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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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nREADME.md\nUNRATE.csv","is_error":true,"tool_use_id":"toolu_014Xf6EA6Cgwsk5xVAi9Dk2F"}]},"uuid":"ac59615d-ccdc-4baa-aab8-8a07fd6c5325","timestamp":"2026-10-30T03:59:07.014Z","toolUseResult":"Error: Exit code 1\n# 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}\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     946 data/UNRATE.csv\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n2025-07-01,4.3\n2025-08-01,4.3\n2025-09-01,4.4\n2025-10-01,\n2025-11-01,4.5\n2025-12-01,4.4\n2026-01-01,4.3\n2026-02-01,4.4\n2026-03-01,4.3\n2026-04-01,4.3\n2026-05-01,4.3\n2026-06-01,4.2\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n935:2025-10-01,\n1","is_error":false}]},"uuid":"c31886df-d5ea-43c0-9cf9-55621c157d50","timestamp":"2026-10-30T03:59:10.027Z","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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\"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}\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     946 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-p out/state; cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question**: Over 36 expanding-window origins (2023-07-01..2026-06-01), does a simple AR-type model beat the naive no-change forecast of US UNRATE at h=1 and h=3 months?\n**Estimands**: out-of-sample RMSE and MAE (pp) of naive and model per horizon, over (origin,h) pairs with non-missing actual; rel. improvement 1 - RMSE_model/RMSE_naive. Meaningful as forecastability of the final-vintage (revised) series; not real-time accuracy. 36 origins -> small-sample, noisy.\n**Outputs**: out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model), out/results.json (keys per TASK.md table), out/report.md (6 sections), out/analysis.py (deterministic), out/claims.json.\n**Information rules**: forecast at t uses only obs dated <= t (estimation, selection, transformations). Naive = latest available obs <= t. Missing actual -> row kept with empty actual, excluded from stats; report n evaluated per horizon. No real-time claims.\n**Invalidity**: using obs > t; tuning model spec on the evaluation errors; missing data making estimation impossible (not the case).\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29 P0 passed: contract written.\" > out/state/log.md\n~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv');print(d.shape,d.dtypes.to_dict());print(d.UNRATE.describe());print('dups',d.observation_date.duplicated().sum())\ndt=pd.to_datetime(d.observation_date);print('gaps',(dt.diff().dt.days>31).sum(), 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2) {'observation_date': dtype('O'), '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\ndups 0\ngaps 0 1948-01-01 00:00:00 2026-09-01 00:00:00\n    observation_date  UNRATE\n933       2025-10-01     NaN\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","is_error":false}]},"uuid":"d5b6f81c-cc0f-4e3f-b208-2498f8072bc6","timestamp":"2026-10-30T03:59:31.346Z","toolUseResult":{"stdout":"(945, 2) {'observation_date': dtype('O'), '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\ndups 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-p out/state; cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question**: Over 36 expanding-window origins (2023-07-01..2026-06-01), does a simple AR-type model beat the naive no-change forecast of US UNRATE at h=1 and h=3 months?\n**Estimands**: out-of-sample RMSE and MAE (pp) of naive and model per horizon, over (origin,h) pairs with non-missing actual; rel. improvement 1 - RMSE_model/RMSE_naive. Meaningful as forecastability of the final-vintage (revised) series; not real-time accuracy. 36 origins -> small-sample, noisy.\n**Outputs**: out/forecasts.csv (144 rows: origin_date,target_date,horizon,forecast,actual,model), out/results.json (keys per TASK.md table), out/report.md (6 sections), out/analysis.py (deterministic), out/claims.json.\n**Information rules**: forecast at t uses only obs dated <= t (estimation, selection, transformations). Naive = latest available obs <= t. Missing actual -> row kept with empty actual, excluded from stats; report n evaluated per horizon. No real-time claims.\n**Invalidity**: using obs > t; tuning model spec on the evaluation errors; missing data making estimation impossible (not the case).\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-29 P0 passed: contract written.\" > out/state/log.md\n~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv');print(d.shape,d.dtypes.to_dict());print(d.UNRATE.describe());print('dups',d.observation_date.duplicated().sum())\ndt=pd.to_datetime(d.observation_date);print('gaps',(dt.diff().dt.days>31).sum(), 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n- File: data/UNRATE.csv, SHA-256 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README).\n- 945 data rows + header, 2 columns: observation_date (YYYY-MM-01 string), UNRATE (float, percent, SA).\n- Range 1948-01-01 .. 2026-09-01, monthly, no gaps in dates, no duplicate dates, all dates on day 01.\n- Missing: 1 value, 2025-10-01 (empty field). Affects: naive/model at origin 2025-10 (use latest earlier obs 2025-09); actual for (origin 2025-09,h1) and (origin 2025-07,h3) -> excluded from error stats; training windows for origins >= 2025-10 (regression rows involving the missing value dropped).\n- Values: min 2.5, max 14.8 (2020-04, COVID spike: 3.5 -> 4.4 -> 14.8 -> 13.2). Genuine data, kept; it is in all training windows. Noted as anomaly; no outlier treatment (decided a priori, not tuned on evaluation errors).\n- Information availability: file is final vintage retrieved 2026-10-30, so all values are revised; violates real-time availability in the vintage sense. Handling: as TASK instructs, evaluate on given data and make no real-time claims. Observations after each origin are masked in code.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29 P1 passed: audit done; one missing value (2025-10). Decision: model = AR(2) on first differences with intercept, OLS, sample from 1948, fixed a priori (no selection on test errors).\nEOF","description":"Write data audit and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2331,"cache_read_input_tokens":8304,"output_tokens":872,"output_tokens_details":{"thinking_tokens":179},"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":2331},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkc5hi5ezgPXJAq2CXe","requestRef":"ee555059-b7ea-4cf6-b9d8-c70bc33db204","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bf985229-6834-4a50-853f-07895f4767ca","timestamp":"2026-10-30T03:59:40.905Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"bf985229-6834-4a50-853f-07895f4767ca","isSidechain":false,"promptId":"79349b31-4ab4-4454-97f0-f44ef311d903","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YZRk5Fbys3YQRZvB8u2LtT","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"e58c1d02-332d-4358-a890-7bc2542d2666","timestamp":"2026-10-30T03:59:40.928Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bf985229-6834-4a50-853f-07895f4767ca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","hash":"d34bcec7141831d56ce78ed1b4d4f8f0e123947694ebaab49d41d9c4ee7bb50e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01YZRk5Fbys3YQRZvB8u2LtT","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n- File: data/UNRATE.csv, SHA-256 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README).\n- 945 data rows + header, 2 columns: observation_date (YYYY-MM-01 string), UNRATE (float, percent, SA).\n- Range 1948-01-01 .. 2026-09-01, monthly, no gaps in dates, no duplicate dates, all dates on day 01.\n- Missing: 1 value, 2025-10-01 (empty field). Affects: naive/model at origin 2025-10 (use latest earlier obs 2025-09); actual for (origin 2025-09,h1) and (origin 2025-07,h3) -> excluded from error stats; training windows for origins >= 2025-10 (regression rows involving the missing value dropped).\n- Values: min 2.5, max 14.8 (2020-04, COVID spike: 3.5 -> 4.4 -> 14.8 -> 13.2). Genuine data, kept; it is in all training windows. Noted as anomaly; no outlier treatment (decided a priori, not tuned on evaluation errors).\n- Information availability: file is final vintage retrieved 2026-10-30, so all values are revised; violates real-time availability in the vintage sense. Handling: as TASK instructs, evaluate on given data and make no real-time claims. Observations after each origin are masked in code.\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-29 P1 passed: audit done; one missing value (2025-10). Decision: model = AR(2) on first differences with intercept, OLS, sample from 1948, fixed a priori (no selection on test errors).\nEOF","description":"Write data audit and 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xD6spL7PtYazPfBKQXn","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Rolling-origin (expanding window) forecasts of US UNRATE: naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nP = 2                      # AR order on first differences (fixed a priori)\nHORIZONS = [1, 3]\nMODEL = \"AR2_diff\"\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str})\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\ny = raw.set_index(\"date\")[\"UNRATE\"].astype(float)\nassert y.index.is_monotonic_increasing and not y.index.duplicated().any()\nassert (y.index == pd.date_range(y.index[0], y.index[-1], freq=\"MS\")).all()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in y.index[y.isna()]]\n\ndef ar_forecast(s, steps):\n    \"\"\"s: series with obs dated <= origin (may contain NaN). Returns forecasts 1..steps months\n    ahead of the last *observed* month.\"\"\"\n    s = s.loc[:s.last_valid_index()]\n    d = s.diff()\n    X = pd.concat({f\"l{i}\": d.shift(i) for i in range(1, P + 1)}, axis=1)\n    reg = pd.concat([d.rename(\"y\"), X], axis=1).dropna()        # drop rows touching missing values\n    A = np.column_stack([np.ones(len(reg)), reg[[f\"l{i}\" for i in range(1, P + 1)]].values])\n    beta, *_ = np.linalg.lstsq(A, reg[\"y\"].values, rcond=None)\n    # forecasting state: interior missing values linearly interpolated using only obs <= origin\n    si = s.interpolate(method=\"linear\", limit_area=\"inside\")\n    lags = list(si.diff().values[-P:][::-1])               # [d_T, d_{T-1}]\n    level, out = si.values[-1], []\n    for _ in range(steps):\n        dhat = beta[0] + np.dot(beta[1:], lags[:P])\n        level += dhat; out.append(level); lags = [dhat] + lags[:P - 1]\n    return np.array(out), len(reg)\n\nrows = []\nfor t in origins:\n    s = y.loc[:t]                                # information rule: only obs dated <= t\n    last_obs = s.last_valid_index()\n    gap = (t.year - last_obs.year) * 12 + (t.month - last_obs.month)   # months origin is ahead of last obs\n    fc, nreg = ar_forecast(s, gap + max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = y.get(tgt, np.nan)\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=h, forecast=float(s.loc[last_obs]), actual=act, model=\"naive\"))\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=h, forecast=float(fc[gap + h - 1]), actual=act, model=MODEL))\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n       \"model_description\": (\"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an \"\n                             \"expanding window starting 1948-01 (regression rows touching missing values dropped), \"\n                             \"re-estimated at every origin; level forecasts by iterating the difference recursion.\"),\n       \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n       \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\ndm = {}\nfor h in HORIZONS:\n    sub = fc_df[fc_df.horizon == h]\n    ev = sub.dropna(subset=[\"actual\"])\n    e = {m: (ev[ev.model == m].set_index(\"origin_date\")[\"actual\"] - ev[ev.model == m].set_index(\"origin_date\")[\"forecast\"])\n         for m in [\"naive\", MODEL]}\n    n = len(e[\"naive\"]); assert n == len(e[MODEL])\n    res[f\"n_evaluated_h{h}\"] = int(n)\n    for m, tag in [(\"naive\", \"naive\"), (MODEL, \"model\")]:\n        res[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e[m] ** 2)))\n        res[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e[m])))\n    # Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free (rectangular) lags, HLN small-sample correction\n    dl = (e[MODEL] ** 2 - e[\"naive\"] ** 2).values\n    dbar = dl.mean(); dc = dl - dbar\n    gam = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    var = (gam[0] + 2 * sum(gam[1:])) / n\n    if var <= 0: var = gam[0] / n\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = hln * dbar / np.sqrt(var)\n    dm[h] = (float(stat), float(2 * stats.t.sf(abs(stat), df=n - 1)), float(dbar))\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"], res[f\"mean_loss_diff_h{h}\"] = dm[h]\nres.update({\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"Missing UNRATE value(s) at {', '.join(missing_dates)} (empty field).\",\n         \"handling\": \"Origin 2025-10-01: naive uses 2025-09 value; AR forecasts iterated from last observed month (2025-09). \"\n                     \"Pairs with missing target (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and \"\n                     \"excluded from error statistics. In later training windows, regression rows touching the missing month are \"\n                     \"dropped; the forecasting state uses linear interpolation between observations dated <= origin.\"},\n        {\"issue\": \"Final (revised) vintage, not real-time data.\",\n         \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"},\n        {\"issue\": \"COVID-19 spike 2020-03..2020-05 (4.4 -> 14.8 -> 13.2) inside every training window.\",\n         \"handling\": \"Kept as genuine data; no outlier treatment (specification fixed a priori, not tuned on test errors).\"}],\n    \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n})\nlast_win = y.loc[:origins[-1]]\nres[\"n_analysed\"] = int(last_win.notna().sum())\nres[\"n_regression_rows_last_window\"] = int(ar_forecast(last_win, 1)[1])\nres[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger (training data for the last window)\nn1 = int(y.notna().sum()); n2 = res[\"n_analysed\"]\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"value not available (2025-10-01)\"),\n    dict(step=2, rule=\"drop observations dated after last origin 2026-06-01\", n_before=n1, n_after=n2, n_removed=n1 - n2,\n         rationale=\"information rule: training only on obs <= origin (these are used only as evaluation actuals)\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\", \"model_description\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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\"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985758089022913,\n \"mae_model_h1\": 0.09565582574189531,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.15977032355322282,\n \"mae_model_h3\": 0.12128483318799818,\n \"rel_improvement_rmse_h1\": -0.023478941924331087,\n \"rel_improvement_rmse_h3\": -0.007601489412623064,\n \"dm_stat_h1\": 2.5036162781392184,\n \"dm_pvalue_h1\": 0.017257584632766314,\n \"mean_loss_diff_h1\": 0.000651553982572098,\n \"dm_stat_h3\": 1.3581793864758895,\n \"dm_pvalue_h3\": 0.18335155056438499,\n \"mean_loss_diff_h3\": 0.0003836991454443411,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"n_regression_rows_last_window\": 935,\n \"random_seed\": null\n}","is_error":false}]},"uuid":"ecf4529b-ba30-466c-8035-b4761c2cf964","timestamp":"2026-10-30T04:00:10.811Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-dev-01\",\n \"model_name\": \"AR2_diff\",\n \"n_origins\": 36,\n \"first_origin\": \"2023-07-01\",\n \"last_origin\": \"2026-06-01\",\n \"n_evaluated_h1\": 35,\n \"rmse_naive_h1\": 0.11710800875382403,\n \"mae_naive_h1\": 0.09142857142857148,\n \"rmse_model_h1\": 0.11985758089022913,\n \"mae_model_h1\": 0.09565582574189531,\n \"n_evaluated_h3\": 35,\n \"rmse_naive_h3\": 0.15856499343441843,\n \"mae_naive_h3\": 0.12000000000000005,\n \"rmse_model_h3\": 0.15977032355322282,\n \"mae_model_h3\": 0.12128483318799818,\n \"rel_improvement_rmse_h1\": -0.023478941924331087,\n \"rel_improvement_rmse_h3\": -0.007601489412623064,\n \"dm_stat_h1\": 2.5036162781392184,\n \"dm_pvalue_h1\": 0.017257584632766314,\n \"mean_loss_diff_h1\": 0.000651553982572098,\n \"dm_stat_h3\": 1.3581793864758895,\n \"dm_pvalue_h3\": 0.18335155056438499,\n \"mean_loss_diff_h3\": 0.0003836991454443411,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"n_input_rows\": {\n  \"UNRATE.csv\": 945\n },\n \"n_analysed\": 941,\n \"n_regression_rows_last_window\": 935,\n \"random_seed\": null\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"80526d7c-2eaa-4339-baf4-3f0281fb1449","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"ecf4529b-ba30-466c-8035-b4761c2cf964","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30","changed":true},"type":"attachment","uuid":"26b7a172-60ef-4f14-980a-7dcbd3622f72","timestamp":"2026-10-30T04:00:10.812Z","rendered":[{"content":"<system-reminder>\nThe date has changed. Today's date is now 2026-10-30. No need to announce the new date — the user's own clock shows it.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","hash":"867929cb1aad7992d6e39d608461bc609184be5f7d8c9a14df82f5204cf98265","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01JPgxD6spL7PtYazPfBKQXn","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Rolling-origin (expanding window) forecasts of US UNRATE: naive vs AR(2) on first differences.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/UNRATE.csv; writes out/results.json, out/forecasts.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/UNRATE.csv\"; OUT = \"out\"\nP = 2                      # AR order on first differences (fixed a priori)\nHORIZONS = [1, 3]\nMODEL = \"AR2_diff\"\norigins = pd.date_range(\"2023-07-01\", \"2026-06-01\", freq=\"MS\")\n\nraw = pd.read_csv(DATA, dtype={\"observation_date\": str})\nn_raw = len(raw)\nraw[\"date\"] = pd.to_datetime(raw[\"observation_date\"], format=\"%Y-%m-%d\")\ny = raw.set_index(\"date\")[\"UNRATE\"].astype(float)\nassert y.index.is_monotonic_increasing and not y.index.duplicated().any()\nassert (y.index == pd.date_range(y.index[0], y.index[-1], freq=\"MS\")).all()\nmissing_dates = [d.strftime(\"%Y-%m-%d\") for d in y.index[y.isna()]]\n\ndef ar_forecast(s, steps):\n    \"\"\"s: series with obs dated <= origin (may contain NaN). Returns forecasts 1..steps months\n    ahead of the last *observed* month.\"\"\"\n    s = s.loc[:s.last_valid_index()]\n    d = s.diff()\n    X = pd.concat({f\"l{i}\": d.shift(i) for i in range(1, P + 1)}, axis=1)\n    reg = pd.concat([d.rename(\"y\"), X], axis=1).dropna()        # drop rows touching missing values\n    A = np.column_stack([np.ones(len(reg)), reg[[f\"l{i}\" for i in range(1, P + 1)]].values])\n    beta, *_ = np.linalg.lstsq(A, reg[\"y\"].values, rcond=None)\n    # forecasting state: interior missing values linearly interpolated using only obs <= origin\n    si = s.interpolate(method=\"linear\", limit_area=\"inside\")\n    lags = list(si.diff().values[-P:][::-1])               # [d_T, d_{T-1}]\n    level, out = si.values[-1], []\n    for _ in range(steps):\n        dhat = beta[0] + np.dot(beta[1:], lags[:P])\n        level += dhat; out.append(level); lags = [dhat] + lags[:P - 1]\n    return np.array(out), len(reg)\n\nrows = []\nfor t in origins:\n    s = y.loc[:t]                                # information rule: only obs dated <= t\n    last_obs = s.last_valid_index()\n    gap = (t.year - last_obs.year) * 12 + (t.month - last_obs.month)   # months origin is ahead of last obs\n    fc, nreg = ar_forecast(s, gap + max(HORIZONS))\n    for h in HORIZONS:\n        tgt = t + pd.DateOffset(months=h)\n        act = y.get(tgt, np.nan)\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=h, forecast=float(s.loc[last_obs]), actual=act, model=\"naive\"))\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=h, forecast=float(fc[gap + h - 1]), actual=act, model=MODEL))\nfc_df = pd.DataFrame(rows)\nfc_df.to_csv(f\"{OUT}/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nres = {\"task_id\": \"F3-dev-01\", \"model_name\": MODEL,\n       \"model_description\": (\"AR(2) with intercept on monthly first differences of UNRATE, estimated by OLS on an \"\n                             \"expanding window starting 1948-01 (regression rows touching missing values dropped), \"\n                             \"re-estimated at every origin; level forecasts by iterating the difference recursion.\"),\n       \"n_origins\": len(origins), \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n       \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\")}\ndm = {}\nfor h in HORIZONS:\n    sub = fc_df[fc_df.horizon == h]\n    ev = sub.dropna(subset=[\"actual\"])\n    e = {m: (ev[ev.model == m].set_index(\"origin_date\")[\"actual\"] - ev[ev.model == m].set_index(\"origin_date\")[\"forecast\"])\n         for m in [\"naive\", MODEL]}\n    n = len(e[\"naive\"]); assert n == len(e[MODEL])\n    res[f\"n_evaluated_h{h}\"] = int(n)\n    for m, tag in [(\"naive\", \"naive\"), (MODEL, \"model\")]:\n        res[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e[m] ** 2)))\n        res[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e[m])))\n    # Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free (rectangular) lags, HLN small-sample correction\n    dl = (e[MODEL] ** 2 - e[\"naive\"] ** 2).values\n    dbar = dl.mean(); dc = dl - dbar\n    gam = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    var = (gam[0] + 2 * sum(gam[1:])) / n\n    if var <= 0: var = gam[0] / n\n    hln = np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    stat = hln * dbar / np.sqrt(var)\n    dm[h] = (float(stat), float(2 * stats.t.sf(abs(stat), df=n - 1)), float(dbar))\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nfor h in HORIZONS:\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"], res[f\"mean_loss_diff_h{h}\"] = dm[h]\nres.update({\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": f\"Missing UNRATE value(s) at {', '.join(missing_dates)} (empty field).\",\n         \"handling\": \"Origin 2025-10-01: naive uses 2025-09 value; AR forecasts iterated from last observed month (2025-09). \"\n                     \"Pairs with missing target (origin 2025-09 h1, origin 2025-07 h3) kept in ledger with empty actual and \"\n                     \"excluded from error statistics. In later training windows, regression rows touching the missing month are \"\n                     \"dropped; the forecasting state uses linear interpolation between observations dated <= origin.\"},\n        {\"issue\": \"Final (revised) vintage, not real-time data.\",\n         \"handling\": \"Errors measure forecastability of the revised series; no real-time validity claimed.\"},\n        {\"issue\": \"COVID-19 spike 2020-03..2020-05 (4.4 -> 14.8 -> 13.2) inside every training window.\",\n         \"handling\": \"Kept as genuine data; no outlier treatment (specification fixed a priori, not tuned on test errors).\"}],\n    \"n_input_rows\": {\"UNRATE.csv\": int(n_raw)},\n})\nlast_win = y.loc[:origins[-1]]\nres[\"n_analysed\"] = int(last_win.notna().sum())\nres[\"n_regression_rows_last_window\"] = int(ar_forecast(last_win, 1)[1])\nres[\"random_seed\"] = None\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger (training data for the last window)\nn1 = int(y.notna().sum()); n2 = res[\"n_analysed\"]\npd.DataFrame([\n    dict(step=1, rule=\"drop rows with missing UNRATE\", n_before=n_raw, n_after=n1, n_removed=n_raw - n1,\n         rationale=\"value not available (2025-10-01)\"),\n    dict(step=2, rule=\"drop observations dated after last origin 2026-06-01\", n_before=n1, n_after=n2, n_removed=n1 - n2,\n         rationale=\"information rule: training only on obs <= origin (these are used only as evaluation actuals)\"),\n]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"data_issues\", \"model_description\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script"}}]}}40{"type":"api-request","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","id":"395b517a-985f-4b50-8728-af308ec66f31","timestamp":"2026-10-30T04:00:10.815Z","version":"2.1.314","querySource":"sdk","shapeHash":"219af5771e57ccc3974acd2e63a5d898f601cd3cfa18a1a978507ec5f42b45cf","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":"8800e7f1-c4c8-46ad-b7e4-57f268fcc4a4","keep":10,"tail":["867929cb1aad7992d6e39d608461bc609184be5f7d8c9a14df82f5204cf98265","e00958201f3cdc38354c1aabe8cd97d51b2d08924b78a33776c99f66b298a8e2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9c801deb4c2deed87043abf215dea835b97732303163859043ea1e6aff3d4acf"}41{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"26b7a172-60ef-4f14-980a-7dcbd3622f72","sessionId":"fd081072-6055-4453-8558-18099d7c36dc"}42{"type":"atis-latch","atis":"","sessionId":"fd081072-6055-4453-8558-18099d7c36dc"}43{"parentUuid":"26b7a172-60ef-4f14-980a-7dcbd3622f72","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkezn8YHsipgFaqzT6u","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":4147,"cache_read_input_tokens":11522,"output_tokens":433,"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":4147},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":715,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkezUGZjZv58nVy1ZtH","requestRef":"395b517a-985f-4b50-8728-af308ec66f31","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"50a639f7-a259-4671-b20c-90e85c9609dd","timestamp":"2026-10-30T04:00:12.894Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"50a639f7-a259-4671-b20c-90e85c9609dd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkezn8YHsipgFaqzT6u","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VYjX41RALxDMahW592RDqC","name":"Bash","input":{"command":"cd out; wc -l forecasts.csv; grep -E '2025-(09|10|07)-01,' forecasts.csv | head -12; cat exclusions.csv; cd ..; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\ny=pd.read_csv('data/UNRATE.csv',index_col=0,parse_dates=True).UNRATE.loc[:'2026-06-01']\nd=y.diff();X=pd.concat([d,d.shift(1),d.shift(2)],axis=1).dropna();import statsmodels.api as sm\nprint(sm.OLS(X.iloc[:,0],sm.add_constant(X.iloc[:,1:])).fit().params)\"","description":"Inspect ledger and last-window coefficients"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4147,"cache_read_input_tokens":11522,"output_tokens":433,"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":4147},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkezUGZjZv58nVy1ZtH","requestRef":"395b517a-985f-4b50-8728-af308ec66f31","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"772dc2a6-febf-4bb9-ae8e-2079cf471029","timestamp":"2026-10-30T04:00:15.140Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"772dc2a6-febf-4bb9-ae8e-2079cf471029","isSidechain":false,"promptId":"79349b31-4ab4-4454-97f0-f44ef311d903","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VYjX41RALxDMahW592RDqC","type":"tool_result","content":"     145 forecasts.csv\n2025-04-01,2025-07-01,3,4.200000,4.300000,naive\n2025-04-01,2025-07-01,3,4.200719,4.300000,AR2_diff\n2025-06-01,2025-07-01,1,4.100000,4.300000,naive\n2025-06-01,2025-07-01,1,4.088469,4.300000,AR2_diff\n2025-06-01,2025-09-01,3,4.100000,4.400000,naive\n2025-06-01,2025-09-01,3,4.097590,4.400000,AR2_diff\n2025-07-01,2025-08-01,1,4.300000,4.300000,naive\n2025-07-01,2025-08-01,1,4.316178,4.300000,AR2_diff\n2025-07-01,2025-10-01,3,4.300000,,naive\n2025-07-01,2025-10-01,3,4.308039,,AR2_diff\n2025-08-01,2025-09-01,1,4.300000,4.400000,naive\n2025-08-01,2025-09-01,1,4.291863,4.400000,AR2_diff\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE,945,944,1,value not available (2025-10-01)\n2,drop observations dated after last origin 2026-06-01,944,941,3,information rule: training only on obs <= origin (these are used only as evaluation actuals)\nconst     0.000337\nUNRATE    0.036585\nUNRATE   -0.042209\ndtype: float64","is_error":false}]},"uuid":"534d6bd5-be18-4535-9008-7ab538ea9906","timestamp":"2026-10-30T04:00:15.928Z","toolUseResult":{"stdout":"     145 forecasts.csv\n2025-04-01,2025-07-01,3,4.200000,4.300000,naive\n2025-04-01,2025-07-01,3,4.200719,4.300000,AR2_diff\n2025-06-01,2025-07-01,1,4.100000,4.300000,naive\n2025-06-01,2025-07-01,1,4.088469,4.300000,AR2_diff\n2025-06-01,2025-09-01,3,4.100000,4.400000,naive\n2025-06-01,2025-09-01,3,4.097590,4.400000,AR2_diff\n2025-07-01,2025-08-01,1,4.300000,4.300000,naive\n2025-07-01,2025-08-01,1,4.316178,4.300000,AR2_diff\n2025-07-01,2025-10-01,3,4.300000,,naive\n2025-07-01,2025-10-01,3,4.308039,,AR2_diff\n2025-08-01,2025-09-01,1,4.300000,4.400000,naive\n2025-08-01,2025-09-01,1,4.291863,4.400000,AR2_diff\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop rows with missing UNRATE,945,944,1,value not available (2025-10-01)\n2,drop observations dated after last origin 2026-06-01,944,941,3,information rule: training only on obs <= origin (these are used only as evaluation actuals)\nconst     0.000337\nUNRATE    0.036585\nUNRATE   -0.042209\ndtype: 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out; wc -l forecasts.csv; grep -E '2025-(09|10|07)-01,' forecasts.csv | head -12; cat exclusions.csv; cd ..; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\ny=pd.read_csv('data/UNRATE.csv',index_col=0,parse_dates=True).UNRATE.loc[:'2026-06-01']\nd=y.diff();X=pd.concat([d,d.shift(1),d.shift(2)],axis=1).dropna();import statsmodels.api as sm\nprint(sm.OLS(X.iloc[:,0],sm.add_constant(X.iloc[:,1:])).fit().params)\"","description":"Inspect ledger and last-window coefficients"}}]}}47{"type":"api-request","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","id":"77831642-3feb-425e-b220-9e7e4052dd2e","timestamp":"2026-10-30T04:00:15.931Z","version":"2.1.314","querySource":"sdk","shapeHash":"219af5771e57ccc3974acd2e63a5d898f601cd3cfa18a1a978507ec5f42b45cf","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":"395b517a-985f-4b50-8728-af308ec66f31","keep":12,"tail":["74d2c2a5d7685fdc86e9adbd14a69f4dfda459d3fa50661365227f3f57998101","8542347fa427d8a9962d9f476531a77693fd72fc59654abebec9959487ba4e22"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c793da32acff7c670be72b5a7a088f8391fd3f1add043f6ba3388714d96a7b11"}48{"parentUuid":"534d6bd5-be18-4535-9008-7ab538ea9906","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkfNeHCBF65cHWy8vB3","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":958,"cache_read_input_tokens":15669,"output_tokens":1532,"output_tokens_details":{"thinking_tokens":59},"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":958},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":432,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXkfNMAZ1ned8cshpZe9","requestRef":"77831642-3feb-425e-b220-9e7e4052dd2e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7b0b09e1-a071-4a27-8583-45da91056554","timestamp":"2026-10-30T04:00:17.652Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"7b0b09e1-a071-4a27-8583-45da91056554","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXkfNeHCBF65cHWy8vB3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TLDD6mf4sNGoWLpkB3D3Fq","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/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')\ny=pd.read_csv('data/UNRATE.csv',index_col=0).UNRATE\nc=[]\nc.append((\"144 ledger rows\",len(f)==144))\nc.append((\"unique (origin,h,model)\",not f.duplicated(['origin_date','horizon','model']).any()))\nc.append((\"target=origin+h\",all(pd.to_datetime(f.target_date)==pd.to_datetime(f.origin_date)+pd.to_timedelta(0,'D')+pd.to_timedelta(0,'D')+pd.Series([pd.DateOffset(months=int(h)) for h in f.horizon]).pipe(lambda s:[pd.Timestamp(o)+d for o,d in zip(f.origin_date,s)]))))\nc.append((\"actuals match data\",all((pd.isna(a) and pd.isna(y.get(t))) or a==y.get(t) for t,a in zip(f.target_date,f.actual))))\nc.append((\"n_eval = non-missing actuals/2\",[r['n_evaluated_h1'],r['n_evaluated_h3']]==[int(f[f.horizon==h].actual.notna().sum()/2) for h in (1,3)]))\nnv=f[f.model=='naive']\nexp=[y.loc[:o].dropna().iloc[-1] for o in nv.origin_date]\nc.append((\"naive = last obs <= origin\",np.allclose(nv.forecast,exp)))\nfor h in (1,3):\n  for m,t in (('naive','naive'),('AR2_diff','model')):\n    s=f[(f.horizon==h)&(f.model==m)].dropna();e=s.actual-s.forecast\n    c.append((f\"rmse/mae {t} h{h} recomputed\",np.isclose(np.sqrt((e**2).mean()),r[f'rmse_{t}_h{h}']) and np.isclose(e.abs().mean(),r[f'mae_{t}_h{h}'])))\n  c.append((f\"rel_impr h{h}\",np.isclose(r[f'rel_improvement_rmse_h{h}'],1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}'])))\nc.append((\"RMSE>=MAE>0\",all(r[f'rmse_{t}_h{h}']>=r[f'mae_{t}_h{h}']>0 for t in ('naive','model') for h in (1,3))))\nc.append((\"exclusion ledger sums 945-4=941\",945-4==r['n_analysed']))\nc.append((\"first<last origin, last origin+3 <= data end\",r['first_origin']<r['last_origin'] and '2026-09-01'<=y.index[-1]))\nc.append((\"h3 naive RMSE > h1 (plausible)\",r['rmse_naive_h3']>r['rmse_naive_h1']))\nwith open('out/state/checks.md','w') as fh:\n  fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (deleted outputs, re-ran analysis.py): results.json and forecasts.csv byte-identical: PASS\\n- Information rule: code slices y.loc[:origin] before any estimation/transformation; leakage check by re-running with all obs > origin truncated is implied by construction (see below)\\n\")\n  for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in c: print(n,ok)\nEOF","description":"Rerun for reproducibility and 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":958,"cache_read_input_tokens":15669,"output_tokens":1532,"output_tokens_details":{"thinking_tokens":59},"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":958},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXkfNMAZ1ned8cshpZe9","requestRef":"77831642-3feb-425e-b220-9e7e4052dd2e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"08cb0a06-e955-4757-a633-1d0cf2c8e3ec","timestamp":"2026-10-30T04:00:29.142Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"08cb0a06-e955-4757-a633-1d0cf2c8e3ec","isSidechain":false,"promptId":"79349b31-4ab4-4454-97f0-f44ef311d903","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nIDENTICAL\nTraceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n    return method(self, other)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/arraylike.py\", line 186, in __add__\n    return self._arith_method(other, operator.add)\n           ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/series.py\", line 6154, in _arith_method\n    return base.IndexOpsMixin._arith_method(self, other, op)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/base.py\", line 1391, in _arith_method\n    result = ops.arithmetic_op(lvalues, rvalues, op)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/ops/array_ops.py\", line 273, in arithmetic_op\n    res_values = op(left, right)\nTypeError: unsupported operand type(s) for +: 'DatetimeArray' and 'list'","is_error":true,"tool_use_id":"toolu_01TLDD6mf4sNGoWLpkB3D3Fq"}]},"uuid":"75324c06-2a82-4d73-bf4c-2bc19cfe8110","timestamp":"2026-10-30T04:00:29.992Z","toolUseResult":"Error: Exit code 1\nIDENTICAL\nTraceback (most recent call last):\n  File \"<stdin>\", line 7, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/ops/common.py\", line 76, in new_method\n    return method(self, other)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/arraylike.py\", line 186, in __add__\n    return self._arith_method(other, operator.add)\n           ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/series.py\", line 6154, in _arith_method\n    return base.IndexOpsMixin._arith_method(self, other, op)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/base.py\", line 1391, in _arith_method\n    result = ops.arithmetic_op(lvalues, rvalues, op)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/ops/array_ops.py\", line 273, in arithmetic_op\n    res_values = op(left, right)\nTypeError: unsupported operand type(s) for +: 'DatetimeArray' and 'list'","sandboxed":false,"sourceToolAssistantUUID":"08cb0a06-e955-4757-a633-1d0cf2c8e3ec","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}51{"type":"api-request-blob","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","hash":"300808d71995ee482e352286397331a2b75a430bba6b536deb3c61dd8331ea4d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01TLDD6mf4sNGoWLpkB3D3Fq","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/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')\ny=pd.read_csv('data/UNRATE.csv',index_col=0).UNRATE\nc=[]\nc.append((\"144 ledger rows\",len(f)==144))\nc.append((\"unique (origin,h,model)\",not f.duplicated(['origin_date','horizon','model']).any()))\nc.append((\"target=origin+h\",all(pd.to_datetime(f.target_date)==pd.to_datetime(f.origin_date)+pd.to_timedelta(0,'D')+pd.to_timedelta(0,'D')+pd.Series([pd.DateOffset(months=int(h)) for h in f.horizon]).pipe(lambda s:[pd.Timestamp(o)+d for o,d in zip(f.origin_date,s)]))))\nc.append((\"actuals match data\",all((pd.isna(a) and pd.isna(y.get(t))) or a==y.get(t) for t,a in zip(f.target_date,f.actual))))\nc.append((\"n_eval = non-missing actuals/2\",[r['n_evaluated_h1'],r['n_evaluated_h3']]==[int(f[f.horizon==h].actual.notna().sum()/2) for h in (1,3)]))\nnv=f[f.model=='naive']\nexp=[y.loc[:o].dropna().iloc[-1] for o in nv.origin_date]\nc.append((\"naive = last obs <= origin\",np.allclose(nv.forecast,exp)))\nfor h in (1,3):\n  for m,t in (('naive','naive'),('AR2_diff','model')):\n    s=f[(f.horizon==h)&(f.model==m)].dropna();e=s.actual-s.forecast\n    c.append((f\"rmse/mae {t} h{h} recomputed\",np.isclose(np.sqrt((e**2).mean()),r[f'rmse_{t}_h{h}']) and np.isclose(e.abs().mean(),r[f'mae_{t}_h{h}'])))\n  c.append((f\"rel_impr h{h}\",np.isclose(r[f'rel_improvement_rmse_h{h}'],1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}'])))\nc.append((\"RMSE>=MAE>0\",all(r[f'rmse_{t}_h{h}']>=r[f'mae_{t}_h{h}']>0 for t in ('naive','model') for h in (1,3))))\nc.append((\"exclusion ledger sums 945-4=941\",945-4==r['n_analysed']))\nc.append((\"first<last origin, last origin+3 <= data end\",r['first_origin']<r['last_origin'] and '2026-09-01'<=y.index[-1]))\nc.append((\"h3 naive RMSE > h1 (plausible)\",r['rmse_naive_h3']>r['rmse_naive_h1']))\nwith open('out/state/checks.md','w') as fh:\n  fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (deleted outputs, re-ran analysis.py): results.json and forecasts.csv byte-identical: PASS\\n- Information rule: code slices y.loc[:origin] before any estimation/transformation; leakage check by re-running with all obs > origin truncated is implied by construction (see below)\\n\")\n  for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in c: print(n,ok)\nEOF","description":"Rerun for reproducibility and consistency 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test forecasts identical for origins<=2024-12: True changed after: True","is_error":false}]},"uuid":"d9e77392-42c0-447a-bb63-f5d4abca6f91","timestamp":"2026-10-30T04:00:37.948Z","toolUseResult":{"stdout":"leak test forecasts identical for origins<=2024-12: True changed after: 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rows\",len(f)==144))\nc.append((\"unique (origin,h,model)\",not f.duplicated(['origin_date','horizon','model']).any()))\nc.append((\"target=origin+h months\",all((pd.Timestamp(o)+pd.DateOffset(months=int(h))).strftime('%Y-%m-%d')==t for o,t,h in zip(f.origin_date,f.target_date,f.horizon))))\nc.append((\"actuals match data\",all((pd.isna(a) and pd.isna(y.get(t))) or a==y.get(t) for t,a in zip(f.target_date,f.actual))))\nc.append((\"n_eval = non-missing actuals\",[r['n_evaluated_h1'],r['n_evaluated_h3']]==[int(f[f.horizon==h].actual.notna().sum()/2) for h in (1,3)]))\nnv=f[f.model=='naive']\nc.append((\"naive = last obs <= origin\",np.allclose(nv.forecast,[y.loc[:o].dropna().iloc[-1] for o in nv.origin_date])))\nfor h in (1,3):\n  for m,t in (('naive','naive'),('AR2_diff','model')):\n    s=f[(f.horizon==h)&(f.model==m)].dropna();e=s.actual-s.forecast\n    c.append((f\"rmse/mae {t} h{h} recomputed from ledger\",bool(np.isclose(np.sqrt((e**2).mean()),r[f'rmse_{t}_h{h}']) and np.isclose(e.abs().mean(),r[f'mae_{t}_h{h}']))))\n  c.append((f\"rel_improvement h{h} identity\",bool(np.isclose(r[f'rel_improvement_rmse_h{h}'],1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']))))\nc.append((\"RMSE>=MAE>0\",all(r[f'rmse_{t}_h{h}']>=r[f'mae_{t}_h{h}']>0 for t in ('naive','model') for h in (1,3))))\nc.append((\"exclusion ledger: 945-1-3=941=n_analysed\",945-1-3==r['n_analysed']))\nc.append((\"first_origin<last_origin; last target 2026-09 within data\",r['first_origin']<r['last_origin'] and y.index[-1]>='2026-09-01'))\nc.append((\"naive RMSE h3 > h1 (plausible magnitude)\",r['rmse_naive_h3']>r['rmse_naive_h1']))\nwith open('out/state/checks.md','w') as fh:\n  fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (outputs deleted, analysis.py re-run): results.json and forecasts.csv byte-identical: PASS\\n- Leakage test: all values after 2024-12-01 set to 9.9 in a temp copy; forecasts for origins <= 2024-12-01 identical, later ones change: PASS\\n\")\n  for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in c: 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":2108,"cache_read_input_tokens":16627,"output_tokens":1971,"output_tokens_details":{"thinking_tokens":220},"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":2108},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXkgQUSNJKesajRb71wQ","requestRef":"74f9ee25-5949-4b15-9d05-59e67ab9e85c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"aec8c08d-1f01-42e5-ad1d-dc8db4285cbb","timestamp":"2026-10-30T04:00:45.138Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"aec8c08d-1f01-42e5-ad1d-dc8db4285cbb","sessionId":"fd081072-6055-4453-8558-18099d7c36dc"}58{"type":"atis-latch","atis":"","sessionId":"fd081072-6055-4453-8558-18099d7c36dc"}59{"parentUuid":"aec8c08d-1f01-42e5-ad1d-dc8db4285cbb","isSidechain":false,"promptId":"79349b31-4ab4-4454-97f0-f44ef311d903","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BmQ1fDcPaYii4thoasQPRp","type":"tool_result","content":"144 ledger rows True\nunique (origin,h,model) True\ntarget=origin+h months True\nactuals match data True\nn_eval = non-missing actuals True\nnaive = last obs <= origin True\nrmse/mae naive h1 recomputed from ledger True\nrmse/mae model h1 recomputed from ledger True\nrel_improvement h1 identity True\nrmse/mae naive h3 recomputed from ledger True\nrmse/mae model h3 recomputed from ledger True\nrel_improvement h3 identity True\nRMSE>=MAE>0 True\nexclusion ledger: 945-1-3=941=n_analysed True\nfirst_origin<last_origin; last target 2026-09 within data True\nnaive RMSE h3 > h1 (plausible magnitude) True","is_error":false}]},"uuid":"05b66278-070f-43e3-851a-f139e61da478","timestamp":"2026-10-30T04:00:45.356Z","toolUseResult":{"stdout":"144 ledger rows True\nunique (origin,h,model) True\ntarget=origin+h months True\nactuals match data True\nn_eval = non-missing actuals True\nnaive = last obs <= origin True\nrmse/mae naive h1 recomputed from ledger True\nrmse/mae model h1 recomputed from ledger True\nrel_improvement h1 identity True\nrmse/mae naive h3 recomputed from ledger True\nrmse/mae model h3 recomputed from ledger True\nrel_improvement h3 identity True\nRMSE>=MAE>0 True\nexclusion ledger: 945-1-3=941=n_analysed True\nfirst_origin<last_origin; last target 2026-09 within data True\nnaive RMSE h3 > h1 (plausible magnitude) 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s=f[(f.horizon==h)&(f.model==m)].dropna();e=s.actual-s.forecast\n    c.append((f\"rmse/mae {t} h{h} recomputed from ledger\",bool(np.isclose(np.sqrt((e**2).mean()),r[f'rmse_{t}_h{h}']) and np.isclose(e.abs().mean(),r[f'mae_{t}_h{h}']))))\n  c.append((f\"rel_improvement h{h} identity\",bool(np.isclose(r[f'rel_improvement_rmse_h{h}'],1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']))))\nc.append((\"RMSE>=MAE>0\",all(r[f'rmse_{t}_h{h}']>=r[f'mae_{t}_h{h}']>0 for t in ('naive','model') for h in (1,3))))\nc.append((\"exclusion ledger: 945-1-3=941=n_analysed\",945-1-3==r['n_analysed']))\nc.append((\"first_origin<last_origin; last target 2026-09 within data\",r['first_origin']<r['last_origin'] and y.index[-1]>='2026-09-01'))\nc.append((\"naive RMSE h3 > h1 (plausible magnitude)\",r['rmse_naive_h3']>r['rmse_naive_h1']))\nwith open('out/state/checks.md','w') as fh:\n  fh.write(\"# Verification checks (P4)\\n\\n- Clean re-run (outputs deleted, analysis.py re-run): results.json and forecasts.csv byte-identical: PASS\\n- Leakage test: all values after 2024-12-01 set to 9.9 in a temp copy; forecasts for origins <= 2024-12-01 identical, later ones change: PASS\\n\")\n  for n,ok in c: fh.write(f\"- {n}: {'PASS' if ok else 'FAIL'}\\n\")\nfor n,ok in c: print(n,ok)\nEOF","description":"Run internal consistency checks"}}]}}61{"type":"api-request-blob","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","hash":"e942d7dcdba904178bd7b035ff7a6a796a83477a0c3f1cc4d74415d9c43f33e4","message":{"role":"user","content":[{"tool_use_id":"toolu_01XE6crC2jJA8Yi5foZLfKyi","type":"tool_result","content":"leak test forecasts identical for origins<=2024-12: True changed after: True","is_error":false},{"tool_use_id":"toolu_01BmQ1fDcPaYii4thoasQPRp","type":"tool_result","content":"144 ledger rows True\nunique (origin,h,model) True\ntarget=origin+h months True\nactuals match data True\nn_eval = non-missing actuals True\nnaive = last obs <= origin True\nrmse/mae naive h1 recomputed from ledger True\nrmse/mae model h1 recomputed from ledger True\nrel_improvement h1 identity True\nrmse/mae naive h3 recomputed from ledger True\nrmse/mae model h3 recomputed from ledger True\nrel_improvement h3 identity True\nRMSE>=MAE>0 True\nexclusion ledger: 945-1-3=941=n_analysed True\nfirst_origin<last_origin; last target 2026-09 within data True\nnaive RMSE h3 > h1 (plausible magnitude) 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>> out/state/log.md <<'EOF'\n- 2026-10-29 P2 passed: naive baseline computed in analysis.py (rmse_naive_h1/h3, mae_naive_*).\n- 2026-10-29 P3 passed: AR2_diff implemented; deterministic, no seed. Extra keys: DM test (dm_stat_h*, dm_pvalue_h*, mean_loss_diff_h*), n_regression_rows_last_window.\n- 2026-10-29 P4: clean re-run identical; leakage test passed; 16 consistency checks pass (checks.md). Last-window AR coefficients ~0 (const 0.0003, 0.037, -0.042), i.e. model is close to a random walk with drift.\nEOF\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\",\"945 rows, 2 cols, no dup dates, no gaps\",\"1 missing value 2025-10-01 recorded\",\"exclusions.csv sums: 945-1-3=941\"],\"notes\":\"data_audit.md\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive forecasts = last obs <= origin verified for all 72 naive rows\",\"naive RMSE/MAE recomputed from ledger\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"no randomness; random_seed null\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"leakage test (future values perturbed) passed\",\"16 consistency checks PASS, see checks.md\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF","description":"Update log and phase 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>> out/state/log.md <<'EOF'\n- 2026-10-29 P2 passed: naive baseline computed in analysis.py (rmse_naive_h1/h3, mae_naive_*).\n- 2026-10-29 P3 passed: AR2_diff implemented; deterministic, no seed. Extra keys: DM test (dm_stat_h*, dm_pvalue_h*, mean_loss_diff_h*), n_regression_rows_last_window.\n- 2026-10-29 P4: clean re-run identical; leakage test passed; 16 consistency checks pass (checks.md). Last-window AR coefficients ~0 (const 0.0003, 0.037, -0.042), i.e. model is close to a random walk with drift.\nEOF\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\",\"945 rows, 2 cols, no dup dates, no gaps\",\"1 missing value 2025-10-01 recorded\",\"exclusions.csv sums: 945-1-3=941\"],\"notes\":\"data_audit.md\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive forecasts = last obs <= origin verified for all 72 naive rows\",\"naive RMSE/MAE recomputed from ledger\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, forecasts.csv, exclusions.csv\",\"no randomness; random_seed null\"],\"notes\":\"\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"leakage test (future values perturbed) passed\",\"16 consistency checks PASS, see checks.md\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF","description":"Update log and phase ledger"}}]}}67{"type":"api-request","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","id":"658a2cc9-3763-4a11-831f-a12142a32918","timestamp":"2026-10-30T04:00:53.332Z","version":"2.1.314","querySource":"sdk","shapeHash":"219af5771e57ccc3974acd2e63a5d898f601cd3cfa18a1a978507ec5f42b45cf","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":"c9206b3a-115e-488d-a1d1-01fffa9fdce2","keep":18,"tail":["d80e122f6e169fb3b83bf7532328d337631d556cf8caab9c85c340b1aadc69a0","afae444916a7ff9eb5dd8798136d9656f2820d21dc532f95f30f669512a025ac"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0459e807fd897201a2676076547dd7709b1ac9d0f34b5284c054a1f6f58e8896"}68{"parentUuid":"e8df7189-5672-4611-acd6-1e2977720b48","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXki8ZHAeiXxDx6o75i9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011QA9tkWaWoR3zBnMkquxNP","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-dev-01 — Rolling-origin forecasts of the US unemployment rate: AR(2) on differences vs naive\n\n## Question\nOver the 36 expanding-window forecast origins 2023-07-01 … 2026-06-01 (`n_origins`, `first_origin`, `last_origin`), does a simple autoregressive model forecast the US civilian unemployment rate (FRED UNRATE, SA, %) better than the naive no-change forecast at horizons of 1 and 3 months? Estimands: out-of-sample RMSE and MAE (percentage points) of both forecasts and the relative RMSE improvement 1 − RMSE_model/RMSE_naive per horizon.\n\n## Data and exclusions\n- `data/UNRATE.csv`, 945 data rows (`n_input_rows`), 1948-01 to 2026-09, SHA-256 matching the README; no duplicate or skipped months (see `out/state/data_audit.md`).\n- One missing value: 2025-10-01 (listed in `data_issues`). How it was handled: (i) at origin 2025-10-01 the naive forecast is the 2025-09 value, and the AR forecast is iterated from 2025-09; (ii) the pairs (origin 2025-09, h=1) and (origin 2025-07, h=3) have no actual. They stay in `out/forecasts.csv` with an empty `actual` and are excluded from the error statistics. So `n_evaluated_h1` = 35 and `n_evaluated_h3` = 35; (iii) in later training windows, regression rows that touch the missing month are dropped. The forecasting state uses linear interpolation between observations dated ≤ origin.\n- Exclusion ledger (`out/exclusions.csv`, last training window): 945 rows → drop 1 missing → drop 3 observations dated after the last origin (used only as actuals) → 941 = `n_analysed`. The AR regression in the last window uses 935 rows (`n_regression_rows_last_window`), because differencing, the two lags and the missing month each cost rows.\n- The COVID spike (2020-04: 14.8%) is kept as genuine data in every training window.\n\n## Method\n- **Naive**: the forecast at every horizon is the latest observation dated ≤ origin.\n- **Model `AR2_diff`** (`model_name`, `model_description`): AR(2) with intercept on monthly first differences, estimated by OLS on an expanding window that starts in 1948-01 and is re-estimated at every origin. Level forecasts come from iterating the difference recursion h steps ahead (plus one extra step at the origin with the missing value). I fixed the specification before looking at any evaluation errors. No model selection was done, so selection cannot leak test information.\n- **Information rule**: at every origin the code keeps only observations dated ≤ origin, before any estimation or transformation. A leakage test supports this: I set all values after 2024-12 to 9.9 in a temporary copy of the data, and the forecasts for origins ≤ 2024-12 did not change (`out/state/checks.md`).\n- **Errors**: actual − forecast on the final-vintage data, over pairs with a non-missing actual. The method is deterministic, so no random seed is used (`random_seed` = null).\n\n## Results\n| horizon | n evaluated | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 (`n_evaluated_h1`) | 0.1171 (`rmse_naive_h1`) | 0.1199 (`rmse_model_h1`) | 0.0914 (`mae_naive_h1`) | 0.0957 (`mae_model_h1`) | −0.0235 (`rel_improvement_rmse_h1`) |\n| h=3 | 35 (`n_evaluated_h3`) | 0.1586 (`rmse_naive_h3`) | 0.1598 (`rmse_model_h3`) | 0.1200 (`mae_naive_h3`) | 0.1213 (`mae_model_h3`) | −0.0076 (`rel_improvement_rmse_h3`) |\n\nThe AR(2)-on-differences model does **not** beat the naive forecast at either horizon. Its RMSE is about 2.3% higher at h=1 and 0.8% higher at h=3, and its MAE is also slightly higher at both horizons. In the last window the estimated coefficients are close to zero (intercept ≈ 0.0003, lag coefficients ≈ 0.04 and −0.04; recorded in `out/state/log.md`, not a results key). So the model behaves almost like a random walk, and its forecasts differ from the naive ones by only a few hundredths of a point.\n\n## Uncertainty\n- Diebold–Mariano test of equal squared-error loss (model minus naive), with the Harvey–Leybourne–Newbold small-sample correction, a rectangular HAC variance with h−1 lags, and t(n−1) reference:\n  - h=1: mean loss difference 0.00065 (`mean_loss_diff_h1`), DM = 2.50 (`dm_stat_h1`), p = 0.017 (`dm_pvalue_h1`). At the 5% level the model is significantly *worse* than naive, but the difference in magnitude is tiny.\n  - h=3: mean loss difference 0.00038 (`mean_loss_diff_h3`), DM = 1.36 (`dm_stat_h3`), p = 0.18 (`dm_pvalue_h3`). The difference is not significant.\n- With only 35 evaluated pairs per horizon, overlapping h=3 errors and a calm sample period (UNRATE moved roughly within 3.5–4.6% here), these comparisons have little power and depend on the period chosen. They should not be generalised to other periods.\n\n## Limitations and open issues\n- **Final vintage**: the errors are computed on revised data. This measures how forecastable the revised series is, not real-time accuracy, and no real-time validity is claimed.\n- I evaluated only one model specification, chosen a priori. Other specifications (e.g. a shorter estimation window, or treating the COVID observations as outliers) might do differently. They were deliberately not tried, to avoid choosing a specification on the test errors. The 2020 spike probably pushes the full-sample AR coefficients towards zero.\n- The handling of the 2025-10 gap (interpolating the forecasting state, dropping regression rows) is a documented choice. It affects only a few origins.\n- The DM test with n = 35 and a HAC variance is approximate.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR2_diff model does not beat the naive forecast: its RMSE and MAE are higher (relative RMSE improvement -0.023).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; single a-priori specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the AR2_diff model does not beat the naive forecast: its RMSE and MAE are slightly higher (relative RMSE improvement -0.008).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"35 overlapping 3-month-ahead errors; final-vintage data.\"},\n {\"id\":\"C3\",\"text\":\"The h=1 loss difference is statistically significant at 5% (DM p=0.017, model worse); the h=3 difference is not (p=0.18).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"mean_loss_diff_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\",\"mean_loss_diff_h3\"],\"status\":\"supported\",\"caveat\":\"Approximate small-sample DM test; the magnitude of the difference is economically negligible.\"},\n {\"id\":\"C4\",\"text\":\"One missing observation (2025-10-01) reduces evaluated pairs to 35 per horizon; 941 observations are in the last training window.\",\"results_keys\":[\"n_evaluated_h1\",\"n_evaluated_h3\",\"n_analysed\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":857,"cache_read_input_tokens":21041,"output_tokens":3011,"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":857},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXki8FvEakpzNnh9YT52","requestRef":"658a2cc9-3763-4a11-831f-a12142a32918","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"89d31f61-3c0c-46dd-8fc1-f838adaf75df","timestamp":"2026-10-30T04:01:18.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RP0023","sessionId":"fd081072-6055-4453-8558-18099d7c36dc","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"89d31f61-3c0c-46dd-8fc1-f838adaf75df","isSidechain":false,"promptId":"79349b31-4ab4-4454-97f0-f44ef311d903","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 41 checks failed)\n  [ok] file:out/results.json: present\n  [ok] file:out/report.md: present\n  [ok] file:out/analysis.py: present\n  [ok] file:out/claims.json: present\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  [ok] section:Question: present\n  [ok] section:Data and exclusions: present\n  [ok] section:Method: present\n  [ok] section:Results: present\n  [ok] section:Uncertainty: present\n  [ok] section:Limitations and open issues: present\n  [ok] claims_json: 4 claims, all keys resolve\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2023-07-01 vs 2026-06-01\n  [ok] reproducibility: regenerated in 0.6s; 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Estimands: out-of-sample RMSE and MAE (percentage points) of both forecasts and the relative RMSE improvement 1 − RMSE_model/RMSE_naive per horizon.\n\n## Data and exclusions\n- `data/UNRATE.csv`, 945 data rows (`n_input_rows`), 1948-01 to 2026-09, SHA-256 matching the README; no duplicate or skipped months (see `out/state/data_audit.md`).\n- One missing value: 2025-10-01 (listed in `data_issues`). How it was handled: (i) at origin 2025-10-01 the naive forecast is the 2025-09 value, and the AR forecast is iterated from 2025-09; (ii) the pairs (origin 2025-09, h=1) and (origin 2025-07, h=3) have no actual. They stay in `out/forecasts.csv` with an empty `actual` and are excluded from the error statistics. So `n_evaluated_h1` = 35 and `n_evaluated_h3` = 35; (iii) in later training windows, regression rows that touch the missing month are dropped. The forecasting state uses linear interpolation between observations dated ≤ origin.\n- Exclusion ledger (`out/exclusions.csv`, last training window): 945 rows → drop 1 missing → drop 3 observations dated after the last origin (used only as actuals) → 941 = `n_analysed`. The AR regression in the last window uses 935 rows (`n_regression_rows_last_window`), because differencing, the two lags and the missing month each cost rows.\n- The COVID spike (2020-04: 14.8%) is kept as genuine data in every training window.\n\n## Method\n- **Naive**: the forecast at every horizon is the latest observation dated ≤ origin.\n- **Model `AR2_diff`** (`model_name`, `model_description`): AR(2) with intercept on monthly first differences, estimated by OLS on an expanding window that starts in 1948-01 and is re-estimated at every origin. Level forecasts come from iterating the difference recursion h steps ahead (plus one extra step at the origin with the missing value). I fixed the specification before looking at any evaluation errors. No model selection was done, so selection cannot leak test information.\n- **Information rule**: at every origin the code keeps only observations dated ≤ origin, before any estimation or transformation. A leakage test supports this: I set all values after 2024-12 to 9.9 in a temporary copy of the data, and the forecasts for origins ≤ 2024-12 did not change (`out/state/checks.md`).\n- **Errors**: actual − forecast on the final-vintage data, over pairs with a non-missing actual. The method is deterministic, so no random seed is used (`random_seed` = null).\n\n## Results\n| horizon | n evaluated | RMSE naive | RMSE AR2_diff | MAE naive | MAE AR2_diff | 1 − RMSE ratio |\n|---|---|---|---|---|---|---|\n| h=1 | 35 (`n_evaluated_h1`) | 0.1171 (`rmse_naive_h1`) | 0.1199 (`rmse_model_h1`) | 0.0914 (`mae_naive_h1`) | 0.0957 (`mae_model_h1`) | −0.0235 (`rel_improvement_rmse_h1`) |\n| h=3 | 35 (`n_evaluated_h3`) | 0.1586 (`rmse_naive_h3`) | 0.1598 (`rmse_model_h3`) | 0.1200 (`mae_naive_h3`) | 0.1213 (`mae_model_h3`) | −0.0076 (`rel_improvement_rmse_h3`) |\n\nThe AR(2)-on-differences model does **not** beat the naive forecast at either horizon. Its RMSE is about 2.3% higher at h=1 and 0.8% higher at h=3, and its MAE is also slightly higher at both horizons. In the last window the estimated coefficients are close to zero (intercept ≈ 0.0003, lag coefficients ≈ 0.04 and −0.04; recorded in `out/state/log.md`, not a results key). So the model behaves almost like a random walk, and its forecasts differ from the naive ones by only a few hundredths of a point.\n\n## Uncertainty\n- Diebold–Mariano test of equal squared-error loss (model minus naive), with the Harvey–Leybourne–Newbold small-sample correction, a rectangular HAC variance with h−1 lags, and t(n−1) reference:\n  - h=1: mean loss difference 0.00065 (`mean_loss_diff_h1`), DM = 2.50 (`dm_stat_h1`), p = 0.017 (`dm_pvalue_h1`). At the 5% level the model is significantly *worse* than naive, but the difference in magnitude is tiny.\n  - h=3: mean loss difference 0.00038 (`mean_loss_diff_h3`), DM = 1.36 (`dm_stat_h3`), p = 0.18 (`dm_pvalue_h3`). The difference is not significant.\n- With only 35 evaluated pairs per horizon, overlapping h=3 errors and a calm sample period (UNRATE moved roughly within 3.5–4.6% here), these comparisons have little power and depend on the period chosen. They should not be generalised to other periods.\n\n## Limitations and open issues\n- **Final vintage**: the errors are computed on revised data. This measures how forecastable the revised series is, not real-time accuracy, and no real-time validity is claimed.\n- I evaluated only one model specification, chosen a priori. Other specifications (e.g. a shorter estimation window, or treating the COVID observations as outliers) might do differently. They were deliberately not tried, to avoid choosing a specification on the test errors. The 2020 spike probably pushes the full-sample AR coefficients towards zero.\n- The handling of the 2025-10 gap (interpolating the forecasting state, dropping regression rows) is a documented choice. It affects only a few origins.\n- The DM test with n = 35 and a HAC variance is approximate.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"At h=1 the AR2_diff model does not beat the naive forecast: its RMSE and MAE are higher (relative RMSE improvement -0.023).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\",\"n_evaluated_h1\"],\"status\":\"supported\",\"caveat\":\"35 evaluated pairs; final-vintage data; single a-priori specification.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the AR2_diff model does not beat the naive forecast: its RMSE and MAE are slightly higher (relative RMSE improvement -0.008).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\",\"n_evaluated_h3\"],\"status\":\"supported\",\"caveat\":\"35 overlapping 3-month-ahead errors; final-vintage data.\"},\n {\"id\":\"C3\",\"text\":\"The h=1 loss difference is 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Its errors were very slightly larger at both one and three months. All phases (P0–P5) are marked passed, and the final gate run passed all 41 checks, including regenerating the results from scratch.\n\n**Model:** an AR(2) with intercept on monthly changes in the unemployment rate, labelled `AR2_diff`. It is estimated by ordinary least squares on all data from 1948 up to each origin and re-estimated at every one of the 36 origins (2023-07 to 2026-06). I fixed this specification before looking at any forecast errors and did not try alternatives.\n\n**Results (35 scored forecasts per horizon):**\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 month | 0.1171 | 0.1199 | 0.0914 | 0.0957 | −0.023 |\n| 3 months | 0.1586 | 0.1598 | 0.1200 | 0.1213 | −0.008 |\n\n- **Significance:** a Diebold–Mariano test of equal squared error gives p = 0.017 at one month, so the model is statistically worse there, though by a negligible amount. At three months the difference is not significant (p = 0.18).\n- **Why the model adds little:** in the last training window its coefficients are close to zero, so it behaves almost like a random walk. The 2020 COVID spike, which I kept in every training window, probably pushes the coefficients towards zero.\n\n**Data handling:**\n- **Missing month:** the only gap is October 2025. At that origin both forecasts start from September 2025. 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