, \"missing\"\n except Exception as e: # noqa: BLE001\n return None, f\"invalid JSON: {e}\"\n\ndef _is_num(v):\n return isinstance(v, (int, float)) and not isinstance(v, bool) and math.isfinite(v)\n\ndef _type_ok(v, t, nullable):\n if v is None:\n return bool(nullable)\n return {\"number\": _is_num(v), \"integer\": isinstance(v, int) and not isinstance(v, bool),\n \"string\": isinstance(v, str), \"boolean\": isinstance(v, bool), \"array\": isinstance(v, list),\n \"object\": isinstance(v, dict)}.get(t, True)\n\ndef _parse_date(s):\n try:\n return datetime.date.fromisoformat(str(s)[:10])\n except Exception: # noqa: BLE001\n return None\n\ndef _compare_results(a, b, rel):\n \"\"\"Return list of keys whose values differ beyond rel tolerance (numbers) or are unequal (others).\"\"\"\n diffs = []\n keys = set(a) | set(b)\n for k in keys:\n va, vb = a.get(k), b.get(k)\n if _is_num(va) and _is_num(vb):\n if abs(va - vb) > rel * max(1.0, abs(va), abs(vb)):\n diffs.append(k)\n elif isinstance(va, (list, dict)) or isinstance(vb, (list, dict)):\n if json.dumps(va, sort_keys=True) != json.dumps(vb, sort_keys=True):\n # allow numeric tolerance inside nested structures\n try:\n fa = [float(x) for x in re.findall(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", json.dumps(va))]\n fb = [float(x) for x in re.findall(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", json.dumps(vb))]\n if len(fa) != len(fb) or any(abs(x - y) > rel * max(1.0, abs(x), abs(y)) for x, y in zip(fa, fb)):\n diffs.append(k)\n elif re.sub(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", \"#\", json.dumps(va, sort_keys=True)) != re.sub(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", \"#\", json.dumps(vb, sort_keys=True)):\n diffs.append(k)\n except Exception: # noqa: BLE001\n diffs.append(k)\n elif va != vb:\n diffs.append(k)\n return sorted(diffs)\n\ndef run_gate(ws, python=None, do_repro=True):\n ws = os.path.abspath(ws)\n checks = []\n def add(cid, ok, detail=\"\"):\n checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n if spec is None:\n add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n return _finish(ws, checks)\n # 1. required files\n for rel in spec.get(\"required_files\", []):\n p = os.path.join(ws, rel)\n ok = os.path.isfile(p) and os.path.getsize(p) > 0\n add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n7:Writes a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n123: pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n127: add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n137: bad.append(f\"phases not terminal: {not_done}\")\n139: # 6. exclusion ledger\n140: ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141: if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153: add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155: add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n215: receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n218: d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221: json.dump(receipt, fh, indent=1)\n222: receipt[\"receipt_path\"] = path\n223: receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225: receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226: return receipt\n245: print(f\"receipt: {r.get('receipt_path')}\")","is_error":false}]},"uuid":"c66d79c9-4968-4977-a76f-2b72b1837bc7","timestamp":"2026-10-30T06:01:11.480Z","toolUseResult":{"stdout":"#!/usr/bin/env python3\n\"\"\"PGAR runtime gate (oracle-free). Standalone: standard library only, so that it runs with any Python ≥ 3.9.\n\nUsage: python pgar_gate.py <workspace_dir> [--python <interpreter>] [--no-repro] [--json]\nReads <workspace_dir>/gate_spec.json and checks the OUTPUT CONTRACT and REPRODUCIBILITY of the deliverables\nunder <workspace_dir>/out. It contains no reference answers and never judges scientific correctness.\nWrites a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n\"\"\"\nimport csv, datetime, json, math, os, re, shutil, subprocess, sys, tempfile, hashlib\n\nGATE_VERSION = \"pgar_gate/1.0\"\nNUMERIC_RE = r\"^#+\\s*\"\n\ndef _utc():\n return datetime.datetime.now(datetime.timezone.utc).strftime(\"%Y%m%dT%H%M%S.%fZ\")\n\ndef _load_json(path):\n try:\n with open(path) as fh:\n return json.load(fh), None\n except FileNotFoundError:\n return None, \"missing\"\n except Exception as e: # noqa: BLE001\n return None, f\"invalid JSON: {e}\"\n\ndef _is_num(v):\n return isinstance(v, (int, float)) and not isinstance(v, bool) and math.isfinite(v)\n\ndef _type_ok(v, t, nullable):\n if v is None:\n return bool(nullable)\n return {\"number\": _is_num(v), \"integer\": isinstance(v, int) and not isinstance(v, bool),\n \"string\": isinstance(v, str), \"boolean\": isinstance(v, bool), \"array\": isinstance(v, list),\n \"object\": isinstance(v, dict)}.get(t, True)\n\ndef _parse_date(s):\n try:\n return datetime.date.fromisoformat(str(s)[:10])\n except Exception: # noqa: BLE001\n return None\n\ndef _compare_results(a, b, rel):\n \"\"\"Return list of keys whose values differ beyond rel tolerance (numbers) or are unequal (others).\"\"\"\n diffs = []\n keys = set(a) | set(b)\n for k in keys:\n va, vb = a.get(k), b.get(k)\n if _is_num(va) and _is_num(vb):\n if abs(va - vb) > rel * max(1.0, abs(va), abs(vb)):\n diffs.append(k)\n elif isinstance(va, (list, dict)) or isinstance(vb, (list, dict)):\n if json.dumps(va, sort_keys=True) != json.dumps(vb, sort_keys=True):\n # allow numeric tolerance inside nested structures\n try:\n fa = [float(x) for x in re.findall(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", json.dumps(va))]\n fb = [float(x) for x in re.findall(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", json.dumps(vb))]\n if len(fa) != len(fb) or any(abs(x - y) > rel * max(1.0, abs(x), abs(y)) for x, y in zip(fa, fb)):\n diffs.append(k)\n elif re.sub(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", \"#\", json.dumps(va, sort_keys=True)) != re.sub(r\"-?\\d+\\.\\d+(?:e-?\\d+)?\", \"#\", json.dumps(vb, sort_keys=True)):\n diffs.append(k)\n except Exception: # noqa: BLE001\n diffs.append(k)\n elif va != vb:\n diffs.append(k)\n return sorted(diffs)\n\ndef run_gate(ws, python=None, do_repro=True):\n ws = os.path.abspath(ws)\n checks = []\n def add(cid, ok, detail=\"\"):\n checks.append({\"id\": cid, \"passed\": bool(ok), \"detail\": detail})\n spec, err = _load_json(os.path.join(ws, \"gate_spec.json\"))\n if spec is None:\n add(\"gate_spec\", False, f\"gate_spec.json {err}\")\n return _finish(ws, checks)\n # 1. required files\n for rel in spec.get(\"required_files\", []):\n p = os.path.join(ws, rel)\n ok = os.path.isfile(p) and os.path.getsize(p) > 0\n add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n7:Writes a receipt to <workspace_dir>/out/state/gate_receipts/<utc>.json and exits 0 if passed, 1 otherwise.\n123: pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n127: add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n137: bad.append(f\"phases not terminal: {not_done}\")\n139: # 6. exclusion ledger\n140: ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141: if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153: add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155: add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n215: receipt = {\"gate_version\": GATE_VERSION, \"workspace\": ws, \"utc\": datetime.datetime.now(datetime.timezone.utc).isoformat(),\n218: d = os.path.join(ws, \"out\", \"state\", \"gate_receipts\"); os.makedirs(d, exist_ok=True)\n221: json.dump(receipt, fh, indent=1)\n222: receipt[\"receipt_path\"] = path\n223: receipt[\"receipt_sha256\"] = hashlib.sha256(open(path, \"rb\").read()).hexdigest()\n225: receipt[\"receipt_path\"] = None; receipt[\"receipt_error\"] = str(e)\n226: return receipt\n245: print(f\"receipt: 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add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n # 2. results.json keys/types\n res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n if not isinstance(res, dict):\n add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n res = {}\n else:\n add(\"results_json\", True, \"parsed\")\n for k, rule in spec.get(\"results_keys\", {}).items():\n if k not in res:\n add(f\"key:{k}\", False, \"missing\")\n else:\n ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n # 3. report sections\n rep = \"\"\n try:\n rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n except Exception: # noqa: BLE001\n pass\n for s in spec.get(\"report_sections\", []):\n ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n fam = set(spec.get(\"family_checks\", []))\n # 4. claims\n claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n if not isinstance(claims, list) or not claims:\n add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n else:\n bad = []\n for i, c in enumerate(claims):\n if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n bad.append(f\"#{i}: missing fields\"); continue\n if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n if missing:\n bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n # 5. phase ledger\n pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n pl, perr = _load_json(pl_path)\n if not isinstance(pl, dict):\n add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n else:\n bad = []\n for pid, st in pl.items():\n if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n bad.append(f\"{pid}: invalid status\"); continue\n if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n bad.append(f\"{pid}: passed without recorded checks\")\n not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n if not_done:\n bad.append(f\"phases not terminal: {not_done}\")\n add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n # 6. exclusion ledger\n ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n try:\n rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n bad = []\n prev_after = None\n for i, r in enumerate(rows):\n nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n if nb - nr != na:\n bad.append(f\"row {i}: n_before - n_removed != n_after\")\n if prev_after is not None and nb != prev_after:\n bad.append(f\"row {i}: n_before != previous n_after\")\n prev_after = na\n add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n except Exception as e: # noqa: BLE001\n add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n # 7. numeric sanity checks\n if \"ci_contains_estimate\" in fam:\n for est, lo, hi in spec.get(\"ci_pairs\", []):\n e, l, h = res.get(est), res.get(lo), res.get(hi)\n if all(_is_num(v) for v in (e, l, h)):\n add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n elif e is None and l is None and h is None:\n add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n else:\n add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n if \"positive_se\" in fam:\n for k in spec.get(\"positive_keys\", []):\n v = res.get(k)\n add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n if \"chronology\" in fam:\n for a, b in spec.get(\"chronology_pairs\", []):\n da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n if \"forecasts_csv_chronology\" in fam:\n fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n try:\n rows = list(csv.DictReader(open(fp, newline=\"\")))\n bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n except Exception as e: # noqa: BLE001\n add(\"forecasts_chronology\", False, f\"forecasts.csv unreadable: {e}\")\n # 8. reproducibility\n if do_repro and os.path.isfile(os.path.join(ws, \"out\", \"analysis.py\")) and isinstance(res, dict) and res:\n py = python or sys.executable\n tmp = tempfile.mkdtemp(prefix=\"pgar_repro_\")\n try:\n shutil.copytree(os.path.join(ws, \"data\"), os.path.join(tmp, \"data\"))\n shutil.copytree(os.path.join(ws, \"out\"), os.path.join(tmp, \"out\"), ignore=shutil.ignore_patterns(\"results.json\", \"state\", \"review\"))\n for extra in (\"TASK.md\", \"gate_spec.json\"):\n if os.path.exists(os.path.join(ws, extra)):\n shutil.copy(os.path.join(ws, extra), tmp)\n t0 = datetime.datetime.now()\n proc = subprocess.run([py, \"out/analysis.py\"], cwd=tmp, capture_output=True, text=True, timeout=int(spec.get(\"analysis_timeout_s\", 300)))\n dur = (datetime.datetime.now() - t0).total_seconds()\n new, nerr = _load_json(os.path.join(tmp, \"out\", \"results.json\"))\n if proc.returncode != 0:\n add(\"reproducibility\", False, f\"analysis.py exit {proc.returncode} after {dur:.1f}s; stderr tail: {proc.stderr[-600:]}\")\n elif not isinstance(new, dict):\n add(\"reproducibility\", False, f\"analysis.py did not regenerate out/results.json ({nerr})\")\n else:\n diffs = _compare_results(res, new, float(spec.get(\"reproducibility_tolerance_rel\", 1e-6)))\n add(\"reproducibility\", not diffs, f\"regenerated in {dur:.1f}s; \" + (\"identical within tolerance\" if not diffs else f\"differences in keys: {diffs}\"))\n except subprocess.TimeoutExpired:\n add(\"reproducibility\", False, \"analysis.py timed out\")\n except Exception as e: # noqa: BLE001\n add(\"reproducibility\", False, f\"repro error: {e}\")\n finally:\n shutil.rmtree(tmp, ignore_errors=True)\n elif do_repro:\n add(\"reproducibility\", False, \"out/analysis.py or out/results.json missing\")\n return _finish(ws, checks)","is_error":false}]},"uuid":"000ff8f3-c74d-4742-adfa-233545992627","timestamp":"2026-10-30T06:01:13.104Z","toolUseResult":{"stdout":" add(f\"file:{rel}\", ok, \"present\" if ok else \"missing or empty\")\n # 2. results.json keys/types\n res, err = _load_json(os.path.join(ws, \"out\", \"results.json\"))\n if not isinstance(res, dict):\n add(\"results_json\", False, f\"out/results.json {err or 'is not an object'}\")\n res = {}\n else:\n add(\"results_json\", True, \"parsed\")\n for k, rule in spec.get(\"results_keys\", {}).items():\n if k not in res:\n add(f\"key:{k}\", False, \"missing\")\n else:\n ok = _type_ok(res[k], rule.get(\"type\"), rule.get(\"nullable\", False))\n add(f\"key:{k}\", ok, \"ok\" if ok else f\"type/nullability mismatch (expected {rule.get('type')}, nullable={rule.get('nullable', False)}, got {type(res[k]).__name__}={res[k]!r:.60})\")\n if spec.get(\"task_id\") and res.get(\"task_id\") not in (None, spec[\"task_id\"]):\n add(\"task_id_matches\", False, f\"results.task_id={res.get('task_id')!r} != {spec['task_id']!r}\")\n # 3. report sections\n rep = \"\"\n try:\n rep = open(os.path.join(ws, \"out\", \"report.md\"), encoding=\"utf-8\", errors=\"replace\").read()\n except Exception: # noqa: BLE001\n pass\n for s in spec.get(\"report_sections\", []):\n ok = re.search(NUMERIC_RE + re.escape(s) + r\"\\s*$\", rep, re.M | re.I) is not None\n add(f\"section:{s}\", ok, \"present\" if ok else \"heading not found\")\n fam = set(spec.get(\"family_checks\", []))\n # 4. claims\n claims, cerr = _load_json(os.path.join(ws, \"out\", \"claims.json\"))\n if \"claims_reference_existing_keys\" in fam or os.path.exists(os.path.join(ws, \"out\", \"claims.json\")):\n if not isinstance(claims, list) or not claims:\n add(\"claims_json\", False, f\"out/claims.json {cerr or 'must be a non-empty list'}\")\n else:\n bad = []\n for i, c in enumerate(claims):\n if not isinstance(c, dict) or not all(k in c for k in (\"id\", \"text\", \"results_keys\", \"status\")):\n bad.append(f\"#{i}: missing fields\"); continue\n if c[\"status\"] not in (\"supported\", \"unsupported\", \"not_evaluated\"):\n bad.append(f\"{c.get('id')}: bad status {c['status']!r}\")\n missing = [k for k in (c.get(\"results_keys\") or []) if k not in res]\n if missing:\n bad.append(f\"{c.get('id')}: results_keys not in results.json: {missing}\")\n add(\"claims_json\", not bad, \"; \".join(bad) if bad else f\"{len(claims)} claims, all keys resolve\")\n # 5. phase ledger\n pl_path = os.path.join(ws, \"out\", \"state\", \"phases.json\")\n if \"phase_ledger_consistent\" in fam and os.path.exists(pl_path):\n pl, perr = _load_json(pl_path)\n if not isinstance(pl, dict):\n add(\"phase_ledger\", False, f\"phases.json {perr or 'not an object'}\")\n else:\n bad = []\n for pid, st in pl.items():\n if not isinstance(st, dict) or st.get(\"status\") not in (\"pending\", \"running\", \"passed\", \"failed\", \"blocked\", \"stale\", \"skipped_optional\"):\n bad.append(f\"{pid}: invalid status\"); continue\n if st.get(\"status\") == \"passed\" and not st.get(\"checks\"):\n bad.append(f\"{pid}: passed without recorded checks\")\n not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n if not_done:\n bad.append(f\"phases not terminal: {not_done}\")\n add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n # 6. exclusion ledger\n ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n try:\n rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n bad = []\n prev_after = None\n for i, r in enumerate(rows):\n nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n if nb - nr != na:\n bad.append(f\"row {i}: n_before - n_removed != n_after\")\n if prev_after is not None and nb != prev_after:\n bad.append(f\"row {i}: n_before != previous n_after\")\n prev_after = na\n add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n except Exception as e: # noqa: BLE001\n add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n # 7. numeric sanity checks\n if \"ci_contains_estimate\" in fam:\n for est, lo, hi in spec.get(\"ci_pairs\", []):\n e, l, h = res.get(est), res.get(lo), res.get(hi)\n if all(_is_num(v) for v in (e, l, h)):\n add(f\"ci:{est}\", l <= e <= h, f\"{l} <= {e} <= {h}\")\n elif e is None and l is None and h is None:\n add(f\"ci:{est}\", True, \"all null (allowed if reported infeasible)\")\n else:\n add(f\"ci:{est}\", False, \"non-numeric CI triple\")\n if \"positive_se\" in fam:\n for k in spec.get(\"positive_keys\", []):\n v = res.get(k)\n add(f\"positive:{k}\", (v is None) or (_is_num(v) and v > 0), f\"{v}\")\n if \"chronology\" in fam:\n for a, b in spec.get(\"chronology_pairs\", []):\n da, db = _parse_date(res.get(a)), _parse_date(res.get(b))\n add(f\"chronology:{a}<={b}\", da is not None and db is not None and da <= db, f\"{res.get(a)} vs {res.get(b)}\")\n if \"forecasts_csv_chronology\" in fam:\n fp = os.path.join(ws, \"out\", \"forecasts.csv\")\n try:\n rows = list(csv.DictReader(open(fp, newline=\"\")))\n bad = sum(1 for r in rows if not (_parse_date(r.get(\"origin_date\")) and _parse_date(r.get(\"target_date\")) and _parse_date(r[\"origin_date\"]) < _parse_date(r[\"target_date\"])))\n add(\"forecasts_chronology\", rows and bad == 0, f\"{bad} rows with target_date <= origin_date out of {len(rows)}\")\n except Exception as e: # noqa: BLE001\n add(\"forecasts_chronology\", False, f\"forecasts.csv unreadable: {e}\")\n # 8. reproducibility\n if do_repro and os.path.isfile(os.path.join(ws, \"out\", \"analysis.py\")) and isinstance(res, dict) and res:\n py = python or sys.executable\n tmp = tempfile.mkdtemp(prefix=\"pgar_repro_\")\n try:\n shutil.copytree(os.path.join(ws, \"data\"), os.path.join(tmp, \"data\"))\n shutil.copytree(os.path.join(ws, \"out\"), os.path.join(tmp, \"out\"), ignore=shutil.ignore_patterns(\"results.json\", \"state\", \"review\"))\n for extra in (\"TASK.md\", \"gate_spec.json\"):\n if os.path.exists(os.path.join(ws, extra)):\n shutil.copy(os.path.join(ws, extra), tmp)\n t0 = datetime.datetime.now()\n proc = subprocess.run([py, \"out/analysis.py\"], cwd=tmp, capture_output=True, text=True, timeout=int(spec.get(\"analysis_timeout_s\", 300)))\n dur = (datetime.datetime.now() - t0).total_seconds()\n new, nerr = _load_json(os.path.join(tmp, \"out\", \"results.json\"))\n if proc.returncode != 0:\n add(\"reproducibility\", False, f\"analysis.py exit {proc.returncode} after {dur:.1f}s; stderr tail: {proc.stderr[-600:]}\")\n elif not isinstance(new, dict):\n add(\"reproducibility\", False, f\"analysis.py did not regenerate out/results.json ({nerr})\")\n else:\n diffs = _compare_results(res, new, float(spec.get(\"reproducibility_tolerance_rel\", 1e-6)))\n add(\"reproducibility\", not diffs, f\"regenerated in {dur:.1f}s; \" + (\"identical within tolerance\" if not diffs else f\"differences in keys: {diffs}\"))\n except subprocess.TimeoutExpired:\n add(\"reproducibility\", False, \"analysis.py timed out\")\n except Exception as e: # noqa: BLE001\n add(\"reproducibility\", False, f\"repro error: {e}\")\n finally:\n shutil.rmtree(tmp, ignore_errors=True)\n elif do_repro:\n add(\"reproducibility\", False, \"out/analysis.py or out/results.json missing\")\n return _finish(ws, 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out/state\ncat > contract.md <<'EOF'\n# Contract (P0) — F3-dev-01\n\n**Question.** Over 36 monthly expanding-window forecast origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast the US unemployment rate (FRED UNRATE, %, SA, final vintage) better than the naive no-change forecast at horizons h=1 and h=3 months?\n\n**Estimands.** Per horizon h∈{1,3}: RMSE and MAE (percentage points) of (a) naive forecast, (b) one chosen model, over (origin,horizon) pairs whose target value is non-missing; and 1 − RMSE_model/RMSE_naive. Meaningful as pseudo-out-of-sample accuracy on the *revised* series (not real-time), over a short 3-year window (≈35 pairs per horizon, overlapping errors at h=3), so sampling uncertainty is large.\n\n**Information rules.** Forecast at origin t uses only observations dated ≤ t, incl. model selection, estimation, transformations. Naive = latest non-missing obs ≤ t. Target = t+h. Missing actual → row kept in ledger with empty `actual`, excluded from stats; report n evaluated per horizon. No real-time claims.\n\n**Outputs.** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json with 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; plus protocol files (exclusions.csv, state/*).\n\n**Invalidity / impossibility.** Using data after the origin (look-ahead), choosing the model based on test-period performance, missing input, fewer than enough targets to evaluate. Data file present and complete except one known missing month, so task is feasible.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 Decision (made before computing any test-period errors): model = AR(2) on first differences of UNRATE with intercept, OLS, estimation sample all available data from 1948-01 up to origin; iterated multi-step forecasts, cumulated to levels. No model selection on the test window. Rationale: standard simple benchmark; pre-specified to avoid test-set snooping.\nEOF","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3370,"cache_read_input_tokens":12689,"output_tokens":1412,"output_tokens_details":{"thinking_tokens":213},"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":3370},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXutJZcRTomKTG2ADHa7","requestRef":"85d359cd-fb99-4bae-97d0-4732d693f5ca","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b6e33fae-14b5-465a-b30e-188126d692eb","timestamp":"2026-10-30T06:01:27.016Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"b6e33fae-14b5-465a-b30e-188126d692eb","isSidechain":false,"promptId":"4757ebb4-cd56-4fc6-a09a-7c216bb898c2","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AJ77oU2FmKKkUZzvkQvp4C","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"3ec0493f-990a-4380-9257-dbe163253688","timestamp":"2026-10-30T06:01:27.068Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b6e33fae-14b5-465a-b30e-188126d692eb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016/out/state","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","hash":"a59492096a2cf89811be507d66c67f5119da52e63040315932cdec45944892f3","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSlQoKEAgTGAI4AUIIdGhpbmtpbmcSDKkigHiBMIlUjvSVYRoMmqjrFw4+WJQVdkIEIjAYONmOYmS0BVbulb1xWDO/FoEtwnUObwQEM7tb7I5rlFX9VxtuGfr4vrICuedHlZgqsgn9pEln5uV1D7Zntwe5NUDls6DJjTaqgWBkX+5ueEVS42zs9x9WiYSJR0HOeVA2rJS3xvs1kh1A268xr/43+KgbY02Dw1fFu5P2skW3l7zGKkMCwub9JZKXHECJvirDhEqOuQPYeXV5kCLDsDrmJZNJKvA8Qwk3qsrEUyOBBLbZ4Vk+qt0W4o2ovsVjJjEGa8E3OG49+NRgIZ7w+UjW8zYdIA81MxU1HFomKLg6UV0B0wUXOWfrCpOUougR1soN5gkbyLSuK22KKUicKrOLmNeyCl1yJ+xoMBULcSm81LNXya/0VuG2ii6JFkLXQgBbSZ8BNwfUj5bi59BwdjjXKMuQVAe7Z3ZrszrB1A6LGpB2qyOYLz1M0T8ynvMeN6v+DfTGH4NAFvJZEcqk5nFEcNdJenW5Nj0jEOGhm/OQQqbaaj1dTBsW9oELBu3NuN0FmESO0hMycz1PZbODyjZ91EZ0Lga0LbRLWwzwcOMzRbx8RCyhsotiTbDbXQMBWq3RMq/RWZ0DbsWirnlkcTcr5Ke3eDVk+MijKmjGf6nBMytPDc1RczHHQOdDKwpKf5sVlUTAgiWXkgESyOyb3Tmc/MdunYQaPBTc7DuWsuRNJR5aQDPW6EFamJ1qUj4Bf2oLkhHJggckTAx1uFzOCwkbeZWc8sA3/0iXE6caRvCJCtLzl8AAmybsDFm4kULcwS0VbERz4D06yZzisrGFX00H/E9Xk5oT85eptzA1SYro1hFqR5xpq2iPxp5QCPYBO/wkDYvYI4Z6Yanh1SbuMTdN8QaoVzIUBdi67Wn0xJs64lk/M7sYzVSF3woDZxJOJcQXgklaL06OV1bB+tZP92JP5UNn/zEheAfImRbjSHXZy4zWHZ2WIExGrD7mly7JWJxSi/8aHJ5Q7XK2tkJWxb+63GL0VEL+y3Fo5CPPUpui/Fhj5g+O9V9TfUy5oFjlnPiduMhVZDMAlQrTcaRN87ePUDpb9oQQuDJSjnqtxQ508dR+eWv3AFojxbc98ixQqdFxdHIta5up4Gvsaa+gnBMQtOGiteqNZbzO1kdP2r3Hu393o/pUGVhl5Wf0TZNjdMW6XVRCy9YuiG5Hd3WEny7eQmcbk+Mivc8i4sznUjiXrKwKXzSZKrAEXHoQiwNI/JNoafj6Bu/89DSF1O7soMKaDcz5ddUnh8mrMsmUHSDxCpgBkz1Lwo2E9C1DOyC8K76T/K+llB3OGgMo311dD/WqC6vD7Kn5JbAI5cGkYNE3xaZmBRxPr2czSbFWy2hvV04kBIljWaU3fwnkRSv3B8P7b5R55DIMZZEXhmcTgr1C2sn7BGGrk9xwWPcyAOMc++062+0d2XPBua3LX5bInpozvpB7EdZBwUb3dI+dTixXUtwRWcpeqpZYl43hVIiMuvB6HJS29P1ZxFHZoh3dEbTqj0CloO/Gv0Z8atTb+3R0jeSPDlT5Cm1VKSiS2q+yU8I7bkkHZqs3eyj3tJzUdW8MnGKr0cuAueps20jifB3UyCiEXTjLpDNcTSp4gyEOOaSndRY0oeCGwA6nCpRreAf6Mxwyr7eu0YHFQx4cW23kK/5ug+t6oMjT3y3w60FVtLyLC8G9bRgB"},{"type":"tool_use","id":"toolu_01AJ77oU2FmKKkUZzvkQvp4C","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract (P0) — F3-dev-01\n\n**Question.** Over 36 monthly expanding-window forecast origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast the US unemployment rate (FRED UNRATE, %, SA, final vintage) better than the naive no-change forecast at horizons h=1 and h=3 months?\n\n**Estimands.** Per horizon h∈{1,3}: RMSE and MAE (percentage points) of (a) naive forecast, (b) one chosen model, over (origin,horizon) pairs whose target value is non-missing; and 1 − RMSE_model/RMSE_naive. 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No real-time claims.\n\n**Outputs.** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json with 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; plus protocol files (exclusions.csv, state/*).\n\n**Invalidity / impossibility.** Using data after the origin (look-ahead), choosing the model based on test-period performance, missing input, fewer than enough targets to evaluate. Data file present and complete except one known missing month, so task is feasible.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 Decision (made before computing any test-period errors): model = AR(2) on first differences of UNRATE with intercept, OLS, estimation sample all available data from 1948-01 up to origin; iterated multi-step forecasts, cumulated to levels. No model selection on the test window. 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No model selection on the test window. Rationale: standard simple benchmark; pre-specified to avoid test-set snooping.\n(945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n1948-01-01 2026-09-01\nmissing 1 observation_date UNRATE\n933 2025-10-01 NaN\ndup dates 0\ngaps 0\ncount 944.000000\nmean 5.652966\nstd 1.703534\nmin 2.500000\n25% 4.300000\n50% 5.450000\n75% 6.700000\nmax 14.800000\nName: UNRATE, dtype: float64\n observation_date UNRATE\n867 2020-04-01 14.8\n868 2020-05-01 13.2\n869 2020-06-01 11.0\n871 2020-08-01 8.4","is_error":false}]},"uuid":"8b670437-6b6c-4f9a-8122-60b1535d0c5c","timestamp":"2026-10-30T06:01:37.376Z","toolUseResult":{"stdout":"state\n\nout/state:\ncontract.md\nlog.md\nphases.json\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 Decision (made before computing any test-period errors): model = AR(2) on first differences of UNRATE with intercept, OLS, estimation sample all available data from 1948-01 up to origin; iterated multi-step forecasts, cumulated to levels. No model selection on the test window. 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README and bundle.json) | 945 | 2 |\n| data/README.md | 62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1 | – | – |\n\n- Schema: `observation_date` (string YYYY-MM-01), `UNRATE` (float, percent of civilian labour force, SA).\n- Date range 1948-01-01 to 2026-09-01; 945 consecutive months, no gaps, no duplicate dates.\n- Missing values: 1 — 2025-10-01 (empty field). (Consistent with the Oct-2025 CPS not being collected; we do not rely on this explanation.)\n- Range 2.5–14.8; mean 5.65. Anomalies: COVID-19 spike, 2020-04 = 14.8 (+10.4 pp from 2020-03), followed by large falls in 2020-05..08. These are genuine observations, kept (no outlier removal; decision made a priori).\n- Information availability: the file is the final vintage (revised data); evaluation therefore is pseudo-out-of-sample on revised data, not real-time. Data after each origin are masked in code at every origin. No input violates the rules beyond this documented vintage caveat.\n\n## Consequences of the missing month for the design\n- Target 2025-10 is missing for (origin 2025-09, h=1) and (origin 2025-07, h=3) → those pairs kept in ledger with empty actual, excluded from stats → expected 35 evaluated pairs per horizon.\n- Origin 2025-10 itself has no observation: naive uses 2025-09 (4.4); model iterates from 2025-09 with h+1 steps.\n- Origins 2025-11 and 2025-12: the AR(2)-in-differences state needs the 2025-10 level; it is linearly interpolated from 2025-09 and 2025-11 (both ≤ origin) for the state vector only. Regression rows touching the missing month are dropped from estimation.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 Session resumed after interruption; P1 audit done (one missing month 2025-10, COVID spike kept). Missing-value handling decided (see data_audit.md).\nEOF","description":"Write data audit and log 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README and bundle.json) | 945 | 2 |\n| data/README.md | 62a5a815ca9b63bd6f91fdb176f2e1cecc2faa8ca816c45277b02e8ffd9a70c1 | – | – |\n\n- Schema: `observation_date` (string YYYY-MM-01), `UNRATE` (float, percent of civilian labour force, SA).\n- Date range 1948-01-01 to 2026-09-01; 945 consecutive months, no gaps, no duplicate dates.\n- Missing values: 1 — 2025-10-01 (empty field). 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No input violates the rules beyond this documented vintage caveat.\n\n## Consequences of the missing month for the design\n- Target 2025-10 is missing for (origin 2025-09, h=1) and (origin 2025-07, h=3) → those pairs kept in ledger with empty actual, excluded from stats → expected 35 evaluated pairs per horizon.\n- Origin 2025-10 itself has no observation: naive uses 2025-09 (4.4); model iterates from 2025-09 with h+1 steps.\n- Origins 2025-11 and 2025-12: the AR(2)-in-differences state needs the 2025-10 level; it is linearly interpolated from 2025-09 and 2025-11 (both ≤ origin) for the state vector only. Regression rows touching the missing month are dropped from estimation.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 Session resumed after interruption; P1 audit done (one missing month 2025-10, COVID spike kept). 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Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nP = 2 # AR order on first differences\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)\ns = pd.Series(raw[\"UNRATE\"].values, index=pd.to_datetime(raw[\"observation_date\"]), dtype=float)\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nassert len(pd.date_range(s.index[0], s.index[-1], freq=\"MS\")) == len(s)\n\n\ndef fit_ar_diff(y):\n \"\"\"OLS of d_t on const, d_{t-1..t-P}; rows with any NaN dropped. y: level series up to origin.\"\"\"\n d = y.diff()\n X = pd.concat([d.shift(k) for k in range(1, P + 1)], axis=1)\n df = pd.concat([d, X], axis=1).dropna()\n Y = df.iloc[:, 0].values\n Xm = np.column_stack([np.ones(len(df)), df.iloc[:, 1:].values])\n beta, *_ = np.linalg.lstsq(Xm, Y, rcond=None)\n return beta, len(df)\n\n\ndef forecast_model(y, origin, h):\n \"\"\"Forecast level at origin+h using only y (already truncated to <= origin).\"\"\"\n beta, nreg = fit_ar_diff(y)\n last_obs = y.last_valid_index()\n # state: interior missing values interpolated from data <= origin (state only, not training)\n ys = y.loc[:last_obs].interpolate(method=\"linear\", limit_area=\"inside\")\n steps = h + (len(pd.date_range(last_obs, origin, freq=\"MS\")) - 1) # extra steps if origin missing\n level = ys.iloc[-1]\n dl = list(ys.diff().iloc[-P:].values[::-1]) # most recent first\n for _ in range(steps):\n dn = beta[0] + sum(beta[k + 1] * dl[k] for k in range(P))\n level += dn\n dl = [dn] + dl[:-1]\n return level, nreg\n\n\nrows = []\nfor t in ORIGINS:\n y = s.loc[:t] # information set: observations dated <= t\n naive = y.dropna().iloc[-1]\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n actual = s.get(tgt, np.nan)\n fm, nreg = forecast_model(y, t, h)\n for m, f in ((\"naive\", naive), (MODEL_NAME, fm)):\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), 10),\n actual=(None if pd.isna(actual) else float(actual)), model=m))\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False)\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, estimated by OLS on all available \"\n \"observations from 1948-01 up to each origin (expanding window; rows touching missing \"\n \"values dropped), iterated h-step forecasts of differences cumulated onto the last \"\n \"observed level.\"),\n \"n_origins\": int(len(ORIGINS)), \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\")}\nextra = {}\nfor h in HORIZONS:\n sub = fc[(fc.horizon == h) & fc.actual.notna()]\n en = (sub[sub.model == \"naive\"].set_index(\"origin_date\").pipe(lambda d: d.actual - d.forecast))\n em = (sub[sub.model == MODEL_NAME].set_index(\"origin_date\").pipe(lambda d: d.actual - d.forecast))\n assert (en.index == em.index).all()\n n = len(en)\n res[f\"n_evaluated_h{h}\"] = int(n)\n for name, e in ((\"naive\", en), (\"model\", em)):\n res[f\"rmse_{name}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{name}_h{h}\"] = float(np.mean(np.abs(e)))\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n # Diebold-Mariano (squared-error loss), rectangular HAC with h-1 lags, Harvey-Leybourne-Newbold correction.\n # Note: with a missing pair the series has a gap; treated as consecutive (approximation, documented).\n dlt = (em.values ** 2 - en.values ** 2)\n dbar = dlt.mean(); dc = dlt - dbar\n gam = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n var = (gam[0] + 2 * sum(gam[1:])) / n\n if var <= 0:\n var = gam[0] / n\n dm = dbar / np.sqrt(var)\n hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n extra[f\"dm_stat_h{h}\"] = float(hln)\n extra[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(hln), df=n - 1))\n extra[f\"mean_error_naive_h{h}\"] = float(en.mean())\n extra[f\"mean_error_model_h{h}\"] = float(em.mean())\n\n# exclusion ledger (observation level, last training window)\nn_nonmiss = int(s.notna().sum())\nlast_window = s.loc[:ORIGINS[-1]]\nn_analysed = int(last_window.notna().sum())\n_, nreg_last = fit_ar_diff(last_window)\nex = pd.DataFrame([\n dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n_nonmiss,\n n_removed=n_raw - n_nonmiss, rationale=\"empty value field (2025-10-01) cannot be used for estimation\"),\n dict(step=2, rule=\"drop observations dated after last origin 2026-06-01\", n_before=n_nonmiss,\n n_after=n_analysed, n_removed=n_nonmiss - n_analysed,\n rationale=\"information rule: last training window uses only data <= last origin (later months are evaluation targets only)\"),\n])\nex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\nres.update({\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"missing observation(s) at {missing_dates}\",\n \"handling\": (\"target pairs with missing actual kept in ledger with empty actual and excluded from error \"\n \"statistics; origin 2025-10 uses latest earlier observation (naive) / iterates one extra step \"\n \"(model); for later origins the missing level is linearly interpolated from data <= origin for \"\n \"the AR state only; regression rows touching it are dropped\")},\n {\"issue\": \"COVID-19 outliers (2020-04 = 14.8%, large swings 2020-04..2020-08) in the estimation sample\",\n \"handling\": \"kept (no outlier treatment, decided a priori); they influence OLS coefficients - see report\"},\n {\"issue\": \"final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"results describe pseudo-out-of-sample accuracy on revised data; no real-time claim\"},\n ],\n \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n \"n_analysed\": n_analysed,\n \"n_regression_rows_last_window\": int(nreg_last),\n \"random_seed\": None,\n})\nres.update(extra)\nbeta_last, _ = fit_ar_diff(last_window)\nres[\"ar_coefficients_last_window\"] = {\"const\": float(beta_last[0]), \"phi1\": float(beta_last[1]), \"phi2\": float(beta_last[2])}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=2)\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} model={res[f'rmse_model_h{h}']:.4f} \"\n f\"rel_impr={res[f'rel_improvement_rmse_h{h}']:.4f} DM={res[f'dm_stat_h{h}']:.3f} p={res[f'dm_pvalue_two_sided_h{h}']:.3f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.csv && wc -l out/forecasts.csv && grep -E '2025-(0[789]|1[012])|2026-01' 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":1352,"cache_read_input_tokens":18754,"output_tokens":3602,"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":1352},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXuw7TTtbGkhNUCproGQ","requestRef":"bb653b21-b77d-4f87-84f0-bc0d347ba05c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b2ed450b-4210-4be8-99bd-bf33fc78e20f","timestamp":"2026-10-30T06:02:20.079Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}65{"type":"last-prompt","lastPrompt":"Your previous session was interrupted by the controller (an infrastructure event, not an error in your work). Continue the task described in `TASK.md` from the current state of the workspace and stop…","leafUuid":"b2ed450b-4210-4be8-99bd-bf33fc78e20f","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}66{"type":"mode","mode":"normal","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}67{"type":"atis-latch","atis":"","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}68{"parentUuid":"b2ed450b-4210-4be8-99bd-bf33fc78e20f","isSidechain":false,"promptId":"ffb08a3a-b6a6-45ae-98ed-30596df983e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015roG73hK7ReSvrJEVZ8EUL","type":"tool_result","content":"h=1: n=35 RMSE naive=0.1171 model=0.1199 rel_impr=-0.0235 DM=2.504 p=0.017\nh=3: n=35 RMSE naive=0.1586 model=0.1598 rel_impr=-0.0076 DM=1.358 p=0.183\n{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on first differences of UNRATE, estimated by OLS on all available observations from 1948-01 up to each origin (expanding window; rows touching missing values dropped), iterated h-step forecasts of differences cumulated onto the last observed level.\",\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\": 0.11985758088999837,\n \"mae_model_h1\": 0.09565582574285725,\n \"rel_improvement_rmse_h1\": -0.023478941922360663,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.1597703235567206,\n \"mae_model_h3\": 0.12128483319714281,\n \"rel_improvement_rmse_h3\": -0.007601489434682085,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"missing observation(s) at ['2025-10-01']\",\n \"handling\": \"target pairs with missing actual kept in ledger with empty actual and excluded from error statistics; origin 2025-10 uses latest earlier observation (naive) / iterates one extra step (model); for later origins the missing level is linearly interpolated from data <= origin for the AR state only; regression rows touching it are dropped\"\n },\n {\n \"issue\": \"COVID-19 outliers (2020-04 = 14.8%, large swings 2020-04..2020-08) in the estimation sample\",\n \"handling\": \"kept (no outlier treatment, decided a priori); they influence OLS coefficients - see report\"\n },\n {\n \"issue\": \"final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"results describe pseudo-out-of-sample accuracy on revised data; no real-time claim\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"n_regression_rows_last_window\": 935,\n \"random_seed\": null,\n \"dm_stat_h1\": 2.503616278597123,\n \"dm_pvalue_two_sided_h1\": 0.017257584613991104,\n \"mean_error_naive_h1\": 0.017142857142857133,\n \"mean_error_model_h1\": 0.017253188068571377,\n \"dm_stat_h3\": 1.3581793932123778,\n \"dm_pvalue_two_sided_h3\": 0.18335154844780102,\n \"mean_error_naive_h3\": 0.03999999999999996,\n \"mean_error_model_h3\": 0.04062121473999998,\n \"ar_coefficients_last_window\": {\n \"const\": 0.0003368050557942884,\n \"phi1\": 0.036584777952152876,\n \"phi2\": -0.04220948335502803\n }\n}step,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,empty value field (2025-10-01) cannot be used for estimation\n2,drop observations dated after last origin 2026-06-01,944,941,3,information rule: last training window uses only data <= last origin (later months are evaluation targets only)\n 145 out/forecasts.csv\n2025-04-01,2025-07-01,3,4.2,4.3,naive\n2025-04-01,2025-07-01,3,4.2007186609,4.3,AR2_diff\n2025-05-01,2025-08-01,3,4.3,4.3,naive\n2025-05-01,2025-08-01,3,4.3003338148,4.3,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\n2025-06-01,2025-09-01,3,4.0975903731,4.4,AR2_diff\n2025-07-01,2025-08-01,1,4.3,4.3,naive\n2025-07-01,2025-08-01,1,4.3161782781,4.3,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3080386634,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.2918625663,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.2925799425,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4041231552,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.4006471591,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.4005029707,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.4012565592,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.5001757858,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4989051502,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.394667901,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.3999869927,4.3,AR2_diff\n2026-01-01,2026-02-01,1,4.3,4.4,naive\n2026-01-01,2026-02-01,1,4.3010021243,4.4,AR2_diff\n2026-01-01,2026-04-01,3,4.3,4.3,naive\n2026-01-01,2026-04-01,3,4.3062948617,4.3,AR2_diff","is_error":false}]},"uuid":"0c9cdacd-3883-4405-8016-f010a4bdc7cc","timestamp":"2026-10-30T06:02:21.010Z","toolUseResult":{"stdout":"h=1: n=35 RMSE naive=0.1171 model=0.1199 rel_impr=-0.0235 DM=2.504 p=0.017\nh=3: n=35 RMSE naive=0.1586 model=0.1598 rel_impr=-0.0076 DM=1.358 p=0.183\n{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"model_description\": \"AR(2) with intercept on first differences of UNRATE, estimated by OLS on all available observations from 1948-01 up to each origin (expanding window; rows touching missing values dropped), iterated h-step forecasts of differences cumulated onto the last observed level.\",\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\": 0.11985758088999837,\n \"mae_model_h1\": 0.09565582574285725,\n \"rel_improvement_rmse_h1\": -0.023478941922360663,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.1597703235567206,\n \"mae_model_h3\": 0.12128483319714281,\n \"rel_improvement_rmse_h3\": -0.007601489434682085,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"missing observation(s) at ['2025-10-01']\",\n \"handling\": \"target pairs with missing actual kept in ledger with empty actual and excluded from error statistics; origin 2025-10 uses latest earlier observation (naive) / iterates one extra step (model); for later origins the missing level is linearly interpolated from data <= origin for the AR state only; regression rows touching it are dropped\"\n },\n {\n \"issue\": \"COVID-19 outliers (2020-04 = 14.8%, large swings 2020-04..2020-08) in the estimation sample\",\n \"handling\": \"kept (no outlier treatment, decided a priori); they influence OLS coefficients - see report\"\n },\n {\n \"issue\": \"final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"results describe pseudo-out-of-sample accuracy on revised data; no real-time claim\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"n_regression_rows_last_window\": 935,\n \"random_seed\": null,\n \"dm_stat_h1\": 2.503616278597123,\n \"dm_pvalue_two_sided_h1\": 0.017257584613991104,\n \"mean_error_naive_h1\": 0.017142857142857133,\n \"mean_error_model_h1\": 0.017253188068571377,\n \"dm_stat_h3\": 1.3581793932123778,\n \"dm_pvalue_two_sided_h3\": 0.18335154844780102,\n \"mean_error_naive_h3\": 0.03999999999999996,\n \"mean_error_model_h3\": 0.04062121473999998,\n \"ar_coefficients_last_window\": {\n \"const\": 0.0003368050557942884,\n \"phi1\": 0.036584777952152876,\n \"phi2\": -0.04220948335502803\n }\n}step,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,empty value field (2025-10-01) cannot be used for estimation\n2,drop observations dated after last origin 2026-06-01,944,941,3,information rule: last training window uses only data <= last origin (later months are evaluation targets only)\n 145 out/forecasts.csv\n2025-04-01,2025-07-01,3,4.2,4.3,naive\n2025-04-01,2025-07-01,3,4.2007186609,4.3,AR2_diff\n2025-05-01,2025-08-01,3,4.3,4.3,naive\n2025-05-01,2025-08-01,3,4.3003338148,4.3,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\n2025-06-01,2025-09-01,3,4.0975903731,4.4,AR2_diff\n2025-07-01,2025-08-01,1,4.3,4.3,naive\n2025-07-01,2025-08-01,1,4.3161782781,4.3,AR2_diff\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.3080386634,,AR2_diff\n2025-08-01,2025-09-01,1,4.3,4.4,naive\n2025-08-01,2025-09-01,1,4.2918625663,4.4,AR2_diff\n2025-08-01,2025-11-01,3,4.3,4.5,naive\n2025-08-01,2025-11-01,3,4.2925799425,4.5,AR2_diff\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.4041231552,,AR2_diff\n2025-09-01,2025-12-01,3,4.4,4.4,naive\n2025-09-01,2025-12-01,3,4.4006471591,4.4,AR2_diff\n2025-10-01,2025-11-01,1,4.4,4.5,naive\n2025-10-01,2025-11-01,1,4.4005029707,4.5,AR2_diff\n2025-10-01,2026-01-01,3,4.4,4.3,naive\n2025-10-01,2026-01-01,3,4.4012565592,4.3,AR2_diff\n2025-11-01,2025-12-01,1,4.5,4.4,naive\n2025-11-01,2025-12-01,1,4.5001757858,4.4,AR2_diff\n2025-11-01,2026-02-01,3,4.5,4.4,naive\n2025-11-01,2026-02-01,3,4.4989051502,4.4,AR2_diff\n2025-12-01,2026-01-01,1,4.4,4.3,naive\n2025-12-01,2026-01-01,1,4.394667901,4.3,AR2_diff\n2025-12-01,2026-03-01,3,4.4,4.3,naive\n2025-12-01,2026-03-01,3,4.3999869927,4.3,AR2_diff\n2026-01-01,2026-02-01,1,4.3,4.4,naive\n2026-01-01,2026-02-01,1,4.3010021243,4.4,AR2_diff\n2026-01-01,2026-04-01,3,4.3,4.3,naive\n2026-01-01,2026-04-01,3,4.3062948617,4.3,AR2_diff","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b2ed450b-4210-4be8-99bd-bf33fc78e20f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}69{"type":"api-request-blob","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","hash":"6b75e6ff4d3517e7ff4b718a8c720b09ef77474359e860c8fc9dda11f4f883a4","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_015roG73hK7ReSvrJEVZ8EUL","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, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"\nOUT = \"out\"\nP = 2 # AR order on first differences\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)\ns = pd.Series(raw[\"UNRATE\"].values, index=pd.to_datetime(raw[\"observation_date\"]), dtype=float)\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\nassert len(pd.date_range(s.index[0], s.index[-1], freq=\"MS\")) == len(s)\n\n\ndef fit_ar_diff(y):\n \"\"\"OLS of d_t on const, d_{t-1..t-P}; rows with any NaN dropped. y: level series up to origin.\"\"\"\n d = y.diff()\n X = pd.concat([d.shift(k) for k in range(1, P + 1)], axis=1)\n df = pd.concat([d, X], axis=1).dropna()\n Y = df.iloc[:, 0].values\n Xm = np.column_stack([np.ones(len(df)), df.iloc[:, 1:].values])\n beta, *_ = np.linalg.lstsq(Xm, Y, rcond=None)\n return beta, len(df)\n\n\ndef forecast_model(y, origin, h):\n \"\"\"Forecast level at origin+h using only y (already truncated to <= origin).\"\"\"\n beta, nreg = fit_ar_diff(y)\n last_obs = y.last_valid_index()\n # state: interior missing values interpolated from data <= origin (state only, not training)\n ys = y.loc[:last_obs].interpolate(method=\"linear\", limit_area=\"inside\")\n steps = h + (len(pd.date_range(last_obs, origin, freq=\"MS\")) - 1) # extra steps if origin missing\n level = ys.iloc[-1]\n dl = list(ys.diff().iloc[-P:].values[::-1]) # most recent first\n for _ in range(steps):\n dn = beta[0] + sum(beta[k + 1] * dl[k] for k in range(P))\n level += dn\n dl = [dn] + dl[:-1]\n return level, nreg\n\n\nrows = []\nfor t in ORIGINS:\n y = s.loc[:t] # information set: observations dated <= t\n naive = y.dropna().iloc[-1]\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=h)\n actual = s.get(tgt, np.nan)\n fm, nreg = forecast_model(y, t, h)\n for m, f in ((\"naive\", naive), (MODEL_NAME, fm)):\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), 10),\n actual=(None if pd.isna(actual) else float(actual)), model=m))\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False)\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n \"model_description\": (\"AR(2) with intercept on first differences of UNRATE, estimated by OLS on all available \"\n \"observations from 1948-01 up to each origin (expanding window; rows touching missing \"\n \"values dropped), iterated h-step forecasts of differences cumulated onto the last \"\n \"observed level.\"),\n \"n_origins\": int(len(ORIGINS)), \"first_origin\": ORIGINS[0].strftime(\"%Y-%m-%d\"),\n \"last_origin\": ORIGINS[-1].strftime(\"%Y-%m-%d\")}\nextra = {}\nfor h in HORIZONS:\n sub = fc[(fc.horizon == h) & fc.actual.notna()]\n en = (sub[sub.model == \"naive\"].set_index(\"origin_date\").pipe(lambda d: d.actual - d.forecast))\n em = (sub[sub.model == MODEL_NAME].set_index(\"origin_date\").pipe(lambda d: d.actual - d.forecast))\n assert (en.index == em.index).all()\n n = len(en)\n res[f\"n_evaluated_h{h}\"] = int(n)\n for name, e in ((\"naive\", en), (\"model\", em)):\n res[f\"rmse_{name}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{name}_h{h}\"] = float(np.mean(np.abs(e)))\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n # Diebold-Mariano (squared-error loss), rectangular HAC with h-1 lags, Harvey-Leybourne-Newbold correction.\n # Note: with a missing pair the series has a gap; treated as consecutive (approximation, documented).\n dlt = (em.values ** 2 - en.values ** 2)\n dbar = dlt.mean(); dc = dlt - dbar\n gam = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n var = (gam[0] + 2 * sum(gam[1:])) / n\n if var <= 0:\n var = gam[0] / n\n dm = dbar / np.sqrt(var)\n hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n extra[f\"dm_stat_h{h}\"] = float(hln)\n extra[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * stats.t.sf(abs(hln), df=n - 1))\n extra[f\"mean_error_naive_h{h}\"] = float(en.mean())\n extra[f\"mean_error_model_h{h}\"] = float(em.mean())\n\n# exclusion ledger (observation level, last training window)\nn_nonmiss = int(s.notna().sum())\nlast_window = s.loc[:ORIGINS[-1]]\nn_analysed = int(last_window.notna().sum())\n_, nreg_last = fit_ar_diff(last_window)\nex = pd.DataFrame([\n dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n_nonmiss,\n n_removed=n_raw - n_nonmiss, rationale=\"empty value field (2025-10-01) cannot be used for estimation\"),\n dict(step=2, rule=\"drop observations dated after last origin 2026-06-01\", n_before=n_nonmiss,\n n_after=n_analysed, n_removed=n_nonmiss - n_analysed,\n rationale=\"information rule: last training window uses only data <= last origin (later months are evaluation targets only)\"),\n])\nex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in s.index[s.isna()]]\nres.update({\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"missing observation(s) at {missing_dates}\",\n \"handling\": (\"target pairs with missing actual kept in ledger with empty actual and excluded from error \"\n \"statistics; origin 2025-10 uses latest earlier observation (naive) / iterates one extra step \"\n \"(model); for later origins the missing level is linearly interpolated from data <= origin for \"\n \"the AR state only; regression rows touching it are dropped\")},\n {\"issue\": \"COVID-19 outliers (2020-04 = 14.8%, large swings 2020-04..2020-08) in the estimation sample\",\n \"handling\": \"kept (no outlier treatment, decided a priori); they influence OLS coefficients - see report\"},\n {\"issue\": \"final-vintage (revised) data, not real-time vintages\",\n \"handling\": \"results describe pseudo-out-of-sample accuracy on revised data; no real-time claim\"},\n ],\n \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n \"n_analysed\": n_analysed,\n \"n_regression_rows_last_window\": int(nreg_last),\n \"random_seed\": None,\n})\nres.update(extra)\nbeta_last, _ = fit_ar_diff(last_window)\nres[\"ar_coefficients_last_window\"] = {\"const\": float(beta_last[0]), \"phi1\": float(beta_last[1]), \"phi2\": float(beta_last[2])}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=2)\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} model={res[f'rmse_model_h{h}']:.4f} \"\n f\"rel_impr={res[f'rel_improvement_rmse_h{h}']:.4f} DM={res[f'dm_stat_h{h}']:.3f} p={res[f'dm_pvalue_two_sided_h{h}']:.3f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.csv && wc -l out/forecasts.csv && grep -E '2025-(0[789]|1[012])|2026-01' out/forecasts.csv","description":"Write and run analysis 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exact\",ok))\nc.append((\"actuals match data\", all((pd.isna(a) and pd.isna(s[t])) or a==s[t] for a,t in zip(f.actual,f.target_date))))\nnv=f[f.model=='naive']; ok=all(v==s.loc[:o].dropna().iloc[-1] for v,o in zip(nv.forecast,nv.origin_date)); c.append((\"naive = last obs <= origin\",ok))\nfor h in (1,3):\n sub=f[(f.horizon==h)&f.actual.notna()]\n c.append((f\"n_eval h{h}={len(sub)//2}\", len(sub)//2==r[f'n_evaluated_h{h}']))\n for m,k in (('naive','naive'),('AR2_diff','model')):\n e=sub[sub.model==m].eval('actual-forecast'); c.append((f\"rmse/mae {k} h{h} recomputed\", np.isclose(np.sqrt((e**2).mean()),r[f'rmse_{k}_h{h}']) and np.isclose(e.abs().mean(),r[f'mae_{k}_h{h}'])))\n c.append((f\"rel impr h{h}\", np.isclose(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}'], r[f'rel_improvement_rmse_h{h}'])))\n c.append((f\"mae<=rmse h{h}\", r[f'mae_naive_h{h}']<=r[f'rmse_naive_h{h}'] and r[f'mae_model_h{h}']<=r[f'rmse_model_h{h}']))\nc.append((\"exclusions: 945-1-3=941=n_analysed\", 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144 rows\nPASS 36 origins\nPASS target>origin all\nPASS target=origin+h\nPASS target=origin+h exact\nPASS actuals match data\nPASS naive = last obs <= origin\nPASS n_eval h1=35\nPASS rmse/mae naive h1 recomputed\nPASS rmse/mae model h1 recomputed\nPASS rel impr h1\nPASS mae<=rmse h1\nPASS n_eval h3=35\nPASS rmse/mae naive h3 recomputed\nPASS rmse/mae model h3 recomputed\nPASS rel impr h3\nPASS mae<=rmse h3\nPASS exclusions: 945-1-3=941=n_analysed\nPASS model forecasts within 0.05pp of naive (magnitude sanity)","is_error":false}]},"uuid":"d207916c-bf15-41c2-90fa-3dd188dae325","timestamp":"2026-10-30T06:02:35.569Z","toolUseResult":{"stdout":"IDENTICAL\n0.016367718600000192\nPASS 144 rows\nPASS 36 origins\nPASS target>origin all\nPASS target=origin+h\nPASS target=origin+h exact\nPASS actuals match data\nPASS naive = last obs <= origin\nPASS n_eval h1=35\nPASS rmse/mae naive h1 recomputed\nPASS rmse/mae model h1 recomputed\nPASS rel impr h1\nPASS mae<=rmse h1\nPASS n_eval h3=35\nPASS rmse/mae naive h3 recomputed\nPASS rmse/mae model h3 recomputed\nPASS rel impr h3\nPASS mae<=rmse h3\nPASS exclusions: 945-1-3=941=n_analysed\nPASS model forecasts within 0.05pp of naive (magnitude sanity)","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6139dc82-d8aa-4583-8199-2bc48e72af2d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}74{"type":"api-request-blob","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","hash":"f8305a9b4795dc58c136b6c6da8a3d1baa8b13069236ccd5b16f5d19ef1f0999","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_018AiLGrPETigU9VP2K6Tun8","name":"Bash","input":{"command":"cp 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> out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n\n| check | outcome |\n|---|---|\n| Clean re-run (results.json, forecasts.csv, exclusions.csv deleted, analysis.py re-run): byte-identical results.json and forecasts.csv | PASS |\n| forecasts.csv has 144 rows, 36 origins × 2 horizons × 2 models | PASS |\n| target_date = origin_date + h months exactly; target > origin for all rows | PASS |\n| `actual` column equals data value at target (empty iff data missing: 2 pairs, target 2025-10-01) | PASS |\n| naive forecast = latest non-missing observation ≤ origin (incl. origin 2025-10 → 2025-09 value) | PASS |\n| n_evaluated_h1 = n_evaluated_h3 = 35 recomputed from ledger | PASS |\n| RMSE/MAE for both models and both horizons recomputed independently from forecasts.csv | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive recomputed | PASS |\n| MAE ≤ RMSE for every model/horizon; all RMSE > 0 | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| No look-ahead: setting every observation after the origin to 99 leaves model forecasts unchanged (origins 2023-07, 2025-10, 2026-06; h=1,3) | PASS |\n| first_origin ≤ last_origin | PASS |\n| Magnitude vs baseline: model forecasts differ from naive by at most 0.016 pp (AR coefficients ≈ 0.04, −0.04), consistent with RMSEs within 2.4% of naive | PASS (noted: model is nearly a random walk) |\n| data/UNRATE.csv SHA-256 unchanged | PASS |\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, no gaps/duplicates\",\"1 missing value (2025-10-01) documented\",\"COVID outliers documented\",\"exclusions.csv sums verified (945-4=941)\"],\"notes\":\"see data_audit.md\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed under same evaluation rule: rmse_naive_h1, rmse_naive_h3, mae_naive_*\",\"naive = last obs <= origin verified\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\",\"AR2_diff pre-specified before test errors seen (log.md)\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"internal consistency checks all PASS (checks.md)\",\"look-ahead perturbation test PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P1-P3 passed; results computed (AR2_diff slightly worse than naive). 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target > origin for all rows | PASS |\n| `actual` column equals data value at target (empty iff data missing: 2 pairs, target 2025-10-01) | PASS |\n| naive forecast = latest non-missing observation ≤ origin (incl. origin 2025-10 → 2025-09 value) | PASS |\n| n_evaluated_h1 = n_evaluated_h3 = 35 recomputed from ledger | PASS |\n| RMSE/MAE for both models and both horizons recomputed independently from forecasts.csv | PASS |\n| rel_improvement = 1 − rmse_model/rmse_naive recomputed | PASS |\n| MAE ≤ RMSE for every model/horizon; all RMSE > 0 | PASS |\n| Exclusion ledger: 945 − 1 − 3 = 941 = n_analysed | PASS |\n| No look-ahead: setting every observation after the origin to 99 leaves model forecasts unchanged (origins 2023-07, 2025-10, 2026-06; h=1,3) | PASS |\n| first_origin ≤ last_origin | PASS |\n| Magnitude vs baseline: model forecasts differ from naive by at most 0.016 pp (AR coefficients ≈ 0.04, −0.04), consistent with RMSEs within 2.4% of naive | PASS (noted: model is nearly a random walk) |\n| data/UNRATE.csv SHA-256 unchanged | PASS |\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, no gaps/duplicates\",\"1 missing value (2025-10-01) documented\",\"COVID outliers documented\",\"exclusions.csv sums verified (945-4=941)\"],\"notes\":\"see data_audit.md\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"naive baseline computed under same evaluation rule: rmse_naive_h1, rmse_naive_h3, mae_naive_*\",\"naive = last obs <= origin verified\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG (random_seed null)\",\"AR2_diff pre-specified before test errors seen (log.md)\"],\"notes\":\"\"},\n \"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"internal consistency checks all PASS (checks.md)\",\"look-ahead perturbation test PASS\"],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P1-P3 passed; 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Each window grows with the origin (expanding window). I measure accuracy with RMSE and MAE in percentage points, and the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` (SHA-256 621ff6fa…a06c, which matches the README) has 945 monthly rows from 1948-01 to 2026-09 (`n_input_rows`). There are no gaps or duplicate dates. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). Two (origin, horizon) pairs target that month: (2025-09, h=1) and (2025-07, h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. That leaves 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 raw rows, minus 1 missing value, minus 3 observations dated after the last origin, gives 941 observations in the last training window (`n_analysed`). That window yields 935 usable regression rows (`n_regression_rows_last_window`).\n- The 2020 COVID observations (14.8% in 2020-04) are real data points. I kept them, a choice made before any forecasts were evaluated.\n\n## Method\n- **Naive:** the forecast is the latest non-missing observation dated ≤ origin. For origin 2025-10, whose own value is missing, this is the 2025-09 value.\n- **Model `AR2_diff`** (`model_name`, `model_description`): an AR(2) with intercept on monthly first differences, fitted by OLS. At each origin it is re-estimated on all observations from 1948-01 up to that origin. Regression rows that involve the missing month are dropped. Forecasts are iterated h steps ahead and the predicted differences are added to the last observed level. For origin 2025-10, the model starts from 2025-09 and runs h+1 steps. For origins 2025-11 and 2025-12, the missing 2025-10 level needed for the lag state is filled by linear interpolation between 2025-09 and 2025-11. Both of those dates are ≤ the origin, so no future information is used.\n- I fixed the specification in `out/state/log.md` before computing any test-period errors and did no model selection on the test window. The method uses no randomness (`random_seed` = null).\n- Every transformation and estimate uses only data dated ≤ origin. A perturbation test confirmed this: setting all post-origin data to 99 left the forecasts unchanged (`out/state/checks.md`).\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 (`rmse_naive_h1`) | 0.1199 (`rmse_model_h1`) | 0.0914 (`mae_naive_h1`) | 0.0957 (`mae_model_h1`) | −0.0235 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1598 (`rmse_model_h3`) | 0.1200 (`mae_naive_h3`) | 0.1213 (`mae_model_h3`) | −0.0076 (`rel_improvement_rmse_h3`) |\n\n- The AR(2) model did **not** beat the naive forecast. Its RMSE was about 2.3% higher at h=1 and 0.8% higher at h=3 (`rel_improvement_rmse_h1`, `rel_improvement_rmse_h3`), and its MAE was also higher at both horizons.\n- The two forecasts are almost identical: they never differ by more than 0.016 pp (`out/state/checks.md`). In the last window the AR coefficients are close to zero (φ1 = 0.037, φ2 = −0.042, `ar_coefficients_last_window`), so the fitted model is essentially a random walk.\n- Both methods tended to under-forecast slightly over this period, when unemployment was drifting up. Mean error (actual − forecast) was about +0.017 pp at h=1 and +0.04 pp at h=3 for both (`mean_error_naive_h1`, `mean_error_model_h1`, `mean_error_naive_h3`, `mean_error_model_h3`).\n\n## Uncertainty\n- I ran a Diebold–Mariano test on squared-error loss, with HAC variance using h−1 lags and the Harvey–Leybourne–Newbold small-sample correction (t reference with n−1 df).\n - h=1: statistic = 2.50, two-sided p = 0.017 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`). The model's loss is higher, but the RMSE difference is tiny (0.003 pp).\n - h=3: statistic = 1.36, p = 0.18 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`).\n- These p-values are only indicative, for three reasons:\n - The AR model nests the random walk, so the standard DM test is not correctly sized. A Clark–West-type adjustment would be more suitable, and I did not run it.\n - There are only 35 evaluation pairs, and the h=3 errors overlap.\n - The pairs on either side of the missing month were treated as consecutive.\n- The data support \"no improvement over naive in this window\". They do not support any claim about a practically meaningful difference.\n\n## Limitations and open issues\n- **Final-vintage data:** the errors are pseudo-out-of-sample on revised data. They are not real-time performance, and I make no claim about real-time validity.\n- **Short window:** the evaluation covers only three years (35 pairs per horizon) of fairly calm, slowly rising unemployment. The result may not carry over to recessions or turning points.\n- **COVID observations:** these values were kept in the estimation sample. A probable reason the AR coefficients are near zero is that OLS is dominated by the 2020 swings, which would flatten the model into a random walk. I did not test this; a sensitivity check (a later sample start or outlier dummies) was not run because the task asks for one pre-specified model.\n- **Missing 2025-10 value:** two pairs are unevaluated. The interpolated lag state for origins 2025-11 and 2025-12 is an assumption, but it affects the model forecast at most marginally.\n- Only one model was evaluated, as the task specifies. The conclusion is about this AR(2)-on-differences specification, not about all time-series models.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 35 evaluated origins at h=1, AR2_diff had higher RMSE (0.1199) than the naive forecast (0.1171); relative improvement -0.0235.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"Final-vintage data; short window.\"},\n {\"id\":\"C2\",\"text\":\"At h=3, AR2_diff had slightly higher RMSE (0.1598) than naive (0.1586); relative improvement -0.0076.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping h=3 errors; 35 pairs.\"},\n {\"id\":\"C3\",\"text\":\"MAE of AR2_diff also exceeded naive MAE at both horizons.\",\"results_keys\":[\"mae_model_h1\",\"mae_naive_h1\",\"mae_model_h3\",\"mae_naive_h3\"],\"status\":\"supported\",\"caveat\":\"Differences are a few thousandths of a percentage point.\"},\n {\"id\":\"C4\",\"text\":\"The fitted AR coefficients are near zero, making the model nearly a random walk.\",\"results_keys\":[\"ar_coefficients_last_window\"],\"status\":\"supported\",\"caveat\":\"Attribution to COVID outliers is a hypothesis, not tested.\"},\n {\"id\":\"C5\",\"text\":\"The DM test indicates higher model loss at h=1 (p=0.017) but not at h=3 (p=0.18); p-values are only indicative because the models are nested and the sample is small.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_two_sided_h1\",\"dm_stat_h3\",\"dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Standard DM test is mis-sized for nested models; Clark-West not run.\"},\n {\"id\":\"C6\",\"text\":\"Two (origin,horizon) pairs targeting the missing 2025-10 month are excluded, leaving 35 evaluated pairs per horizon; 941 observations in the last training window.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_analysed\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -15","description":"Write report, claims; run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1285,"cache_read_input_tokens":28647,"output_tokens":3393,"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":1285},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXv1eVNFPTWvpuNchWsK","requestRef":"607ba579-c93e-4137-8e15-930f865267f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c8681fc5-1f36-4ecb-bebc-3ff0c38cf620","timestamp":"2026-10-30T06:03:20.980Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}91{"type":"last-prompt","lastPrompt":"Your previous session was interrupted by the controller (an infrastructure event, not an error in your work). Continue the task described in `TASK.md` from the current state of the workspace and stop…","leafUuid":"c8681fc5-1f36-4ecb-bebc-3ff0c38cf620","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}92{"type":"mode","mode":"normal","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}93{"type":"atis-latch","atis":"","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc"}94{"parentUuid":"c8681fc5-1f36-4ecb-bebc-3ff0c38cf620","isSidechain":false,"promptId":"ffb08a3a-b6a6-45ae-98ed-30596df983e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JMAUiFCBWPz1x1EjGCjB1Q","type":"tool_result","content":" [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 6 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] chronology:first_origin<=last_origin: 2023-07-01 vs 2026-06-01\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0016/out/state/gate_receipts/20261030T060321.751925Z.json","is_error":false}]},"uuid":"afd5cbb0-eaf1-4438-ad45-f1084b51d49e","timestamp":"2026-10-30T06:03:21.760Z","toolUseResult":{"stdout":" [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 6 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 steps consistent\n [ok] chronology:first_origin<=last_origin: 2023-07-01 vs 2026-06-01\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0016/out/state/gate_receipts/20261030T060321.751925Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c8681fc5-1f36-4ecb-bebc-3ff0c38cf620","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}95{"type":"api-request-blob","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","hash":"e03429074db355e0d30ccd82445e30e71132393a5b952e7fe15a444446a5acbc","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims, then the gate."},{"type":"tool_use","id":"toolu_01JMAUiFCBWPz1x1EjGCjB1Q","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Can AR(2) on differences beat the naive forecast of US unemployment?\n\n## Question\nOver 36 monthly forecast origins from 2023-07-01 to 2026-06-01 (`first_origin`, `last_origin`, `n_origins`), does a simple autoregressive model forecast the US civilian unemployment rate (FRED UNRATE, %, SA) better than the naive no-change forecast at 1- and 3-month horizons? Each window grows with the origin (expanding window). I measure accuracy with RMSE and MAE in percentage points, and the relative improvement 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` (SHA-256 621ff6fa…a06c, which matches the README) has 945 monthly rows from 1948-01 to 2026-09 (`n_input_rows`). There are no gaps or duplicate dates. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). Two (origin, horizon) pairs target that month: (2025-09, h=1) and (2025-07, h=3). Both stay in `out/forecasts.csv` with an empty `actual` and are left out of the error statistics. That leaves 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- Exclusion ledger (`out/exclusions.csv`): 945 raw rows, minus 1 missing value, minus 3 observations dated after the last origin, gives 941 observations in the last training window (`n_analysed`). That window yields 935 usable regression rows (`n_regression_rows_last_window`).\n- The 2020 COVID observations (14.8% in 2020-04) are real data points. I kept them, a choice made before any forecasts were evaluated.\n\n## Method\n- **Naive:** the forecast is the latest non-missing observation dated ≤ origin. For origin 2025-10, whose own value is missing, this is the 2025-09 value.\n- **Model `AR2_diff`** (`model_name`, `model_description`): an AR(2) with intercept on monthly first differences, fitted by OLS. At each origin it is re-estimated on all observations from 1948-01 up to that origin. Regression rows that involve the missing month are dropped. Forecasts are iterated h steps ahead and the predicted differences are added to the last observed level. For origin 2025-10, the model starts from 2025-09 and runs h+1 steps. For origins 2025-11 and 2025-12, the missing 2025-10 level needed for the lag state is filled by linear interpolation between 2025-09 and 2025-11. Both of those dates are ≤ the origin, so no future information is used.\n- I fixed the specification in `out/state/log.md` before computing any test-period errors and did no model selection on the test window. The method uses no randomness (`random_seed` = null).\n- Every transformation and estimate uses only data dated ≤ origin. A perturbation test confirmed this: setting all post-origin data to 99 left the forecasts unchanged (`out/state/checks.md`).\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 (`rmse_naive_h1`) | 0.1199 (`rmse_model_h1`) | 0.0914 (`mae_naive_h1`) | 0.0957 (`mae_model_h1`) | −0.0235 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1598 (`rmse_model_h3`) | 0.1200 (`mae_naive_h3`) | 0.1213 (`mae_model_h3`) | −0.0076 (`rel_improvement_rmse_h3`) |\n\n- The AR(2) model did **not** beat the naive forecast. Its RMSE was about 2.3% higher at h=1 and 0.8% higher at h=3 (`rel_improvement_rmse_h1`, `rel_improvement_rmse_h3`), and its MAE was also higher at both horizons.\n- The two forecasts are almost identical: they never differ by more than 0.016 pp (`out/state/checks.md`). In the last window the AR coefficients are close to zero (φ1 = 0.037, φ2 = −0.042, `ar_coefficients_last_window`), so the fitted model is essentially a random walk.\n- Both methods tended to under-forecast slightly over this period, when unemployment was drifting up. Mean error (actual − forecast) was about +0.017 pp at h=1 and +0.04 pp at h=3 for both (`mean_error_naive_h1`, `mean_error_model_h1`, `mean_error_naive_h3`, `mean_error_model_h3`).\n\n## Uncertainty\n- I ran a Diebold–Mariano test on squared-error loss, with HAC variance using h−1 lags and the Harvey–Leybourne–Newbold small-sample correction (t reference with n−1 df).\n - h=1: statistic = 2.50, two-sided p = 0.017 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`). The model's loss is higher, but the RMSE difference is tiny (0.003 pp).\n - h=3: statistic = 1.36, p = 0.18 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`).\n- These p-values are only indicative, for three reasons:\n - The AR model nests the random walk, so the standard DM test is not correctly sized. A Clark–West-type adjustment would be more suitable, and I did not run it.\n - There are only 35 evaluation pairs, and the h=3 errors overlap.\n - The pairs on either side of the missing month were treated as consecutive.\n- The data support \"no improvement over naive in this window\". They do not support any claim about a practically meaningful difference.\n\n## Limitations and open issues\n- **Final-vintage data:** the errors are pseudo-out-of-sample on revised data. They are not real-time performance, and I make no claim about real-time validity.\n- **Short window:** the evaluation covers only three years (35 pairs per horizon) of fairly calm, slowly rising unemployment. The result may not carry over to recessions or turning points.\n- **COVID observations:** these values were kept in the estimation sample. A probable reason the AR coefficients are near zero is that OLS is dominated by the 2020 swings, which would flatten the model into a random walk. I did not test this; a sensitivity check (a later sample start or outlier dummies) was not run because the task asks for one pre-specified model.\n- **Missing 2025-10 value:** two pairs are unevaluated. The interpolated lag state for origins 2025-11 and 2025-12 is an assumption, but it affects the model forecast at most marginally.\n- Only one model was evaluated, as the task specifies. The conclusion is about this AR(2)-on-differences specification, not about all time-series models.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 35 evaluated origins at h=1, AR2_diff had higher RMSE (0.1199) than the naive forecast (0.1171); relative improvement -0.0235.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"Final-vintage data; short window.\"},\n {\"id\":\"C2\",\"text\":\"At h=3, AR2_diff had slightly higher RMSE (0.1598) than naive (0.1586); relative improvement -0.0076.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping h=3 errors; 35 pairs.\"},\n {\"id\":\"C3\",\"text\":\"MAE of AR2_diff also exceeded naive MAE at both horizons.\",\"results_keys\":[\"mae_model_h1\",\"mae_naive_h1\",\"mae_model_h3\",\"mae_naive_h3\"],\"status\":\"supported\",\"caveat\":\"Differences are a few thousandths of a percentage point.\"},\n {\"id\":\"C4\",\"text\":\"The fitted AR coefficients are near zero, making the model nearly a random walk.\",\"results_keys\":[\"ar_coefficients_last_window\"],\"status\":\"supported\",\"caveat\":\"Attribution to COVID outliers is a hypothesis, not tested.\"},\n {\"id\":\"C5\",\"text\":\"The DM test indicates higher model loss at h=1 (p=0.017) but not at h=3 (p=0.18); p-values are only indicative because the models are nested and the sample is small.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_two_sided_h1\",\"dm_stat_h3\",\"dm_pvalue_two_sided_h3\"],\"status\":\"supported\",\"caveat\":\"Standard DM test is mis-sized for nested models; Clark-West not run.\"},\n {\"id\":\"C6\",\"text\":\"Two (origin,horizon) pairs targeting the missing 2025-10 month are excluded, leaving 35 evaluated pairs per horizon; 941 observations in the last training window.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_analysed\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -15","description":"Write report, claims; run gate"}}]}}96{"type":"api-request","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","id":"55575ea5-1841-4ade-9c28-04cf14aebec0","timestamp":"2026-10-30T06:03:21.763Z","version":"2.1.314","querySource":"sdk","shapeHash":"4e1d5bc1569d5f6ee7b21c934e6ec40908a758b852e5440cebf7a05bc2ef1968","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":"607ba579-c93e-4137-8e15-930f865267f7","keep":28,"tail":["e03429074db355e0d30ccd82445e30e71132393a5b952e7fe15a444446a5acbc","6e334e3d2617d2c4294fa90d935f9c4f3d277d3bf88e7656d93bb0e459853fe0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e28e7d0f67fdb9e082949442e86ac83a20c6b416e21111cf1c91a022760df5e9"}97{"parentUuid":"afd5cbb0-eaf1-4438-ad45-f1084b51d49e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXv3o57tbfbEoPWLXP89","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":3733,"cache_read_input_tokens":29932,"output_tokens":721,"output_tokens_details":{"thinking_tokens":45},"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":3733},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":596,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXv3neZhu5foejebFSG9","requestRef":"55575ea5-1841-4ade-9c28-04cf14aebec0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"330dddfb-88a0-4a63-b9bc-7ec09d7473a5","timestamp":"2026-10-30T06:03:23.576Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}98{"parentUuid":"330dddfb-88a0-4a63-b9bc-7ec09d7473a5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXv3o57tbfbEoPWLXP89","type":"message","role":"assistant","content":[{"type":"text","text":"The only failure is the phase ledger, which is expected because P4 and P5 are still open. Next I'll mark both phases complete and run the gate again."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3733,"cache_read_input_tokens":29932,"output_tokens":721,"output_tokens_details":{"thinking_tokens":45},"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":3733},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXv3neZhu5foejebFSG9","requestRef":"55575ea5-1841-4ade-9c28-04cf14aebec0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"97d95abd-72b4-46d0-a3d8-16219322dc94","timestamp":"2026-10-30T06:03:23.577Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}99{"parentUuid":"97d95abd-72b4-46d0-a3d8-16219322dc94","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXv3o57tbfbEoPWLXP89","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018jwVWLg3rXDXkYqT7jcp74","name":"Bash","input":{"command":"R1=$(ls out/state/gate_receipts | tail -1)\n~/wp29/venv/bin/python - <<EOF\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":p['P4']['checks']+[\"gate run end of P4: receipt $R1 - all contract/repro checks ok; only failure was phase_ledger non-terminal (P4/P5 open), resolved by this update\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with all 6 sections, every number cites a results key\",\"claims.json 6 claims, keys resolve\",\"final gate run recorded in log.md / gate_receipts\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | grep -E \"FAIL|passed|receipt\"; R2=$(ls out/state/gate_receipts | tail -1)\n~/wp29/venv/bin/python - <<EOF\nimport json\nr=json.load(open('out/state/gate_receipts/$R2')); print('passed',r['passed'])\np=json.load(open('out/state/phases.json')); p['P5']['checks'].append(\"gate run end of P5: receipt $R2 passed=%s\"%r['passed']); json.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate $R1), P5 passed; final gate $R2. Task complete.\" >> out/state/log.md","description":"Finalize phases and rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3733,"cache_read_input_tokens":29932,"output_tokens":721,"output_tokens_details":{"thinking_tokens":45},"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":3733},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXv3neZhu5foejebFSG9","requestRef":"55575ea5-1841-4ade-9c28-04cf14aebec0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2dff0362-0ffd-4c8e-9fe4-74c3acca7e4a","timestamp":"2026-10-30T06:03:28.168Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}100{"parentUuid":"2dff0362-0ffd-4c8e-9fe4-74c3acca7e4a","isSidechain":false,"promptId":"ffb08a3a-b6a6-45ae-98ed-30596df983e8","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018jwVWLg3rXDXkYqT7jcp74","type":"tool_result","content":"receipt: /Users/simon-pierreboucher/wp29/runs/RR0016/out/state/gate_receipts/20261030T060328.958349Z.json\npassed True","is_error":false}]},"uuid":"a1cf4f55-5624-4556-bf4e-e248d5063adf","timestamp":"2026-10-30T06:03:28.995Z","toolUseResult":{"stdout":"receipt: /Users/simon-pierreboucher/wp29/runs/RR0016/out/state/gate_receipts/20261030T060328.958349Z.json\npassed True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2dff0362-0ffd-4c8e-9fe4-74c3acca7e4a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}101{"type":"api-request-blob","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","hash":"c5f72d471385eecc014aec600806cfadcf48799e5db84e42facd8c4d6c5f335f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The only failure is the phase ledger, which is expected because P4 and P5 are still open. Next I'll mark both phases complete and run the gate again."},{"type":"tool_use","id":"toolu_018jwVWLg3rXDXkYqT7jcp74","name":"Bash","input":{"command":"R1=$(ls out/state/gate_receipts | tail -1)\n~/wp29/venv/bin/python - <<EOF\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":p['P4']['checks']+[\"gate run end of P4: receipt $R1 - all contract/repro checks ok; only failure was phase_ledger non-terminal (P4/P5 open), resolved by this update\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with all 6 sections, every number cites a results key\",\"claims.json 6 claims, keys resolve\",\"final gate run recorded in log.md / gate_receipts\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | grep -E \"FAIL|passed|receipt\"; R2=$(ls out/state/gate_receipts | tail -1)\n~/wp29/venv/bin/python - <<EOF\nimport json\nr=json.load(open('out/state/gate_receipts/$R2')); print('passed',r['passed'])\np=json.load(open('out/state/phases.json')); p['P5']['checks'].append(\"gate run end of P5: receipt $R2 passed=%s\"%r['passed']); json.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate $R1), P5 passed; final gate $R2. Task complete.\" >> out/state/log.md","description":"Finalize phases and rerun gate"}}]}}102{"type":"api-request","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","id":"c0131aeb-7382-46b9-a3d0-3de259d7fe73","timestamp":"2026-10-30T06:03:28.999Z","version":"2.1.314","querySource":"sdk","shapeHash":"4e1d5bc1569d5f6ee7b21c934e6ec40908a758b852e5440cebf7a05bc2ef1968","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":"55575ea5-1841-4ade-9c28-04cf14aebec0","keep":30,"tail":["c5f72d471385eecc014aec600806cfadcf48799e5db84e42facd8c4d6c5f335f","bf9a00bb7e01a492476ad21507353e168d373cc8c7ff91fedd29dd867c8f3b91"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",31,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"551869ee33682831f60f3eb14710362b0b34cd671171058da420d19114e1e53d"}103{"parentUuid":"a1cf4f55-5624-4556-bf4e-e248d5063adf","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXv4KrHbrqvznHse4FMM","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":784,"cache_read_input_tokens":33665,"output_tokens":933,"output_tokens_details":{"thinking_tokens":36},"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":784},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6172,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXv4KafgEmWmh5J8M8fg","requestRef":"c0131aeb-7382-46b9-a3d0-3de259d7fe73","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c07809b4-3086-42b8-a7e2-11e01ba5062d","timestamp":"2026-10-30T06:03:36.293Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}104{"parentUuid":"c07809b4-3086-42b8-a7e2-11e01ba5062d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXv4KrHbrqvznHse4FMM","type":"message","role":"assistant","content":[{"type":"text","text":"The simple model did not beat the naive forecast: it was slightly worse at both the 1-month and 3-month horizons. The final gate run passed, and a clean re-run of `out/analysis.py` reproduces `out/results.json` exactly.\n\nThe model is an AR(2) with an intercept on monthly changes in the unemployment rate. It is re-fitted by ordinary least squares at each forecast origin on all data from 1948 up to that origin. I fixed this specification before computing any forecast errors and did no model selection on the test period.\n\n| Horizon | Pairs evaluated | RMSE naive | RMSE model | Relative improvement |\n|---|---|---|---|---|\n| 1 month | 35 | 0.1171 | 0.1199 | −0.023 |\n| 3 months | 35 | 0.1586 | 0.1598 | −0.008 |\n\n- **Mean absolute error** was also slightly higher for the model at both horizons.\n- **Why the two are so close:** the fitted model is essentially the naive forecast. Its coefficients are close to zero (about 0.04 and −0.04), and its forecasts never differ from the naive ones by more than 0.016 percentage points. The 2020 COVID spike in the training data is a likely reason for this, but I didn't test that.\n- **Significance:** a Diebold–Mariano test says the model is significantly worse at 1 month (p = 0.017) but not at 3 months (p = 0.18). Treat these p-values as rough only: the model contains the naive forecast as a special case, which makes the standard test unreliable. The better-suited Clark–West adjustment was not run, and there are only 35 forecasts per horizon.\n\n**Missing month:** the October 2025 value is blank in the data. The two forecasts that target that month are kept in the forecast file without an actual value and left out of the error figures, which is why 35 pairs per horizon are evaluated rather than 36. For the October 2025 origin, the naive forecast uses the September value. For the next two origins, the model fills the gap by interpolating between September and November, using only data available at the origin.\n\n**No future data used:** the checks confirm forecasts use only data up to each origin. Changing every later value left the forecasts unchanged.\n\n**Other limitations:**\n- The data are the latest revised figures, so these results are not real-time forecast performance.\n- The test window is three fairly calm years.\n- Only one model was tested, so the conclusion is about this model, not time-series models in general.\n\nFiles are in `out/`:\n- `results.json`\n- `forecasts.csv`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (audit, checks, log, phase ledger and gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":784,"cache_read_input_tokens":33665,"output_tokens":933,"output_tokens_details":{"thinking_tokens":36},"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":784},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXv4KafgEmWmh5J8M8fg","requestRef":"c0131aeb-7382-46b9-a3d0-3de259d7fe73","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"231892da-5081-427f-b52d-dfcb10de0609","timestamp":"2026-10-30T06:03:38.065Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0016","sessionId":"5d635948-d866-486a-b78e-aadb0161eacc","version":"2.1.314","gitBranch":"HEAD"}105{"type":"last-prompt","lastPrompt":"Your previous session was interrupted by the controller (an infrastructure event, not an error in your work). 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