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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  168 simon-pierreboucher  staff  5376 oct.  30 01:30 ..\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":"e3d70552-79b4-46f7-85cf-0adce862214e","timestamp":"2026-10-30T05:30:56.956Z","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}     946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\n2025-10-01,\n2025-11-01,4.5\n2025-12-01,4.4\n2026-01-01,4.3\n2026-02-01,4.4\n2026-03-01,4.3\n2026-04-01,4.3\n2026-05-01,4.3\n2026-06-01,4.2\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n935:2025-10-01,","is_error":false}]},"uuid":"cdf3fb2f-7a50-4e1f-b71e-0b9c35e76f80","timestamp":"2026-10-30T05:30:59.618Z","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\": 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** Over 36 rolling expanding-window origins (2023-07-01 … 2026-06-01), does a simple autoregressive-type model forecast the US unemployment rate (UNRATE, %, SA, monthly, final vintage) better than the naive no-change forecast at h=1 and h=3 months?\n\n**Estimands.** Per horizon h∈{1,3}: out-of-sample RMSE and MAE (percentage points) of (a) naive, (b) one pre-specified model; relative improvement 1 − RMSE_model/RMSE_naive. Meaningful as descriptive forecast-accuracy measures on the revised (final-vintage) series over this specific 36-origin window; not real-time accuracy; small sample (≤36 pairs, overlapping for h=3).\n\n**Information rules.** Forecast at origin t uses only observations dated ≤ t (estimation, selection, transformation). Naive = latest available observation ≤ t. Target t+h with missing actual stays in ledger with empty actual, excluded from errors; report n evaluated per horizon.\n\n**Outputs.** out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json (keys: task_id, model_name, model_description, n_origins, first_origin, last_origin, n_evaluated_h1, n_evaluated_h3, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json.\n\n**Invalidating conditions.** Use of data dated > t at origin t; model chosen by looking at evaluation-period errors; missing actuals treated as zeros; fewer than 36 origins available in data.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md 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code 1\nTraceback (most recent call last):\n  File \"<string>\", line 3, in <module>\n    d=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n  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\"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 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-c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.dtypes.to_dict()); print(d.UNRATE.describe()); print(d[d.UNRATE.isna()])\nprint(d.observation_date.diff().dt.days.value_counts().sort_index().to_dict())\nprint((d.observation_date.dt.day!=1).sum(), d.observation_date.is_monotonic_increasing)\nprint(d[d.observation_date.between('2020-02-01','2020-08-01')])\n\"","description":"Audit data schema and anomalies"}}]}}34{"type":"api-request","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","id":"a4fd7731-8e95-4548-b3c3-ab7195ad087c","timestamp":"2026-10-30T05:31:24.229Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfb65f0e2dfc33ceb7e4c55b620bc32c70234268aa21616301197fada178fc29","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":"2f7b9747-3f45-4b6b-b7f4-b3a345c6d590","keep":8,"tail":["9aa5c27b570c55df93a13d0a7f17d00d214eccbee12d90df5698644ec3a22262","d870d9e167e627d891521d20ce21c50f735509efe1b5b1e86e580bf8099711ee"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"744c499cd5492b3a8ee72dcbd80d4fc432336521daa018b5713039e864306fcb"}35{"parentUuid":"00b3aea1-992e-4dfd-a8a6-2e6b86a5ad77","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXscSgM1XicR7FThrLo4","type":"message","role":"assistant","content":[{"type":"text","text":"Working directory moved; rerun from workspace root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":999,"cache_read_input_tokens":10998,"output_tokens":353,"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":999},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXscSRU4fbjS4szfhwNf","requestRef":"a4fd7731-8e95-4548-b3c3-ab7195ad087c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"68297444-2aad-4984-82cf-cc98dfcb73ec","timestamp":"2026-10-30T05:31:25.422Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004/out/state","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"68297444-2aad-4984-82cf-cc98dfcb73ec","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXscSgM1XicR7FThrLo4","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CoKWETeTdXHaTgphYLnxmu","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RR0004 && ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.dtypes.to_dict()); print(d.UNRATE.describe()); print(d[d.UNRATE.isna()])\nprint(d.observation_date.diff().dt.days.value_counts().sort_index().to_dict())\nprint((d.observation_date.dt.day!=1).sum(), d.observation_date.is_monotonic_increasing, d.observation_date.duplicated().sum())\nprint(d[d.observation_date.between('2020-02-01','2020-08-01')])\n\"","description":"Audit data schema and anomalies"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":999,"cache_read_input_tokens":10998,"output_tokens":353,"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":999},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXscSRU4fbjS4szfhwNf","requestRef":"a4fd7731-8e95-4548-b3c3-ab7195ad087c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"66b182e7-11cc-4b31-bc83-89a4d68f563f","timestamp":"2026-10-30T05:31:27.411Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004/out/state","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"66b182e7-11cc-4b31-bc83-89a4d68f563f","isSidechain":false,"promptId":"fe2fe456-9fa4-493d-afcb-0292ba94b1a4","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CoKWETeTdXHaTgphYLnxmu","type":"tool_result","content":"(945, 2) {'observation_date': dtype('<M8[ns]'), 'UNRATE': dtype('float64')}\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\n933       2025-10-01     NaN\n{28.0: 59, 29.0: 20, 30.0: 314, 31.0: 551}\n0 True 0\n    observation_date  UNRATE\n865       2020-02-01     3.5\n866       2020-03-01     4.4\n867       2020-04-01    14.8\n868       2020-05-01    13.2\n869       2020-06-01    11.0\n870       2020-07-01    10.2\n871       2020-08-01     8.4","is_error":false}]},"uuid":"6462ac94-b365-4c92-9e4b-c14ae19cacaa","timestamp":"2026-10-30T05:31:27.634Z","toolUseResult":{"stdout":"(945, 2) {'observation_date': dtype('<M8[ns]'), 'UNRATE': dtype('float64')}\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\n933       2025-10-01     NaN\n{28.0: 59, 29.0: 20, 30.0: 314, 31.0: 551}\n0 True 0\n    observation_date  UNRATE\n865       2020-02-01     3.5\n866       2020-03-01     4.4\n867       2020-04-01    14.8\n868       2020-05-01    13.2\n869       2020-06-01    11.0\n870       2020-07-01    10.2\n871       2020-08-01     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pd\nd=pd.read_csv('data/UNRATE.csv',parse_dates=['observation_date'])\nprint(d.shape,d.dtypes.to_dict()); print(d.UNRATE.describe()); print(d[d.UNRATE.isna()])\nprint(d.observation_date.diff().dt.days.value_counts().sort_index().to_dict())\nprint((d.observation_date.dt.day!=1).sum(), d.observation_date.is_monotonic_increasing, d.observation_date.duplicated().sum())\nprint(d[d.observation_date.between('2020-02-01','2020-08-01')])\n\"","description":"Audit data schema and 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> out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin forecasts of UNRATE, naive vs ARIMA(2,1,0).\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.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nMODEL_NAME = \"ARIMA210\"\nHORIZONS = [1, 3]\nSEED = None  # no randomness used\n\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"))\nn_raw = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns = raw.set_index(\"observation_date\")[\"UNRATE\"].astype(float)\ns.index = pd.DatetimeIndex(s.index, freq=\"MS\")\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nmissing = s[s.isna()].index\n\nrows = []\nfor t in origins:\n    train = s.loc[:t]                      # only observations dated <= t\n    naive = train.dropna().iloc[-1]\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, maxiter=200)\n    fc = res.forecast(steps=max(HORIZONS))  # forecasts for t+1..t+3 (Kalman state handles gaps)\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(naive), actual=act, model=\"naive\"))\n        rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(fc.loc[tgt]), actual=act, model=MODEL_NAME))\nled = pd.DataFrame(rows)\nassert (led.target_date > led.origin_date).all()\nout = led.copy()\nout[\"origin_date\"] = out.origin_date.dt.strftime(\"%Y-%m-%d\")\nout[\"target_date\"] = out.target_date.dt.strftime(\"%Y-%m-%d\")\nout[\"forecast\"] = out.forecast.round(10)\nout.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\ndef err(model, h):\n    d = led[(led.model == model) & (led.horizon == h)].dropna(subset=[\"actual\"])\n    e = d.actual - d.forecast\n    return len(d), float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n     \"model_description\": (\"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), \"\n                           \"estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months \"\n                           \"treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, \"\n                           \"re-estimated at every origin; h-step forecasts by recursive iteration.\"),\n     \"n_origins\": int(len(origins)),\n     \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    n, rn, mn = err(\"naive\", h)\n    n2, rm, mm = err(MODEL_NAME, h)\n    assert n == n2\n    R[f\"n_evaluated_h{h}\"] = n\n    R[f\"rmse_naive_h{h}\"], R[f\"mae_naive_h{h}\"] = rn, mn\n    R[f\"rmse_model_h{h}\"], R[f\"mae_model_h{h}\"] = rm, mm\n    R[f\"rel_improvement_rmse_h{h}\"] = 1 - rm / rn\norder = [\"task_id\", \"model_name\", \"model_description\", \"n_origins\", \"first_origin\", \"last_origin\",\n         \"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n         \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n         \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]\nR = {k: R[k] for k in order}\nmiss_pairs = led[(led.model == \"naive\") & led.actual.isna()]\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [{\"issue\": f\"Missing UNRATE value(s) at {', '.join(d.strftime('%Y-%m-%d') for d in missing)}\",\n                     \"handling\": (\"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier \"\n                                  \"observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; \"\n                                  \"(origin,horizon) pairs with a missing target (\"\n                                  + \"; \".join(f\"{o.strftime('%Y-%m-%d')} h={h}\" for o, h in zip(miss_pairs.origin_date, miss_pairs.horizon))\n                                  + \") are in the ledger with empty actual and excluded from error statistics.\")},\n                    {\"issue\": \"Final (revised) vintage data, not real-time vintages\",\n                     \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"}]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger (observations in the last training window)\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"empty value field in source (2025-10-01); treated as missing, not imputed\"),\n    dict(step=2, rule=\"keep observations dated <= last origin (2026-06-01) for the last training window\",\n         n_before=n1, n_after=R[\"n_analysed\"], n_removed=n1 - R[\"n_analysed\"],\n         rationale=\"information-availability rule; later months used only as actuals\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nfor k in order[6:]:\n    print(f\"{k}: {R[k]}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && grep -c . out/forecasts.csv && grep ',,' 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":4,"cache_creation_input_tokens":1248,"cache_read_input_tokens":12640,"output_tokens":2727,"output_tokens_details":{"thinking_tokens":20},"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":1248},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsduSwA5MJwy9kqmeW2","requestRef":"10a22126-fe1c-4e64-8626-313ead6346e4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5562d696-aaf1-4211-b8c4-1d89a55dbcb1","timestamp":"2026-10-30T05:32:05.161Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"5562d696-aaf1-4211-b8c4-1d89a55dbcb1","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UWuKNkuoSq5ZR2gQAou2pX","type":"tool_result","content":"n_evaluated_h1: 35\nn_evaluated_h3: 35\nrmse_naive_h1: 0.11710800875382403\nmae_naive_h1: 0.09142857142857148\nrmse_naive_h3: 0.15856499343441843\nmae_naive_h3: 0.12000000000000005\nrmse_model_h1: 0.11979041413031663\nmae_model_h1: 0.09561928226076515\nrmse_model_h3: 0.1593741901249531\nmae_model_h3: 0.12090394601678622\nrel_improvement_rmse_h1: -0.022905396522720833\nrel_improvement_rmse_h3: -0.005103249292343737\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,\"empty value field in source (2025-10-01); treated as missing, not imputed\"\n2,keep observations dated <= last origin (2026-06-01) for the last training window,944,941,3,information-availability rule; later months used only as actuals\n145\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.307063775,,ARIMA210\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.403716752,,ARIMA210","is_error":false}]},"uuid":"3f99e87d-1182-46e1-9782-cff29497893c","timestamp":"2026-10-30T05:32:06.458Z","toolUseResult":{"stdout":"n_evaluated_h1: 35\nn_evaluated_h3: 35\nrmse_naive_h1: 0.11710800875382403\nmae_naive_h1: 0.09142857142857148\nrmse_naive_h3: 0.15856499343441843\nmae_naive_h3: 0.12000000000000005\nrmse_model_h1: 0.11979041413031663\nmae_model_h1: 0.09561928226076515\nrmse_model_h3: 0.1593741901249531\nmae_model_h3: 0.12090394601678622\nrel_improvement_rmse_h1: -0.022905396522720833\nrel_improvement_rmse_h3: -0.005103249292343737\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE value,945,944,1,\"empty value field in source (2025-10-01); treated as missing, not imputed\"\n2,keep observations dated <= last origin (2026-06-01) for the last training window,944,941,3,information-availability rule; later months used only as actuals\n145\n2025-07-01,2025-10-01,3,4.3,,naive\n2025-07-01,2025-10-01,3,4.307063775,,ARIMA210\n2025-09-01,2025-10-01,1,4.4,,naive\n2025-09-01,2025-10-01,1,4.403716752,,ARIMA210","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5562d696-aaf1-4211-b8c4-1d89a55dbcb1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"b0255ee516859a44cae5535a213e697455f6b896d8c7f4978ee96ef00b710abf","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01UWuKNkuoSq5ZR2gQAou2pX","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-dev-01: rolling-origin forecasts of UNRATE, naive vs ARIMA(2,1,0).\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.\n\"\"\"\nimport json, os, warnings\nimport numpy as np\nimport pandas as pd\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\n\nwarnings.filterwarnings(\"ignore\")\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nOUT = os.path.join(ROOT, \"out\")\nMODEL_NAME = \"ARIMA210\"\nHORIZONS = [1, 3]\nSEED = None  # no randomness used\n\nraw = pd.read_csv(os.path.join(ROOT, \"data\", \"UNRATE.csv\"))\nn_raw = len(raw)\nraw[\"observation_date\"] = pd.to_datetime(raw[\"observation_date\"])\ns = raw.set_index(\"observation_date\")[\"UNRATE\"].astype(float)\ns.index = pd.DatetimeIndex(s.index, freq=\"MS\")\nassert s.index.is_monotonic_increasing and not s.index.duplicated().any()\n\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\nmissing = s[s.isna()].index\n\nrows = []\nfor t in origins:\n    train = s.loc[:t]                      # only observations dated <= t\n    naive = train.dropna().iloc[-1]\n    mod = SARIMAX(train, order=(2, 1, 0), trend=\"n\")\n    res = mod.fit(disp=False, maxiter=200)\n    fc = res.forecast(steps=max(HORIZONS))  # forecasts for t+1..t+3 (Kalman state handles gaps)\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = s.get(tgt, np.nan)\n        rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(naive), actual=act, model=\"naive\"))\n        rows.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(fc.loc[tgt]), actual=act, model=MODEL_NAME))\nled = pd.DataFrame(rows)\nassert (led.target_date > led.origin_date).all()\nout = led.copy()\nout[\"origin_date\"] = out.origin_date.dt.strftime(\"%Y-%m-%d\")\nout[\"target_date\"] = out.target_date.dt.strftime(\"%Y-%m-%d\")\nout[\"forecast\"] = out.forecast.round(10)\nout.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10g\")\n\ndef err(model, h):\n    d = led[(led.model == model) & (led.horizon == h)].dropna(subset=[\"actual\"])\n    e = d.actual - d.forecast\n    return len(d), float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nR = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL_NAME,\n     \"model_description\": (\"ARIMA(2,1,0) without constant (AR(2) on first differences of the level), \"\n                           \"estimated by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter; missing months \"\n                           \"treated as missing) on an expanding window starting 1948-01-01 and ending at each origin, \"\n                           \"re-estimated at every origin; h-step forecasts by recursive iteration.\"),\n     \"n_origins\": int(len(origins)),\n     \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"), \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\nfor h in HORIZONS:\n    n, rn, mn = err(\"naive\", h)\n    n2, rm, mm = err(MODEL_NAME, h)\n    assert n == n2\n    R[f\"n_evaluated_h{h}\"] = n\n    R[f\"rmse_naive_h{h}\"], R[f\"mae_naive_h{h}\"] = rn, mn\n    R[f\"rmse_model_h{h}\"], R[f\"mae_model_h{h}\"] = rm, mm\n    R[f\"rel_improvement_rmse_h{h}\"] = 1 - rm / rn\norder = [\"task_id\", \"model_name\", \"model_description\", \"n_origins\", \"first_origin\", \"last_origin\",\n         \"n_evaluated_h1\", \"n_evaluated_h3\", \"rmse_naive_h1\", \"mae_naive_h1\", \"rmse_naive_h3\", \"mae_naive_h3\",\n         \"rmse_model_h1\", \"mae_model_h1\", \"rmse_model_h3\", \"mae_model_h3\",\n         \"rel_improvement_rmse_h1\", \"rel_improvement_rmse_h3\"]\nR = {k: R[k] for k in order}\nmiss_pairs = led[(led.model == \"naive\") & led.actual.isna()]\nR[\"feasibility\"] = \"complete\"\nR[\"feasibility_reason\"] = None\nR[\"data_issues\"] = [{\"issue\": f\"Missing UNRATE value(s) at {', '.join(d.strftime('%Y-%m-%d') for d in missing)}\",\n                     \"handling\": (\"Kept as missing (no imputation). Naive at a missing origin uses the latest earlier \"\n                                  \"observation; the ARIMA Kalman filter skips the missing month in estimation and forecasting; \"\n                                  \"(origin,horizon) pairs with a missing target (\"\n                                  + \"; \".join(f\"{o.strftime('%Y-%m-%d')} h={h}\" for o, h in zip(miss_pairs.origin_date, miss_pairs.horizon))\n                                  + \") are in the ledger with empty actual and excluded from error statistics.\")},\n                    {\"issue\": \"Final (revised) vintage data, not real-time vintages\",\n                     \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"}]\nR[\"n_input_rows\"] = {\"UNRATE.csv\": int(n_raw)}\nlast_train = s.loc[:origins[-1]]\nR[\"n_analysed\"] = int(last_train.notna().sum())\nR[\"random_seed\"] = SEED\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(R, f, indent=2)\n\n# exclusion ledger (observations in the last training window)\nn1 = int(s.notna().sum())\nexcl = pd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE value\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"empty value field in source (2025-10-01); treated as missing, not imputed\"),\n    dict(step=2, rule=\"keep observations dated <= last origin (2026-06-01) for the last training window\",\n         n_before=n1, n_after=R[\"n_analysed\"], n_removed=n1 - R[\"n_analysed\"],\n         rationale=\"information-availability rule; later months used only as actuals\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nfor k in order[6:]:\n    print(f\"{k}: {R[k]}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && grep -c . out/forecasts.csv && grep ',,' out/forecasts.csv","description":"Write and run analysis 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Next I'm adding a Diebold–Mariano test to quantify uncertainty."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":836,"cache_read_input_tokens":17048,"output_tokens":1018,"output_tokens_details":{"thinking_tokens":61},"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":836},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsfwjfhLoMz9AHSNQYn","requestRef":"24f82111-28bd-4b31-b632-a548b1d32527","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"97a0ec33-72f1-4547-ad7b-6bb02ca69fa9","timestamp":"2026-10-30T05:32:14.191Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"97a0ec33-72f1-4547-ad7b-6bb02ca69fa9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsfx1HzYwtd2W3ispsU","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0133rwizSjHgn4BrfijV1vpi","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; t=open(p).read()\nt=t.replace('''R = {k: R[k] for k in order}''','''# Diebold-Mariano test (squared-error loss, HAC variance with h-1 Newey-West lags,\n# Harvey-Leybourne-Newbold small-sample correction, t(n-1) reference). Positive stat = model worse.\nfrom scipy import stats\nfor h in HORIZONS:\n    a = led[(led.model == \"naive\") & (led.horizon == h)].reset_index(drop=True)\n    b = led[(led.model == MODEL_NAME) & (led.horizon == h)].reset_index(drop=True)\n    ok = a.actual.notna().values\n    d = ((b.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2).values[ok]\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    lrv = np.sum(dc ** 2) / n\n    for k in range(1, h):\n        lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n    dm = dbar / np.sqrt(lrv / n)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    R[f\"dm_stat_h{h}\"] = float(dm * hln)\n    R[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm * hln), df=n - 1))\norder += [\"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"]\nR = {k: R[k] for k in order}''')\nopen(p,'w').write(t)\nEOF\nrm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py; cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL","description":"Add DM test, rerun twice, compare"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":836,"cache_read_input_tokens":17048,"output_tokens":1018,"output_tokens_details":{"thinking_tokens":61},"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":836},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsfwjfhLoMz9AHSNQYn","requestRef":"24f82111-28bd-4b31-b632-a548b1d32527","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"96052382-cc2b-4d64-be02-9c773781b1e1","timestamp":"2026-10-30T05:32:20.395Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"96052382-cc2b-4d64-be02-9c773781b1e1","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0133rwizSjHgn4BrfijV1vpi","type":"tool_result","content":"n_evaluated_h1: 35\nn_evaluated_h3: 35\nrmse_naive_h1: 0.11710800875382403\nmae_naive_h1: 0.09142857142857148\nrmse_naive_h3: 0.15856499343441843\nmae_naive_h3: 0.12000000000000005\nrmse_model_h1: 0.11979041413031663\nmae_model_h1: 0.09561928226076515\nrmse_model_h3: 0.1593741901249531\nmae_model_h3: 0.12090394601678622\nrel_improvement_rmse_h1: -0.022905396522720833\nrel_improvement_rmse_h3: -0.005103249292343737\ndm_stat_h1: 2.4559640158118414\ndm_pvalue_h1: 0.019315858142969447\ndm_stat_h3: 1.0949160137919336\ndm_pvalue_h3: 0.2812494464012166\nIDENTICAL","is_error":false}]},"uuid":"8391a6dc-1bfc-4dcf-b442-4dea39c9f922","timestamp":"2026-10-30T05:32:22.948Z","toolUseResult":{"stdout":"n_evaluated_h1: 35\nn_evaluated_h3: 35\nrmse_naive_h1: 0.11710800875382403\nmae_naive_h1: 0.09142857142857148\nrmse_naive_h3: 0.15856499343441843\nmae_naive_h3: 0.12000000000000005\nrmse_model_h1: 0.11979041413031663\nmae_model_h1: 0.09561928226076515\nrmse_model_h3: 0.1593741901249531\nmae_model_h3: 0.12090394601678622\nrel_improvement_rmse_h1: -0.022905396522720833\nrel_improvement_rmse_h3: -0.005103249292343737\ndm_stat_h1: 2.4559640158118414\ndm_pvalue_h1: 0.019315858142969447\ndm_stat_h3: 1.0949160137919336\ndm_pvalue_h3: 0.2812494464012166\nIDENTICAL","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"96052382-cc2b-4d64-be02-9c773781b1e1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}71{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"25d86d3a80d20e527be90c890e859a6800671be5af62b9f361b6bd1d66324a9e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Coefficients are near zero, so the model is close to a random walk (likely partly due to the 2020 outlier; that will be reported). Next I'm adding a Diebold–Mariano test to quantify uncertainty."},{"type":"tool_use","id":"toolu_0133rwizSjHgn4BrfijV1vpi","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; t=open(p).read()\nt=t.replace('''R = {k: R[k] for k in order}''','''# Diebold-Mariano test (squared-error loss, HAC variance with h-1 Newey-West lags,\n# Harvey-Leybourne-Newbold small-sample correction, t(n-1) reference). Positive stat = model worse.\nfrom scipy import stats\nfor h in HORIZONS:\n    a = led[(led.model == \"naive\") & (led.horizon == h)].reset_index(drop=True)\n    b = led[(led.model == MODEL_NAME) & (led.horizon == h)].reset_index(drop=True)\n    ok = a.actual.notna().values\n    d = ((b.actual - b.forecast) ** 2 - (a.actual - a.forecast) ** 2).values[ok]\n    n = len(d); dbar = d.mean(); dc = d - dbar\n    lrv = np.sum(dc ** 2) / n\n    for k in range(1, h):\n        lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n    dm = dbar / np.sqrt(lrv / n)\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    R[f\"dm_stat_h{h}\"] = float(dm * hln)\n    R[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(dm * hln), df=n - 1))\norder += [\"dm_stat_h1\", \"dm_pvalue_h1\", \"dm_stat_h3\", \"dm_pvalue_h3\"]\nR = {k: R[k] for k in order}''')\nopen(p,'w').write(t)\nEOF\nrm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py; cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL","description":"Add DM test, rerun twice, compare"}}]}}72{"type":"api-request","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","id":"43bc1f12-8d9c-4483-8ef6-2be7a9dda538","timestamp":"2026-10-30T05:32:22.951Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfb65f0e2dfc33ceb7e4c55b620bc32c70234268aa21616301197fada178fc29","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":"24f82111-28bd-4b31-b632-a548b1d32527","keep":20,"tail":["25d86d3a80d20e527be90c890e859a6800671be5af62b9f361b6bd1d66324a9e","6d7090bb92497c1b11f3c43fe067ec56e40cbcb20ad27b6d0e8a8107453cef2a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e945ae9e3ce73b7f7ceb15a8685806b37eee3734a16fb694572ab38ff04980d2"}73{"parentUuid":"8391a6dc-1bfc-4dcf-b442-4dea39c9f922","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgn3XrQVJyYiL9VjG5","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":1306,"cache_read_input_tokens":17884,"output_tokens":1279,"output_tokens_details":{"thinking_tokens":105},"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":1306},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":752,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsgmTJbqRfj5wr1j9Po","requestRef":"43bc1f12-8d9c-4483-8ef6-2be7a9dda538","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6f23150d-8ca6-49ca-886c-a51e223edfd0","timestamp":"2026-10-30T05:32:25.169Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}74{"parentUuid":"6f23150d-8ca6-49ca-886c-a51e223edfd0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsgn3XrQVJyYiL9VjG5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0198dNc8FA5Xa66vcdK2zVnt","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nR=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date']); e=pd.read_csv('out/exclusions.csv')\ns=pd.read_csv('data/UNRATE.csv',index_col=0,parse_dates=True)['UNRATE']\nc=[]\nc.append((\"ledger has 144 rows\", len(f)==144))\nc.append((\"36 unique origins, 2023-07-01..2026-06-01\", f.origin_date.nunique()==36 and str(f.origin_date.min().date())=='2023-07-01' and str(f.origin_date.max().date())=='2026-06-01'))\nc.append((\"target = origin + h months\", all(f.target_date==[o+pd.DateOffset(months=int(h)) for o,h in zip(f.origin_date,f.horizon)])))\nc.append((\"naive = last non-missing obs <= origin\", all(f[f.model=='naive'].apply(lambda r: r.forecast==s.loc[:r.origin_date].dropna().iloc[-1],axis=1))))\nc.append((\"actuals match data\", all((f.actual.isna()&s.reindex(f.target_date).isna().values)|(f.actual.values==s.reindex(f.target_date).values))))\nc.append((\"n_evaluated = 36 - missing targets\", R['n_evaluated_h1']==int(f[(f.horizon==1)&(f.model=='naive')].actual.notna().sum())==35 and R['n_evaluated_h3']==35))\nc.append((\"RMSE >= MAE > 0\", all(R[f'rmse_{m}_h{h}']>=R[f'mae_{m}_h{h}']>0 for m in ['naive','model'] for h in [1,3])))\nc.append((\"rel_improvement recomputed\", all(abs(R[f'rel_improvement_rmse_h{h}']-(1-R[f'rmse_model_h{h}']/R[f'rmse_naive_h{h}']))<1e-12 for h in [1,3])))\nc.append((\"exclusion ledger sums: 945 - removed = n_analysed\", 945-e.n_removed.sum()==R['n_analysed']==941))\nc.append((\"h3 error >= h1 error (plausible magnitudes)\", R['rmse_naive_h3']>R['rmse_naive_h1']))\nc.append((\"model forecasts within 0.2pp of naive (consistent with near-zero AR coefs)\", float((f[f.model!='naive'].forecast.values-f[f.model=='naive'].forecast.values).__abs__().max())<0.2))\nc.append((\"first_origin < last_origin\", R['first_origin']<R['last_origin']))\nwith open('out/state/checks.md','w') as fh:\n    fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (deleted results.json/forecasts.csv, ran twice): byte-identical outputs — PASS\\n\")\n    for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\"); print(n,ok)\nEOF","description":"Run internal consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1306,"cache_read_input_tokens":17884,"output_tokens":1279,"output_tokens_details":{"thinking_tokens":105},"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":1306},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsgmTJbqRfj5wr1j9Po","requestRef":"43bc1f12-8d9c-4483-8ef6-2be7a9dda538","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"db7daf8a-ca69-4c31-8d54-48d2d543b064","timestamp":"2026-10-30T05:32:33.513Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}75{"parentUuid":"db7daf8a-ca69-4c31-8d54-48d2d543b064","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0198dNc8FA5Xa66vcdK2zVnt","type":"tool_result","content":"ledger has 144 rows True\n36 unique origins, 2023-07-01..2026-06-01 True\ntarget = origin + h months True\nnaive = last non-missing obs <= origin True\nactuals match data True\nn_evaluated = 36 - missing targets True\nRMSE >= MAE > 0 True\nrel_improvement recomputed True\nexclusion ledger sums: 945 - removed = n_analysed True\nh3 error >= h1 error (plausible magnitudes) True\nmodel forecasts within 0.2pp of naive (consistent with near-zero AR coefs) True\nfirst_origin < last_origin True","is_error":false}]},"uuid":"18ef0e8c-fd2f-4199-9587-077434747cf1","timestamp":"2026-10-30T05:32:33.713Z","toolUseResult":{"stdout":"ledger has 144 rows True\n36 unique origins, 2023-07-01..2026-06-01 True\ntarget = origin + h months True\nnaive = last non-missing obs <= origin True\nactuals match data True\nn_evaluated = 36 - missing targets True\nRMSE >= MAE > 0 True\nrel_improvement recomputed True\nexclusion ledger sums: 945 - removed = n_analysed True\nh3 error >= h1 error (plausible magnitudes) True\nmodel forecasts within 0.2pp of naive (consistent with near-zero AR coefs) True\nfirst_origin < last_origin True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"db7daf8a-ca69-4c31-8d54-48d2d543b064","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}76{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"e6ef391778fdceaa2d4436655c3c0602cfb88727cba666e80f273c2d9f9d6ad8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSpQgKEAgTGAI4AUIIdGhpbmtpbmcSDIN9c8mzGvKsnZaE/RoMAi4iUn0Vs17cFcXGIjBt3L4vLsddPTY+OXEKPtCM8LPz3H7jhPhb/53p1XjQDHzULM7ZxN7KQH0NXVzKXFYqwgd+Ouki6BIFpyLcbCTISdC0l8bI4gOQR/VNeLSSnjoCQfiativDzoEYGsa46IoDQuSeJBtk/rRf/GUMiSkp87kgjBA3638YMPsDZioDbbNrznsIPJkBBbUXfa10EXTfRwRn7t3nT67RSL38nJqF5nRofo9i6Z+eLS+RFkDll3oe5AixFW0odi39WvGV5hWCm04Ml0efOcKGvUauUq8VfcpVZ3jD3b4QKaJO7vb8UnjYf2uX92I2WMXRwTteTv03O3Ar6j9sL6UtSzhGOcSjCKUtRDgg5BBysBGmFrIJ3xd74Z5o4I0hqkI1KSpaHxwyxCEzCV8t4SXJ8cb9j6NH0FuETJ8g1TCnOEjLzV5qrs+qUQ/nDjTQUOQRhrDy6kDHZdmsl4lH1hYgncikbxkoziR62k2hBT9EgB3pNcg7XfdX61deNRlKoECtYGwZ9I+Qx0Lx8QL8wznlLXYFFxLTB/pRadosL+0jr6JdIaWAL+IoJrAQIuvFy++0CGT/bEcpx0O6UA54Es27CTQ5OIzMY0RY7VCuANHJTgUc9eKIkcd5KdlGvuXVga1wE+b0NdQ1xqFMPZ5PniNGfcuSj3DxrrikZUlutfjqA42ltrnhtkyyU8E1OeN+4XzrdH7j6xCGMSAWlZGTEXKmbEbrmxJBYk9DJSGkKes6rgZwl6+/5ShFgcqO9BeqP21EmZx1zIlvXIZ/0bZAMWPtJztSgxXr1fJk7NHm/3D6Tp7botsvDXtqRUOUXqs8q35+viAkWbbz2DYdPDiUafbdaqkWG0UDlU3g+mfJcl9Dfksk+qLp77u29/DI8HkPFACo+XSfNA1+T/cYEmC/d69aINvCGyZdyNt9IHz+AxIl2JYdWv7m3nvZFQr1+tgal0lTzSXA0K2GzStLXqjHQQUsxo4U48BRBNvRynUSBUnudcSvjVKkFSmR23QrCroNRQaTcRn8HuaHMnJSxgjD+/PJIerdE9R/9xmV4WjHbYk/fAeSOvtjD8t6qYnjyxB0afyj4wNWi9AtVG01Tl4coFUMXoVmDcF3Lxg1O6LcUuKFtM8HH3/8YJIaAm2JpTcie2MDdWf5AgQxJ17GsXiuu4DRJ04RHbwJWUFGlqTEHJxx+shnfyC00kc8Cmh4IFlr9KpQK9YAoRuGLAVw2HzYerdCxVJlJXZX8EXRLDN9xiYD4pahQWaGizqmCgdZeRB9eC449o0ukJI6cIyXHyZsdzJ6gxf4TMQsoaTMA/IH1/bBiaZIwF0BdCMk/KY3FKwt/HZTEBdSoHTkuBgq7hgB"},{"type":"tool_use","id":"toolu_0198dNc8FA5Xa66vcdK2zVnt","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nR=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date']); e=pd.read_csv('out/exclusions.csv')\ns=pd.read_csv('data/UNRATE.csv',index_col=0,parse_dates=True)['UNRATE']\nc=[]\nc.append((\"ledger has 144 rows\", len(f)==144))\nc.append((\"36 unique origins, 2023-07-01..2026-06-01\", f.origin_date.nunique()==36 and str(f.origin_date.min().date())=='2023-07-01' and str(f.origin_date.max().date())=='2026-06-01'))\nc.append((\"target = origin + h months\", all(f.target_date==[o+pd.DateOffset(months=int(h)) for o,h in zip(f.origin_date,f.horizon)])))\nc.append((\"naive = last non-missing obs <= origin\", all(f[f.model=='naive'].apply(lambda r: r.forecast==s.loc[:r.origin_date].dropna().iloc[-1],axis=1))))\nc.append((\"actuals match data\", all((f.actual.isna()&s.reindex(f.target_date).isna().values)|(f.actual.values==s.reindex(f.target_date).values))))\nc.append((\"n_evaluated = 36 - missing targets\", R['n_evaluated_h1']==int(f[(f.horizon==1)&(f.model=='naive')].actual.notna().sum())==35 and R['n_evaluated_h3']==35))\nc.append((\"RMSE >= MAE > 0\", all(R[f'rmse_{m}_h{h}']>=R[f'mae_{m}_h{h}']>0 for m in ['naive','model'] for h in [1,3])))\nc.append((\"rel_improvement recomputed\", all(abs(R[f'rel_improvement_rmse_h{h}']-(1-R[f'rmse_model_h{h}']/R[f'rmse_naive_h{h}']))<1e-12 for h in [1,3])))\nc.append((\"exclusion ledger sums: 945 - removed = n_analysed\", 945-e.n_removed.sum()==R['n_analysed']==941))\nc.append((\"h3 error >= h1 error (plausible magnitudes)\", R['rmse_naive_h3']>R['rmse_naive_h1']))\nc.append((\"model forecasts within 0.2pp of naive (consistent with near-zero AR coefs)\", float((f[f.model!='naive'].forecast.values-f[f.model=='naive'].forecast.values).__abs__().max())<0.2))\nc.append((\"first_origin < last_origin\", R['first_origin']<R['last_origin']))\nwith open('out/state/checks.md','w') as fh:\n    fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (deleted results.json/forecasts.csv, ran twice): byte-identical outputs — PASS\\n\")\n    for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\"); print(n,ok)\nEOF","description":"Run internal consistency checks"}}]}}77{"type":"api-request","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","id":"67b4a34f-06b0-4aa2-b91a-7f88d63e96a0","timestamp":"2026-10-30T05:32:33.716Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfb65f0e2dfc33ceb7e4c55b620bc32c70234268aa21616301197fada178fc29","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":"43bc1f12-8d9c-4483-8ef6-2be7a9dda538","keep":22,"tail":["e6ef391778fdceaa2d4436655c3c0602cfb88727cba666e80f273c2d9f9d6ad8","4fed176a4d77216fd3529447f83eef215426be14c13d7dec8c40facb440c7f3c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5a8f86fb5c39bd5bd347bab32635d15380c8c78d04bc4e2dc83287c3ffbccc3b"}78{"parentUuid":"18ef0e8c-fd2f-4199-9587-077434747cf1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXshZiA3pERBXFuac3FZ","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":1483,"cache_read_input_tokens":19190,"output_tokens":715,"output_tokens_details":{"thinking_tokens":106},"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":1483},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1035,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXshZVGDjnwmvqtb3ESc","requestRef":"67b4a34f-06b0-4aa2-b91a-7f88d63e96a0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4c06178a-253e-4997-8b42-d87d59d00f21","timestamp":"2026-10-30T05:32:36.051Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}79{"parentUuid":"4c06178a-253e-4997-8b42-d87d59d00f21","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXshZiA3pERBXFuac3FZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CsWPHafkGi6HgzYuoQp914","name":"Bash","input":{"command":"echo \"- Information rule: each origin's estimation uses \\`s.loc[:t]\\` only (code inspection); targets strictly after origins (checked above) — PASS\" >> out/state/checks.md\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, no dup dates, no calendar gaps\",\"1 missing value 2025-10-01 documented\",\"exclusions.csv written\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive forecasts computed for 36 origins x 2 horizons; rmse/mae_naive keys in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, no randomness (random_seed null)\",\"ARIMA(2,1,0) pre-specified before evaluation\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"12 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed; P4 checks pass; running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Update phases and 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":1483,"cache_read_input_tokens":19190,"output_tokens":715,"output_tokens_details":{"thinking_tokens":106},"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":1483},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXshZVGDjnwmvqtb3ESc","requestRef":"67b4a34f-06b0-4aa2-b91a-7f88d63e96a0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a9d2d41d-191b-4efe-a1b1-a92417c054ce","timestamp":"2026-10-30T05:32:40.594Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}80{"parentUuid":"a9d2d41d-191b-4efe-a1b1-a92417c054ce","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CsWPHafkGi6HgzYuoQp914","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 41 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:n_evaluated_h1: ok\n  [ok] key:n_evaluated_h3: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\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 1.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053241.917299Z.json\n20261030T053241.917299Z.json","is_error":false}]},"uuid":"4876fcd2-de85-4119-b0ae-17e17bdd392e","timestamp":"2026-10-30T05:32:41.924Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 41 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:n_evaluated_h1: ok\n  [ok] key:n_evaluated_h3: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\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 1.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053241.917299Z.json\n20261030T053241.917299Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a9d2d41d-191b-4efe-a1b1-a92417c054ce","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}81{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"bb94315a4ee640e1ac43ff9d91fdc23dcfb6ba1117b8f5178f57c0a7733708a2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01CsWPHafkGi6HgzYuoQp914","name":"Bash","input":{"command":"echo \"- Information rule: each origin's estimation uses \\`s.loc[:t]\\` only (code inspection); targets strictly after origins (checked above) — PASS\" >> out/state/checks.md\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"945 rows, 2 cols, no dup dates, no calendar gaps\",\"1 missing value 2025-10-01 documented\",\"exclusions.csv written\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive forecasts computed for 36 origins x 2 horizons; rmse/mae_naive keys in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, no randomness (random_seed null)\",\"ARIMA(2,1,0) pre-specified before evaluation\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"12 consistency checks PASS (checks.md)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2,P3 passed; P4 checks pass; running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; 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Next I'll write the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1586,"cache_read_input_tokens":20673,"output_tokens":3480,"output_tokens_details":{"thinking_tokens":66},"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":1586},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsiAatc24cquRFoNMQH","requestRef":"c0ca8a84-8a36-4ef0-878f-dfbb00e33836","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"27ee68d9-f4c4-492f-9a00-722a2dd5224a","timestamp":"2026-10-30T05:32:44.771Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}88{"parentUuid":"27ee68d9-f4c4-492f-9a00-722a2dd5224a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsiArFbVUGbRrWCfMyk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JreSBMefYWTwSmQmfAZ9cy","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver the 36 expanding-window forecast origins 2023-07-01 … 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, %, SA, final vintage) more accurately than the naive no-change forecast at horizons of 1 and 3 months? Accuracy is measured by out-of-sample RMSE and MAE, and the relative improvement is 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 data rows (`n_input_rows`), covering 1948-01 to 2026-09. Its SHA-256 matches the README. There are no duplicate dates and no calendar gaps. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). It was not imputed. Observation ledger (`out/exclusions.csv`): 945 rows → drop 1 missing → 944 → keep only observations dated ≤ the last origin → 941 observations in the last training window (`n_analysed`).\n- Forecast pairs: the ledger `out/forecasts.csv` holds all 144 rows. The missing month is the target of two pairs: (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3). Both stay in the ledger with an empty `actual` and are left out of the error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- At origin 2025-10-01 (whose own value is missing), the naive forecast uses the 2025-09-01 value. The ARIMA model forecasts from its Kalman-filter state given the data through that origin.\n\n## Method\n- **Naive baseline:** the latest non-missing observation dated ≤ t, used for every horizon.\n- **Model (`model_name` = ARIMA210):** see `model_description`. It is an ARIMA(2,1,0) without a constant, i.e. an AR(2) on monthly changes. It is fitted by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter, with missing months treated as missing). The sample starts 1948-01-01 and ends at each origin t, and the model is re-fitted at every origin. 3-step forecasts are built recursively.\n- I fixed this specification before computing any evaluation error (`out/state/log.md`). No other model was tried on the evaluation window. All estimation uses only observations dated ≤ t. No randomness is involved (`random_seed` = null).\n- **Uncertainty:** a Diebold–Mariano test of equal mean squared error (keys `dm_stat_h*`, `dm_pvalue_h*`). It uses a Newey–West variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and a t(n−1) reference. A positive statistic means the model's squared errors are larger than the naive ones.\n- Replication: `~/wp29/venv/bin/python out/analysis.py`.\n\n## Results\n| horizon | n | RMSE naive | MAE naive | RMSE ARIMA210 | MAE ARIMA210 | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\n(Units: percentage points.) The ARIMA(2,1,0) model did **not** beat the naive forecast at either horizon:\n- At h=1 its RMSE is about 2.3% higher than naive (`rel_improvement_rmse_h1`).\n- At h=3 its RMSE is about 0.5% higher (`rel_improvement_rmse_h3`).\n- Its MAE is also higher at both horizons (`mae_model_h1`, `mae_model_h3`).\n\nThe estimated AR coefficients on monthly changes are close to zero. In a diagnostic fit on the last window they were about 0.04 and −0.04 (this was run outside `analysis.py` and is not in `results.json`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** the Diebold–Mariano statistic is 2.46 with p = 0.019 (`dm_stat_h1`, `dm_pvalue_h1`). Taken at face value, the model's loss is significantly higher than naive at the 5% level.\n- **h=3:** the statistic is 1.09 with p = 0.28 (`dm_stat_h3`, `dm_pvalue_h3`), so there is no significant difference.\n- Caveats on these tests:\n  - The model nests the random walk, and its forecasts are almost the same as naive. The standard DM test is not designed for nested models (Clark–West would be the usual alternative, and it was not computed). So these p-values are approximate.\n  - With 35 evaluation points, and overlapping errors at h=3, all accuracy estimates are imprecise.\n  - The absolute differences in RMSE are below 0.003 pp.\n\n## Limitations and open issues\n- **Final vintage:** the evaluation uses revised data, so it is not a real-time assessment.\n- **One specification only:** a single model was fixed in advance. The result does not show that no AR-type model can beat naive. The 2020 COVID spike (14.8% in 2020-04) is kept in every estimation window. It probably pulls the AR coefficients toward zero, which makes the model nearly a random walk. Alternatives such as a 2020 dummy, a later sample start or robust estimation were deliberately not explored, because choosing among them on this evaluation window would be selection on test data.\n- **Missing month:** the 2025-10 observation is missing. It removes one evaluated pair per horizon, and the training windows after 2025-10 contain a gap, which the Kalman filter handles.\n- **Short window:** 36 origins in a relatively calm period (the rate stayed between about 3.5% and 4.6%). The results may not carry over to recessions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The pre-specified ARIMA(2,1,0) model does not improve on the naive no-change forecast at h=1: its RMSE is about 2.3% higher.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"mae_model_h1\",\"mae_naive_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; only one model specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the ARIMA(2,1,0) model is also slightly worse than naive: RMSE about 0.5% higher and MAE also higher.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"mae_model_h3\",\"mae_naive_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-step errors; the difference is very small.\"},\n {\"id\":\"C3\",\"text\":\"The h=1 loss difference is statistically significant by the Diebold-Mariano test (p about 0.02); the h=3 difference is not (p about 0.28).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"The DM test is not designed for nested models and the forecasts are nearly identical, so the p-values are approximate.\"},\n {\"id\":\"C4\",\"text\":\"35 of the 36 origin pairs per horizon were evaluated, because the 2025-10-01 observation is missing.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"12 consistency checks PASS (checks.md)\",\"gate run 20261030T053241.917299Z.json: all contract/reproducibility checks ok; only failures were P5 deliverables (report, claims, sections, P4/P5 status)\"],\"notes\":\"receipt out/state/gate_receipts/20261030T053241.917299Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json written (4 claims)\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T053241); P5 report/claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1586,"cache_read_input_tokens":20673,"output_tokens":3480,"output_tokens_details":{"thinking_tokens":66},"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":1586},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXsiAatc24cquRFoNMQH","requestRef":"c0ca8a84-8a36-4ef0-878f-dfbb00e33836","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f302c76a-a089-4aa7-93c6-5af1cba3b975","timestamp":"2026-10-30T05:33:11.561Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}89{"parentUuid":"f302c76a-a089-4aa7-93c6-5af1cba3b975","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JreSBMefYWTwSmQmfAZ9cy","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053312.876857Z.json","is_error":false}]},"uuid":"d3cb6db4-01ca-4d56-94ec-1e761ae5ec42","timestamp":"2026-10-30T05:33:12.883Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053312.876857Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f302c76a-a089-4aa7-93c6-5af1cba3b975","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}90{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"3109766c6dafe820a973dd96a0215f3a049a1cabac0174149e8ee571ced1f413","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 gate run is as expected: every contract and reproducibility check passes, and the only failures are P5 deliverables that don't exist yet. Next I'll write the report and claims."},{"type":"tool_use","id":"toolu_01JreSBMefYWTwSmQmfAZ9cy","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate\n\n## Question\nOver the 36 expanding-window forecast origins 2023-07-01 … 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive-type model forecast the US civilian unemployment rate (FRED UNRATE, %, SA, final vintage) more accurately than the naive no-change forecast at horizons of 1 and 3 months? Accuracy is measured by out-of-sample RMSE and MAE, and the relative improvement is 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 data rows (`n_input_rows`), covering 1948-01 to 2026-09. Its SHA-256 matches the README. There are no duplicate dates and no calendar gaps. Full audit: `out/state/data_audit.md`.\n- One value is missing: 2025-10-01 (`data_issues`). It was not imputed. Observation ledger (`out/exclusions.csv`): 945 rows → drop 1 missing → 944 → keep only observations dated ≤ the last origin → 941 observations in the last training window (`n_analysed`).\n- Forecast pairs: the ledger `out/forecasts.csv` holds all 144 rows. The missing month is the target of two pairs: (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3). Both stay in the ledger with an empty `actual` and are left out of the error statistics. That gives 35 evaluated pairs per horizon (`n_evaluated_h1`, `n_evaluated_h3`).\n- At origin 2025-10-01 (whose own value is missing), the naive forecast uses the 2025-09-01 value. The ARIMA model forecasts from its Kalman-filter state given the data through that origin.\n\n## Method\n- **Naive baseline:** the latest non-missing observation dated ≤ t, used for every horizon.\n- **Model (`model_name` = ARIMA210):** see `model_description`. It is an ARIMA(2,1,0) without a constant, i.e. an AR(2) on monthly changes. It is fitted by Gaussian maximum likelihood (statsmodels SARIMAX, Kalman filter, with missing months treated as missing). The sample starts 1948-01-01 and ends at each origin t, and the model is re-fitted at every origin. 3-step forecasts are built recursively.\n- I fixed this specification before computing any evaluation error (`out/state/log.md`). No other model was tried on the evaluation window. All estimation uses only observations dated ≤ t. No randomness is involved (`random_seed` = null).\n- **Uncertainty:** a Diebold–Mariano test of equal mean squared error (keys `dm_stat_h*`, `dm_pvalue_h*`). It uses a Newey–West variance with h−1 lags, the Harvey–Leybourne–Newbold small-sample correction and a t(n−1) reference. A positive statistic means the model's squared errors are larger than the naive ones.\n- Replication: `~/wp29/venv/bin/python out/analysis.py`.\n\n## Results\n| horizon | n | RMSE naive | MAE naive | RMSE ARIMA210 | MAE ARIMA210 | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| 1 | 35 | 0.1171 (`rmse_naive_h1`) | 0.0914 (`mae_naive_h1`) | 0.1198 (`rmse_model_h1`) | 0.0956 (`mae_model_h1`) | −0.0229 (`rel_improvement_rmse_h1`) |\n| 3 | 35 | 0.1586 (`rmse_naive_h3`) | 0.1200 (`mae_naive_h3`) | 0.1594 (`rmse_model_h3`) | 0.1209 (`mae_model_h3`) | −0.0051 (`rel_improvement_rmse_h3`) |\n\n(Units: percentage points.) The ARIMA(2,1,0) model did **not** beat the naive forecast at either horizon:\n- At h=1 its RMSE is about 2.3% higher than naive (`rel_improvement_rmse_h1`).\n- At h=3 its RMSE is about 0.5% higher (`rel_improvement_rmse_h3`).\n- Its MAE is also higher at both horizons (`mae_model_h1`, `mae_model_h3`).\n\nThe estimated AR coefficients on monthly changes are close to zero. In a diagnostic fit on the last window they were about 0.04 and −0.04 (this was run outside `analysis.py` and is not in `results.json`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\n\n## Uncertainty\n- **h=1:** the Diebold–Mariano statistic is 2.46 with p = 0.019 (`dm_stat_h1`, `dm_pvalue_h1`). Taken at face value, the model's loss is significantly higher than naive at the 5% level.\n- **h=3:** the statistic is 1.09 with p = 0.28 (`dm_stat_h3`, `dm_pvalue_h3`), so there is no significant difference.\n- Caveats on these tests:\n  - The model nests the random walk, and its forecasts are almost the same as naive. The standard DM test is not designed for nested models (Clark–West would be the usual alternative, and it was not computed). So these p-values are approximate.\n  - With 35 evaluation points, and overlapping errors at h=3, all accuracy estimates are imprecise.\n  - The absolute differences in RMSE are below 0.003 pp.\n\n## Limitations and open issues\n- **Final vintage:** the evaluation uses revised data, so it is not a real-time assessment.\n- **One specification only:** a single model was fixed in advance. The result does not show that no AR-type model can beat naive. The 2020 COVID spike (14.8% in 2020-04) is kept in every estimation window. It probably pulls the AR coefficients toward zero, which makes the model nearly a random walk. Alternatives such as a 2020 dummy, a later sample start or robust estimation were deliberately not explored, because choosing among them on this evaluation window would be selection on test data.\n- **Missing month:** the 2025-10 observation is missing. It removes one evaluated pair per horizon, and the training windows after 2025-10 contain a gap, which the Kalman filter handles.\n- **Short window:** 36 origins in a relatively calm period (the rate stayed between about 3.5% and 4.6%). The results may not carry over to recessions.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The pre-specified ARIMA(2,1,0) model does not improve on the naive no-change forecast at h=1: its RMSE is about 2.3% higher.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rel_improvement_rmse_h1\",\"mae_model_h1\",\"mae_naive_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; only one model specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the ARIMA(2,1,0) model is also slightly worse than naive: RMSE about 0.5% higher and MAE also higher.\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"rel_improvement_rmse_h3\",\"mae_model_h3\",\"mae_naive_h3\"],\"status\":\"supported\",\"caveat\":\"Overlapping 3-step errors; the difference is very small.\"},\n {\"id\":\"C3\",\"text\":\"The h=1 loss difference is statistically significant by the Diebold-Mariano test (p about 0.02); the h=3 difference is not (p about 0.28).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"The DM test is not designed for nested models and the forecasts are nearly identical, so the p-values are approximate.\"},\n {\"id\":\"C4\",\"text\":\"35 of the 36 origin pairs per horizon were evaluated, because the 2025-10-01 observation is missing.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_origins\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"12 consistency checks PASS (checks.md)\",\"gate run 20261030T053241.917299Z.json: all contract/reproducibility checks ok; only failures were P5 deliverables (report, claims, sections, P4/P5 status)\"],\"notes\":\"receipt out/state/gate_receipts/20261030T053241.917299Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json written (4 claims)\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T053241); P5 report/claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, run final gate"}}]}}91{"type":"api-request","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","id":"5eb80daa-06eb-4fae-bd03-b75f17b511f9","timestamp":"2026-10-30T05:33:12.885Z","version":"2.1.314","querySource":"sdk","shapeHash":"cfb65f0e2dfc33ceb7e4c55b620bc32c70234268aa21616301197fada178fc29","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":"c0ca8a84-8a36-4ef0-878f-dfbb00e33836","keep":26,"tail":["3109766c6dafe820a973dd96a0215f3a049a1cabac0174149e8ee571ced1f413","b3935cc955335730f51e762542ec89b3c2fe161b5ad0b1fb82cb81c31736fbb0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"dc05804cb6dac7e17723fb54aadac7bd1a3a515729e633c7aae20b957e1d5539"}92{"parentUuid":"d3cb6db4-01ca-4d56-94ec-1e761ae5ec42","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXskTPy9qGLdVRVWZvoT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSrAgKEAgTGAI4AUIIdGhpbmtpbmcSDLw+BLJSEsBEd3xE9RoMKlJwXTPGYyz6trP0IjDK99vwGLHV59XqIN+1M8CtfcZb9K/yNV84y21npNDTyFouHgZkpsab0HFojDHjmrMqyQeIFvY/qJJamuYMEYkSpSpQ4oAyoSbgBA9LyMCNHCANn/NP8WqLcjqL6EDss/XQiTsWZLQm/YSuIP4k4b6pZAEFv+8lVFM17sRh/kgo7ssAumtGTUqC7zpJrr47wJI7r4lpUfIs3ZViWZvPAzoCuQzb+yEB/8RrQTqrB/bnKPv9x8cGdajYFEgrzloUIm6deDIbZ07Ta7PGY7iw5BA3bA1OmU6HRznHnX1joPGmjgN96ujNDCrVEZUCWtEZXusM/X6slo4cz9fcSo0T7XmxhbihBkIzfWOp5e3KzQJvPXkxLQLC6Nq4G8AOgaeT+dLOXpIKvZu4TW83/AosAIvh9fJM114Od8BsjDuk26qyqVCB/61o+DakU3l4xV87Eo/2Ctrga/Cv3d4+7trRe9aQb5GJO1Q+q/OUPV24yV7Qcxo+fmwfZOLgFoGFHOczJX2E2qKWHTxsEr3PcbU1phJw6D1o9yh1vwvH7KxPYhXdaQw4b4eetAVoBu81jYJFwLtbsC89QoKGKnvvf4nIw3vYS5SppViw6K65Ldn9ip0nWIzVd37xU/3wZRMerpPxHimafIeDOQPCklUQsxISNl6yjMQTamWE1PWGK5bXnyGnqlyXOYDE5imVeUmrQnutW94xE0kyhtTOq1u26GX8SfsoM0xfOmncZKj4altrOsWmBEPvHZMB3aF0+jPUFozUt5IzneNntVTTmwVOtHzpRMpFV9+M0E4O+yOwqarJPZ1YlymPQk5r2Qre8g0VMD0IZOHg9dSwE3d6hlcO8jBEm1cq8aupG8Y+flvpkCJTmsnVbF8aT0AsUUAvcxCaaNJMeMM10ZJNcNSUnXJkBTvJUOET8M+2wX+/9TahylTqEAeqG2sLCg55bcKgKHb4Nb85cM+XQ0OGr8nM8nedAeog81xEEBnIPnT78QCfSeMPSmJ54GTadY5MzqwxYTbhE7n3uSn/GHJNDAmBa8IVpOfaWAlENiVTZ/4xuSv8vVHPJ9NlPQyp+y0e69iG0gjxMKEYbOILHMANqPMVL8MnqatyTKacdE8KP4mgEcjA2gs5cjDNMhLuVrTflFes4KfLIIIxSlx8mDmwmcywIKseTB+7D3XzZY6bTdIJ1/VshrD/EyjYt5a6dL4bdZbAx60wZoWZt2h+oMIcVwx+jHv9tpJxEho891Op7v+r5g8qJRVfxMEOdQtooHDt7raIwpzLfPzhOWYD573EwKNiRt7SCtsqj7yoTDvTKpXd5CcVMbKH+B8IYqNmCqrAC4B2bcaJR9YkeC5odkPXGlO9rPgqwkgYAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3575,"cache_read_input_tokens":22259,"output_tokens":1045,"output_tokens_details":{"thinking_tokens":165},"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":3575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1792,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXskSxgGdJp7cfd5vFph","requestRef":"5eb80daa-06eb-4fae-bd03-b75f17b511f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e0b185d1-4088-4fdd-b50a-57f7f1f0cac5","timestamp":"2026-10-30T05:33:16.241Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}93{"parentUuid":"e0b185d1-4088-4fdd-b50a-57f7f1f0cac5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXskTPy9qGLdVRVWZvoT","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"The final gate has passed. I'm now moving the AR coefficients from the report into `analysis.py` and `results.json` so each number can be traced.\n\n","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":3575,"cache_read_input_tokens":22259,"output_tokens":1045,"output_tokens_details":{"thinking_tokens":165},"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":3575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2,"thinkingDisplay":"updates","apiBlockIndex":1,"requestId":"req_011CgXskSxgGdJp7cfd5vFph","requestRef":"5eb80daa-06eb-4fae-bd03-b75f17b511f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f48768ed-c0fa-421b-a769-e8c9e6d2949e","timestamp":"2026-10-30T05:33:16.245Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}94{"parentUuid":"f48768ed-c0fa-421b-a769-e8c9e6d2949e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXskTPy9qGLdVRVWZvoT","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GhvMuQKLnxtVwTpwZut3ZM","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; t=open(p).read()\nt=t.replace('''    fc = res.forecast(steps=max(HORIZONS))''','''    fc = res.forecast(steps=max(HORIZONS))\n    last_params = res.params''')\nt=t.replace('''order += [\"dm_stat_h1\"''','''R[\"ar1_coef_last_window\"] = float(last_params[\"ar.L1\"])\nR[\"ar2_coef_last_window\"] = float(last_params[\"ar.L2\"])\norder += [\"ar1_coef_last_window\", \"ar2_coef_last_window\"]\norder += [\"dm_stat_h1\"''')\nopen(p,'w').write(t)\np='out/report.md'; t=open(p).read()\nt=t.replace(\"In a diagnostic fit on the last window they were about 0.04 and −0.04 (this was run outside `analysis.py` and is not in `results.json`).\",\"In the last window they are 0.037 and −0.042 (`ar1_coef_last_window`, `ar2_coef_last_window`).\")\nopen(p,'w').write(t)\nEOF\n~/wp29/venv/bin/python out/analysis.py | tail -3; grep -n ar1_coef out/report.md\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nc=json.load(open('out/claims.json'))\nc.append({\"id\":\"C5\",\"text\":\"The estimated AR coefficients on monthly changes in the last window are close to zero, so the model's forecasts are nearly identical to naive.\",\"results_keys\":[\"ar1_coef_last_window\",\"ar2_coef_last_window\"],\"status\":\"supported\",\"caveat\":\"Probably influenced by the 2020 COVID outlier kept in the sample.\"})\njson.dump(c,open('out/claims.json','w'),indent=1)\np=json.load(open('out/state/phases.json')); p['P5']['checks'].append(\"AR coefficients moved into analysis.py/results.json; claim C5 added; gate re-run\")\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Added AR coefficient keys to results; reran gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Add coefficient keys, 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":3575,"cache_read_input_tokens":22259,"output_tokens":1045,"output_tokens_details":{"thinking_tokens":165},"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":3575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXskSxgGdJp7cfd5vFph","requestRef":"5eb80daa-06eb-4fae-bd03-b75f17b511f9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"51901e4d-204e-4644-a660-3b8148e40a98","timestamp":"2026-10-30T05:33:21.611Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}95{"type":"last-prompt","lastPrompt":"Your","leafUuid":"51901e4d-204e-4644-a660-3b8148e40a98","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909"}96{"type":"mode","mode":"normal","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909"}97{"type":"atis-latch","atis":"","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909"}98{"parentUuid":"51901e4d-204e-4644-a660-3b8148e40a98","isSidechain":false,"promptId":"4859887c-664a-4a0c-9554-862a632c8426","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GhvMuQKLnxtVwTpwZut3ZM","type":"tool_result","content":"dm_pvalue_h1: 0.019315858142969447\ndm_stat_h3: 1.0949160137919336\ndm_pvalue_h3: 0.2812494464012166\n30:The estimated AR coefficients on monthly changes are close to zero. In the last window they are 0.037 and −0.042 (`ar1_coef_last_window`, `ar2_coef_last_window`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053324.181256Z.json","is_error":false}]},"uuid":"bd205be2-a75a-4597-a55a-6e8dc71ddd44","timestamp":"2026-10-30T05:33:24.186Z","toolUseResult":{"stdout":"dm_pvalue_h1: 0.019315858142969447\ndm_stat_h3: 1.0949160137919336\ndm_pvalue_h3: 0.2812494464012166\n30:The estimated AR coefficients on monthly changes are close to zero. In the last window they are 0.037 and −0.042 (`ar1_coef_last_window`, `ar2_coef_last_window`). So the model's forecasts barely differ from the naive ones: by less than 0.2 pp in every case (`out/state/checks.md`).\nPGAR GATE pgar_gate/1.0 — PASSED (0 of 41 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RR0004/out/state/gate_receipts/20261030T053324.181256Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"51901e4d-204e-4644-a660-3b8148e40a98","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}99{"type":"api-request-blob","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","hash":"7b63539dee935aea1fdb219b6cb2c4d664a562d4fd4398701ba0571931b79474","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"thinking","thinking":"The final gate has passed. I'm now moving the AR coefficients from the report into `analysis.py` and `results.json` so each number can be traced.\n\n","signature":"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"},{"type":"tool_use","id":"toolu_01GhvMuQKLnxtVwTpwZut3ZM","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; 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The required files are written and the final gate passes (0 of 41 checks failed); re-running `out/analysis.py` gives identical output.\n\n**Model:** ARIMA(2,1,0) without a constant, i.e. an AR(2) on monthly changes. It is fitted on all data from 1948 up to each origin and re-fitted at every one of the 36 origins (2023-07 to 2026-06). I fixed this before computing any errors and tried no other model.\n\n| horizon | pairs evaluated | RMSE naive | RMSE model | relative improvement | MAE naive | MAE model |\n|---|---|---|---|---|---|---|\n| 1 month | 35 | 0.1171 | 0.1198 | −2.3% | 0.0914 | 0.0956 |\n| 3 months | 35 | 0.1586 | 0.1594 | −0.5% | 0.1200 | 0.1209 |\n\nErrors are in percentage points.\n\n- **Statistical test:** a Diebold–Mariano test gives p = 0.019 at one month (the model is significantly worse) and p = 0.28 at three months (no significant difference). These p-values are only approximate: this test isn't designed for a model that contains the naive forecast as a special case, and the forecasts are nearly identical. The usual alternative (Clark–West) was not computed.\n- **Why the model behaves like naive:** its estimated coefficients in the last window are 0.037 and −0.042, so its forecasts never differ from naive by more than 0.2 pp. The 2020 COVID spike (14.8% in April 2020) is kept in every training window and probably pulls those coefficients towards zero.\n- **Missing month:** October 2025 has no value in the data. It is not imputed, and the two forecasts that target it stay in the ledger with an empty actual but are left out of the errors, so 35 of 36 pairs count per horizon. For the October 2025 origin itself, the naive forecast uses the September value.\n\n**Limitations:**\n- The data are the current revised series, so this is not a real-time test.\n- 35 points per horizon from a fairly calm period give imprecise estimates.\n- One fixed model can't show that no autoregressive model beats naive. I deliberately didn't try alternatives (a COVID dummy, a later start date) because picking among them on these same 36 months would mean choosing on the test data.\n\nFiles are in `out/`:\n- `results.json`\n- `forecasts.csv`\n- `analysis.py`\n- `report.md`\n- `claims.json`\n- `exclusions.csv`\n- `state/`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":277,"cache_read_input_tokens":27148,"output_tokens":927,"output_tokens_details":{"thinking_tokens":51},"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":277},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsmVHbx3pCznEAjocBx","requestRef":"929b1604-a716-40a7-b41d-0a96937f1c83","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f5bfbeaf-f42d-41f1-bbd7-878c6134d8fd","timestamp":"2026-10-30T05:33:36.276Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RR0004","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","version":"2.1.314","gitBranch":"HEAD"}107{"type":"last-prompt","lastPrompt":"Your","leafUuid":"f5bfbeaf-f42d-41f1-bbd7-878c6134d8fd","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909"}108{"type":"cost-state","sessionId":"727cd347-eb09-4dbc-b743-f37588d83909","totalCostUSD":0.5001564,"totalAPIDuration":151342,"totalAPIDurationWithoutRetries":151332,"totalToolDuration":10274,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":161762,"startTime":1793338253301,"modelUsage":{"claude-opus-5-5":{"inputTokens":34,"outputTokens":16313,"thinkingTokens":2182,"cacheReadInputTokens":220777,"cacheCreationInputTokens":25921,"cacheCreationEphemeral5mInputTokens":25921,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5001564}},"hasUnknownModelCost":false}109