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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`. 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The estimands are the real-time out-of-sample RMSE and MAE, over 8 quarterly origins and horizons h ∈ {1, 2} quarters, of (a) the naive forecast and (b) one model of your choice, evaluated against the *first-release* value of each target quarter, together with the relative improvement 1 − RMSE_model / RMSE_naive per horizon, and the mean absolute revision between the first release and the current value of the h = 1 target quarters.\n\n## Inputs\nAll files concern the series A191RL1Q225SBEA — US real GDP, percent change from preceding period, seasonally adjusted annual rate, quarterly (BEA via FRED/ALFRED); quarters are dated by their first day. See `data/README.md` for URLs, retrieval times (2026-10-30 UTC), SHA-256 and licence.\n- `data/vintages/A191RL1Q225SBEA_vintage_<YYYY-MM-DD>.csv` — 8 files, one per forecast origin (2024-01-31, 2024-04-30, 2024-07-31, 2024-10-31, 2025-01-31, 2025-04-30, 2025-07-31, 2025-12-31): the complete series exactly as it was published on that date (ALFRED; header `observation_date,A191RL1Q225SBEA_<yyyymmdd>`; values as of that date; the last row is the most recently published quarter at that date).\n- `data/current_vintage.csv` — the same series as published on 2026-10-30 (FRED; header `observation_date,A191RL1Q225SBEA`), containing all revisions known today.\n- `data/first_release.csv` — columns `quarter`, `first_release_value`, `first_release_vintage_date`: the first published value of each quarter from 2023Q3 to 2026Q3 and the (month-end) vintage date at which it was first available.\n\n## Information-availability rules\n- The 8 forecast origins are the 8 vintage dates. At origin d, **the only usable target data are the contents of the vintage file dated d** (its training sample ends at the last quarter in that file, and all values, including those of earlier quarters, are the ones in that file). `current_vintage.csv` must not be used for estimation or as forecast input at any origin; it is provided only to compute the revision statistic and for discussion.\n- Horizons: h = 1 and h = 2 quarters after the last quarter contained in the origin's vintage file.\n- Actuals: the first-release value of the target quarter from `data/first_release.csv`.\n- Naive baseline: for both horizons, the forecast is the last value in the origin's vintage file.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, horizon, model), columns: `origin_date` (the vintage date, YYYY-MM-DD), `target_date` (quarter start, YYYY-MM-DD), `horizon` (1 or 2), `forecast`, `actual` (first-release value), `model` (`naive` or the value of `model_name`), `vintage_date` (the vintage file used), `train_last_date` (last quarter in the training data), `train_last_value` (the value of that quarter in the training data used), `n_train` (number of observations in the training data used). Expected: 8 × 2 × 2 = 32 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-02\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, estimation sample, method) |\n| `n_origins` | integer | — | 8 |\n| `first_origin`, `last_origin` | string | date | first and last vintage dates |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h2`, `mae_naive_h2` | number | percentage points (annualised growth) | naive errors vs first releases |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h2`, `mae_model_h2` | number | percentage points | your model's errors vs first releases |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h2` | number | fraction | 1 − rmse_model / rmse_naive |\n| `mean_abs_revision_h1_targets` | number | percentage points | mean over the 8 h = 1 target quarters of \\|current-vintage value − first-release value\\| |\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 | — | data rows read per input file (excluding headers), keyed by path relative to `data/` (e.g. \"current_vintage.csv\", \"first_release.csv\", \"vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\") |\n| `n_analysed` | integer | — | observations in the last origin's training data |\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:\ncurrent_vintage.csv\nfirst_release.csv\nREADME.md\nvintages\n\n./data/vintages:\nA191RL1Q225SBEA_vintage_2024-01-31.csv\nA191RL1Q225SBEA_vintage_2024-04-30.csv\nA191RL1Q225SBEA_vintage_2024-07-31.csv\nA191RL1Q225SBEA_vintage_2024-10-31.csv\nA191RL1Q225SBEA_vintage_2025-01-31.csv\nA191RL1Q225SBEA_vintage_2025-04-30.csv\nA191RL1Q225SBEA_vintage_2025-07-31.csv\nA191RL1Q225SBEA_vintage_2025-12-31.csv","is_error":true,"tool_use_id":"toolu_014dtrPmDyt6kfsQUQ7dXcTM"}]},"uuid":"f8843518-f705-4cb4-8acf-6e1047113a78","timestamp":"2026-10-30T04:18:25.555Z","toolUseResult":"Error: Exit code 1\n# F3-conf-02 — Real-time forecasts of US real GDP growth from data vintages\n\n## Scientific question\nHow accurately could US quarterly real GDP growth have been forecast one and two quarters ahead *at the time*, using only the data that had actually been published at each forecast origin, and does a simple time-series model beat the naive forecast in this real-time setting? The estimands are the real-time out-of-sample RMSE and MAE, over 8 quarterly origins and horizons h ∈ {1, 2} quarters, of (a) the naive forecast and (b) one model of your choice, evaluated against the *first-release* value of each target quarter, together with the relative improvement 1 − RMSE_model / RMSE_naive per horizon, and the mean absolute revision between the first release and the current value of the h = 1 target quarters.\n\n## Inputs\nAll files concern the series A191RL1Q225SBEA — US real GDP, percent change from preceding period, seasonally adjusted annual rate, quarterly (BEA via FRED/ALFRED); quarters are dated by their first day. See `data/README.md` for URLs, retrieval times (2026-10-30 UTC), SHA-256 and licence.\n- `data/vintages/A191RL1Q225SBEA_vintage_<YYYY-MM-DD>.csv` — 8 files, one per forecast origin (2024-01-31, 2024-04-30, 2024-07-31, 2024-10-31, 2025-01-31, 2025-04-30, 2025-07-31, 2025-12-31): the complete series exactly as it was published on that date (ALFRED; header `observation_date,A191RL1Q225SBEA_<yyyymmdd>`; values as of that date; the last row is the most recently published quarter at that date).\n- `data/current_vintage.csv` — the same series as published on 2026-10-30 (FRED; header `observation_date,A191RL1Q225SBEA`), containing all revisions known today.\n- `data/first_release.csv` — columns `quarter`, `first_release_value`, `first_release_vintage_date`: the first published value of each quarter from 2023Q3 to 2026Q3 and the (month-end) vintage date at which it was first available.\n\n## Information-availability rules\n- The 8 forecast origins are the 8 vintage dates. At origin d, **the only usable target data are the contents of the vintage file dated d** (its training sample ends at the last quarter in that file, and all values, including those of earlier quarters, are the ones in that file). `current_vintage.csv` must not be used for estimation or as forecast input at any origin; it is provided only to compute the revision statistic and for discussion.\n- Horizons: h = 1 and h = 2 quarters after the last quarter contained in the origin's vintage file.\n- Actuals: the first-release value of the target quarter from `data/first_release.csv`.\n- Naive baseline: for both horizons, the forecast is the last value in the origin's vintage file.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, horizon, model), columns: `origin_date` (the vintage date, YYYY-MM-DD), `target_date` (quarter start, YYYY-MM-DD), `horizon` (1 or 2), `forecast`, `actual` (first-release value), `model` (`naive` or the value of `model_name`), `vintage_date` (the vintage file used), `train_last_date` (last quarter in the training data), `train_last_value` (the value of that quarter in the training data used), `n_train` (number of observations in the training data used). Expected: 8 × 2 × 2 = 32 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-02\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, estimation sample, method) |\n| `n_origins` | integer | — | 8 |\n| `first_origin`, `last_origin` | string | date | first and last vintage dates |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h2`, `mae_naive_h2` | number | percentage points (annualised growth) | naive errors vs first releases |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h2`, `mae_model_h2` | number | percentage points | your model's errors vs first releases |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h2` | number | fraction | 1 − rmse_model / rmse_naive |\n| `mean_abs_revision_h1_targets` | number | percentage points | mean over the 8 h = 1 target quarters of \\|current-vintage value − first-release value\\| |\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 | — | data rows read per input file (excluding headers), keyed by path relative to `data/` (e.g. \"current_vintage.csv\", \"first_release.csv\", \"vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\") |\n| `n_analysed` | integer | — | observations in the last origin's training data |\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:\ncurrent_vintage.csv\nfirst_release.csv\nREADME.md\nvintages\n\n./data/vintages:\nA191RL1Q225SBEA_vintage_2024-01-31.csv\nA191RL1Q225SBEA_vintage_2024-04-30.csv\nA191RL1Q225SBEA_vintage_2024-07-31.csv\nA191RL1Q225SBEA_vintage_2024-10-31.csv\nA191RL1Q225SBEA_vintage_2025-01-31.csv\nA191RL1Q225SBEA_vintage_2025-04-30.csv\nA191RL1Q225SBEA_vintage_2025-07-31.csv\nA191RL1Q225SBEA_vintage_2025-12-31.csv","sandboxed":false,"sourceToolAssistantUUID":"13b14586-0d87-438d-bb8f-5110170eec44","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","hash":"0e5e6bf6fd44f3ad03cc8ca3ed9a1136839209558cd543a22b2350493bfec6c8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSpAUKEAgTGAI4AUIIdGhpbmtpbmcSDDuzyw+bvu+sWrM7ShoM9BBH0kBK+Qb0weS9IjB5XKDWZB/JGjljI8IXSfxYAHhLhyz2lEUh6dtqwc1bm6tYMcEH2+8TDwQPGXdmd3wqwQSzrkIgrmw30mWUFfiZakjWbVIBcbMWJGXbLHJbwNom4a0Nz/+2tjrx+zQLvgHWnraJH2qYhJrT5hgSpiuOMZ7zFARNSEbLY9yj1EMUCGWCeACUP46muJXdmFXWkNPs7zsbuOR+1yT+ProTm+jLG1MMhNYD2X6vba+rL1ULcS807RDd/VMaOTG3Bw41iOgqSA7iVrQJPk1OlnXqLZZWVecAwgEN2mmgSSEG9JTWHdUYIKfHJexy19H1aFemE0KQzbc6O9ug5DAIGvHK4zsnI+Ww/kxF0lNff+KCm817SFFezTrP6EkREUS+usklZgMd298UugYKg4l9HiC0o4e5+jCYcINuQDctiW+sGnPUTX1JPGEXB8XW9/vPE4k9K64QXZsusTWkZdXCXZQYBwzn0Yuj7O14dsM0jwNW5zKP+BsUrU/2PNotsMclWpZCF9dXcHwcDFYvEnzRIBwBAUOU3VoiH15Yo/MpdUFdC2lgIrCJNz4D3e1jVfCgL5imM9+zEFXtRuaLHuB6B/tdgR1w3Vsk92XAYQXLIp5EYjOhzt70lYx9iHQ62uCOBHfXRGdm73/ZTppFe4Je5oedspeaKILXFpI9lPtSgBS7BWLW5eKuIk62Pdkav/E5DTFyMy3pqXK5EG1NKfEgesuETGLwsKCE5YzExcmCMYECY90CVId1CcfO2sksCwOTmf+smrPRBsO8Yy5nzSMmwItSCbDK5siOmmcjhjYxeErF2VcEdzRpygsXvnDmsP1w79tcwrpH+kNJGAE="},{"type":"tool_use","id":"toolu_014dtrPmDyt6kfsQUQ7dXcTM","name":"Bash","input":{"command":"cat TASK.md; 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cat gate_spec.json | head -60; cat data/first_release.csv; for f in data/vintages/*.csv data/current_vintage.csv; do echo \"== $f\"; wc -l < $f; head -3 $f; tail -4 $f; done; shasum -a 256 data/*.csv data/vintages/*.csv","description":"Inspect data files and README"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2586,"cache_read_input_tokens":4315,"output_tokens":189,"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":2586},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXn3fiAUHUZnJCfpkwZZ","requestRef":"9f12ecb9-b4e5-4809-835f-e62066726585","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a275cc69-8afe-4079-a52a-b342acde80b4","timestamp":"2026-10-30T04:18:27.504Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"a275cc69-8afe-4079-a52a-b342acde80b4","isSidechain":false,"promptId":"b4031036-e465-46aa-97b4-58842f4c56e6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01P9htfZhScMejYa3zC3Q8hY","type":"tool_result","content":"# data/README.md — F3-conf-02\n\nAll files are real data for the FRED/ALFRED series **A191RL1Q225SBEA** — Real Gross Domestic Product, United States, *percent change from preceding period, seasonally adjusted annual rate*, quarterly (source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.1). Quarters are dated by their first day (YYYY-MM-01). License: FRED/ALFRED terms of use (https://fred.stlouisfed.org/legal/); BEA data are public domain.\n\n## vintages/ — the series as it existed on each origin date (ALFRED)\nRetrieved 2026-10-30 between 03:24:52Z and 03:25:10Z from `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=A191RL1Q225SBEA&vintage_date=<date>`, saved exactly as served (header `observation_date,A191RL1Q225SBEA_<yyyymmdd>`). Each file contains every quarter that had been published as of that date, with the values as published at that date (advance/second/third estimates and annual/comprehensive revisions as of then).\n\n| file | vintage date | last quarter in file (value) | rows | SHA-256 |\n|---|---|---|---|---|\n| `vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv` | 2024-01-31 | 2023-10-01 (3.3) | 307 | 7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243 |\n| `vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv` | 2024-04-30 | 2024-01-01 (1.6) | 308 | 62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb |\n| `vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv` | 2024-07-31 | 2024-04-01 (2.8) | 309 | 6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da |\n| `vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv` | 2024-10-31 | 2024-07-01 (2.8) | 310 | c4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv` | 2025-01-31 | 2024-10-01 (2.3) | 311 | 10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv` | 2025-04-30 | 2025-01-01 (-0.3) | 312 | 1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a |\n| `vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv` | 2025-07-31 | 2025-04-01 (3.0) | 313 | f1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv` | 2025-12-31 | 2025-07-01 (4.3) | 314 | c111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7 |\n\nNote: vintage dates are month-ends chosen just after the BEA advance estimate of the preceding quarter. The advance estimate of 2025Q3 was delayed by the October–November 2025 federal shutdown (it was not available on 2025-10-31), so the eighth origin is 2025-12-31 instead of 2025-10-31.\n\n## current_vintage.csv\nThe same series as served by FRED on 2026-10-30T03:20:36Z (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=A191RL1Q225SBEA`), 318 rows 1947Q2 … 2026-07-01 — the latest revised values. SHA-256 1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562.\n\n## first_release.csv\nColumns `quarter`, `first_release_value`, `first_release_vintage_date`: for each quarter from 2023Q3 on, the value in the earliest month-end ALFRED vintage (grid: last day of every month from 2023-09-30 to 2026-09-30, plus 2026-10-29) that contains the quarter — i.e. the BEA advance estimate as it stood at that month-end. 13 rows. SHA-256 ab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb.\n\n## Vintage caveat\n`vintages/*.csv` are genuine real-time datasets (ALFRED); `current_vintage.csv` is not — it contains revisions that were unknown at the origins. Historical real-time validity can only be claimed for computations that use, at each origin, the vintage file dated at that origin.\n{\n \"task_id\": \"F3-conf-02\",\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 \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mean_abs_revision_h1_targets\": {\"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_h2\", \"rmse_model_h1\", \"rmse_model_h2\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nquarter,first_release_value,first_release_vintage_date\n2023-07-01,4.9,2023-10-31\n2023-10-01,3.3,2024-01-31\n2024-01-01,1.6,2024-04-30\n2024-04-01,2.8,2024-07-31\n2024-07-01,2.8,2024-10-31\n2024-10-01,2.3,2025-01-31\n2025-01-01,-0.3,2025-04-30\n2025-04-01,3.0,2025-07-31\n2025-07-01,4.3,2025-12-31\n2025-10-01,1.4,2026-02-28\n2026-01-01,2.0,2026-04-30\n2026-04-01,1.5,2026-07-31\n2026-07-01,3.4,2026-10-29\n== data/vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n 308\nobservation_date,A191RL1Q225SBEA_20240131\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-01-01,2.2\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.3\n== data/vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n 309\nobservation_date,A191RL1Q225SBEA_20240430\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.6\n== data/vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\n 310\nobservation_date,A191RL1Q225SBEA_20240731\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.4\n2024-04-01,2.8\n== data/vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n 311\nobservation_date,A191RL1Q225SBEA_20241031\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-10-01,3.2\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,2.8\n== data/vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n 312\nobservation_date,A191RL1Q225SBEA_20250131\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.3\n== data/vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\n 313\nobservation_date,A191RL1Q225SBEA_20250430\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.3\n== data/vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\n 314\nobservation_date,A191RL1Q225SBEA_20250731\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.5\n2025-04-01,3.0\n== data/vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv\n 315\nobservation_date,A191RL1Q225SBEA_20251231\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-10-01,1.9\n2025-01-01,-0.6\n2025-04-01,3.8\n2025-07-01,4.3\n== data/current_vintage.csv\n 319\nobservation_date,A191RL1Q225SBEA\n1947-04-01,-1.0\n1947-07-01,-0.8\n2025-10-01,0.2\n2026-01-01,2.5\n2026-04-01,2.2\n2026-07-01,3.4\n1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562 data/current_vintage.csv\nab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb data/first_release.csv\n7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243 data/vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb data/vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da data/vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\nc4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003 data/vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2 data/vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a data/vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\nf1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9 data/vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\nc111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7 data/vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv","is_error":false}]},"uuid":"3ee52e60-4bd1-43c7-87aa-8419f9ee92f8","timestamp":"2026-10-30T04:18:27.612Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-02\n\nAll files are real data for the FRED/ALFRED series **A191RL1Q225SBEA** — Real Gross Domestic Product, United States, *percent change from preceding period, seasonally adjusted annual rate*, quarterly (source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.1). Quarters are dated by their first day (YYYY-MM-01). License: FRED/ALFRED terms of use (https://fred.stlouisfed.org/legal/); BEA data are public domain.\n\n## vintages/ — the series as it existed on each origin date (ALFRED)\nRetrieved 2026-10-30 between 03:24:52Z and 03:25:10Z from `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=A191RL1Q225SBEA&vintage_date=<date>`, saved exactly as served (header `observation_date,A191RL1Q225SBEA_<yyyymmdd>`). Each file contains every quarter that had been published as of that date, with the values as published at that date (advance/second/third estimates and annual/comprehensive revisions as of then).\n\n| file | vintage date | last quarter in file (value) | rows | SHA-256 |\n|---|---|---|---|---|\n| `vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv` | 2024-01-31 | 2023-10-01 (3.3) | 307 | 7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243 |\n| `vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv` | 2024-04-30 | 2024-01-01 (1.6) | 308 | 62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb |\n| `vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv` | 2024-07-31 | 2024-04-01 (2.8) | 309 | 6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da |\n| `vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv` | 2024-10-31 | 2024-07-01 (2.8) | 310 | c4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv` | 2025-01-31 | 2024-10-01 (2.3) | 311 | 10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv` | 2025-04-30 | 2025-01-01 (-0.3) | 312 | 1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a |\n| `vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv` | 2025-07-31 | 2025-04-01 (3.0) | 313 | f1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9 |\n| `vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv` | 2025-12-31 | 2025-07-01 (4.3) | 314 | c111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7 |\n\nNote: vintage dates are month-ends chosen just after the BEA advance estimate of the preceding quarter. The advance estimate of 2025Q3 was delayed by the October–November 2025 federal shutdown (it was not available on 2025-10-31), so the eighth origin is 2025-12-31 instead of 2025-10-31.\n\n## current_vintage.csv\nThe same series as served by FRED on 2026-10-30T03:20:36Z (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=A191RL1Q225SBEA`), 318 rows 1947Q2 … 2026-07-01 — the latest revised values. SHA-256 1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562.\n\n## first_release.csv\nColumns `quarter`, `first_release_value`, `first_release_vintage_date`: for each quarter from 2023Q3 on, the value in the earliest month-end ALFRED vintage (grid: last day of every month from 2023-09-30 to 2026-09-30, plus 2026-10-29) that contains the quarter — i.e. the BEA advance estimate as it stood at that month-end. 13 rows. SHA-256 ab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb.\n\n## Vintage caveat\n`vintages/*.csv` are genuine real-time datasets (ALFRED); `current_vintage.csv` is not — it contains revisions that were unknown at the origins. Historical real-time validity can only be claimed for computations that use, at each origin, the vintage file dated at that origin.\n{\n \"task_id\": \"F3-conf-02\",\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 \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_naive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mae_naive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mae_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h2\": {\"type\": \"number\", \"nullable\": false},\n \"mean_abs_revision_h1_targets\": {\"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_h2\", \"rmse_model_h1\", \"rmse_model_h2\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\nquarter,first_release_value,first_release_vintage_date\n2023-07-01,4.9,2023-10-31\n2023-10-01,3.3,2024-01-31\n2024-01-01,1.6,2024-04-30\n2024-04-01,2.8,2024-07-31\n2024-07-01,2.8,2024-10-31\n2024-10-01,2.3,2025-01-31\n2025-01-01,-0.3,2025-04-30\n2025-04-01,3.0,2025-07-31\n2025-07-01,4.3,2025-12-31\n2025-10-01,1.4,2026-02-28\n2026-01-01,2.0,2026-04-30\n2026-04-01,1.5,2026-07-31\n2026-07-01,3.4,2026-10-29\n== data/vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n 308\nobservation_date,A191RL1Q225SBEA_20240131\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-01-01,2.2\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.3\n== data/vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n 309\nobservation_date,A191RL1Q225SBEA_20240430\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.6\n== data/vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\n 310\nobservation_date,A191RL1Q225SBEA_20240731\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.4\n2024-04-01,2.8\n== data/vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n 311\nobservation_date,A191RL1Q225SBEA_20241031\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-10-01,3.2\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,2.8\n== data/vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n 312\nobservation_date,A191RL1Q225SBEA_20250131\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.3\n== data/vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\n 313\nobservation_date,A191RL1Q225SBEA_20250430\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.3\n== data/vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\n 314\nobservation_date,A191RL1Q225SBEA_20250731\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.5\n2025-04-01,3.0\n== data/vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv\n 315\nobservation_date,A191RL1Q225SBEA_20251231\n1947-04-01,-1.0\n1947-07-01,-0.8\n2024-10-01,1.9\n2025-01-01,-0.6\n2025-04-01,3.8\n2025-07-01,4.3\n== data/current_vintage.csv\n 319\nobservation_date,A191RL1Q225SBEA\n1947-04-01,-1.0\n1947-07-01,-0.8\n2025-10-01,0.2\n2026-01-01,2.5\n2026-04-01,2.2\n2026-07-01,3.4\n1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562 data/current_vintage.csv\nab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb data/first_release.csv\n7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243 data/vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb data/vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da data/vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\nc4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003 data/vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2 data/vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a data/vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\nf1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9 data/vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\nc111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7 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> out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** Real-time out-of-sample accuracy of 1- and 2-quarter-ahead forecasts of US real GDP growth (A191RL1Q225SBEA, % q/q SAAR) at 8 forecast origins (vintage dates 2024-01-31 … 2025-12-31), naive vs one simple time-series model, scored against first-release values.\n**Estimands.** RMSE and MAE per horizon h∈{1,2} for naive (last value in the origin's vintage) and the model; rel. improvement 1−RMSE_model/RMSE_naive per h; mean |current − first release| over the 8 h=1 target quarters. Meaningful under: each origin uses only its own vintage file; first release = advance estimate as at month-end; 8 origins → very small sample, errors serially correlated across h=2 overlaps.\n**Outputs.** out/forecasts.csv (32 rows; origin_date,target_date,horizon,forecast,actual,model,vintage_date,train_last_date,train_last_value,n_train); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, rmse/mae naive & model h1/h2, rel_improvement_rmse_h1/h2, mean_abs_revision_h1_targets, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus state files and out/exclusions.csv.\n**Information rules.** At origin d use only vintage file dated d (all values as in it). current_vintage.csv only for revision statistic. Actuals from first_release.csv. Horizons relative to last quarter in the vintage file.\n**Invalidity.** Using current vintage or later vintages for estimation; missing first-release actuals for targets; model choice tuned on evaluation outcomes.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written. Starting P1 audit.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2649,"cache_read_input_tokens":11933,"output_tokens":1090,"output_tokens_details":{"thinking_tokens":98},"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":2649},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn5REaVZUArVT9d5dX5","requestRef":"5c88cba1-5e49-4261-b3e2-a5edc5b88d2f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f7b1cc22-f24a-4a3a-92cd-d462a77649a4","timestamp":"2026-10-30T04:18:58.522Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"f7b1cc22-f24a-4a3a-92cd-d462a77649a4","isSidechain":false,"promptId":"b4031036-e465-46aa-97b4-58842f4c56e6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F63zGxtDEj2CPvTBJ39Pe3","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"6c1bf5b8-8531-4c45-963f-40b32275aa7b","timestamp":"2026-10-30T04:18:58.578Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f7b1cc22-f24a-4a3a-92cd-d462a77649a4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","hash":"78865ca94d2f424795ef2ae4cf84ad28eb77db2e491c1dff62925d50aa231a71","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01F63zGxtDEj2CPvTBJ39Pe3","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract (P0)\n**Question.** Real-time out-of-sample accuracy of 1- and 2-quarter-ahead forecasts of US real GDP growth (A191RL1Q225SBEA, % q/q SAAR) at 8 forecast origins (vintage dates 2024-01-31 … 2025-12-31), naive vs one simple time-series model, scored against first-release values.\n**Estimands.** RMSE and MAE per horizon h∈{1,2} for naive (last value in the origin's vintage) and the model; rel. improvement 1−RMSE_model/RMSE_naive per h; mean |current − first release| over the 8 h=1 target quarters. Meaningful under: each origin uses only its own vintage file; first release = advance estimate as at month-end; 8 origins → very small sample, errors serially correlated across h=2 overlaps.\n**Outputs.** out/forecasts.csv (32 rows; origin_date,target_date,horizon,forecast,actual,model,vintage_date,train_last_date,train_last_value,n_train); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, rmse/mae naive & model h1/h2, rel_improvement_rmse_h1/h2, mean_abs_revision_h1_targets, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; plus state files and out/exclusions.csv.\n**Information rules.** At origin d use only vintage file dated d (all values as in it). current_vintage.csv only for revision statistic. Actuals from first_release.csv. Horizons relative to last quarter in the vintage file.\n**Invalidity.** Using current vintage or later vintages for estimation; missing first-release actuals for targets; model choice tuned on evaluation outcomes.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written. 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writes out/results.json, out/forecasts.csv, out/exclusions.csv.\nDeterministic: no randomness used.\"\"\"\nimport json, os, glob, hashlib\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA, OUT = \"data\", \"out\"\nORIGINS = [\"2024-01-31\", \"2024-04-30\", \"2024-07-31\", \"2024-10-31\",\n \"2025-01-31\", \"2025-04-30\", \"2025-07-31\", \"2025-12-31\"]\nMODEL = \"ar1_post1985_exCovid\"\nSAMPLE_START = pd.Timestamp(\"1985-01-01\")\nCOVID_ROWS = [pd.Timestamp(x) for x in (\"2020-04-01\", \"2020-07-01\", \"2020-10-01\")]\n\n\ndef read_series(path):\n d = pd.read_csv(path)\n d.columns = [\"date\", \"value\"]\n d[\"date\"] = pd.to_datetime(d[\"date\"])\n return d\n\n\ndef ar1_design(s):\n \"\"\"s: DataFrame(date,value) from one vintage. Returns y, x, and ledger counts.\"\"\"\n n0 = len(s)\n w = s[s.date >= SAMPLE_START].reset_index(drop=True)\n n1 = len(w)\n rows = pd.DataFrame({\"date\": w.date.iloc[1:].values,\n \"y\": w.value.iloc[1:].values,\n \"ylag\": w.value.iloc[:-1].values})\n # consecutive-quarter check\n assert (pd.to_datetime(rows.date).values - w.date.iloc[:-1].values).astype(\"timedelta64[D]\").astype(int).max() <= 92\n n2 = len(rows)\n rows = rows[~rows.date.isin(COVID_ROWS)].reset_index(drop=True)\n n3 = len(rows)\n return rows, (n0, n1, n2, n3)\n\n\ndef fit_ar1(rows):\n X = np.column_stack([np.ones(len(rows)), rows.ylag.values])\n beta, *_ = np.linalg.lstsq(X, rows.y.values, rcond=None)\n return beta # c, phi\n\n\ndef dm_hln(e_model, e_naive):\n \"\"\"Diebold-Mariano with Harvey-Leybourne-Newbold correction, squared loss, h=1 lag var.\"\"\"\n d = e_model ** 2 - e_naive ** 2\n n = len(d)\n dbar = d.mean()\n var = d.var(ddof=0) / n\n stat = dbar / np.sqrt(var)\n stat *= np.sqrt((n + 1 - 2 + 0) / n) # HLN factor for h=1\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\n\ndef main():\n n_input_rows, data_issues = {}, []\n fr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"quarter\", \"first_release_vintage_date\"])\n n_input_rows[\"first_release.csv\"] = int(len(fr))\n cur = read_series(os.path.join(DATA, \"current_vintage.csv\"))\n n_input_rows[\"current_vintage.csv\"] = int(len(cur))\n frmap = dict(zip(fr.quarter, fr.first_release_value))\n frdate = dict(zip(fr.quarter, fr.first_release_vintage_date))\n\n recs, ledger = [], None\n for o in ORIGINS:\n rel = f\"vintages/A191RL1Q225SBEA_vintage_{o}.csv\"\n s = read_series(os.path.join(DATA, rel))\n n_input_rows[rel] = int(len(s))\n assert s.value.notna().all() and not s.date.duplicated().any()\n last_d, last_v = s.date.iloc[-1], float(s.value.iloc[-1])\n # vintage's last value should equal that quarter's first release (sanity, not used)\n if last_d in frmap and abs(frmap[last_d] - last_v) > 1e-9:\n data_issues.append({\"issue\": f\"vintage {o}: last value {last_v} != first release {frmap[last_d]}\",\n \"handling\": \"vintage value used as forecast input (real-time rule)\"})\n rows, counts = ar1_design(s)\n c, phi = fit_ar1(rows)\n f = last_v\n for h in (1, 2):\n tgt = last_d + pd.DateOffset(months=3 * h)\n actual = frmap.get(tgt, np.nan)\n assert frdate[tgt] > pd.Timestamp(o), \"actual must be published after origin\"\n f = c + phi * f\n base = dict(origin_date=o, target_date=tgt.strftime(\"%Y-%m-%d\"), horizon=h, actual=actual,\n vintage_date=o, train_last_date=last_d.strftime(\"%Y-%m-%d\"), train_last_value=last_v)\n recs.append({**base, \"forecast\": last_v, \"model\": \"naive\", \"n_train\": int(len(s))})\n recs.append({**base, \"forecast\": float(f), \"model\": MODEL, \"n_train\": int(counts[3]),\n \"_c\": c, \"_phi\": phi})\n if o == ORIGINS[-1]:\n ledger = counts\n fc = pd.DataFrame(recs)\n fc[\"error\"] = fc.actual - fc.forecast\n cols = [\"origin_date\", \"target_date\", \"horizon\", \"forecast\", \"actual\", \"model\", \"vintage_date\",\n \"train_last_date\", \"train_last_value\", \"n_train\"]\n os.makedirs(OUT, exist_ok=True)\n fc[cols].to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\n # exclusion ledger (last origin's model estimation sample)\n n0, n1, n2, n3 = ledger\n ex = pd.DataFrame([\n dict(step=1, rule=\"keep quarters >= 1985Q1 (last-origin vintage 2025-12-31)\", n_before=n0, n_after=n1,\n n_removed=n0 - n1, rationale=\"pre-specified post-Great-Moderation estimation window\"),\n dict(step=2, rule=\"drop first quarter of window (no lag available for AR(1))\", n_before=n1, n_after=n2,\n n_removed=n1 - n2, rationale=\"AR(1) regression requires y_{t-1}\"),\n dict(step=3, rule=\"drop regression rows with t in 2020Q2,2020Q3,2020Q4\", n_before=n2, n_after=n3,\n n_removed=n2 - n3, rationale=\"COVID outliers (-28/+35 pp) as dependent or lagged value would dominate OLS\"),\n ])\n ex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n res = {\"task_id\": \"F3-conf-02\", \"model_name\": MODEL,\n \"model_description\": (\"AR(1) with intercept estimated by OLS, separately at each origin, on that origin's vintage \"\n \"from 1985Q1 to its last quarter, excluding regression rows whose dependent quarter is \"\n \"2020Q2-2020Q4; h=2 forecast by iterating the AR(1).\"),\n \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\n for m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n for h in (1, 2):\n e = fc[(fc.model == m) & (fc.horizon == h)].error.values\n res[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e)))\n for h in (1, 2):\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n t1 = fc[(fc.model == \"naive\") & (fc.horizon == 1)]\n curmap = dict(zip(cur.date, cur.value))\n revs = [curmap[pd.Timestamp(t)] - a for t, a in zip(t1.target_date, t1.actual)]\n res[\"mean_abs_revision_h1_targets\"] = float(np.mean(np.abs(revs)))\n res[\"feasibility\"] = \"complete\"\n res[\"feasibility_reason\"] = None\n res[\"data_issues\"] = data_issues\n res[\"n_input_rows\"] = n_input_rows\n res[\"n_analysed\"] = int(n3)\n res[\"random_seed\"] = None\n # supplementary\n for h in (1, 2):\n em = fc[(fc.model == MODEL) & (fc.horizon == h)].error.values\n en = fc[(fc.model == \"naive\") & (fc.horizon == h)].error.values\n st, p = dm_hln(em, en)\n res[f\"supp_dm_hln_stat_h{h}\"] = st\n res[f\"supp_dm_hln_pvalue_h{h}\"] = p\n res[f\"supp_n_origins_model_abs_err_lt_naive_h{h}\"] = int(np.sum(np.abs(em) < np.abs(en)))\n res[f\"supp_mean_error_naive_h{h}\"] = float(en.mean())\n res[f\"supp_mean_error_model_h{h}\"] = float(em.mean())\n mod = fc[(fc.model == MODEL) & (fc.horizon == 1)]\n res[\"supp_ar1_phi_range\"] = [float(mod._phi.min()), float(mod._phi.max())]\n res[\"supp_ar1_implied_mean_range\"] = [float((mod._c / (1 - mod._phi)).min()), float((mod._c / (1 - mod._phi)).max())]\n res[\"supp_revisions_h1_targets\"] = {t: float(r) for t, r in zip(t1.target_date, revs)}\n with open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=2)\n print(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\", \"mean_abs\"))}, indent=1))\n\n\nif __name__ == \"__main__\":\n main()\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/forecasts.csv && cat out/exclusions.csv && grep -A30 supp out/results.json","description":"Write and run analysis 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\"rmse_naive_h1\": 2.0102860492974624,\n \"mae_naive_h1\": 1.6874999999999996,\n \"rmse_naive_h2\": 2.265226258014859,\n \"mae_naive_h2\": 1.8124999999999998,\n \"rmse_model_h1\": 1.4835747469436587,\n \"mae_model_h1\": 1.233214264194708,\n \"rmse_model_h2\": 1.3976488356542833,\n \"mae_model_h2\": 1.029519649931495,\n \"rel_improvement_rmse_h1\": 0.26200813687080715,\n \"rel_improvement_rmse_h2\": 0.3829981306683602,\n \"mean_abs_revision_h1_targets\": 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> out/analysis.py <<'EOF'\n\"\"\"Real-time forecasts of US real GDP growth (F3-conf-02).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\nDeterministic: no randomness used.\"\"\"\nimport json, os, glob, hashlib\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA, OUT = \"data\", \"out\"\nORIGINS = [\"2024-01-31\", \"2024-04-30\", \"2024-07-31\", \"2024-10-31\",\n \"2025-01-31\", \"2025-04-30\", \"2025-07-31\", \"2025-12-31\"]\nMODEL = \"ar1_post1985_exCovid\"\nSAMPLE_START = pd.Timestamp(\"1985-01-01\")\nCOVID_ROWS = [pd.Timestamp(x) for x in (\"2020-04-01\", \"2020-07-01\", \"2020-10-01\")]\n\n\ndef read_series(path):\n d = pd.read_csv(path)\n d.columns = [\"date\", \"value\"]\n d[\"date\"] = pd.to_datetime(d[\"date\"])\n return d\n\n\ndef ar1_design(s):\n \"\"\"s: DataFrame(date,value) from one vintage. Returns y, x, and ledger counts.\"\"\"\n n0 = len(s)\n w = s[s.date >= SAMPLE_START].reset_index(drop=True)\n n1 = len(w)\n rows = pd.DataFrame({\"date\": w.date.iloc[1:].values,\n \"y\": w.value.iloc[1:].values,\n \"ylag\": w.value.iloc[:-1].values})\n # consecutive-quarter check\n assert (pd.to_datetime(rows.date).values - w.date.iloc[:-1].values).astype(\"timedelta64[D]\").astype(int).max() <= 92\n n2 = len(rows)\n rows = rows[~rows.date.isin(COVID_ROWS)].reset_index(drop=True)\n n3 = len(rows)\n return rows, (n0, n1, n2, n3)\n\n\ndef fit_ar1(rows):\n X = np.column_stack([np.ones(len(rows)), rows.ylag.values])\n beta, *_ = np.linalg.lstsq(X, rows.y.values, rcond=None)\n return beta # c, phi\n\n\ndef dm_hln(e_model, e_naive):\n \"\"\"Diebold-Mariano with Harvey-Leybourne-Newbold correction, squared loss, h=1 lag var.\"\"\"\n d = e_model ** 2 - e_naive ** 2\n n = len(d)\n dbar = d.mean()\n var = d.var(ddof=0) / n\n stat = dbar / np.sqrt(var)\n stat *= np.sqrt((n + 1 - 2 + 0) / n) # HLN factor for h=1\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\n\ndef main():\n n_input_rows, data_issues = {}, []\n fr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"quarter\", \"first_release_vintage_date\"])\n n_input_rows[\"first_release.csv\"] = int(len(fr))\n cur = read_series(os.path.join(DATA, \"current_vintage.csv\"))\n n_input_rows[\"current_vintage.csv\"] = int(len(cur))\n frmap = dict(zip(fr.quarter, fr.first_release_value))\n frdate = dict(zip(fr.quarter, fr.first_release_vintage_date))\n\n recs, ledger = [], None\n for o in ORIGINS:\n rel = f\"vintages/A191RL1Q225SBEA_vintage_{o}.csv\"\n s = read_series(os.path.join(DATA, rel))\n n_input_rows[rel] = int(len(s))\n assert s.value.notna().all() and not s.date.duplicated().any()\n last_d, last_v = s.date.iloc[-1], float(s.value.iloc[-1])\n # vintage's last value should equal that quarter's first release (sanity, not used)\n if last_d in frmap and abs(frmap[last_d] - last_v) > 1e-9:\n data_issues.append({\"issue\": f\"vintage {o}: last value {last_v} != first release {frmap[last_d]}\",\n \"handling\": \"vintage value used as forecast input (real-time rule)\"})\n rows, counts = ar1_design(s)\n c, phi = fit_ar1(rows)\n f = last_v\n for h in (1, 2):\n tgt = last_d + pd.DateOffset(months=3 * h)\n actual = frmap.get(tgt, np.nan)\n assert frdate[tgt] > pd.Timestamp(o), \"actual must be published after origin\"\n f = c + phi * f\n base = dict(origin_date=o, target_date=tgt.strftime(\"%Y-%m-%d\"), horizon=h, actual=actual,\n vintage_date=o, train_last_date=last_d.strftime(\"%Y-%m-%d\"), train_last_value=last_v)\n recs.append({**base, \"forecast\": last_v, \"model\": \"naive\", \"n_train\": int(len(s))})\n recs.append({**base, \"forecast\": float(f), \"model\": MODEL, \"n_train\": int(counts[3]),\n \"_c\": c, \"_phi\": phi})\n if o == ORIGINS[-1]:\n ledger = counts\n fc = pd.DataFrame(recs)\n fc[\"error\"] = fc.actual - fc.forecast\n cols = [\"origin_date\", \"target_date\", \"horizon\", \"forecast\", \"actual\", \"model\", \"vintage_date\",\n \"train_last_date\", \"train_last_value\", \"n_train\"]\n os.makedirs(OUT, exist_ok=True)\n fc[cols].to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\n # exclusion ledger (last origin's model estimation sample)\n n0, n1, n2, n3 = ledger\n ex = pd.DataFrame([\n dict(step=1, rule=\"keep quarters >= 1985Q1 (last-origin vintage 2025-12-31)\", n_before=n0, n_after=n1,\n n_removed=n0 - n1, rationale=\"pre-specified post-Great-Moderation estimation window\"),\n dict(step=2, rule=\"drop first quarter of window (no lag available for AR(1))\", n_before=n1, n_after=n2,\n n_removed=n1 - n2, rationale=\"AR(1) regression requires y_{t-1}\"),\n dict(step=3, rule=\"drop regression rows with t in 2020Q2,2020Q3,2020Q4\", n_before=n2, n_after=n3,\n n_removed=n2 - n3, rationale=\"COVID outliers (-28/+35 pp) as dependent or lagged value would dominate OLS\"),\n ])\n ex.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\n res = {\"task_id\": \"F3-conf-02\", \"model_name\": MODEL,\n \"model_description\": (\"AR(1) with intercept estimated by OLS, separately at each origin, on that origin's vintage \"\n \"from 1985Q1 to its last quarter, excluding regression rows whose dependent quarter is \"\n \"2020Q2-2020Q4; h=2 forecast by iterating the AR(1).\"),\n \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\n for m, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n for h in (1, 2):\n e = fc[(fc.model == m) & (fc.horizon == h)].error.values\n res[f\"rmse_{tag}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{tag}_h{h}\"] = float(np.mean(np.abs(e)))\n for h in (1, 2):\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n t1 = fc[(fc.model == \"naive\") & (fc.horizon == 1)]\n curmap = dict(zip(cur.date, cur.value))\n revs = [curmap[pd.Timestamp(t)] - a for t, a in zip(t1.target_date, t1.actual)]\n res[\"mean_abs_revision_h1_targets\"] = float(np.mean(np.abs(revs)))\n res[\"feasibility\"] = \"complete\"\n res[\"feasibility_reason\"] = None\n res[\"data_issues\"] = data_issues\n res[\"n_input_rows\"] = n_input_rows\n res[\"n_analysed\"] = int(n3)\n res[\"random_seed\"] = None\n # supplementary\n for h in (1, 2):\n em = fc[(fc.model == MODEL) & (fc.horizon == h)].error.values\n en = fc[(fc.model == \"naive\") & (fc.horizon == h)].error.values\n st, p = dm_hln(em, en)\n res[f\"supp_dm_hln_stat_h{h}\"] = st\n res[f\"supp_dm_hln_pvalue_h{h}\"] = p\n res[f\"supp_n_origins_model_abs_err_lt_naive_h{h}\"] = int(np.sum(np.abs(em) < np.abs(en)))\n res[f\"supp_mean_error_naive_h{h}\"] = float(en.mean())\n res[f\"supp_mean_error_model_h{h}\"] = float(em.mean())\n mod = fc[(fc.model == MODEL) & (fc.horizon == 1)]\n res[\"supp_ar1_phi_range\"] = [float(mod._phi.min()), float(mod._phi.max())]\n res[\"supp_ar1_implied_mean_range\"] = [float((mod._c / (1 - mod._phi)).min()), float((mod._c / (1 - mod._phi)).max())]\n res[\"supp_revisions_h1_targets\"] = {t: float(r) for t, r in zip(t1.target_date, revs)}\n with open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n json.dump(res, fh, indent=2)\n print(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\", \"mean_abs\"))}, indent=1))\n\n\nif __name__ == \"__main__\":\n main()\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/forecasts.csv && cat out/exclusions.csv && grep -A30 supp out/results.json","description":"Write and run analysis 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\nold=s[s.index('def dm_hln'):s.index('def main')]\nnew='''def dm_hln(e_model, e_naive, h):\n \"\"\"Diebold-Mariano test (squared loss) with Harvey-Leybourne-Newbold small-sample correction;\n long-run variance uses autocovariances up to lag h-1 (rectangular).\"\"\"\n d = e_model ** 2 - e_naive ** 2\n n = len(d)\n dbar = d.mean()\n dc = d - dbar\n lrv = np.sum(dc * dc) / n\n for k in range(1, h):\n lrv += 2 * np.sum(dc[k:] * dc[:-k]) / n\n if lrv <= 0:\n return None, None\n stat = dbar / np.sqrt(lrv / n)\n stat *= np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n p = 2 * stats.t.sf(abs(stat), df=n - 1)\n return float(stat), float(p)\n\n\n'''\ns=s.replace(old,new).replace('st, p = dm_hln(em, en)','st, p = dm_hln(em, en, h)')\ns=s.replace(''' fc = pd.DataFrame(recs)''',''' data_issues.append({\"issue\": \"Origins are not equally spaced: the 8th origin is 2025-12-31 (5 months after 2025-07-31) because the 2025Q3 advance estimate was delayed by the Oct-Nov 2025 federal shutdown (data/README.md).\",\n \"handling\": \"Used as given; horizons are defined relative to the last quarter in each vintage, so targets remain consecutive quarters.\"})\n data_issues.append({\"issue\": \"All vintages contain extreme COVID-19 observations (2020Q2 about -28, 2020Q3 about +35 pp SAAR).\",\n \"handling\": \"AR(1) regression rows with dependent quarter 2020Q2-2020Q4 excluded (pre-specified; see out/exclusions.csv). Naive forecast unaffected.\"})\n data_issues.append({\"issue\": \"Target quarter 2025Q4 first release became available only at the 2026-02-28 vintage (shutdown delay).\",\n \"handling\": \"first_release.csv used as given; it is after the origin, so no look-ahead.\"})\n fc = pd.DataFrame(recs)''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -E \"dm_hln\" out/results.json","description":"Fix DM test for h=2, add data issues, 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nSHA-256 computed with `shasum -a 256`; all 10 match data/README.md.\n\n| file | sha256 | data rows | cols | range | NaN | dup dates | gaps>1q | min/max |\n|---|---|---|---|---|---|---|---|---|\n| vintages/..._2024-01-31.csv | 7ce218fb…f243 | 307 | 2 | 1947-04-01..2023-10-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-04-30.csv | 62c1dfce…97cb | 308 | 2 | ..2024-01-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-07-31.csv | 6f942591…79da | 309 | 2 | ..2024-04-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-10-31.csv | c4c51ede…0003 | 310 | 2 | ..2024-07-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-01-31.csv | 10a5e02e…aee2 | 311 | 2 | ..2024-10-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-04-30.csv | 1f19c975…085d1a | 312 | 2 | ..2025-01-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-07-31.csv | f1e7c556…57a9 | 313 | 2 | ..2025-04-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-12-31.csv | c111c070…a8d4a7 | 314 | 2 | ..2025-07-01 | 0 | 0 | 0 | -28.0/34.9 |\n| current_vintage.csv | 1af2590d…0562 | 318 | 2 | 1947-04-01..2026-07-01 | 0 | 0 | 0 | -28.0/34.9 |\n| first_release.csv | ab843c9f…95bb | 13 | 3 | quarters 2023-07-01..2026-07-01 | 0 | 0 | 0 | -0.3/4.9 |\n\nUnits: percent change from preceding quarter, SAAR (percentage points of annualised growth). Quarters dated by first day.\n\nAnomalies / notes:\n- COVID quarters 2020Q2/Q3 are extreme outliers in every vintage -> handled in model estimation (exclusion ledger).\n- 8th origin 2025-12-31 (shutdown-delayed advance estimate); spacing irregular.\n- Last value of each vintage equals first_release value of that quarter (checked in analysis.py; no mismatch).\n- Every target quarter (2024Q1..2026Q1) has a first release with vintage date strictly after its origin (asserted in analysis.py).\n\nInformation-availability handling: current_vintage.csv is read only for the revision statistic; first_release.csv only for actuals. No input violates the rules as long as each origin uses only its own vintage file, which analysis.py enforces (one file per origin loop iteration).\nModel specification (AR(1), 1985Q1+ window, COVID rows excluded) was fixed before computing any forecast errors.\nEOF\necho \"- 2026-10-30 P1 passed: audit written, hashes match. Decision: model = AR(1) OLS, 1985Q1+, exclude rows t=2020Q2..Q4, iterated h=2; fixed before evaluation.\n- 2026-10-30 P2 passed: naive baseline in results.json (rmse_naive_*, mae_naive_*).\n- 2026-10-30 P3 passed: analysis.py written; DM-HLN supplementary test added (lag h-1 LRV).\" >> out/state/log.md\nrm -f out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\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');ex=pd.read_csv('out/exclusions.csv')\nprint(len(f), f.groupby(['model','horizon']).size().tolist())\nprint('ledger', ex.n_before[0]-ex.n_removed.sum()==r['n_analysed'], (ex.n_before-ex.n_removed==ex.n_after).all())\nprint('rmse>=mae', all(r[f'rmse_{m}_h{h}']>=r[f'mae_{m}_h{h}'] for m in ['naive','model'] for h in [1,2]))\nprint('rel', [abs(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']-r[f'rel_improvement_rmse_h{h}'])<1e-12 for h in [1,2]])\nprint('target>train_last', (pd.to_datetime(f.target_date)>pd.to_datetime(f.train_last_date)).all(), (f.vintage_date==f.origin_date).all())\n# independent recompute of naive rmse\nfor h in [1,2]:\n g=f[(f.model=='naive')&(f.horizon==h)]; print(h,np.sqrt(((g.actual-g.forecast)**2).mean()))\n# check train_last_value against vintage file\nfor o in f.origin_date.unique():\n v=pd.read_csv(f'data/vintages/A191RL1Q225SBEA_vintage_{o}.csv'); print(o, v.iloc[-1,1]==f[f.origin_date==o].train_last_value.iloc[0], len(v)==f[(f.origin_date==o)&(f.model=='naive')].n_train.iloc[0])\nEOF","description":"Write audit, rerun reproducibility, 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":1486,"cache_read_input_tokens":22367,"output_tokens":2331,"output_tokens_details":{"thinking_tokens":81},"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":1486},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn9FfiWcj1tnuw8aKuY","requestRef":"b520b70d-405a-4044-8cf0-b1d4cc90b1c6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4e0787c8-767a-430f-adc4-643e8eb49da6","timestamp":"2026-10-30T04:20:04.086Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"4e0787c8-767a-430f-adc4-643e8eb49da6","isSidechain":false,"promptId":"b4031036-e465-46aa-97b4-58842f4c56e6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AWM5yCGWiF7qou1tvCEQqx","type":"tool_result","content":"IDENTICAL\n32 [8, 8, 8, 8]\nledger True True\nrmse>=mae True\nrel [True, True]\ntarget>train_last True True\n1 2.0102860492974624\n2 2.265226258014859\n2024-01-31 True True\n2024-04-30 True True\n2024-07-31 True True\n2024-10-31 True True\n2025-01-31 True True\n2025-04-30 True True\n2025-07-31 True True\n2025-12-31 True True","is_error":false}]},"uuid":"f854c89c-d04e-45e8-a3dc-fecb6315c20b","timestamp":"2026-10-30T04:20:05.834Z","toolUseResult":{"stdout":"IDENTICAL\n32 [8, 8, 8, 8]\nledger True True\nrmse>=mae True\nrel [True, True]\ntarget>train_last True True\n1 2.0102860492974624\n2 2.265226258014859\n2024-01-31 True True\n2024-04-30 True True\n2024-07-31 True True\n2024-10-31 True True\n2025-01-31 True True\n2025-04-30 True True\n2025-07-31 True True\n2025-12-31 True True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4e0787c8-767a-430f-adc4-643e8eb49da6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","hash":"d703dfadcbe590becd3fe1c22435875de965bda4bfcd62bd23ce598e83260884","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01AWM5yCGWiF7qou1tvCEQqx","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nSHA-256 computed with `shasum -a 256`; all 10 match data/README.md.\n\n| file | sha256 | data rows | cols | range | NaN | dup dates | gaps>1q | min/max |\n|---|---|---|---|---|---|---|---|---|\n| vintages/..._2024-01-31.csv | 7ce218fb…f243 | 307 | 2 | 1947-04-01..2023-10-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-04-30.csv | 62c1dfce…97cb | 308 | 2 | ..2024-01-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-07-31.csv | 6f942591…79da | 309 | 2 | ..2024-04-01 | 0 | 0 | 0 | -28.0/34.8 |\n| vintages/..._2024-10-31.csv | c4c51ede…0003 | 310 | 2 | ..2024-07-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-01-31.csv | 10a5e02e…aee2 | 311 | 2 | ..2024-10-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-04-30.csv | 1f19c975…085d1a | 312 | 2 | ..2025-01-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-07-31.csv | f1e7c556…57a9 | 313 | 2 | ..2025-04-01 | 0 | 0 | 0 | -28.1/35.2 |\n| vintages/..._2025-12-31.csv | c111c070…a8d4a7 | 314 | 2 | ..2025-07-01 | 0 | 0 | 0 | -28.0/34.9 |\n| current_vintage.csv | 1af2590d…0562 | 318 | 2 | 1947-04-01..2026-07-01 | 0 | 0 | 0 | -28.0/34.9 |\n| first_release.csv | ab843c9f…95bb | 13 | 3 | quarters 2023-07-01..2026-07-01 | 0 | 0 | 0 | -0.3/4.9 |\n\nUnits: percent change from preceding quarter, SAAR (percentage points of annualised growth). Quarters dated by first day.\n\nAnomalies / notes:\n- COVID quarters 2020Q2/Q3 are extreme outliers in every vintage -> handled in model estimation (exclusion ledger).\n- 8th origin 2025-12-31 (shutdown-delayed advance estimate); spacing irregular.\n- Last value of each vintage equals first_release value of that quarter (checked in analysis.py; no mismatch).\n- Every target quarter (2024Q1..2026Q1) has a first release with vintage date strictly after its origin (asserted in analysis.py).\n\nInformation-availability handling: current_vintage.csv is read only for the revision statistic; first_release.csv only for actuals. No input violates the rules as long as each origin uses only its own vintage file, which analysis.py enforces (one file per origin loop iteration).\nModel specification (AR(1), 1985Q1+ window, COVID rows excluded) was fixed before computing any forecast errors.\nEOF\necho \"- 2026-10-30 P1 passed: audit written, hashes match. Decision: model = AR(1) OLS, 1985Q1+, exclude rows t=2020Q2..Q4, iterated h=2; fixed before evaluation.\n- 2026-10-30 P2 passed: naive baseline in results.json (rmse_naive_*, mae_naive_*).\n- 2026-10-30 P3 passed: analysis.py written; DM-HLN supplementary test added (lag h-1 LRV).\" >> out/state/log.md\nrm -f out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\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');ex=pd.read_csv('out/exclusions.csv')\nprint(len(f), f.groupby(['model','horizon']).size().tolist())\nprint('ledger', ex.n_before[0]-ex.n_removed.sum()==r['n_analysed'], (ex.n_before-ex.n_removed==ex.n_after).all())\nprint('rmse>=mae', all(r[f'rmse_{m}_h{h}']>=r[f'mae_{m}_h{h}'] for m in ['naive','model'] for h in [1,2]))\nprint('rel', [abs(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']-r[f'rel_improvement_rmse_h{h}'])<1e-12 for h in [1,2]])\nprint('target>train_last', (pd.to_datetime(f.target_date)>pd.to_datetime(f.train_last_date)).all(), (f.vintage_date==f.origin_date).all())\n# independent recompute of naive rmse\nfor h in [1,2]:\n g=f[(f.model=='naive')&(f.horizon==h)]; print(h,np.sqrt(((g.actual-g.forecast)**2).mean()))\n# check train_last_value against vintage file\nfor o in f.origin_date.unique():\n v=pd.read_csv(f'data/vintages/A191RL1Q225SBEA_vintage_{o}.csv'); print(o, v.iloc[-1,1]==f[f.origin_date==o].train_last_value.iloc[0], len(v)==f[(f.origin_date==o)&(f.model=='naive')].n_train.iloc[0])\nEOF","description":"Write audit, rerun reproducibility, consistency checks"}}]}}53{"type":"api-request","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","id":"8de3222d-d272-4870-a768-95533474a3cf","timestamp":"2026-10-30T04:20:05.838Z","version":"2.1.314","querySource":"sdk","shapeHash":"4273f8f8d6ac3d7a48892e0dc260f2b06a96a55ced8886d9155ff9bc6d2f815b","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":"b520b70d-405a-4044-8cf0-b1d4cc90b1c6","keep":14,"tail":["d703dfadcbe590becd3fe1c22435875de965bda4bfcd62bd23ce598e83260884","66cf59914c2cb1d8b20917c3f924ad8a23c1cf44762b966219f254ee4a2f8585"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bc644fdefd91ecc0ceb26386fed99dc003fd8bfd1a5f37d8634b76540d4f48a3"}54{"parentUuid":"f854c89c-d04e-45e8-a3dc-fecb6315c20b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnB4mgqqcCzso4d6jtb","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PZ4Hgz4opN2aXd2sFpUJkb","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted outputs, ran twice) -> results.json and forecasts.csv byte-identical | PASS |\n| forecasts.csv has 32 rows, 8 per (model,horizon) | PASS |\n| Exclusion ledger: 314 - 151 - 1 - 3 = 159 = n_analysed; each step n_before-n_removed=n_after | PASS |\n| RMSE >= MAE for every model/horizon; all RMSE > 0 | PASS |\n| rel_improvement_rmse_h{1,2} == 1 - rmse_model/rmse_naive | PASS |\n| target_date > train_last_date for all rows; vintage_date == origin_date | PASS |\n| train_last_value and n_train (naive) equal last value / row count of the origin's vintage file | PASS |\n| Naive RMSE independently recomputed from forecasts.csv matches results.json | PASS |\n| first_release_vintage_date of every target > origin (assert in analysis.py) | PASS |\n| SHA-256 of all inputs match data/README.md | PASS |\n| Magnitudes: RMSEs 1.4-2.3 pp, same order as GDP growth volatility; AR(1) phi 0.33-0.34, implied mean 2.6-2.7% — plausible | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Record checks and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2490,"cache_read_input_tokens":23853,"output_tokens":603,"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":2490},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXnB4V5LCxoVx89gPWS4","requestRef":"8de3222d-d272-4870-a768-95533474a3cf","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b5f559c3-b95f-4e93-af9f-741cbd90b352","timestamp":"2026-10-30T04:20:10.969Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"b5f559c3-b95f-4e93-af9f-741cbd90b352","isSidechain":false,"promptId":"b4031036-e465-46aa-97b4-58842f4c56e6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PZ4Hgz4opN2aXd2sFpUJkb","type":"tool_result","content":" [ok] key:last_origin: ok\n [ok] key:rmse_naive_h1: ok\n [ok] key:mae_naive_h1: ok\n [ok] key:rmse_naive_h2: ok\n [ok] key:mae_naive_h2: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h2: ok\n [ok] key:mae_model_h2: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h2: ok\n [ok] key:mean_abs_revision_h1_targets: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] chronology:first_origin<=last_origin: 2024-01-31 vs 2025-12-31\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0065/out/state/gate_receipts/20261030T042011.753918Z.json","is_error":false}]},"uuid":"20584a66-093e-4bfd-be85-09f2054ce138","timestamp":"2026-10-30T04:20:11.762Z","toolUseResult":{"stdout":" [ok] key:last_origin: ok\n [ok] key:rmse_naive_h1: ok\n [ok] key:mae_naive_h1: ok\n [ok] key:rmse_naive_h2: ok\n [ok] key:mae_naive_h2: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h2: ok\n [ok] key:mae_model_h2: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h2: ok\n [ok] key:mean_abs_revision_h1_targets: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 3 steps consistent\n [ok] chronology:first_origin<=last_origin: 2024-01-31 vs 2025-12-31\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: 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forecasts.csv byte-identical | PASS |\n| forecasts.csv has 32 rows, 8 per (model,horizon) | PASS |\n| Exclusion ledger: 314 - 151 - 1 - 3 = 159 = n_analysed; each step n_before-n_removed=n_after | PASS |\n| RMSE >= MAE for every model/horizon; all RMSE > 0 | PASS |\n| rel_improvement_rmse_h{1,2} == 1 - rmse_model/rmse_naive | PASS |\n| target_date > train_last_date for all rows; vintage_date == origin_date | PASS |\n| train_last_value and n_train (naive) equal last value / row count of the origin's vintage file | PASS |\n| Naive RMSE independently recomputed from forecasts.csv matches results.json | PASS |\n| first_release_vintage_date of every target > origin (assert in analysis.py) | PASS |\n| SHA-256 of all inputs match data/README.md | PASS |\n| Magnitudes: RMSEs 1.4-2.3 pp, same order as GDP growth volatility; AR(1) phi 0.33-0.34, implied mean 2.6-2.7% — plausible | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Record checks and 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> out/report.md <<'EOF'\n# Real-time forecasts of US real GDP growth from data vintages (F3-conf-02)\n\n## Question\nHow accurately could US quarterly real GDP growth (A191RL1Q225SBEA, % q/q SAAR) have been forecast 1 and 2 quarters ahead using only the data published at each of 8 forecast origins (2024-01-31 … 2025-12-31; `first_origin`, `last_origin`, `n_origins`), scored against first-release values, and does a simple time-series model beat the naive (last-value) forecast?\n\n## Data and exclusions\n- Inputs: 8 ALFRED vintage files (307–314 rows each), `current_vintage.csv` (318 rows), `first_release.csv` (13 rows); row counts in `n_input_rows`. All SHA-256 hashes match `data/README.md`; no missing values, duplicate dates or gaps (`out/state/data_audit.md`).\n- Real-time rule: at each origin only that origin's vintage file is used (training sample = whole file, values as in that file). `current_vintage.csv` is used only for the revision statistic; `first_release.csv` only for actuals. Every target's first release is dated after its origin (asserted in code).\n- Exclusions apply only to the model's estimation sample (`out/exclusions.csv`, shown for the last origin): 314 vintage observations → keep 1985Q1 onward (−151) → drop first window quarter for the lag (−1) → drop regression rows whose dependent quarter is 2020Q2–2020Q4 (−3; COVID outliers of about −28/+35 pp) → 159 regression observations (`n_analysed`). The naive forecast uses the full vintage file.\n- Data issues recorded in `data_issues`: irregular 8th origin (2025-12-31, shutdown-delayed advance estimate), COVID outliers, delayed first release of 2025Q4.\n\n## Method\n- Naive: forecast for h = 1 and h = 2 = last value in the origin's vintage file.\n- Model (`model_name` = `ar1_post1985_exCovid`; `model_description`): AR(1) with intercept, OLS, re-estimated at each origin on that vintage from 1985Q1 to its last quarter, excluding the COVID regression rows; h = 2 by iterating the AR(1). The specification was fixed before any forecast errors were computed (no tuning on the evaluation sample).\n- Errors = first-release value − forecast; RMSE and MAE over the 8 origins per horizon; `rel_improvement_rmse_h*` = 1 − RMSE_model/RMSE_naive.\n- Revision statistic: mean over the 8 h = 1 target quarters (2024Q1–2025Q4) of |current-vintage value − first release|.\n- Supplementary: Diebold–Mariano test on squared-error differentials with Harvey–Leybourne–Newbold correction (long-run variance with lags up to h−1), t(7) reference. No randomness (`random_seed` = null).\n\n## Results\n| | naive | AR(1) |\n|---|---|---|\n| RMSE h=1 (pp) | 2.010 (`rmse_naive_h1`) | 1.484 (`rmse_model_h1`) |\n| MAE h=1 (pp) | 1.687 (`mae_naive_h1`) | 1.233 (`mae_model_h1`) |\n| RMSE h=2 (pp) | 2.265 (`rmse_naive_h2`) | 1.398 (`rmse_model_h2`) |\n| MAE h=2 (pp) | 1.812 (`mae_naive_h2`) | 1.030 (`mae_model_h2`) |\n\n- Relative RMSE improvement of the AR(1) over naive: 0.262 at h = 1 (`rel_improvement_rmse_h1`) and 0.383 at h = 2 (`rel_improvement_rmse_h2`).\n- The AR(1) had a smaller absolute error than naive at 5 of 8 origins for h = 1 (`supp_n_origins_model_abs_err_lt_naive_h1`) and 8 of 8 for h = 2 (`supp_n_origins_model_abs_err_lt_naive_h2`).\n- The estimated AR(1) was very stable across vintages: φ between 0.335 and 0.343 (`supp_ar1_phi_range`), implied mean 2.62–2.66 % (`supp_ar1_implied_mean_range`); the model's gain comes from shrinking the last observation toward the mean, which helped most after the volatile 2025Q1 (−0.3) and 2025Q3 (4.3) prints.\n- Mean absolute revision between first release and the current (2026-10-30) value of the h = 1 targets: 0.5625 pp (`mean_abs_revision_h1_targets`; individual revisions in `supp_revisions_h1_targets`, range −1.2 to +1.0 pp). This is about a third of the AR(1) h = 1 MAE, so first-release \"truth\" is itself noisy.\n\n## Uncertainty\nWith only 8 origins the comparison is very imprecise. The DM-HLN test does not reject equal squared-error accuracy at conventional levels: h = 1 statistic −1.49, p = 0.18 (`supp_dm_hln_stat_h1`, `supp_dm_hln_pvalue_h1`); h = 2 statistic −1.75, p = 0.12 (`supp_dm_hln_stat_h2`, `supp_dm_hln_pvalue_h2`). The point estimates favour the AR(1), but the evidence that it beats the naive forecast in this window is not statistically significant. h = 2 errors overlap across consecutive origins (serial correlation), which the lag-1 long-run variance only partly addresses with n = 8.\n\n## Limitations and open issues\n- 8 origins (2024–2025) is a tiny, specific evaluation period, including the tariff-related 2025Q1 contraction; results may not generalise.\n- The 1985Q1 start and the COVID-row exclusion are judgement calls (pre-specified, not tuned); other reasonable choices (full sample, AR(p)) were not evaluated, by design of a single-model task.\n- Origins are irregularly spaced (2025-12-31 instead of 2025-10-31 due to the federal shutdown); horizons are defined relative to the last published quarter, so targets remain the next two quarters.\n- Actuals are advance estimates as at month-end; scoring against later vintages would give different numbers (revisions average 0.56 pp in absolute value).\n- `n_train` for naive rows is the full vintage length; for model rows it is the AR(1) regression sample size. `n_analysed` = 159 is the model regression sample at the last origin.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Real-time naive forecast RMSE vs first releases was 2.01 pp at h=1 and 2.27 pp at h=2 over 8 origins.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h2\",\"mae_naive_h1\",\"mae_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Only 8 origins in 2024-2025.\"},\n {\"id\":\"C2\",\"text\":\"The AR(1) (1985Q1+, COVID rows excluded) had lower RMSE than naive: 1.48 pp at h=1 and 1.40 pp at h=2, relative improvements 0.26 and 0.38.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_model_h2\",\"mae_model_h1\",\"mae_model_h2\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\"],\"status\":\"supported\",\"caveat\":\"Point estimates only; see C3.\"},\n {\"id\":\"C3\",\"text\":\"The AR(1) advantage is not statistically significant with 8 origins (DM-HLN p = 0.18 at h=1, 0.12 at h=2).\",\"results_keys\":[\"supp_dm_hln_pvalue_h1\",\"supp_dm_hln_pvalue_h2\",\"supp_dm_hln_stat_h1\",\"supp_dm_hln_stat_h2\"],\"status\":\"supported\",\"caveat\":\"DM test has low power and approximate size at n=8; h=2 errors are serially correlated.\"},\n {\"id\":\"C4\",\"text\":\"The AR(1) beat naive in absolute error at 5/8 origins for h=1 and 8/8 for h=2.\",\"results_keys\":[\"supp_n_origins_model_abs_err_lt_naive_h1\",\"supp_n_origins_model_abs_err_lt_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Counts, not tests.\"},\n {\"id\":\"C5\",\"text\":\"The mean absolute revision from first release to the 2026-10-30 value for the 8 h=1 targets is 0.56 pp.\",\"results_keys\":[\"mean_abs_revision_h1_targets\",\"supp_revisions_h1_targets\"],\"status\":\"supported\",\"caveat\":\"Later revisions may still occur.\"},\n {\"id\":\"C6\",\"text\":\"The AR(1) estimates were stable across vintages (phi 0.335-0.343, implied mean 2.62-2.66%).\",\"results_keys\":[\"supp_ar1_phi_range\",\"supp_ar1_implied_mean_range\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\necho ok","description":"Write report and claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1274,"cache_read_input_tokens":26343,"output_tokens":3307,"output_tokens_details":{"thinking_tokens":79},"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":1274},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnBW4iLF5Vok3pvDTU1","requestRef":"248b9a50-17bd-4a3f-a7a5-599731bc46d5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9a67a96a-1f06-4701-b8a1-66e8ccb8050f","timestamp":"2026-10-30T04:20:39.668Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"9a67a96a-1f06-4701-b8a1-66e8ccb8050f","isSidechain":false,"promptId":"b4031036-e465-46aa-97b4-58842f4c56e6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RZyrWkr1ukBfj7fMUwu8zT","type":"tool_result","content":"ok","is_error":false}]},"uuid":"1c6030d9-8d35-4664-b3b0-a0738c1cde4a","timestamp":"2026-10-30T04:20:39.719Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9a67a96a-1f06-4701-b8a1-66e8ccb8050f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0065","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","version":"2.1.314","gitBranch":"HEAD"}61{"type":"api-request-blob","sessionId":"8caf757a-cf77-44df-a7f5-c227a3152bb1","hash":"cc039494b4ae6f478faf3cedd078b550f1b0ca02b7d5d8c8ce53944bc4b88a89","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01RZyrWkr1ukBfj7fMUwu8zT","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Real-time forecasts of US real GDP growth from data vintages (F3-conf-02)\n\n## Question\nHow accurately could US quarterly real GDP growth (A191RL1Q225SBEA, % q/q SAAR) have been forecast 1 and 2 quarters ahead using only the data published at each of 8 forecast origins (2024-01-31 … 2025-12-31; `first_origin`, `last_origin`, `n_origins`), scored against first-release values, and does a simple time-series model beat the naive (last-value) forecast?\n\n## Data and exclusions\n- Inputs: 8 ALFRED vintage files (307–314 rows each), `current_vintage.csv` (318 rows), `first_release.csv` (13 rows); row counts in `n_input_rows`. All SHA-256 hashes match `data/README.md`; no missing values, duplicate dates or gaps (`out/state/data_audit.md`).\n- Real-time rule: at each origin only that origin's vintage file is used (training sample = whole file, values as in that file). `current_vintage.csv` is used only for the revision statistic; `first_release.csv` only for actuals. Every target's first release is dated after its origin (asserted in code).\n- Exclusions apply only to the model's estimation sample (`out/exclusions.csv`, shown for the last origin): 314 vintage observations → keep 1985Q1 onward (−151) → drop first window quarter for the lag (−1) → drop regression rows whose dependent quarter is 2020Q2–2020Q4 (−3; COVID outliers of about −28/+35 pp) → 159 regression observations (`n_analysed`). The naive forecast uses the full vintage file.\n- Data issues recorded in `data_issues`: irregular 8th origin (2025-12-31, shutdown-delayed advance estimate), COVID outliers, delayed first release of 2025Q4.\n\n## Method\n- Naive: forecast for h = 1 and h = 2 = last value in the origin's vintage file.\n- Model (`model_name` = `ar1_post1985_exCovid`; `model_description`): AR(1) with intercept, OLS, re-estimated at each origin on that vintage from 1985Q1 to its last quarter, excluding the COVID regression rows; h = 2 by iterating the AR(1). The specification was fixed before any forecast errors were computed (no tuning on the evaluation sample).\n- Errors = first-release value − forecast; RMSE and MAE over the 8 origins per horizon; `rel_improvement_rmse_h*` = 1 − RMSE_model/RMSE_naive.\n- Revision statistic: mean over the 8 h = 1 target quarters (2024Q1–2025Q4) of |current-vintage value − first release|.\n- Supplementary: Diebold–Mariano test on squared-error differentials with Harvey–Leybourne–Newbold correction (long-run variance with lags up to h−1), t(7) reference. No randomness (`random_seed` = null).\n\n## Results\n| | naive | AR(1) |\n|---|---|---|\n| RMSE h=1 (pp) | 2.010 (`rmse_naive_h1`) | 1.484 (`rmse_model_h1`) |\n| MAE h=1 (pp) | 1.687 (`mae_naive_h1`) | 1.233 (`mae_model_h1`) |\n| RMSE h=2 (pp) | 2.265 (`rmse_naive_h2`) | 1.398 (`rmse_model_h2`) |\n| MAE h=2 (pp) | 1.812 (`mae_naive_h2`) | 1.030 (`mae_model_h2`) |\n\n- Relative RMSE improvement of the AR(1) over naive: 0.262 at h = 1 (`rel_improvement_rmse_h1`) and 0.383 at h = 2 (`rel_improvement_rmse_h2`).\n- The AR(1) had a smaller absolute error than naive at 5 of 8 origins for h = 1 (`supp_n_origins_model_abs_err_lt_naive_h1`) and 8 of 8 for h = 2 (`supp_n_origins_model_abs_err_lt_naive_h2`).\n- The estimated AR(1) was very stable across vintages: φ between 0.335 and 0.343 (`supp_ar1_phi_range`), implied mean 2.62–2.66 % (`supp_ar1_implied_mean_range`); the model's gain comes from shrinking the last observation toward the mean, which helped most after the volatile 2025Q1 (−0.3) and 2025Q3 (4.3) prints.\n- Mean absolute revision between first release and the current (2026-10-30) value of the h = 1 targets: 0.5625 pp (`mean_abs_revision_h1_targets`; individual revisions in `supp_revisions_h1_targets`, range −1.2 to +1.0 pp). This is about a third of the AR(1) h = 1 MAE, so first-release \"truth\" is itself noisy.\n\n## Uncertainty\nWith only 8 origins the comparison is very imprecise. The DM-HLN test does not reject equal squared-error accuracy at conventional levels: h = 1 statistic −1.49, p = 0.18 (`supp_dm_hln_stat_h1`, `supp_dm_hln_pvalue_h1`); h = 2 statistic −1.75, p = 0.12 (`supp_dm_hln_stat_h2`, `supp_dm_hln_pvalue_h2`). The point estimates favour the AR(1), but the evidence that it beats the naive forecast in this window is not statistically significant. h = 2 errors overlap across consecutive origins (serial correlation), which the lag-1 long-run variance only partly addresses with n = 8.\n\n## Limitations and open issues\n- 8 origins (2024–2025) is a tiny, specific evaluation period, including the tariff-related 2025Q1 contraction; results may not generalise.\n- The 1985Q1 start and the COVID-row exclusion are judgement calls (pre-specified, not tuned); other reasonable choices (full sample, AR(p)) were not evaluated, by design of a single-model task.\n- Origins are irregularly spaced (2025-12-31 instead of 2025-10-31 due to the federal shutdown); horizons are defined relative to the last published quarter, so targets remain the next two quarters.\n- Actuals are advance estimates as at month-end; scoring against later vintages would give different numbers (revisions average 0.56 pp in absolute value).\n- `n_train` for naive rows is the full vintage length; for model rows it is the AR(1) regression sample size. `n_analysed` = 159 is the model regression sample at the last origin.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Real-time naive forecast RMSE vs first releases was 2.01 pp at h=1 and 2.27 pp at h=2 over 8 origins.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h2\",\"mae_naive_h1\",\"mae_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Only 8 origins in 2024-2025.\"},\n {\"id\":\"C2\",\"text\":\"The AR(1) (1985Q1+, COVID rows excluded) had lower RMSE than naive: 1.48 pp at h=1 and 1.40 pp at h=2, relative improvements 0.26 and 0.38.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_model_h2\",\"mae_model_h1\",\"mae_model_h2\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\"],\"status\":\"supported\",\"caveat\":\"Point estimates only; see C3.\"},\n {\"id\":\"C3\",\"text\":\"The AR(1) advantage is not statistically significant with 8 origins (DM-HLN p = 0.18 at h=1, 0.12 at h=2).\",\"results_keys\":[\"supp_dm_hln_pvalue_h1\",\"supp_dm_hln_pvalue_h2\",\"supp_dm_hln_stat_h1\",\"supp_dm_hln_stat_h2\"],\"status\":\"supported\",\"caveat\":\"DM test has low power and approximate size at n=8; h=2 errors are serially correlated.\"},\n {\"id\":\"C4\",\"text\":\"The AR(1) beat naive in absolute error at 5/8 origins for h=1 and 8/8 for h=2.\",\"results_keys\":[\"supp_n_origins_model_abs_err_lt_naive_h1\",\"supp_n_origins_model_abs_err_lt_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Counts, not tests.\"},\n {\"id\":\"C5\",\"text\":\"The mean absolute revision from first release to the 2026-10-30 value for the 8 h=1 targets is 0.56 pp.\",\"results_keys\":[\"mean_abs_revision_h1_targets\",\"supp_revisions_h1_targets\"],\"status\":\"supported\",\"caveat\":\"Later revisions may still occur.\"},\n {\"id\":\"C6\",\"text\":\"The AR(1) estimates were stable across vintages (phi 0.335-0.343, implied mean 2.62-2.66%).\",\"results_keys\":[\"supp_ar1_phi_range\",\"supp_ar1_implied_mean_range\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\necho ok","description":"Write report and 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The simple model (an AR(1)) had lower forecast errors than the naive forecast at both horizons, but with only 8 forecast origins the difference is not statistically significant. The gate passes, and running `out/analysis.py` twice from a clean state gives identical output.\n\n**Setup.** At each of the 8 origins (2024-01-31 to 2025-12-31) I used only the data file published on that date, and scored forecasts against the first published value of each target quarter. The model is an AR(1) with an intercept: each quarter's growth regressed on the previous quarter's. It is re-estimated at each origin on data from 1985Q1 onward, leaving out 2020Q2–2020Q4 because the COVID swings (about −28 and +35 points) would distort it. For two quarters ahead it is applied twice. I chose this setup before computing any forecast errors.\n\nErrors are in percentage points of annualised growth:\n\n| | Naive RMSE | Model RMSE | Naive MAE | Model MAE | RMSE improvement |\n|---|---|---|---|---|---|\n| 1 quarter ahead | 2.01 | 1.48 | 1.69 | 1.23 | 26% |\n| 2 quarters ahead | 2.27 | 1.40 | 1.81 | 1.03 | 38% |\n\n- **Origin by origin:** the model's error was smaller than the naive forecast's at 5 of 8 origins one quarter ahead, and at all 8 two quarters ahead.\n- **Significance:** a Diebold–Mariano test (with the small-sample correction) does not reject equal accuracy: p = 0.18 one quarter ahead and p = 0.12 two quarters ahead.\n- **Revisions:** the first published values of the 8 one-quarter-ahead target quarters have since been revised by 0.56 points on average (largest change 1.2 points). That is about half the model's average error, so the first releases used as \"actuals\" are themselves imprecise.\n\n**Limitations:**\n- The test window is short and specific (2024–2025).\n- The 1985 start and the COVID exclusion are judgement calls, and I tested no other model specifications.\n- The origins are unevenly spaced: the last one is 2025-12-31 instead of 2025-10-31 because the federal shutdown delayed the 2025Q3 release.\n- Two-quarter-ahead errors from consecutive origins overlap, which the test only partly accounts for with so few points.\n\nEverything is in `out/`. 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