spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:18:40.260Z","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:18:40.271Z","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745"}4{"parentUuid":null,"isSidechain":false,"promptId":"db6166b8-8d55-4a5e-8d5e-ae3ce261a64b","type":"user","message":{"role":"user","content":"Begin"},"uuid":"a736dc96-7ed6-4708-9192-86a762364876","timestamp":"2026-10-30T04:18:40.277Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","shapeHash":"9de5beaed85b287ec7574bf6f549e23abd2c8c16d5a74bb19918aad71f6b8f41","timestamp":"2026-10-30T04:18:40.294Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0067\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimands are the 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.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:44 .\ndrwxr-xr-x  50 simon-pierreboucher  staff  1600 oct.  30 00:18 ..\n-rw-r--r--   1 simon-pierreboucher  staff  1641 oct.  29 23:44 bundle.json\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 data\n-rw-r--r--   1 simon-pierreboucher  staff  2045 oct.  29 23:43 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  5610 oct.  29 23:43 TASK.md\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:44 ..\n-rw-r--r--   1 simon-pierreboucher  staff  4860 oct.  29 23:41 current_vintage.csv\n-rw-r--r--   1 simon-pierreboucher  staff   394 oct.  29 23:41 first_release.csv\n-rw-r--r--   1 simon-pierreboucher  staff  3628 oct.  29 23:41 README.md\ndrwxr-xr-x  10 simon-pierreboucher  staff   320 oct.  29 23:41 vintages","is_error":false}]},"uuid":"1ea7983d-5b3a-4172-8616-9d59d8a8e546","timestamp":"2026-10-30T04:18:41.903Z","toolUseResult":{"stdout":"# 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). 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cat gate_spec.json; cat data/first_release.csv; cd data/vintages; for f in *; do echo \"== $f\"; wc -l $f; head -2 $f; tail -4 $f; done; cd ..; head -3 current_vintage.csv; tail -14 current_vintage.csv; wc -l current_vintage.csv; shasum -a 256 *.csv vintages/*","description":"Inspect data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2789,"cache_read_input_tokens":4315,"output_tokens":209,"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":2789},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXn4u4sh6AshbUJzyt3J","requestRef":"f920ccbd-15a2-4c33-8299-1703913a40bc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"07da3625-eb06-4c41-879c-18a9f9303d33","timestamp":"2026-10-30T04:18:43.780Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"07da3625-eb06-4c41-879c-18a9f9303d33","isSidechain":false,"promptId":"db6166b8-8d55-4a5e-8d5e-ae3ce261a64b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YNxMPd2ehgYViG4gem5xcx","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== A191RL1Q225SBEA_vintage_2024-01-31.csv\n     308 A191RL1Q225SBEA_vintage_2024-01-31.csv\nobservation_date,A191RL1Q225SBEA_20240131\n1947-04-01,-1.0\n2023-01-01,2.2\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.3\n== A191RL1Q225SBEA_vintage_2024-04-30.csv\n     309 A191RL1Q225SBEA_vintage_2024-04-30.csv\nobservation_date,A191RL1Q225SBEA_20240430\n1947-04-01,-1.0\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.6\n== A191RL1Q225SBEA_vintage_2024-07-31.csv\n     310 A191RL1Q225SBEA_vintage_2024-07-31.csv\nobservation_date,A191RL1Q225SBEA_20240731\n1947-04-01,-1.0\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.4\n2024-04-01,2.8\n== A191RL1Q225SBEA_vintage_2024-10-31.csv\n     311 A191RL1Q225SBEA_vintage_2024-10-31.csv\nobservation_date,A191RL1Q225SBEA_20241031\n1947-04-01,-1.0\n2023-10-01,3.2\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,2.8\n== A191RL1Q225SBEA_vintage_2025-01-31.csv\n     312 A191RL1Q225SBEA_vintage_2025-01-31.csv\nobservation_date,A191RL1Q225SBEA_20250131\n1947-04-01,-1.0\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.3\n== A191RL1Q225SBEA_vintage_2025-04-30.csv\n     313 A191RL1Q225SBEA_vintage_2025-04-30.csv\nobservation_date,A191RL1Q225SBEA_20250430\n1947-04-01,-1.0\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.3\n== A191RL1Q225SBEA_vintage_2025-07-31.csv\n     314 A191RL1Q225SBEA_vintage_2025-07-31.csv\nobservation_date,A191RL1Q225SBEA_20250731\n1947-04-01,-1.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.5\n2025-04-01,3.0\n== A191RL1Q225SBEA_vintage_2025-12-31.csv\n     315 A191RL1Q225SBEA_vintage_2025-12-31.csv\nobservation_date,A191RL1Q225SBEA_20251231\n1947-04-01,-1.0\n2024-10-01,1.9\n2025-01-01,-0.6\n2025-04-01,3.8\n2025-07-01,4.3\nobservation_date,A191RL1Q225SBEA\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-04-01,2.2\n2023-07-01,4.5\n2023-10-01,3.6\n2024-01-01,1.4\n2024-04-01,3.6\n2024-07-01,3.2\n2024-10-01,2.4\n2025-01-01,0.1\n2025-04-01,4.0\n2025-07-01,3.9\n2025-10-01,0.2\n2026-01-01,2.5\n2026-04-01,2.2\n2026-07-01,3.4\n     319 current_vintage.csv\n1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562  current_vintage.csv\nab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb  first_release.csv\n7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243  vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb  vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da  vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\nc4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003  vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2  vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a  vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\nf1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9  vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\nc111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7  vintages/A191RL1Q225SBEA_vintage_2025-12-31.csv","is_error":false}]},"uuid":"022e4c67-e523-47f1-99cb-eeee89732a8f","timestamp":"2026-10-30T04:18:43.868Z","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== A191RL1Q225SBEA_vintage_2024-01-31.csv\n     308 A191RL1Q225SBEA_vintage_2024-01-31.csv\nobservation_date,A191RL1Q225SBEA_20240131\n1947-04-01,-1.0\n2023-01-01,2.2\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.3\n== A191RL1Q225SBEA_vintage_2024-04-30.csv\n     309 A191RL1Q225SBEA_vintage_2024-04-30.csv\nobservation_date,A191RL1Q225SBEA_20240430\n1947-04-01,-1.0\n2023-04-01,2.1\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.6\n== A191RL1Q225SBEA_vintage_2024-07-31.csv\n     310 A191RL1Q225SBEA_vintage_2024-07-31.csv\nobservation_date,A191RL1Q225SBEA_20240731\n1947-04-01,-1.0\n2023-07-01,4.9\n2023-10-01,3.4\n2024-01-01,1.4\n2024-04-01,2.8\n== A191RL1Q225SBEA_vintage_2024-10-31.csv\n     311 A191RL1Q225SBEA_vintage_2024-10-31.csv\nobservation_date,A191RL1Q225SBEA_20241031\n1947-04-01,-1.0\n2023-10-01,3.2\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,2.8\n== A191RL1Q225SBEA_vintage_2025-01-31.csv\n     312 A191RL1Q225SBEA_vintage_2025-01-31.csv\nobservation_date,A191RL1Q225SBEA_20250131\n1947-04-01,-1.0\n2024-01-01,1.6\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.3\n== A191RL1Q225SBEA_vintage_2025-04-30.csv\n     313 A191RL1Q225SBEA_vintage_2025-04-30.csv\nobservation_date,A191RL1Q225SBEA_20250430\n1947-04-01,-1.0\n2024-04-01,3.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.3\n== A191RL1Q225SBEA_vintage_2025-07-31.csv\n     314 A191RL1Q225SBEA_vintage_2025-07-31.csv\nobservation_date,A191RL1Q225SBEA_20250731\n1947-04-01,-1.0\n2024-07-01,3.1\n2024-10-01,2.4\n2025-01-01,-0.5\n2025-04-01,3.0\n== A191RL1Q225SBEA_vintage_2025-12-31.csv\n     315 A191RL1Q225SBEA_vintage_2025-12-31.csv\nobservation_date,A191RL1Q225SBEA_20251231\n1947-04-01,-1.0\n2024-10-01,1.9\n2025-01-01,-0.6\n2025-04-01,3.8\n2025-07-01,4.3\nobservation_date,A191RL1Q225SBEA\n1947-04-01,-1.0\n1947-07-01,-0.8\n2023-04-01,2.2\n2023-07-01,4.5\n2023-10-01,3.6\n2024-01-01,1.4\n2024-04-01,3.6\n2024-07-01,3.2\n2024-10-01,2.4\n2025-01-01,0.1\n2025-04-01,4.0\n2025-07-01,3.9\n2025-10-01,0.2\n2026-01-01,2.5\n2026-04-01,2.2\n2026-07-01,3.4\n     319 current_vintage.csv\n1af2590d79adb8d3c6fad4328d306c96bb6ed9980120eae58bb9471fffbb0562  current_vintage.csv\nab843c9fc4e6b700f5bb95169f15fb506f7a7a1a3c22a91b31cb29031c5795bb  first_release.csv\n7ce218fb21aba2e9ee40736a1d3ee02becde628a21242ed43340dd1411d6f243  vintages/A191RL1Q225SBEA_vintage_2024-01-31.csv\n62c1dfce6e53079837f706a5edd6f14731c91d3f630b2cad666325f2259197cb  vintages/A191RL1Q225SBEA_vintage_2024-04-30.csv\n6f94259143ad489c021648dfebe7fda9ea6d17d2bbf22a43761d1abf291179da  vintages/A191RL1Q225SBEA_vintage_2024-07-31.csv\nc4c51edee290f73a951171c30a9931f4a4bc2a7584aeb92ed9ce3de1cb9e0003  vintages/A191RL1Q225SBEA_vintage_2024-10-31.csv\n10a5e02e6ef0134dbb4091470ad28b7f59c24462364352b16fcbfb644e0faee2  vintages/A191RL1Q225SBEA_vintage_2025-01-31.csv\n1f19c975a6c3b721e0c4b82c014aca160c4694791bf63751e8c79c24b5085d1a  vintages/A191RL1Q225SBEA_vintage_2025-04-30.csv\nf1e7c5563a6e313a58391f750023f8ea60e5a47c7f03df41f7ab593e6b2157a9  vintages/A191RL1Q225SBEA_vintage_2025-07-31.csv\nc111c070f3d074de0e725bb6164aaa83ccbf17ceda9bac9356e7f50f42a8d4a7  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no such file or directory: data\nvintages/A191RL1Q225SBEA_vintage_2024-01-31.csv (307, 2) 0 0 0 1947-04-01 2023-10-01 -28.0 34.8\nvintages/A191RL1Q225SBEA_vintage_2024-04-30.csv (308, 2) 0 0 0 1947-04-01 2024-01-01 -28.0 34.8\nvintages/A191RL1Q225SBEA_vintage_2024-07-31.csv (309, 2) 0 0 0 1947-04-01 2024-04-01 -28.0 34.8\nvintages/A191RL1Q225SBEA_vintage_2024-10-31.csv (310, 2) 0 0 0 1947-04-01 2024-07-01 -28.1 35.2\nvintages/A191RL1Q225SBEA_vintage_2025-01-31.csv (311, 2) 0 0 0 1947-04-01 2024-10-01 -28.1 35.2\nvintages/A191RL1Q225SBEA_vintage_2025-04-30.csv (312, 2) 0 0 0 1947-04-01 2025-01-01 -28.1 35.2\nvintages/A191RL1Q225SBEA_vintage_2025-07-31.csv (313, 2) 0 0 0 1947-04-01 2025-04-01 -28.1 35.2\nvintages/A191RL1Q225SBEA_vintage_2025-12-31.csv (314, 2) 0 0 0 1947-04-01 2025-07-01 -28.0 34.9\ncurrent_vintage.csv (318, 2) 0 0 0 1947-04-01 2026-07-01 -28.0 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2) 0 0 0 1947-04-01 2026-07-01 -28.0 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/Users/simon-pierreboucher/wp29/runs/RC0067 && mkdir -p out/state && pwd\ncat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-02\n\n**Question.** How accurately could US quarterly real GDP growth (A191RL1Q225SBEA, % q/q SAAR) have been forecast 1 and 2 quarters ahead in real time, using only the vintage published at each origin; does a simple time-series model beat the naive (last value) forecast?\n\n**Estimands.** Over 8 origins (vintage dates 2024-01-31 … 2025-12-31) and h ∈ {1,2} (quarters after the last quarter in the origin's vintage): RMSE and MAE of naive and of one model, vs first-release values of the targets; rel improvement 1 − RMSE_model/RMSE_naive per h; mean |current − first release| over the 8 h=1 target quarters. Meaningful under: first release is the evaluation target (not \"truth\"); 8 errors per horizon → very large sampling uncertainty; h=2 errors overlap with h=1 errors of the next origin (serially correlated).\n\n**Outputs.** out/forecasts.csv (32 rows; columns 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 out/exclusions.csv and out/state/*.\n\n**Information rules.** At origin d only the vintage file dated d may be used (all values as in that file). current_vintage.csv only for revision statistic/discussion. Actuals = first_release.csv. Naive = last value of vintage, both horizons.\n\n**Invalidity / impossibility.** Using current vintage or later vintages in training; a first-release vintage date not after the origin (would mean actual known at origin — check); missing first releases for any target; vintage last quarter mismatched with stated.\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. P1 started.\" > out/state/log.md","description":"Write P0 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":2115,"cache_read_input_tokens":12315,"output_tokens":1177,"output_tokens_details":{"thinking_tokens":67},"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":2115},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn67t5VcHXsTeaT2Cdq","requestRef":"41ee301b-1d9c-4c8e-9f56-e500f515b77f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fd3b24bc-1290-45ec-9ba0-9745a48af0d3","timestamp":"2026-10-30T04:19:08.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067/data","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"fd3b24bc-1290-45ec-9ba0-9745a48af0d3","isSidechain":false,"promptId":"db6166b8-8d55-4a5e-8d5e-ae3ce261a64b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014bsh2hpwF6ECzs88kpjuBM","type":"tool_result","content":"/Users/simon-pierreboucher/wp29/runs/RC0067","is_error":false}]},"uuid":"e599aa1d-3a1f-4f15-ac19-3c546536b7d5","timestamp":"2026-10-30T04:19:08.492Z","toolUseResult":{"stdout":"/Users/simon-pierreboucher/wp29/runs/RC0067","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fd3b24bc-1290-45ec-9ba0-9745a48af0d3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","hash":"f8a74618ad420842658a247c05b9bfa7ab60527c226476902fd9b33728bfe777","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014bsh2hpwF6ECzs88kpjuBM","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0067 && mkdir -p out/state && pwd\ncat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-02\n\n**Question.** How accurately could US quarterly real GDP growth (A191RL1Q225SBEA, % q/q SAAR) have been forecast 1 and 2 quarters ahead in real time, using only the vintage published at each origin; does a simple time-series model beat the naive (last value) forecast?\n\n**Estimands.** Over 8 origins (vintage dates 2024-01-31 … 2025-12-31) and h ∈ {1,2} (quarters after the last quarter in the origin's vintage): RMSE and MAE of naive and of one model, vs first-release values of the targets; rel improvement 1 − RMSE_model/RMSE_naive per h; mean |current − first release| over the 8 h=1 target quarters. Meaningful under: first release is the evaluation target (not \"truth\"); 8 errors per horizon → very large sampling uncertainty; h=2 errors overlap with h=1 errors of the next origin (serially correlated).\n\n**Outputs.** out/forecasts.csv (32 rows; columns 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 out/exclusions.csv and out/state/*.\n\n**Information rules.** At origin d only the vintage file dated d may be used (all values as in that file). current_vintage.csv only for revision statistic/discussion. Actuals = first_release.csv. Naive = last value of vintage, both horizons.\n\n**Invalidity / impossibility.** Using current vintage or later vintages in training; a first-release vintage date not after the origin (would mean actual known at origin — check); missing first releases for any target; vintage last quarter mismatched with stated.\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. P1 started.\" > out/state/log.md","description":"Write P0 contract, phase ledger, log"}}]}}34{"type":"api-request","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","id":"5bbc0eb5-5229-436e-8b64-fc3136288a54","timestamp":"2026-10-30T04:19:08.495Z","version":"2.1.314","querySource":"sdk","shapeHash":"9de5beaed85b287ec7574bf6f549e23abd2c8c16d5a74bb19918aad71f6b8f41","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":"41ee301b-1d9c-4c8e-9f56-e500f515b77f","keep":8,"tail":["f8a74618ad420842658a247c05b9bfa7ab60527c226476902fd9b33728bfe777","45c218b74ec910d230f4794fa3caa1a3732cf63b45750f35c54bc69abb9099b5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d0965ea9cf8303f073f5576abfeabb2773f14aa2a9f7b11e6a3767e2bfe3256d"}35{"parentUuid":"e599aa1d-3a1f-4f15-ac19-3c546536b7d5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn6s1eDj5GwJK3zyeSR","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1207,"cache_read_input_tokens":14430,"output_tokens":4326,"output_tokens_details":{"thinking_tokens":205},"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":1207},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1538,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXn6riYYwPGC3uvwMiWi","requestRef":"5bbc0eb5-5229-436e-8b64-fc3136288a54","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"92315803-5d7b-4711-9ad0-50b7356faba7","timestamp":"2026-10-30T04:19:10.753Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"92315803-5d7b-4711-9ad0-50b7356faba7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXn6s1eDj5GwJK3zyeSR","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0172GYxkDzH5d8LPg5sA5jDv","name":"Bash","input":{"command":"cat >> out/state/log.md <<'EOF'\n- 2026-10-30 Decision (made before computing any forecast errors): model = AR(1) with intercept, OLS, estimation sample 1985Q1..last quarter of the origin vintage, with 2020Q2 and 2020Q3 (pandemic outliers, -28/+35% SAAR) set to missing so that no regression pair uses them; iterated forecasts for h=2. Rationale: post-Great-Moderation sample; pandemic swings would otherwise dominate OLS. No tuning on evaluation results.\nEOF\ncat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-02: real-time forecasts of US real GDP growth from ALFRED vintages.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\"\"\"\nimport json, os, glob\nimport numpy as np\nimport pandas as pd\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\"]\nSAMPLE_START = pd.Timestamp(\"1985-01-01\")\nEXCLUDED_Q = [pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-07-01\")]\nMODEL = \"ar1_post1985\"\nDESC = (\"AR(1) with intercept estimated by OLS on the origin vintage, sample 1985Q1 to the last quarter \"\n        \"of that vintage, with 2020Q2 and 2020Q3 set to missing (no regression pair uses them); \"\n        \"h=2 forecast obtained by iterating the AR(1) recursion.\")\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.set_index(\"date\")[\"value\"].astype(float)\n\ndef qshift(ts, k):\n    return ts + pd.DateOffset(months=3 * k)\n\nn_input_rows = {}\nvint = {}\nfor o in ORIGINS:\n    rel = f\"vintages/A191RL1Q225SBEA_vintage_{o}.csv\"\n    s = read_series(os.path.join(DATA, rel))\n    vint[o] = s\n    n_input_rows[rel] = int(len(s))\ncur = read_series(os.path.join(DATA, \"current_vintage.csv\"))  # used ONLY for revision statistic\nn_input_rows[\"current_vintage.csv\"] = int(len(cur))\nfr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"quarter\", \"first_release_vintage_date\"])\nn_input_rows[\"first_release.csv\"] = int(len(fr))\nfr_val = fr.set_index(\"quarter\")[\"first_release_value\"].astype(float)\nfr_vd = fr.set_index(\"quarter\")[\"first_release_vintage_date\"]\n\ndata_issues = []\nfor o, s in vint.items():\n    assert s.index.is_monotonic_increasing and not s.index.duplicated().any() and s.notna().all()\n    assert (s.index.to_series().diff().dropna().dt.days <= 92).all()\ndata_issues.append({\"issue\": \"Pandemic outliers 2020Q2/2020Q3 (about -28% and +35% SAAR) in every vintage.\",\n                    \"handling\": \"Set to missing in the AR(1) estimation sample (documented in out/exclusions.csv); naive forecasts unaffected.\"})\ndata_issues.append({\"issue\": \"Historical values differ across vintages (annual revisions, e.g. 2024Q4 2.3 -> 2.4 -> 1.9 across vintages).\",\n                    \"handling\": \"Each origin uses only its own vintage file; current vintage used only for the revision statistic.\"})\ndata_issues.append({\"issue\": \"Eighth origin is 2025-12-31 rather than 2025-10-31 (2025Q3 advance estimate delayed by the federal shutdown); its h=1 target 2025Q4 first release dated 2026-02-28.\",\n                    \"handling\": \"Used as given in TASK.md; origins are not equally spaced in calendar time but each still forecasts 1 and 2 quarters past its last published quarter.\"})\n\ndef fit_ar1(s):\n    y = s[s.index >= SAMPLE_START].copy()\n    n_after_start = len(y)\n    y.loc[y.index.isin(EXCLUDED_Q)] = np.nan\n    lag = y.shift(1)\n    # require the lag to be the immediately preceding quarter (index is contiguous quarterly)\n    m = y.notna() & lag.notna()\n    X = np.column_stack([np.ones(m.sum()), lag[m].values])\n    beta, *_ = np.linalg.lstsq(X, y[m].values, rcond=None)\n    return beta, int(y.notna().sum()), n_after_start, int(m.sum())\n\nrows, excl = [], []\nfits = {}\nfor o in ORIGINS:\n    s = vint[o]\n    T, yT = s.index[-1], float(s.iloc[-1])\n    beta, n_used, n_after_start, n_pairs = fit_ar1(s)\n    fits[o] = {\"c\": float(beta[0]), \"phi\": float(beta[1]), \"n_pairs\": n_pairs}\n    excl.append({\"step\": f\"{o}:sample_start\", \"rule\": \"drop quarters before 1985-01-01 (model only)\",\n                 \"n_before\": len(s), \"n_after\": n_after_start, \"n_removed\": len(s) - n_after_start,\n                 \"rationale\": \"post-Great-Moderation estimation sample for AR(1)\"})\n    excl.append({\"step\": f\"{o}:pandemic\", \"rule\": \"set 2020-04-01 and 2020-07-01 to missing (model only)\",\n                 \"n_before\": n_after_start, \"n_after\": n_used, \"n_removed\": n_after_start - n_used,\n                 \"rationale\": \"extreme pandemic swings would dominate OLS AR coefficient\"})\n    f_ar = {}\n    prev = yT\n    for h in (1, 2):\n        prev = beta[0] + beta[1] * prev\n        f_ar[h] = float(prev)\n    for h in (1, 2):\n        tgt = qshift(T, h)\n        assert tgt in fr_val.index, f\"missing first release for {tgt}\"\n        assert fr_vd[tgt] >= pd.Timestamp(o), \"actual published before origin\"\n        for model, fc, ntr in ((\"naive\", yT, len(s)), (MODEL, f_ar[h], n_used)):\n            rows.append({\"origin_date\": o, \"target_date\": tgt.strftime(\"%Y-%m-%d\"), \"horizon\": h,\n                         \"forecast\": round(float(fc), 10), \"actual\": float(fr_val[tgt]), \"model\": model,\n                         \"vintage_date\": o, \"train_last_date\": T.strftime(\"%Y-%m-%d\"),\n                         \"train_last_value\": yT, \"n_train\": int(ntr)})\n\nfc = pd.DataFrame(rows)\nfc[\"error\"] = fc[\"actual\"] - fc[\"forecast\"]\nfc.drop(columns=\"error\").to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False)\npd.DataFrame(excl).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\ndef metrics(model, h):\n    e = fc[(fc.model == model) & (fc.horizon == h)][\"error\"].values\n    assert len(e) == len(ORIGINS)\n    return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nres = {\"task_id\": \"F3-conf-02\", \"model_name\": MODEL, \"model_description\": DESC,\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\nfor model, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n    for h in (1, 2):\n        r, a = metrics(model, h)\n        res[f\"rmse_{tag}_h{h}\"] = round(r, 10)\n        res[f\"mae_{tag}_h{h}\"] = round(a, 10)\nfor h in (1, 2):\n    res[f\"rel_improvement_rmse_h{h}\"] = round(1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"], 10)\nh1t = pd.to_datetime(fc[(fc.model == \"naive\") & (fc.horizon == 1)][\"target_date\"])\nrev = [abs(float(cur[t]) - float(fr_val[t])) for t in h1t]\nres[\"mean_abs_revision_h1_targets\"] = round(float(np.mean(rev)), 10)\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = data_issues\nres[\"n_input_rows\"] = n_input_rows\nres[\"n_analysed\"] = int(fc[(fc.origin_date == ORIGINS[-1]) & (fc.model == MODEL)][\"n_train\"].iloc[0])\nres[\"random_seed\"] = None\n# supplementary diagnostics (not required keys)\nres[\"supp_ar1_coefficients_by_origin\"] = fits\nres[\"supp_abs_revision_h1_targets\"] = {t.strftime(\"%Y-%m-%d\"): round(r, 10) for t, r in zip(h1t, rev)}\nfor model, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n    for h in (1, 2):\n        res[f\"supp_mean_error_{tag}_h{h}\"] = round(float(fc[(fc.model == model) & (fc.horizon == h)][\"error\"].mean()), 10)\n# Diebold-Mariano-type comparison (squared-error loss), simple t-stat on loss differential, small-sample caveat\nfrom scipy import stats\nfor h in (1, 2):\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)][\"error\"].values\n    em = fc[(fc.model == MODEL) & (fc.horizon == h)][\"error\"].values\n    d = en ** 2 - em ** 2\n    n = len(d)\n    # HAC variance with h-1 lags (rectangular), fallback to plain variance if non-positive\n    g0 = np.mean((d - d.mean()) ** 2)\n    v = g0 + sum(2 * np.mean((d[k:] - d.mean()) * (d[:-k] - d.mean())) for k in range(1, h))\n    if v <= 0:\n        v = g0\n    dm = float(d.mean() / np.sqrt(v / n))\n    hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    res[f\"supp_dm_hln_stat_h{h}\"] = round(float(hln), 10)\n    res[f\"supp_dm_hln_pvalue_two_sided_h{h}\"] = round(float(2 * stats.t.sf(abs(hln), df=n - 1)), 10)\n\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\", \"mean_abs\", \"supp_dm\"))}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/forecasts.csv && cat out/exclusions.csv | tail -3 && python3 -c \"import json;print(json.load(open('out/results.json'))['supp_ar1_coefficients_by_origin'])\"","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1207,"cache_read_input_tokens":14430,"output_tokens":4326,"output_tokens_details":{"thinking_tokens":205},"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":1207},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXn6riYYwPGC3uvwMiWi","requestRef":"5bbc0eb5-5229-436e-8b64-fc3136288a54","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"491e4366-3aae-465f-9741-084c6105e067","timestamp":"2026-10-30T04:19:41.664Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"491e4366-3aae-465f-9741-084c6105e067","isSidechain":false,"promptId":"db6166b8-8d55-4a5e-8d5e-ae3ce261a64b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0172GYxkDzH5d8LPg5sA5jDv","type":"tool_result","content":"{\n  \"rmse_naive_h1\": 2.0102860493,\n  \"mae_naive_h1\": 1.6875,\n  \"rmse_naive_h2\": 2.265226258,\n  \"mae_naive_h2\": 1.8125,\n  \"rmse_model_h1\": 1.4835747469,\n  \"mae_model_h1\": 1.2332142642,\n  \"rmse_model_h2\": 1.3976488357,\n  \"mae_model_h2\": 1.0295196499,\n  \"rel_improvement_rmse_h1\": 0.2620081369,\n  \"rel_improvement_rmse_h2\": 0.3829981306,\n  \"mean_abs_revision_h1_targets\": 0.5625,\n  \"supp_dm_hln_stat_h1\": 1.4911492058,\n  \"supp_dm_hln_pvalue_two_sided_h1\": 0.1795464115,\n  \"supp_dm_hln_stat_h2\": 1.8510836005,\n  \"supp_dm_hln_pvalue_two_sided_h2\": 0.106597457\n}\norigin_date,target_date,horizon,forecast,actual,model,vintage_date,train_last_date,train_last_value,n_train\n2024-01-31,2024-01-01,1,3.3,1.6,naive,2024-01-31,2023-10-01,3.3,307\n2024-01-31,2024-01-01,1,2.8594858907,1.6,ar1_post1985,2024-01-31,2023-10-01,3.3,154\n2024-01-31,2024-04-01,2,3.3,2.8,naive,2024-01-31,2023-10-01,3.3,307\n2024-01-31,2024-04-01,2,2.7095380799,2.8,ar1_post1985,2024-01-31,2023-10-01,3.3,154\n2024-04-30,2024-04-01,1,1.6,2.8,naive,2024-04-30,2024-01-01,1.6,308\n2024-04-30,2024-04-01,1,2.2739273039,2.8,ar1_post1985,2024-04-30,2024-01-01,1.6,155\n2024-04-30,2024-07-01,2,1.6,2.8,naive,2024-04-30,2024-01-01,1.6,308\n2024-04-30,2024-07-01,2,2.5027880684,2.8,ar1_post1985,2024-04-30,2024-01-01,1.6,155\n2024-07-31,2024-07-01,1,2.8,2.8,naive,2024-07-31,2024-04-01,2.8,309\n2024-07-31,2024-07-01,1,2.683914729,2.8,ar1_post1985,2024-07-31,2024-04-01,2.8,156\n2024-07-31,2024-10-01,2,2.8,2.3,naive,2024-07-31,2024-04-01,2.8,309\n2024-07-31,2024-10-01,2,2.6446265316,2.3,ar1_post1985,2024-07-31,2024-04-01,2.8,156\n2024-10-31,2024-10-01,1,2.8,2.3,naive,2024-10-31,2024-07-01,2.8,310\n2024-10-31,2024-10-01,1,2.7052430423,2.3,ar1_post1985,2024-10-31,2024-07-01,2.8,157\n2024-10-31,2025-01-01,2,2.8,-0.3,naive,2024-10-31,2024-07-01,2.8,310\n2024-10-31,2025-01-01,2,2.6728789463,-0.3,ar1_post1985,2024-10-31,2024-07-01,2.8,157\n2025-01-31,2025-01-01,1,2.3,-0.3,naive,2025-01-31,2024-10-01,2.3,311\n2025-01-31,2025-01-01,1,2.5331955789,-0.3,ar1_post1985,2025-01-31,2024-10-01,2.3,158\n2025-01-31,2025-04-01,2,2.3,3.0,naive,2025-01-31,2024-10-01,2.3,311\n2025-01-31,2025-04-01,2,2.6128140336,3.0,ar1_post1985,2025-01-31,2024-10-01,2.3,158\n2025-04-30,2025-04-01,1,-0.3,3.0,naive,2025-04-30,2025-01-01,-0.3,312\n2025-04-30,2025-04-01,1,1.6237209695,3.0,ar1_post1985,2025-04-30,2025-01-01,-0.3,159\n2025-04-30,2025-07-01,2,-0.3,4.3,naive,2025-04-30,2025-01-01,-0.3,312\n2025-04-30,2025-07-01,2,2.2831285554,4.3,ar1_post1985,2025-04-30,2025-01-01,-0.3,159\n2025-07-31,2025-07-01,1,3.0,4.3,naive,2025-07-31,2025-04-01,3.0,313\n2025-07-31,2025-07-01,1,2.761291972,4.3,ar1_post1985,2025-07-31,2025-04-01,3.0,160\n2025-07-31,2025-10-01,2,3.0,1.4,naive,2025-07-31,2025-04-01,3.0,313\n2025-07-31,2025-10-01,2,2.6808360098,1.4,ar1_post1985,2025-07-31,2025-04-01,3.0,160\n2025-12-31,2025-10-01,1,4.3,1.4,naive,2025-12-31,2025-07-01,4.3,314\n2025-12-31,2025-10-01,1,3.2106445759,1.4,ar1_post1985,2025-12-31,2025-07-01,4.3,161\n2025-12-31,2026-01-01,2,4.3,2.0,naive,2025-12-31,2025-07-01,4.3,314\n2025-12-31,2026-01-01,2,2.8460844491,2.0,ar1_post1985,2025-12-31,2025-07-01,4.3,161\n2025-07-31:pandemic,set 2020-04-01 and 2020-07-01 to missing (model only),162,160,2,extreme pandemic swings would dominate OLS AR coefficient\n2025-12-31:sample_start,drop quarters before 1985-01-01 (model only),314,163,151,post-Great-Moderation estimation sample for AR(1)\n2025-12-31:pandemic,set 2020-04-01 and 2020-07-01 to missing (model only),163,161,2,extreme pandemic swings would dominate OLS AR coefficient\n{'2024-01-31': {'c': 1.7361898026074702, 'phi': 0.34039275398089675, 'n_pairs': 152}, '2024-04-30': {'c': 1.7305790509680756, 'phi': 0.33959265807517736, 'n_pairs': 153}, '2024-07-31': {'c': 1.7362755341851663, 'phi': 0.3384425695751649, 'n_pairs': 154}, '2024-10-31': {'c': 1.7489072640017447, 'phi': 0.3415484922496021, 'n_pairs': 155}, '2025-01-31': {'c': 1.747921489650478, 'phi': 0.34142351706726565, 'n_pairs': 156}, '2025-04-30': {'c': 1.7265541136483835, 'phi': 0.342777147170324, 'n_pairs': 157}, '2025-07-31': {'c': 1.7501492451503575, 'phi': 0.33704757560277687, 'n_pairs': 158}, '2025-12-31': {'c': 1.7716206256012257, 'phi': 0.33465673263863993, 'n_pairs': 159}}","is_error":false}]},"uuid":"3e507ef2-38db-4d85-a881-a372c2574fc3","timestamp":"2026-10-30T04:19:42.376Z","toolUseResult":{"stdout":"{\n  \"rmse_naive_h1\": 2.0102860493,\n  \"mae_naive_h1\": 1.6875,\n  \"rmse_naive_h2\": 2.265226258,\n  \"mae_naive_h2\": 1.8125,\n  \"rmse_model_h1\": 1.4835747469,\n  \"mae_model_h1\": 1.2332142642,\n  \"rmse_model_h2\": 1.3976488357,\n  \"mae_model_h2\": 1.0295196499,\n  \"rel_improvement_rmse_h1\": 0.2620081369,\n  \"rel_improvement_rmse_h2\": 0.3829981306,\n  \"mean_abs_revision_h1_targets\": 0.5625,\n  \"supp_dm_hln_stat_h1\": 1.4911492058,\n  \"supp_dm_hln_pvalue_two_sided_h1\": 0.1795464115,\n  \"supp_dm_hln_stat_h2\": 1.8510836005,\n  \"supp_dm_hln_pvalue_two_sided_h2\": 0.106597457\n}\norigin_date,target_date,horizon,forecast,actual,model,vintage_date,train_last_date,train_last_value,n_train\n2024-01-31,2024-01-01,1,3.3,1.6,naive,2024-01-31,2023-10-01,3.3,307\n2024-01-31,2024-01-01,1,2.8594858907,1.6,ar1_post1985,2024-01-31,2023-10-01,3.3,154\n2024-01-31,2024-04-01,2,3.3,2.8,naive,2024-01-31,2023-10-01,3.3,307\n2024-01-31,2024-04-01,2,2.7095380799,2.8,ar1_post1985,2024-01-31,2023-10-01,3.3,154\n2024-04-30,2024-04-01,1,1.6,2.8,naive,2024-04-30,2024-01-01,1.6,308\n2024-04-30,2024-04-01,1,2.2739273039,2.8,ar1_post1985,2024-04-30,2024-01-01,1.6,155\n2024-04-30,2024-07-01,2,1.6,2.8,naive,2024-04-30,2024-01-01,1.6,308\n2024-04-30,2024-07-01,2,2.5027880684,2.8,ar1_post1985,2024-04-30,2024-01-01,1.6,155\n2024-07-31,2024-07-01,1,2.8,2.8,naive,2024-07-31,2024-04-01,2.8,309\n2024-07-31,2024-07-01,1,2.683914729,2.8,ar1_post1985,2024-07-31,2024-04-01,2.8,156\n2024-07-31,2024-10-01,2,2.8,2.3,naive,2024-07-31,2024-04-01,2.8,309\n2024-07-31,2024-10-01,2,2.6446265316,2.3,ar1_post1985,2024-07-31,2024-04-01,2.8,156\n2024-10-31,2024-10-01,1,2.8,2.3,naive,2024-10-31,2024-07-01,2.8,310\n2024-10-31,2024-10-01,1,2.7052430423,2.3,ar1_post1985,2024-10-31,2024-07-01,2.8,157\n2024-10-31,2025-01-01,2,2.8,-0.3,naive,2024-10-31,2024-07-01,2.8,310\n2024-10-31,2025-01-01,2,2.6728789463,-0.3,ar1_post1985,2024-10-31,2024-07-01,2.8,157\n2025-01-31,2025-01-01,1,2.3,-0.3,naive,2025-01-31,2024-10-01,2.3,311\n2025-01-31,2025-01-01,1,2.5331955789,-0.3,ar1_post1985,2025-01-31,2024-10-01,2.3,158\n2025-01-31,2025-04-01,2,2.3,3.0,naive,2025-01-31,2024-10-01,2.3,311\n2025-01-31,2025-04-01,2,2.6128140336,3.0,ar1_post1985,2025-01-31,2024-10-01,2.3,158\n2025-04-30,2025-04-01,1,-0.3,3.0,naive,2025-04-30,2025-01-01,-0.3,312\n2025-04-30,2025-04-01,1,1.6237209695,3.0,ar1_post1985,2025-04-30,2025-01-01,-0.3,159\n2025-04-30,2025-07-01,2,-0.3,4.3,naive,2025-04-30,2025-01-01,-0.3,312\n2025-04-30,2025-07-01,2,2.2831285554,4.3,ar1_post1985,2025-04-30,2025-01-01,-0.3,159\n2025-07-31,2025-07-01,1,3.0,4.3,naive,2025-07-31,2025-04-01,3.0,313\n2025-07-31,2025-07-01,1,2.761291972,4.3,ar1_post1985,2025-07-31,2025-04-01,3.0,160\n2025-07-31,2025-10-01,2,3.0,1.4,naive,2025-07-31,2025-04-01,3.0,313\n2025-07-31,2025-10-01,2,2.6808360098,1.4,ar1_post1985,2025-07-31,2025-04-01,3.0,160\n2025-12-31,2025-10-01,1,4.3,1.4,naive,2025-12-31,2025-07-01,4.3,314\n2025-12-31,2025-10-01,1,3.2106445759,1.4,ar1_post1985,2025-12-31,2025-07-01,4.3,161\n2025-12-31,2026-01-01,2,4.3,2.0,naive,2025-12-31,2025-07-01,4.3,314\n2025-12-31,2026-01-01,2,2.8460844491,2.0,ar1_post1985,2025-12-31,2025-07-01,4.3,161\n2025-07-31:pandemic,set 2020-04-01 and 2020-07-01 to missing (model only),162,160,2,extreme pandemic swings would dominate OLS AR coefficient\n2025-12-31:sample_start,drop quarters before 1985-01-01 (model only),314,163,151,post-Great-Moderation estimation sample for AR(1)\n2025-12-31:pandemic,set 2020-04-01 and 2020-07-01 to missing (model only),163,161,2,extreme pandemic swings would dominate OLS AR coefficient\n{'2024-01-31': {'c': 1.7361898026074702, 'phi': 0.34039275398089675, 'n_pairs': 152}, '2024-04-30': {'c': 1.7305790509680756, 'phi': 0.33959265807517736, 'n_pairs': 153}, '2024-07-31': {'c': 1.7362755341851663, 'phi': 0.3384425695751649, 'n_pairs': 154}, '2024-10-31': {'c': 1.7489072640017447, 'phi': 0.3415484922496021, 'n_pairs': 155}, '2025-01-31': {'c': 1.747921489650478, 'phi': 0.34142351706726565, 'n_pairs': 156}, '2025-04-30': {'c': 1.7265541136483835, 'phi': 0.342777147170324, 'n_pairs': 157}, '2025-07-31': {'c': 1.7501492451503575, 'phi': 0.33704757560277687, 'n_pairs': 158}, '2025-12-31': 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>> out/state/log.md <<'EOF'\n- 2026-10-30 Decision (made before computing any forecast errors): model = AR(1) with intercept, OLS, estimation sample 1985Q1..last quarter of the origin vintage, with 2020Q2 and 2020Q3 (pandemic outliers, -28/+35% SAAR) set to missing so that no regression pair uses them; iterated forecasts for h=2. Rationale: post-Great-Moderation sample; pandemic swings would otherwise dominate OLS. No tuning on evaluation results.\nEOF\ncat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-02: real-time forecasts of US real GDP growth from ALFRED vintages.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv.\"\"\"\nimport json, os, glob\nimport numpy as np\nimport pandas as pd\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\"]\nSAMPLE_START = pd.Timestamp(\"1985-01-01\")\nEXCLUDED_Q = [pd.Timestamp(\"2020-04-01\"), pd.Timestamp(\"2020-07-01\")]\nMODEL = \"ar1_post1985\"\nDESC = (\"AR(1) with intercept estimated by OLS on the origin vintage, sample 1985Q1 to the last quarter \"\n        \"of that vintage, with 2020Q2 and 2020Q3 set to missing (no regression pair uses them); \"\n        \"h=2 forecast obtained by iterating the AR(1) recursion.\")\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.set_index(\"date\")[\"value\"].astype(float)\n\ndef qshift(ts, k):\n    return ts + pd.DateOffset(months=3 * k)\n\nn_input_rows = {}\nvint = {}\nfor o in ORIGINS:\n    rel = f\"vintages/A191RL1Q225SBEA_vintage_{o}.csv\"\n    s = read_series(os.path.join(DATA, rel))\n    vint[o] = s\n    n_input_rows[rel] = int(len(s))\ncur = read_series(os.path.join(DATA, \"current_vintage.csv\"))  # used ONLY for revision statistic\nn_input_rows[\"current_vintage.csv\"] = int(len(cur))\nfr = pd.read_csv(os.path.join(DATA, \"first_release.csv\"), parse_dates=[\"quarter\", \"first_release_vintage_date\"])\nn_input_rows[\"first_release.csv\"] = int(len(fr))\nfr_val = fr.set_index(\"quarter\")[\"first_release_value\"].astype(float)\nfr_vd = fr.set_index(\"quarter\")[\"first_release_vintage_date\"]\n\ndata_issues = []\nfor o, s in vint.items():\n    assert s.index.is_monotonic_increasing and not s.index.duplicated().any() and s.notna().all()\n    assert (s.index.to_series().diff().dropna().dt.days <= 92).all()\ndata_issues.append({\"issue\": \"Pandemic outliers 2020Q2/2020Q3 (about -28% and +35% SAAR) in every vintage.\",\n                    \"handling\": \"Set to missing in the AR(1) estimation sample (documented in out/exclusions.csv); naive forecasts unaffected.\"})\ndata_issues.append({\"issue\": \"Historical values differ across vintages (annual revisions, e.g. 2024Q4 2.3 -> 2.4 -> 1.9 across vintages).\",\n                    \"handling\": \"Each origin uses only its own vintage file; current vintage used only for the revision statistic.\"})\ndata_issues.append({\"issue\": \"Eighth origin is 2025-12-31 rather than 2025-10-31 (2025Q3 advance estimate delayed by the federal shutdown); its h=1 target 2025Q4 first release dated 2026-02-28.\",\n                    \"handling\": \"Used as given in TASK.md; origins are not equally spaced in calendar time but each still forecasts 1 and 2 quarters past its last published quarter.\"})\n\ndef fit_ar1(s):\n    y = s[s.index >= SAMPLE_START].copy()\n    n_after_start = len(y)\n    y.loc[y.index.isin(EXCLUDED_Q)] = np.nan\n    lag = y.shift(1)\n    # require the lag to be the immediately preceding quarter (index is contiguous quarterly)\n    m = y.notna() & lag.notna()\n    X = np.column_stack([np.ones(m.sum()), lag[m].values])\n    beta, *_ = np.linalg.lstsq(X, y[m].values, rcond=None)\n    return beta, int(y.notna().sum()), n_after_start, int(m.sum())\n\nrows, excl = [], []\nfits = {}\nfor o in ORIGINS:\n    s = vint[o]\n    T, yT = s.index[-1], float(s.iloc[-1])\n    beta, n_used, n_after_start, n_pairs = fit_ar1(s)\n    fits[o] = {\"c\": float(beta[0]), \"phi\": float(beta[1]), \"n_pairs\": n_pairs}\n    excl.append({\"step\": f\"{o}:sample_start\", \"rule\": \"drop quarters before 1985-01-01 (model only)\",\n                 \"n_before\": len(s), \"n_after\": n_after_start, \"n_removed\": len(s) - n_after_start,\n                 \"rationale\": \"post-Great-Moderation estimation sample for AR(1)\"})\n    excl.append({\"step\": f\"{o}:pandemic\", \"rule\": \"set 2020-04-01 and 2020-07-01 to missing (model only)\",\n                 \"n_before\": n_after_start, \"n_after\": n_used, \"n_removed\": n_after_start - n_used,\n                 \"rationale\": \"extreme pandemic swings would dominate OLS AR coefficient\"})\n    f_ar = {}\n    prev = yT\n    for h in (1, 2):\n        prev = beta[0] + beta[1] * prev\n        f_ar[h] = float(prev)\n    for h in (1, 2):\n        tgt = qshift(T, h)\n        assert tgt in fr_val.index, f\"missing first release for {tgt}\"\n        assert fr_vd[tgt] >= pd.Timestamp(o), \"actual published before origin\"\n        for model, fc, ntr in ((\"naive\", yT, len(s)), (MODEL, f_ar[h], n_used)):\n            rows.append({\"origin_date\": o, \"target_date\": tgt.strftime(\"%Y-%m-%d\"), \"horizon\": h,\n                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= metrics(model, h)\n        res[f\"rmse_{tag}_h{h}\"] = round(r, 10)\n        res[f\"mae_{tag}_h{h}\"] = round(a, 10)\nfor h in (1, 2):\n    res[f\"rel_improvement_rmse_h{h}\"] = round(1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"], 10)\nh1t = pd.to_datetime(fc[(fc.model == \"naive\") & (fc.horizon == 1)][\"target_date\"])\nrev = [abs(float(cur[t]) - float(fr_val[t])) for t in h1t]\nres[\"mean_abs_revision_h1_targets\"] = round(float(np.mean(rev)), 10)\nres[\"feasibility\"] = \"complete\"\nres[\"feasibility_reason\"] = None\nres[\"data_issues\"] = data_issues\nres[\"n_input_rows\"] = n_input_rows\nres[\"n_analysed\"] = int(fc[(fc.origin_date == ORIGINS[-1]) & (fc.model == MODEL)][\"n_train\"].iloc[0])\nres[\"random_seed\"] = None\n# supplementary diagnostics (not required keys)\nres[\"supp_ar1_coefficients_by_origin\"] = fits\nres[\"supp_abs_revision_h1_targets\"] = {t.strftime(\"%Y-%m-%d\"): round(r, 10) for t, r in zip(h1t, rev)}\nfor model, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n    for h in (1, 2):\n        res[f\"supp_mean_error_{tag}_h{h}\"] = round(float(fc[(fc.model == model) & (fc.horizon == h)][\"error\"].mean()), 10)\n# Diebold-Mariano-type comparison (squared-error loss), simple t-stat on loss differential, small-sample caveat\nfrom scipy import stats\nfor h in (1, 2):\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)][\"error\"].values\n    em = fc[(fc.model == MODEL) & (fc.horizon == h)][\"error\"].values\n    d = en ** 2 - em ** 2\n    n = len(d)\n    # HAC variance with h-1 lags (rectangular), fallback to plain variance if non-positive\n    g0 = np.mean((d - d.mean()) ** 2)\n    v = g0 + sum(2 * np.mean((d[k:] - d.mean()) * (d[:-k] - d.mean())) for k in range(1, h))\n    if v <= 0:\n        v = g0\n    dm = float(d.mean() / np.sqrt(v / n))\n    hln = dm * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    res[f\"supp_dm_hln_stat_h{h}\"] = round(float(hln), 10)\n    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   # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153:            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155:            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")","is_error":false}]},"uuid":"45363afe-7ac5-44d5-84aa-b75a11946ec7","timestamp":"2026-10-30T04:19:45.777Z","toolUseResult":{"stdout":"139:    # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n153:            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155:            add(\"exclusion_ledger\", False, f\"unreadable: 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           not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")\n    # 7. numeric sanity checks\n    if \"ci_contains_estimate\" in fam:\n        for est, lo, hi in spec.get(\"ci_pairs\", []):\n            e, l, h = res.get(est), res.get(lo), res.get(hi)\n            if all(_is_num(v) for v in (e, l, h)):","is_error":false}]},"uuid":"006355f2-6c4a-49f2-9420-3c5bbb1ca964","timestamp":"2026-10-30T04:19:46.918Z","toolUseResult":{"stdout":"            not_done = [pid for pid, st in pl.items() if isinstance(st, dict) and st.get(\"status\") not in (\"passed\", \"skipped_optional\", \"blocked\")]\n            if not_done:\n                bad.append(f\"phases not terminal: {not_done}\")\n            add(\"phase_ledger\", not bad, \"; \".join(bad) if bad else \"consistent\")\n    # 6. exclusion ledger\n    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n        try:\n            rows = list(csv.DictReader(open(ex_path, newline=\"\")))\n            bad = []\n            prev_after = None\n            for i, r in enumerate(rows):\n                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n                if nb - nr != na:\n                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n                if prev_after is not None and nb != prev_after:\n                    bad.append(f\"row {i}: n_before != previous n_after\")\n                prev_after = na\n            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n        except Exception as e:  # noqa: BLE001\n            add(\"exclusion_ledger\", False, 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Value range in vintages about -28.1 .. 35.2 (2020Q2/Q3 pandemic).\n\nAnomalies / notes:\n- Pandemic outliers 2020Q2 (≈-28) and 2020Q3 (≈+35) in all vintages → set to missing in the AR(1) estimation sample only (see exclusions.csv; per-origin in exclusions_by_origin.csv).\n- Values of earlier quarters differ across vintages (revisions), as expected for real-time data; each origin uses only its own file.\n- Last quarter of each vintage matches README; and equals first_release value of that quarter (consistent: the vintage dates are the first-release dates).\n- Eighth origin is 2025-12-31 (shutdown delay); h=1 target 2025Q4 first released 2026-02-28.\n- Every target's first_release_vintage_date is ≥ origin date (h=2 target of origin 2025-07-31 is 2025Q3, first released 2025-12-31 — after the origin). Asserted in analysis.py.\n\nInformation-availability: current_vintage.csv violates the real-time rule for estimation; it is read only for the revision statistic. 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AR(1) cross-checked with statsmodels OLS (last origin c=1.7716, phi=0.3347, 159 pairs).\n- 2026-10-30 Exclusion ledger changed to single chain for last origin (gate requires chained rows); per-origin ledger in exclusions_by_origin.csv.\nEOF","description":"Write audit, rerun from clean, compare"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":821,"cache_read_input_tokens":24449,"output_tokens":1503,"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":821},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnAcNCg5E2tvD8Mte1u","requestRef":"44fb1666-0b47-48eb-8791-ab2ee060af84","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"625d7478-e335-442c-a271-5ae1b1434ca3","timestamp":"2026-10-30T04:20:12.002Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"625d7478-e335-442c-a271-5ae1b1434ca3","isSidechain":false,"promptId":"db6166b8-8d55-4a5e-8d5e-ae3ce261a64b","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RFaMJXL3Sjv3XzzKqA8Y7Q","type":"tool_result","content":"IDENTICAL","is_error":false}]},"uuid":"a2b1625f-83f0-4475-b3eb-26c13dc96886","timestamp":"2026-10-30T04:20:12.732Z","toolUseResult":{"stdout":"IDENTICAL","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"625d7478-e335-442c-a271-5ae1b1434ca3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","hash":"9cb052e5e6acfa424d3ce27fab3d667701b83d3c02d908c8ac8c3ba93818f24c","message":{"role":"assistant","content":[{"type":"text","text":"Matches. Now the audit, checks, and clean re-run."},{"type":"tool_use","id":"toolu_01RFaMJXL3Sjv3XzzKqA8Y7Q","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n\nAll SHA-256 values computed with `shasum -a 256` match data/README.md.\n\n| file | SHA-256 | data rows | cols | range | missing | dup dates | gaps |\n|---|---|---|---|---|---|---|---|\n| vintages/..._2024-01-31.csv | 7ce218fb…f243 | 307 | 2 | 1947-04-01..2023-10-01 | 0 | 0 | 0 |\n| vintages/..._2024-04-30.csv | 62c1dfce…97cb | 308 | 2 | ..2024-01-01 | 0 | 0 | 0 |\n| vintages/..._2024-07-31.csv | 6f942591…79da | 309 | 2 | ..2024-04-01 | 0 | 0 | 0 |\n| vintages/..._2024-10-31.csv | c4c51ede…0003 | 310 | 2 | ..2024-07-01 | 0 | 0 | 0 |\n| vintages/..._2025-01-31.csv | 10a5e02e…aee2 | 311 | 2 | ..2024-10-01 | 0 | 0 | 0 |\n| vintages/..._2025-04-30.csv | 1f19c975…5085d1a | 312 | 2 | ..2025-01-01 | 0 | 0 | 0 |\n| vintages/..._2025-07-31.csv | f1e7c556…57a9 | 313 | 2 | ..2025-04-01 | 0 | 0 | 0 |\n| vintages/..._2025-12-31.csv | c111c070…42a8d4a7 | 314 | 2 | ..2025-07-01 | 0 | 0 | 0 |\n| current_vintage.csv | 1af2590d…0562 | 318 | 2 | 1947-04-01..2026-07-01 | 0 | 0 | 0 |\n| first_release.csv | ab843c9f…95bb | 13 | 3 | quarters 2023-07-01..2026-07-01 | 0 | 0 | – |\n\nUnits: percent change from preceding quarter, SAAR (pp of annualised growth). Value range in vintages about -28.1 .. 35.2 (2020Q2/Q3 pandemic).\n\nAnomalies / notes:\n- Pandemic outliers 2020Q2 (≈-28) and 2020Q3 (≈+35) in all vintages → set to missing in the AR(1) estimation sample only (see exclusions.csv; per-origin in exclusions_by_origin.csv).\n- Values of earlier quarters differ across vintages (revisions), as expected for real-time data; each origin uses only its own file.\n- Last quarter of each vintage matches README; and equals first_release value of that quarter (consistent: the vintage dates are the first-release dates).\n- Eighth origin is 2025-12-31 (shutdown delay); h=1 target 2025Q4 first released 2026-02-28.\n- Every target's first_release_vintage_date is ≥ origin date (h=2 target of origin 2025-07-31 is 2025Q3, first released 2025-12-31 — after the origin). Asserted in analysis.py.\n\nInformation-availability: current_vintage.csv violates the real-time rule for estimation; it is read only for the revision statistic. No other input violates the rules.\nEOF\nrm -f out/results.json out/forecasts.csv out/exclusions*.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo IDENTICAL\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P1 passed (audit written; hashes match). P2 baseline (naive) and P3 AR(1) computed in out/analysis.py. AR(1) cross-checked with statsmodels OLS (last origin c=1.7716, phi=0.3347, 159 pairs).\n- 2026-10-30 Exclusion ledger changed to single chain for last origin (gate requires chained rows); per-origin ledger in exclusions_by_origin.csv.\nEOF","description":"Write audit, rerun from clean, compare"}}]}}71{"type":"api-request","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","id":"aad6b7b2-de67-4a2b-a42e-f8aab2771061","timestamp":"2026-10-30T04:20:12.735Z","version":"2.1.314","querySource":"sdk","shapeHash":"9de5beaed85b287ec7574bf6f549e23abd2c8c16d5a74bb19918aad71f6b8f41","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":"44fb1666-0b47-48eb-8791-ab2ee060af84","keep":22,"tail":["9cb052e5e6acfa424d3ce27fab3d667701b83d3c02d908c8ac8c3ba93818f24c","29f6e9c879f07050ba53fc6e3d91a3d470932e73c1ed41efa21dfa38112c9774"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b1fe3f247f72f840e3483b846280aca05892f011980dc8aeac958cf896476bf6"}72{"parentUuid":"a2b1625f-83f0-4475-b3eb-26c13dc96886","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnBbgo1MzUL1s1VPpC8","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UgbTdF8rdMAtmExoaVGkhX","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n1. Clean re-run: deleted out/results.json, forecasts.csv, exclusions*.csv; re-ran analysis.py; results.json byte-identical to previous run (cmp). PASS\n2. forecasts.csv has 32 rows (8 origins × 2 horizons × 2 models). PASS\n3. Hand check naive h1 RMSE: errors -1.7,1.2,0,-0.5,-2.6,3.3,1.3,-2.9 → sqrt(32.33/8)=2.0103 = rmse_naive_h1. PASS\n4. Hand check revision: |cur-first| for 2024Q1..2025Q4 = .2,.8,.4,.1,.4,1.0,.4,1.2 → 0.5625 = mean_abs_revision_h1_targets. PASS\n5. AR(1) OLS cross-checked with statsmodels for last origin: identical coefficients and h=1 forecast 3.2106. PASS\n6. Chronology: every target first_release_vintage_date ≥ origin (asserted in code); train_last_date < target_date; first_origin < last_origin. PASS\n7. Exclusion ledger: 314 − 151 − 2 = 161 = n_analysed = n_train of last-origin model rows. PASS\n8. RMSE ≥ MAE for every model/horizon; all RMSE > 0; rel_improvement = 1 − rmse_model/rmse_naive recomputed. PASS\n9. Information rule: current_vintage.csv used only in the revision statistic (code inspection). 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Clean re-run: deleted out/results.json, forecasts.csv, exclusions*.csv; re-ran analysis.py; results.json byte-identical to previous run (cmp). PASS\n2. forecasts.csv has 32 rows (8 origins × 2 horizons × 2 models). PASS\n3. Hand check naive h1 RMSE: errors -1.7,1.2,0,-0.5,-2.6,3.3,1.3,-2.9 → sqrt(32.33/8)=2.0103 = rmse_naive_h1. PASS\n4. Hand check revision: |cur-first| for 2024Q1..2025Q4 = .2,.8,.4,.1,.4,1.0,.4,1.2 → 0.5625 = mean_abs_revision_h1_targets. PASS\n5. AR(1) OLS cross-checked with statsmodels for last origin: identical coefficients and h=1 forecast 3.2106. PASS\n6. Chronology: every target first_release_vintage_date ≥ origin (asserted in code); train_last_date < target_date; first_origin < last_origin. PASS\n7. Exclusion ledger: 314 − 151 − 2 = 161 = n_analysed = n_train of last-origin model rows. PASS\n8. RMSE ≥ MAE for every model/horizon; all RMSE > 0; rel_improvement = 1 − rmse_model/rmse_naive recomputed. PASS\n9. Information rule: current_vintage.csv used only in the revision statistic (code inspection). 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> out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 10 inputs match README\",\"row/col counts, missing, duplicates, quarterly gaps checked (none)\",\"first-release dates >= origins checked\",\"exclusion ledger written\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive RMSE/MAE computed for h=1,2 with first-release actuals\",\"naive h1 RMSE hand-verified 2.0103\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic (no randomness)\",\"AR(1) cross-checked vs statsmodels\"],\"notes\":\"model pre-specified before evaluation, see log.md\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"clean re-run byte-identical\",\"identities in checks.md all PASS\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Update ledger and run gate 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 [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2024-01-31 vs 2025-12-31\n  [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0067/out/state/gate_receipts/20261030T042026.005951Z.json\n20261030T042026.005951Z.json","is_error":false}]},"uuid":"26454381-20ea-4d18-9d9e-4fe8b79d0266","timestamp":"2026-10-30T04:20:26.018Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 40 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: 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> out/report.md <<'EOF'\n# F3-conf-02 — Real-time forecasts of US real GDP growth from data vintages\n\n## Question\nHow accurately could US quarterly real GDP growth (A191RL1Q225SBEA, % change q/q at a seasonally adjusted annual rate) have been forecast 1 and 2 quarters ahead using only the data published at each of 8 forecast origins (`first_origin` = 2024-01-31 to `last_origin` = 2025-12-31, `n_origins` = 8)? And does a simple time-series model beat the naive (last-value) forecast when both are scored against first-release values?\n\n## Data and exclusions\n- Inputs: 8 ALFRED vintage files (307–314 data rows each), `current_vintage.csv` (318 rows) and `first_release.csv` (13 rows). Row counts are in `n_input_rows`. All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). No missing values, duplicate dates or quarterly gaps.\n- Information rule: at each origin the forecasts use only the vintage file dated at that origin. `current_vintage.csv` is used only for the revision statistic. The code asserts that every target's first-release date is on or after the origin date.\n- Exclusions (model only; the naive forecast uses the full vintage): quarters before 1985Q1 are dropped, and 2020Q2 and 2020Q3 (pandemic swings of about −28 and +35 % SAAR) are set to missing. For the last origin: 314 − 151 − 2 = 161 observations (`n_analysed`). The ledger is in `out/exclusions.csv`; the same rules applied at every origin are listed in `out/exclusions_by_origin.csv`.\n- Data issues and how each was handled are listed in `data_issues`. They include the 2025-12-31 eighth origin (the 2025Q3 advance estimate was delayed by the federal shutdown) and revisions of past quarters between vintages.\n\n## Method\n- Naive: the forecast for h = 1 and h = 2 is the last value in the origin's vintage file.\n- Model (`model_name` = ar1_post1985; `model_description`): an AR(1) with intercept, estimated by OLS on the origin's vintage from 1985Q1 to the last published quarter, with 2020Q2 and 2020Q3 set to missing. Any lag pair that involves those quarters is dropped. The h = 2 forecast iterates the AR(1). I fixed this specification before computing any forecast errors (`out/state/log.md`) and did not tune it on the evaluation set. The method has no randomness (`random_seed` = null).\n- Targets are the quarters h = 1 and h = 2 after the vintage's last quarter. The actual for each target is its first-release value. Metrics are RMSE and MAE over the 8 origins per horizon, and relative improvement = 1 − RMSE_model / RMSE_naive.\n- Revision statistic: the mean of |current-vintage value − first release| over the 8 h = 1 target quarters (2024Q1–2025Q4).\n- Per-row forecasts are in `out/forecasts.csv` (32 rows).\n\n## Results\n| | h = 1 | h = 2 |\n|---|---|---|\n| RMSE naive | 2.010 (`rmse_naive_h1`) | 2.265 (`rmse_naive_h2`) |\n| MAE naive | 1.688 (`mae_naive_h1`) | 1.813 (`mae_naive_h2`) |\n| RMSE AR(1) | 1.484 (`rmse_model_h1`) | 1.398 (`rmse_model_h2`) |\n| MAE AR(1) | 1.233 (`mae_model_h1`) | 1.030 (`mae_model_h2`) |\n| 1 − RMSE ratio | 0.262 (`rel_improvement_rmse_h1`) | 0.383 (`rel_improvement_rmse_h2`) |\n\nUnits are percentage points of annualised growth.\n- In this sample the AR(1) has a lower RMSE than the naive forecast: 26 % lower at h = 1 and 38 % lower at h = 2. The AR(1) pulls forecasts toward the post-1985 mean (about 2.6 %), and this helps most after unusual quarters, such as the −0.3 first release for 2025Q1 and the 4.3 for 2025Q3.\n- Mean forecast errors (actual − forecast) are small and negative for both models: naive −0.24 / −0.19 and AR(1) −0.34 / −0.33 at h = 1 / h = 2 (`supp_mean_error_*`).\n- The mean absolute revision from first release to the 2026-10-30 vintage for the h = 1 targets is 0.5625 pp (`mean_abs_revision_h1_targets`). That is roughly a third of the AR(1) h = 1 RMSE, so the first-release target itself carries non-trivial measurement noise.\n\n## Uncertainty\n- Each metric rests on only 8 forecast errors. The h = 2 errors also overlap in time with the h = 1 errors of the next origin.\n- A Diebold–Mariano test with the Harvey–Leybourne–Newbold small-sample correction, using squared-error loss (naive minus AR(1)), gives statistics of 1.49 at h = 1 (`supp_dm_hln_stat_h1`, two-sided p = 0.18, `supp_dm_hln_pvalue_two_sided_h1`) and 1.85 at h = 2 (`supp_dm_hln_stat_h2`, p = 0.11, `supp_dm_hln_pvalue_two_sided_h2`).\n- The AR(1) advantage is therefore not statistically significant at conventional levels. The asymptotic justification of the test is also weak with n = 8.\n- A single quarter can move the RMSEs substantially. 2025Q1 (−0.3) alone contributes a naive h = 1 squared error of 6.76 out of a total of 32.33.\n\n## Limitations and open issues\n- The evaluation window is short (2024Q1–2026Q1 targets). It includes an unusual 2025Q1 contraction linked to import front-running, so the results do not generalise to other periods.\n- I specified only one model. The 1985 sample start and the pandemic treatment are judgement calls made before evaluation; I did not run other specifications, so I cannot say how sensitive the results are to these choices.\n- Actuals are first releases, as the task requires. Against current-vintage values the ranking could differ; I did not evaluate this, because current values are not real-time information.\n- The origins are not equally spaced (the 2025-12-31 origin replaces 2025-10-31). The first-release grid in `first_release.csv` uses month-end vintages, so a \"first release\" is the advance estimate as it stood at that month-end.\n- `feasibility` = \"complete\": every required quantity was computed.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In real time over 8 origins, the naive forecast had RMSE 2.010 (h=1) and 2.265 (h=2) pp against first releases.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h2\",\"mae_naive_h1\",\"mae_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Only 8 errors per horizon.\"},\n {\"id\":\"C2\",\"text\":\"The pre-specified AR(1) (1985Q1+, pandemic quarters masked) had lower RMSE than naive: 1.484 at h=1 and 1.398 at h=2, i.e. relative improvements of 26% and 38%.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_model_h2\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"mae_model_h1\",\"mae_model_h2\"],\"status\":\"supported\",\"caveat\":\"Point estimates in a short sample.\"},\n {\"id\":\"C3\",\"text\":\"The AR(1) advantage is not statistically significant at conventional levels (HLN-corrected DM p = 0.18 at h=1, 0.11 at h=2).\",\"results_keys\":[\"supp_dm_hln_stat_h1\",\"supp_dm_hln_pvalue_two_sided_h1\",\"supp_dm_hln_stat_h2\",\"supp_dm_hln_pvalue_two_sided_h2\"],\"status\":\"supported\",\"caveat\":\"DM test approximations are weak with n=8.\"},\n {\"id\":\"C4\",\"text\":\"First releases of the h=1 target quarters were later revised by 0.5625 pp on average in absolute value (to the 2026-10-30 vintage).\",\"results_keys\":[\"mean_abs_revision_h1_targets\"],\"status\":\"supported\",\"caveat\":\"Later vintages may revise further.\"},\n {\"id\":\"C5\",\"text\":\"Both models' mean errors are small and negative (forecasts slightly too high on average).\",\"results_keys\":[\"supp_mean_error_naive_h1\",\"supp_mean_error_naive_h2\",\"supp_mean_error_model_h1\",\"supp_mean_error_model_h2\"],\"status\":\"supported\",\"caveat\":\"No bias test performed; 8 observations.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 10 inputs match README\",\"row/col counts, missing, duplicates, quarterly gaps checked (none)\",\"first-release dates >= origins checked\",\"exclusion ledger written\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive RMSE/MAE computed for h=1,2 with first-release actuals\",\"naive h1 RMSE hand-verified 2.0103\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic (no randomness)\",\"AR(1) cross-checked vs statsmodels\"],\"notes\":\"model pre-specified before evaluation, see log.md\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"identities in checks.md all PASS\",\"gate run receipt out/state/gate_receipts/20261030T042026.005951Z.json: all P4-relevant checks (keys, reproducibility, exclusion ledger, chronology) ok; failures were only P5 deliverables not yet written\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections written, keys cited\",\"claims.json written, keys exist in results.json\",\"final gate run (see receipt listed in notes)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T042026). 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And does a simple time-series model beat the naive (last-value) forecast when both are scored against first-release values?\n\n## Data and exclusions\n- Inputs: 8 ALFRED vintage files (307–314 data rows each), `current_vintage.csv` (318 rows) and `first_release.csv` (13 rows). Row counts are in `n_input_rows`. All SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). No missing values, duplicate dates or quarterly gaps.\n- Information rule: at each origin the forecasts use only the vintage file dated at that origin. `current_vintage.csv` is used only for the revision statistic. The code asserts that every target's first-release date is on or after the origin date.\n- Exclusions (model only; the naive forecast uses the full vintage): quarters before 1985Q1 are dropped, and 2020Q2 and 2020Q3 (pandemic swings of about −28 and +35 % SAAR) are set to missing. For the last origin: 314 − 151 − 2 = 161 observations (`n_analysed`). The ledger is in `out/exclusions.csv`; the same rules applied at every origin are listed in `out/exclusions_by_origin.csv`.\n- Data issues and how each was handled are listed in `data_issues`. They include the 2025-12-31 eighth origin (the 2025Q3 advance estimate was delayed by the federal shutdown) and revisions of past quarters between vintages.\n\n## Method\n- Naive: the forecast for h = 1 and h = 2 is the last value in the origin's vintage file.\n- Model (`model_name` = ar1_post1985; `model_description`): an AR(1) with intercept, estimated by OLS on the origin's vintage from 1985Q1 to the last published quarter, with 2020Q2 and 2020Q3 set to missing. Any lag pair that involves those quarters is dropped. The h = 2 forecast iterates the AR(1). I fixed this specification before computing any forecast errors (`out/state/log.md`) and did not tune it on the evaluation set. The method has no randomness (`random_seed` = null).\n- Targets are the quarters h = 1 and h = 2 after the vintage's last quarter. The actual for each target is its first-release value. Metrics are RMSE and MAE over the 8 origins per horizon, and relative improvement = 1 − RMSE_model / RMSE_naive.\n- Revision statistic: the mean of |current-vintage value − first release| over the 8 h = 1 target quarters (2024Q1–2025Q4).\n- Per-row forecasts are in `out/forecasts.csv` (32 rows).\n\n## Results\n| | h = 1 | h = 2 |\n|---|---|---|\n| RMSE naive | 2.010 (`rmse_naive_h1`) | 2.265 (`rmse_naive_h2`) |\n| MAE naive | 1.688 (`mae_naive_h1`) | 1.813 (`mae_naive_h2`) |\n| RMSE AR(1) | 1.484 (`rmse_model_h1`) | 1.398 (`rmse_model_h2`) |\n| MAE AR(1) | 1.233 (`mae_model_h1`) | 1.030 (`mae_model_h2`) |\n| 1 − RMSE ratio | 0.262 (`rel_improvement_rmse_h1`) | 0.383 (`rel_improvement_rmse_h2`) |\n\nUnits are percentage points of annualised growth.\n- In this sample the AR(1) has a lower RMSE than the naive forecast: 26 % lower at h = 1 and 38 % lower at h = 2. The AR(1) pulls forecasts toward the post-1985 mean (about 2.6 %), and this helps most after unusual quarters, such as the −0.3 first release for 2025Q1 and the 4.3 for 2025Q3.\n- Mean forecast errors (actual − forecast) are small and negative for both models: naive −0.24 / −0.19 and AR(1) −0.34 / −0.33 at h = 1 / h = 2 (`supp_mean_error_*`).\n- The mean absolute revision from first release to the 2026-10-30 vintage for the h = 1 targets is 0.5625 pp (`mean_abs_revision_h1_targets`). That is roughly a third of the AR(1) h = 1 RMSE, so the first-release target itself carries non-trivial measurement noise.\n\n## Uncertainty\n- Each metric rests on only 8 forecast errors. The h = 2 errors also overlap in time with the h = 1 errors of the next origin.\n- A Diebold–Mariano test with the Harvey–Leybourne–Newbold small-sample correction, using squared-error loss (naive minus AR(1)), gives statistics of 1.49 at h = 1 (`supp_dm_hln_stat_h1`, two-sided p = 0.18, `supp_dm_hln_pvalue_two_sided_h1`) and 1.85 at h = 2 (`supp_dm_hln_stat_h2`, p = 0.11, `supp_dm_hln_pvalue_two_sided_h2`).\n- The AR(1) advantage is therefore not statistically significant at conventional levels. The asymptotic justification of the test is also weak with n = 8.\n- A single quarter can move the RMSEs substantially. 2025Q1 (−0.3) alone contributes a naive h = 1 squared error of 6.76 out of a total of 32.33.\n\n## Limitations and open issues\n- The evaluation window is short (2024Q1–2026Q1 targets). It includes an unusual 2025Q1 contraction linked to import front-running, so the results do not generalise to other periods.\n- I specified only one model. The 1985 sample start and the pandemic treatment are judgement calls made before evaluation; I did not run other specifications, so I cannot say how sensitive the results are to these choices.\n- Actuals are first releases, as the task requires. Against current-vintage values the ranking could differ; I did not evaluate this, because current values are not real-time information.\n- The origins are not equally spaced (the 2025-12-31 origin replaces 2025-10-31). The first-release grid in `first_release.csv` uses month-end vintages, so a \"first release\" is the advance estimate as it stood at that month-end.\n- `feasibility` = \"complete\": every required quantity was computed.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"In real time over 8 origins, the naive forecast had RMSE 2.010 (h=1) and 2.265 (h=2) pp against first releases.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h2\",\"mae_naive_h1\",\"mae_naive_h2\"],\"status\":\"supported\",\"caveat\":\"Only 8 errors per horizon.\"},\n {\"id\":\"C2\",\"text\":\"The pre-specified AR(1) (1985Q1+, pandemic quarters masked) had lower RMSE than naive: 1.484 at h=1 and 1.398 at h=2, i.e. relative improvements of 26% and 38%.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_model_h2\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"mae_model_h1\",\"mae_model_h2\"],\"status\":\"supported\",\"caveat\":\"Point estimates in a short sample.\"},\n {\"id\":\"C3\",\"text\":\"The AR(1) advantage is not statistically significant at conventional levels (HLN-corrected DM p = 0.18 at h=1, 0.11 at h=2).\",\"results_keys\":[\"supp_dm_hln_stat_h1\",\"supp_dm_hln_pvalue_two_sided_h1\",\"supp_dm_hln_stat_h2\",\"supp_dm_hln_pvalue_two_sided_h2\"],\"status\":\"supported\",\"caveat\":\"DM test approximations are weak with n=8.\"},\n {\"id\":\"C4\",\"text\":\"First releases of the h=1 target quarters were later revised by 0.5625 pp on average in absolute value (to the 2026-10-30 vintage).\",\"results_keys\":[\"mean_abs_revision_h1_targets\"],\"status\":\"supported\",\"caveat\":\"Later vintages may revise further.\"},\n {\"id\":\"C5\",\"text\":\"Both models' mean errors are small and negative (forecasts slightly too high on average).\",\"results_keys\":[\"supp_mean_error_naive_h1\",\"supp_mean_error_naive_h2\",\"supp_mean_error_model_h1\",\"supp_mean_error_model_h2\"],\"status\":\"supported\",\"caveat\":\"No bias test performed; 8 observations.\"}\n]\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of all 10 inputs match README\",\"row/col counts, missing, duplicates, quarterly gaps checked (none)\",\"first-release dates >= origins checked\",\"exclusion ledger written\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive RMSE/MAE computed for h=1,2 with first-release actuals\",\"naive h1 RMSE hand-verified 2.0103\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic (no randomness)\",\"AR(1) cross-checked vs statsmodels\"],\"notes\":\"model pre-specified before evaluation, see log.md\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"identities in checks.md all PASS\",\"gate run receipt out/state/gate_receipts/20261030T042026.005951Z.json: all P4-relevant checks (keys, reproducibility, exclusion ledger, chronology) ok; failures were only P5 deliverables not yet written\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections written, keys cited\",\"claims.json written, keys exist in results.json\",\"final gate run (see receipt listed in notes)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T042026). 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completed the task. The simple model (an AR(1)) beat the naive forecast in this sample, but with only 8 forecasts per horizon the difference is not statistically significant. The final gate run passed all 40 checks, and re-running `out/analysis.py` from a clean state reproduces `out/results.json` exactly.\n\n**Results** (percentage points of annualised growth, scored against the first-release value of each target quarter):\n\n| | h = 1 | h = 2 |\n|---|---|---|\n| Naive RMSE / MAE | 2.010 / 1.688 | 2.265 / 1.813 |\n| AR(1) RMSE / MAE | 1.484 / 1.233 | 1.398 / 1.030 |\n| RMSE improvement over naive | 26.2 % | 38.3 % |\n\n- **Model:** an AR(1) with intercept, fitted by ordinary least squares on each origin's vintage only, using data from 1985Q1 onward. The two pandemic quarters (2020Q2 at about −28 % and 2020Q3 at about +35 %) were treated as missing. The h = 2 forecast applies the model twice. I fixed this specification before computing any forecast errors and did not tune it afterwards.\n- **Significance:** a standard small-sample test comparing the two models' forecast errors (Diebold–Mariano with the Harvey–Leybourne–Newbold correction) gives p = 0.18 at h = 1 and p = 0.11 at h = 2. I added this test and the mean forecast errors as extra outputs beyond the required ones.\n- **Revisions:** the first releases of the 8 h = 1 target quarters have since been revised by 0.5625 pp on average, measured against today's data. That is about a third of the AR(1) h = 1 RMSE, so the first-release targets themselves are noticeably noisy.\n- **Data:** all input file checksums match `data/README.md`. The current-vintage file was used only for the revision figure, never for fitting or as forecast input. The code also confirms that no target's first release was published before its forecast origin.\n\n**Limitations:**\n- With only 8 errors per horizon, a single quarter moves the results a lot. The −0.3 first release for 2025Q1 alone accounts for about a fifth of the naive h = 1 squared error.\n- I specified only one model and did not test how much the results depend on the 1985 start or on dropping the pandemic quarters.\n- The origins are not evenly spaced: the last one is 2025-12-31 instead of 2025-10-31, because the 2025Q3 GDP estimate was delayed by the federal shutdown.\n\nThe exclusion ledger (`out/exclusions.csv`) covers only the last origin's training data (314 − 151 − 2 = 161 observations), because the gate requires its rows to chain. The same two exclusions at every origin are listed in `out/exclusions_by_origin.csv`.\n\nEverything is in `out/`. The main files are `analysis.py`, `results.json`, `forecasts.csv` (32 rows), `report.md` and `claims.json`; my working notes and gate receipts are in `out/state/`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":202,"cache_read_input_tokens":34419,"output_tokens":1071,"output_tokens_details":{"thinking_tokens":37},"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":202},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnFhsV7aai9PHvQ2q6j","requestRef":"0b8a26d1-7d51-4b83-8358-0d28588bbb03","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d03558ae-1e9a-468c-af5c-22efa67bf1da","timestamp":"2026-10-30T04:21:17.861Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0067","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","version":"2.1.314","gitBranch":"HEAD"}105{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d03558ae-1e9a-468c-af5c-22efa67bf1da","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745"}106{"type":"cost-state","sessionId":"619e98eb-36f1-4903-a9fa-9de48aa5f745","totalCostUSD":0.5994898000000001,"totalAPIDuration":151315,"totalAPIDurationWithoutRetries":151303,"totalToolDuration":6238,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":157855,"startTime":1793333920015,"modelUsage":{"claude-opus-5-5":{"inputTokens":38,"outputTokens":17906,"thinkingTokens":2270,"cacheReadInputTokens":378164,"cacheCreationInputTokens":33117,"cacheCreationEphemeral5mInputTokens":33117,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5994898000000001}},"hasUnknownModelCost":false}107