spb/wp29_uqo

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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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This is an end-to-end forecasting project: question → data audit → real-time baselines → your model → rolling-origin evaluation → uncertainty → figure → short paper. The estimands are, for horizons h ∈ {1, 3} months and over the 10 forecast origins defined below: (a) the out-of-sample RMSE and MAE of three baselines (naive, seasonal naive, AR(1)) and of your model, all evaluated against the first-release value of the target; (b) the relative improvement of your model over the naive forecast, 1 − RMSE_model / RMSE_naive; (c) the Diebold–Mariano statistic of your model against the naive forecast under squared-error loss, with a long-run variance that is robust to the serial correlation of multi-step forecast errors; (d) the mean absolute revision between the first-release growth of the h = 1 target months and their currently published growth. Report what the data show, including a negative result if your model does not beat the naive forecast.\n\n## Inputs\nAll series are monthly, seasonally adjusted, dated by the first day of the month, and come from FRED/ALFRED (Federal Reserve Bank of St. Louis); full provenance, retrieval times (2026-10-30 UTC), SHA-256 and terms are in `data/README.md`.\n- `data/vintages/RSAFS_vintage_<YYYY-MM-DD>.csv` — 10 files, one per forecast origin (2025-10-31, 2025-11-30, 2025-12-31, 2026-01-31, 2026-02-28, 2026-03-31, 2026-04-30, 2026-05-31, 2026-06-30, 2026-07-31): the series RSAFS — Advance Retail Sales: Retail Trade and Food Services, total, millions of dollars, SA (US Census Bureau) — exactly as it was published on that date (ALFRED vintage; header `observation_date,RSAFS_<yyyymmdd>`; first observation 1992-01; the last row is the most recent month published at that date). The target variable is the year-over-year growth g_m = 100 · ln(L_m / L_{m−12}) in percent, where L is the level in the relevant vintage.\n- `data/vintages/PAYEMS_vintage_<YYYY-MM-DD>.csv` — 10 files, same dates: the series PAYEMS — All Employees, Total Nonfarm, thousands of persons, SA (US Bureau of Labor Statistics) — as published on that date (ALFRED vintage; first observation 1939-01). A candidate predictor.\n- `data/UMCSENT.csv` — the series UMCSENT — University of Michigan Index of Consumer Sentiment (1966Q1 = 100) — current vintage as served by FRED on 2026-10-30 (header `observation_date,UMCSENT`; 1952-11 to 2026-09; the field is empty for the months before 1978 in which the survey was not conducted). A candidate predictor; see the availability rule below.\n- `data/RSAFS_current.csv` — RSAFS as served by FRED on 2026-10-30 (header `observation_date,RSAFS`; 1992-01 to 2026-09), containing all revisions known today. **Provided only for the revision analysis (estimand (d)); it may not enter any forecast.**\n- `data/first_release.csv` — one row per target month from 2024-06 to 2026-09: `target_month`, `first_release_vintage` (the first month-end ALFRED vintage in the designer's grid 2024-06-30 … 2026-10-29 in which the month appears), `level_first_release` (the RSAFS level of the month in that vintage), `level_prev_year_same_vintage` (the level of the month twelve months earlier in the same vintage) and `yoy_first_release` = 100 · ln(level_first_release / level_prev_year_same_vintage), 6 decimals. This is the \"actual\" against which every forecast is evaluated.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- Forecast origins are the 10 vintage dates listed above. A forecast made at origin V may use **only** the RSAFS vintage file dated V, the PAYEMS vintage file dated V, and the UMCSENT values of months on or before the month of V (the final sentiment reading of a month is published before the end of that month and is not revised afterwards; the current UMCSENT file is used with this rule). Estimation samples, model selection, transformations and the values plugged into a forecast must all respect this rule at every origin.\n- Let last(V) be the most recent month present in the RSAFS vintage dated V (it is not always the month before V: publication delays occurred). The target of origin V at horizon h is the month last(V) + h; the actual is `yoy_first_release` of that month in `data/first_release.csv`.\n- Baselines (all three required, computed from the RSAFS vintage dated V):\n  - **naive**: the forecast for both horizons is g_{last(V)}, the last available growth value in the vintage;\n  - **seasonal naive**: the forecast for target month T is g_{T−12} from the vintage;\n  - **AR(1)**: ordinary least squares of g_m on a constant and g_{m−1} over all consecutive pairs with 1993-02 ≤ m ≤ last(V) in the vintage, then iterated (plug-in) forecasts h steps beyond last(V).\n- Your model is free (it may use the vintage dynamics, PAYEMS and/or UMCSENT), but it must obey the availability rule and must be described in `model_description`.\n- Diebold–Mariano test (model vs naive, per horizon): loss differential d_t = e²_naive,t − e²_model,t over the 10 origins; `dm_stat_h` = d̄ / sqrt(σ̂²_LR / n) where σ̂²_LR is the Bartlett-kernel (Newey–West) long-run variance of d_t with truncation lag h − 1 (lag 0 only at h = 1; lags 1 and 2 with weights 2/3 and 1/3 at h = 3), without the Harvey–Leybourne–Newbold small-sample factor; `dm_pvalue_h` is the two-sided p-value from the standard normal. You may add other uncertainty measures (e.g. a bootstrap interval for the RMSE difference) in the report.\n- The series `RSAFS_current.csv` is used only for estimand (d): mean over the 10 h = 1 target months of |g^current_T − yoy_first_release_T|, where g^current is the growth computed from the current file.\n\n## Required outputs\n- `out/forecasts.csv` — exactly one row per (origin, horizon, model): 10 origins × 2 horizons × 4 models = 80 rows, columns `origin_date` (vintage date, YYYY-MM-DD), `target_date` (YYYY-MM-01), `horizon` (1 or 3), `forecast` (percent), `actual_first_release` (percent, from `first_release.csv`), `model` (one of `naive`, `snaive`, `ar1`, `model`), `vintage_used` (the vintage date of the RSAFS file used, YYYY-MM-DD).\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T1-forecast\" |\n| `model_name` | string | — | short name of your model |\n| `model_description` | string | — | specification, predictors, estimation window, alignment of predictors with the availability rule |\n| `n_origins` | integer | — | 10 |\n| `first_origin`, `last_origin` | string | — | first and last origin dates (YYYY-MM-DD) |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percent | naive baseline errors against first-release actuals |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h3`, `mae_snaive_h3` | number | percent | seasonal-naive baseline errors |\n| `rmse_ar1_h1`, `mae_ar1_h1`, `rmse_ar1_h3`, `mae_ar1_h3` | number | percent | AR(1) baseline errors |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percent | your model's errors |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | — | 1 − RMSE_model / RMSE_naive |\n| `dm_stat_h1`, `dm_pvalue_h1`, `dm_stat_h3`, `dm_pvalue_h3` | number | — | Diebold–Mariano statistic and two-sided p-value, model vs naive, as defined above |\n| `conclusion_code_h1`, `conclusion_code_h3` | string | — | one of `model_better_significant`, `model_better_not_significant`, `naive_better_not_significant`, `naive_better_significant` (\"better\" = lower RMSE; \"significant\" = `dm_pvalue_h` < 0.05) |\n| `mean_abs_revision_h1_targets` | number | percent | estimand (d) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected (irregular publication timing, missing values, revisions that affect the design, …) and how it was handled; empty list if none |\n| `n_input_rows` | object | rows | {file name: number of data rows (lines after the header)} for every file under `data/` that you read |\n| `n_analysed` | integer or null | — | number of forecast–actual pairs evaluated for your model (both horizons) |\n| `random_seed` | integer or null | — | seed used, if any (e.g. bootstrap) |\n\n- `out/table1.csv` — the RMSE/MAE table: one row per (model, horizon) with columns `model`, `horizon`, `rmse`, `mae`, `n`.\n- `out/figure1.png` — first-release actuals and the forecasts of the naive baseline and of your model over the target months, one panel per horizon (or an equivalent single figure).\n- `out/report.md` — a short paper with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The \"Data and exclusions\" section must document the real-time design (which vintage feeds which forecast, publication delays observed) and the \"Uncertainty\" section must interpret the Diebold–Mariano results given the small number of origins.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/forecasts.csv`, `out/table1.csv` and `out/figure1.png` 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, matplotlib available). Do not modify `data/`.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. The project is designed to be completed in well under 45 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nfirst_release.csv\nREADME.md\nRSAFS_current.csv\nUMCSENT.csv\nvintages\n\n./data/vintages:\nPAYEMS_vintage_2025-10-31.csv\nPAYEMS_vintage_2025-11-30.csv\nPAYEMS_vintage_2025-12-31.csv\nPAYEMS_vintage_2026-01-31.csv\nPAYEMS_vintage_2026-02-28.csv\nPAYEMS_vintage_2026-03-31.csv\nPAYEMS_vintage_2026-04-30.csv\nPAYEMS_vintage_2026-05-31.csv\nPAYEMS_vintage_2026-06-30.csv\nPAYEMS_vintage_2026-07-31.csv\nRSAFS_vintage_2025-10-31.csv\nRSAFS_vintage_2025-11-30.csv\nRSAFS_vintage_2025-12-31.csv\nRSAFS_vintage_2026-01-31.csv\nRSAFS_vintage_2026-02-28.csv\nRSAFS_vintage_2026-03-31.csv\nRSAFS_vintage_2026-04-30.csv\nRSAFS_vintage_2026-05-31.csv\nRSAFS_vintage_2026-06-30.csv\nRSAFS_vintage_2026-07-31.csv","is_error":true,"tool_use_id":"toolu_01Q29nXufwZNDPCLb6fXBCZc"}]},"uuid":"c1121cd1-8c9e-4c5b-a566-1487dc474b7b","timestamp":"2026-10-30T05:33:58.142Z","toolUseResult":"Error: Exit code 1\n# T1-forecast — Real-time forecasting of US retail-sales growth from data vintages: a complete mini-project\n\n## Scientific question\nHow well could the year-over-year growth of US retail and food services sales have been forecast *in real time* — using only the data actually published at each forecast origin — one and three months beyond the last published month, and does a model of your own design beat the no-change (naive) forecast once the forecast errors are measured against the first published value of each target month? This is an end-to-end forecasting project: question → data audit → real-time baselines → your model → rolling-origin evaluation → uncertainty → figure → short paper. The estimands are, for horizons h ∈ {1, 3} months and over the 10 forecast origins defined below: (a) the out-of-sample RMSE and MAE of three baselines (naive, seasonal naive, AR(1)) and of your model, all evaluated against the first-release value of the target; (b) the relative improvement of your model over the naive forecast, 1 − RMSE_model / RMSE_naive; (c) the Diebold–Mariano statistic of your model against the naive forecast under squared-error loss, with a long-run variance that is robust to the serial correlation of multi-step forecast errors; (d) the mean absolute revision between the first-release growth of the h = 1 target months and their currently published growth. Report what the data show, including a negative result if your model does not beat the naive forecast.\n\n## Inputs\nAll series are monthly, seasonally adjusted, dated by the first day of the month, and come from FRED/ALFRED (Federal Reserve Bank of St. Louis); full provenance, retrieval times (2026-10-30 UTC), SHA-256 and terms are in `data/README.md`.\n- `data/vintages/RSAFS_vintage_<YYYY-MM-DD>.csv` — 10 files, one per forecast origin (2025-10-31, 2025-11-30, 2025-12-31, 2026-01-31, 2026-02-28, 2026-03-31, 2026-04-30, 2026-05-31, 2026-06-30, 2026-07-31): the series RSAFS — Advance Retail Sales: Retail Trade and Food Services, total, millions of dollars, SA (US Census Bureau) — exactly as it was published on that date (ALFRED vintage; header `observation_date,RSAFS_<yyyymmdd>`; first observation 1992-01; the last row is the most recent month published at that date). The target variable is the year-over-year growth g_m = 100 · ln(L_m / L_{m−12}) in percent, where L is the level in the relevant vintage.\n- `data/vintages/PAYEMS_vintage_<YYYY-MM-DD>.csv` — 10 files, same dates: the series PAYEMS — All Employees, Total Nonfarm, thousands of persons, SA (US Bureau of Labor Statistics) — as published on that date (ALFRED vintage; first observation 1939-01). A candidate predictor.\n- `data/UMCSENT.csv` — the series UMCSENT — University of Michigan Index of Consumer Sentiment (1966Q1 = 100) — current vintage as served by FRED on 2026-10-30 (header `observation_date,UMCSENT`; 1952-11 to 2026-09; the field is empty for the months before 1978 in which the survey was not conducted). A candidate predictor; see the availability rule below.\n- `data/RSAFS_current.csv` — RSAFS as served by FRED on 2026-10-30 (header `observation_date,RSAFS`; 1992-01 to 2026-09), containing all revisions known today. **Provided only for the revision analysis (estimand (d)); it may not enter any forecast.**\n- `data/first_release.csv` — one row per target month from 2024-06 to 2026-09: `target_month`, `first_release_vintage` (the first month-end ALFRED vintage in the designer's grid 2024-06-30 … 2026-10-29 in which the month appears), `level_first_release` (the RSAFS level of the month in that vintage), `level_prev_year_same_vintage` (the level of the month twelve months earlier in the same vintage) and `yoy_first_release` = 100 · ln(level_first_release / level_prev_year_same_vintage), 6 decimals. This is the \"actual\" against which every forecast is evaluated.\n- `data/README.md` — provenance.\n\n## Information-availability rules\n- Forecast origins are the 10 vintage dates listed above. A forecast made at origin V may use **only** the RSAFS vintage file dated V, the PAYEMS vintage file dated V, and the UMCSENT values of months on or before the month of V (the final sentiment reading of a month is published before the end of that month and is not revised afterwards; the current UMCSENT file is used with this rule). Estimation samples, model selection, transformations and the values plugged into a forecast must all respect this rule at every origin.\n- Let last(V) be the most recent month present in the RSAFS vintage dated V (it is not always the month before V: publication delays occurred). The target of origin V at horizon h is the month last(V) + h; the actual is `yoy_first_release` of that month in `data/first_release.csv`.\n- Baselines (all three required, computed from the RSAFS vintage dated V):\n  - **naive**: the forecast for both horizons is g_{last(V)}, the last available growth value in the vintage;\n  - **seasonal naive**: the forecast for target month T is g_{T−12} from the vintage;\n  - **AR(1)**: ordinary least squares of g_m on a constant and g_{m−1} over all consecutive pairs with 1993-02 ≤ m ≤ last(V) in the vintage, then iterated (plug-in) forecasts h steps beyond last(V).\n- Your model is free (it may use the vintage dynamics, PAYEMS and/or UMCSENT), but it must obey the availability rule and must be described in `model_description`.\n- Diebold–Mariano test (model vs naive, per horizon): loss differential d_t = e²_naive,t − e²_model,t over the 10 origins; `dm_stat_h` = d̄ / sqrt(σ̂²_LR / n) where σ̂²_LR is the Bartlett-kernel (Newey–West) long-run variance of d_t with truncation lag h − 1 (lag 0 only at h = 1; lags 1 and 2 with weights 2/3 and 1/3 at h = 3), without the Harvey–Leybourne–Newbold small-sample factor; `dm_pvalue_h` is the two-sided p-value from the standard normal. You may add other uncertainty measures (e.g. a bootstrap interval for the RMSE difference) in the report.\n- The series `RSAFS_current.csv` is used only for estimand (d): mean over the 10 h = 1 target months of |g^current_T − yoy_first_release_T|, where g^current is the growth computed from the current file.\n\n## Required outputs\n- `out/forecasts.csv` — exactly one row per (origin, horizon, model): 10 origins × 2 horizons × 4 models = 80 rows, columns `origin_date` (vintage date, YYYY-MM-DD), `target_date` (YYYY-MM-01), `horizon` (1 or 3), `forecast` (percent), `actual_first_release` (percent, from `first_release.csv`), `model` (one of `naive`, `snaive`, `ar1`, `model`), `vintage_used` (the vintage date of the RSAFS file used, YYYY-MM-DD).\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"T1-forecast\" |\n| `model_name` | string | — | short name of your model |\n| `model_description` | string | — | specification, predictors, estimation window, alignment of predictors with the availability rule |\n| `n_origins` | integer | — | 10 |\n| `first_origin`, `last_origin` | string | — | first and last origin dates (YYYY-MM-DD) |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percent | naive baseline errors against first-release actuals |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h3`, `mae_snaive_h3` | number | percent | seasonal-naive baseline errors |\n| `rmse_ar1_h1`, `mae_ar1_h1`, `rmse_ar1_h3`, `mae_ar1_h3` | number | percent | AR(1) baseline errors |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percent | your model's errors |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | — | 1 − RMSE_model / RMSE_naive |\n| `dm_stat_h1`, `dm_pvalue_h1`, `dm_stat_h3`, `dm_pvalue_h3` | number | — | Diebold–Mariano statistic and two-sided p-value, model vs naive, as defined above |\n| `conclusion_code_h1`, `conclusion_code_h3` | string | — | one of `model_better_significant`, `model_better_not_significant`, `naive_better_not_significant`, `naive_better_significant` (\"better\" = lower RMSE; \"significant\" = `dm_pvalue_h` < 0.05) |\n| `mean_abs_revision_h1_targets` | number | percent | estimand (d) |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected (irregular publication timing, missing values, revisions that affect the design, …) and how it was handled; empty list if none |\n| `n_input_rows` | object | rows | {file name: number of data rows (lines after the header)} for every file under `data/` that you read |\n| `n_analysed` | integer or null | — | number of forecast–actual pairs evaluated for your model (both horizons) |\n| `random_seed` | integer or null | — | seed used, if any (e.g. bootstrap) |\n\n- `out/table1.csv` — the RMSE/MAE table: one row per (model, horizon) with columns `model`, `horizon`, `rmse`, `mae`, `n`.\n- `out/figure1.png` — first-release actuals and the forecasts of the naive baseline and of your model over the target months, one panel per horizon (or an equivalent single figure).\n- `out/report.md` — a short paper with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. The \"Data and exclusions\" section must document the real-time design (which vintage feeds which forecast, publication delays observed) and the \"Uncertainty\" section must interpret the Diebold–Mariano results given the small number of origins.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/forecasts.csv`, `out/table1.csv` and `out/figure1.png` 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, matplotlib available). Do not modify `data/`.\n- Budget class: transfer mini-project — 12.00 USD cap, 60 minutes wall, 150 turns per session. 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Let me look at the data."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4679,"cache_read_input_tokens":4315,"output_tokens":245,"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":4679},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsonFNnXXLvXnApwHrp","requestRef":"e21277c3-5603-428b-819e-da482b96162b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b1d02ca4-2478-4745-b517-53d9d97c19f3","timestamp":"2026-10-30T05:33:59.385Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"b1d02ca4-2478-4745-b517-53d9d97c19f3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsonSJ1zpmNrC3Uvk3D","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018qi5s1W7cHZD3hkRx8oBZr","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; cat data/first_release.csv; cd data/vintages; for f in *; do echo \"$f $(wc -l <$f) $(head -1 $f) $(tail -2 $f|tr '\\n' ' ')\"; done; cd ..; head -3 UMCSENT.csv; tail -3 UMCSENT.csv; tail -3 RSAFS_current.csv; wc -l *.csv","description":"Inspect data files and gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4679,"cache_read_input_tokens":4315,"output_tokens":245,"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":4679},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsonFNnXXLvXnApwHrp","requestRef":"e21277c3-5603-428b-819e-da482b96162b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d164e1bf-b181-4468-839a-c936e1bd127f","timestamp":"2026-10-30T05:34:00.877Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}21{"parentUuid":"d164e1bf-b181-4468-839a-c936e1bd127f","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018qi5s1W7cHZD3hkRx8oBZr","type":"tool_result","content":"# Data provenance — T1-forecast\n\nAll observed series were retrieved by the task designer on 2026-10-30 (between 04:00 and 04:01 UTC) from the Federal Reserve Bank of St. Louis FRED and ALFRED services, exactly as served (CSV, one header line, `observation_date` in YYYY-MM-DD, empty value field where the source reports no value). No value was altered. Row counts are data rows (lines after the header).\n\n| file | content | URL | retrieved (UTC) | data rows | SHA-256 |\n|---|---|---|---|---|---|\n| `vintages/PAYEMS_vintage_2025-10-31.csv` | PAYEMS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 1040 | d4b4c909ed321fbf8021c385beb1512ac000009e22bb20bc27c791a248d33c60 |\n| `vintages/PAYEMS_vintage_2025-11-30.csv` | PAYEMS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 1041 | a05ef7fc50cfc339207788f9d4205a619717bc44a335488d7790ae823bdb7b77 |\n| `vintages/PAYEMS_vintage_2025-12-31.csv` | PAYEMS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 1043 | 829dae087af61916e87994220a3e5ebf5853fa43cc163b3798715603ec8bb031 |\n| `vintages/PAYEMS_vintage_2026-01-31.csv` | PAYEMS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 1044 | 8fd01c5d2740e869458721a66b81bb2e247085e1e60cb93f3cb06bedf01885d7 |\n| `vintages/PAYEMS_vintage_2026-02-28.csv` | PAYEMS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 1045 | 4a913e67ac5ffbb4d127f03f6f10ce0647998779629579617dabf1344d072db3 |\n| `vintages/PAYEMS_vintage_2026-03-31.csv` | PAYEMS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 1046 | 919de86b07be90466e19844b54482ef8a3f6c8da7f9fe2a3ef3af0d02923c92e |\n| `vintages/PAYEMS_vintage_2026-04-30.csv` | PAYEMS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 1047 | 06a8d663d9aaa61c6d5bda15d2893b10fd127b1d64fa16d47335341f62431c7a |\n| `vintages/PAYEMS_vintage_2026-05-31.csv` | PAYEMS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 1048 | e2a152e874139acdebfc1715ffff9c1e5320f639f1443fff976b389724a5932d |\n| `vintages/PAYEMS_vintage_2026-06-30.csv` | PAYEMS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 1049 | 19fbc6cd3547ffad1e22f32ac76f91758859804654b77622a4f42eac2d4e286a |\n| `vintages/PAYEMS_vintage_2026-07-31.csv` | PAYEMS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 1050 | 3a3a6603fcc31c6eb2ee3293143888ab3440bbe3a962565eb39711b92019df41 |\n| `vintages/RSAFS_vintage_2025-10-31.csv` | RSAFS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 404 | 3ecb0832a391e7261145af41a7a951f823df3a08d476162721022a0922d37e36 |\n| `vintages/RSAFS_vintage_2025-11-30.csv` | RSAFS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 405 | 4a683d85374690f1e73d66c4f890156d0d32cd8b27d5d67b72582b9a0dd825a4 |\n| `vintages/RSAFS_vintage_2025-12-31.csv` | RSAFS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 406 | 436df4b5509b1dfe61ea11b8f98dce27f012ff6b0255eb194eea564ad7e880dd |\n| `vintages/RSAFS_vintage_2026-01-31.csv` | RSAFS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 407 | b92ab6a5c1dbdc5653e85c56fdb7dc0646f1fca7227ed6e64f1424e17446aa9e |\n| `vintages/RSAFS_vintage_2026-02-28.csv` | RSAFS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 408 | 2af0a3aaeeba81efddb2613d326e3f96b5a1c88c41eb30ad384ab3cc4b289a77 |\n| `vintages/RSAFS_vintage_2026-03-31.csv` | RSAFS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 409 | 0316fdf238d55d9f109b0be85e8c2d2eb00aee57016c0328d39405b349a8815f |\n| `vintages/RSAFS_vintage_2026-04-30.csv` | RSAFS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 411 | 02dcc697a1db34753194974b1481dbd6a3ffcfe333864850e5ddd0e3202d2b8f |\n| `vintages/RSAFS_vintage_2026-05-31.csv` | RSAFS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 412 | 8c3fe3530213589635ad6bb086f78ed613c459bf435c4558a5c51c597e19d87b |\n| `vintages/RSAFS_vintage_2026-06-30.csv` | RSAFS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 413 | 2f103157f4a9b7a3c90c63af2033b9570d5e334205878714bbd35c8b304d7524 |\n| `vintages/RSAFS_vintage_2026-07-31.csv` | RSAFS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 414 | 52c04a8fce2effa727f3ef267eaee04e13aef20f8aaabc982277d08fc5b8e5b2 |\n| `RSAFS_current.csv` | RSAFS (current vintage, all revisions to date) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=RSAFS` | 2026-10-30T04:00Z | 417 | 627d8bb386ac80f4aede3a9496b63040df584875fb766d455b08ebd84311468f |\n| `UMCSENT.csv` | UMCSENT (current vintage) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UMCSENT` | 2026-10-30T04:00Z | 887 | 3076277f9f21f4bd3dc77fd68add84d417e5cce23f3adb96dc70313b3ad2dd46 |\n| `first_release.csv` | derived by the task designer from the month-end ALFRED vintages of RSAFS 2024-06-30 … 2026-10-29 (29 vintages; the 19 not shipped here were used only to date the first release of each month) | (derived) | 2026-10-30T04:00Z | 28 | 1d1ae8b3cacf7799aaefc7e0a969b3886e7979c36fce02ccf77d7c79fb7343e4 |\n\n## Series\n- **RSAFS** — Advance Retail Sales: Retail Trade and Food Services, Total. Source: U.S. Census Bureau, Advance Monthly Sales for Retail and Food Services (via FRED). Units: millions of dollars, seasonally adjusted, monthly, 1992-01 onward. The advance estimate of a month is normally published in the middle of the following month and is revised in the two following monthly releases and in annual revisions; a month-end ALFRED vintage therefore normally contains the months up to the month before the vintage date. The vintage grid in this bundle shows departures from this pattern (e.g. the vintages of 2025-10-31 and 2026-03-31 end two months before their date; the vintage of 2026-04-30 adds two months at once), which reflect the actual publication calendar; the task rules refer to the last month present in each vintage, not to the calendar.\n- **PAYEMS** — All Employees, Total Nonfarm. Source: U.S. Bureau of Labor Statistics, Current Employment Statistics (via FRED). Units: thousands of persons, seasonally adjusted, monthly, 1939-01 onward. Published in the Employment Situation release on the first Friday (normally) of the following month and revised in the two following releases and in annual benchmark revisions; the ALFRED vintages dated as the RSAFS vintages give the data available at each origin.\n- **UMCSENT** — University of Michigan: Consumer Sentiment, index 1966Q1 = 100, monthly (quarterly before 1978; empty fields where no survey was conducted). Source: University of Michigan, Surveys of Consumers (via FRED). The preliminary reading of a month is published in the middle of the month and the final reading at the end of the month; the series is not revised afterwards. The current FRED vintage is provided; at a month-end origin the final reading of the origin month is available (verified on the ALFRED vintages of 2025-10-31, 2026-03-31 and 2026-07-31, which contain the origin month).\n- **first_release.csv** — the first month-end ALFRED vintage in which each target month appears, the level printed there, the level of the month twelve months earlier in the same vintage, and the year-over-year growth 100·ln(ratio) (6 decimals). \"First release\" is defined at month-end granularity: for RSAFS the advance estimate is the only release of a month that can appear in the month-end vintage of the following month, so the value equals the advance estimate except where the publication calendar was disrupted (then it is the first published value as of the first month-end on which it existed).\n\n## Terms\nFRED/ALFRED data are provided by the Federal Reserve Bank of St. Louis under its terms of use (https://fred.stlouisfed.org/legal/); the underlying series are U.S. government statistics (Census Bureau, BLS: public domain) and the University of Michigan Surveys of Consumers index (redistributed by FRED with permission; cite \"University of Michigan, University of Michigan: Consumer Sentiment [UMCSENT], retrieved from FRED\"). The data are reproduced here unchanged for research use.\n{\n  \"task_id\": \"T1-forecast\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\", \"out/table1.csv\", \"out/figure1.png\"],\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}, \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_snaive_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_snaive_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ar1_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_ar1_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ar1_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_ar1_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false}, \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"dm_stat_h1\": {\"type\": \"number\", \"nullable\": false}, \"dm_pvalue_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"dm_stat_h3\": {\"type\": \"number\", \"nullable\": false}, \"dm_pvalue_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"conclusion_code_h1\": {\"type\": \"string\", \"nullable\": false}, \"conclusion_code_h3\": {\"type\": \"string\", \"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\", \"forecasts_csv_chronology\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_snaive_h1\", \"rmse_snaive_h3\", \"rmse_ar1_h1\", \"rmse_ar1_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n  \"forecasts_csv\": {\"path\": \"out/forecasts.csv\", \"origin_col\": \"origin_date\", \"target_col\": \"target_date\", \"vintage_col\": \"vintage_used\", \"rule\": \"vintage_used <= origin_date < target_date\", \"expected_rows\": 80},\n  \"analysis_timeout_s\": 600,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\ntarget_month,first_release_vintage,level_first_release,level_prev_year_same_vintage,yoy_first_release\r\n2024-06-01,2024-07-31,704324,688630,2.253436\r\n2024-07-01,2024-08-31,709668,691250,2.62957\r\n2024-08-01,2024-09-30,710773,695940,2.108966\r\n2024-09-01,2024-10-31,714358,702160,1.722294\r\n2024-10-01,2024-11-30,718867,698956,2.808857\r\n2024-11-01,2024-12-31,724609,698105,3.726268\r\n2024-12-01,2025-01-31,729191,701672,3.846964\r\n2025-01-01,2025-02-28,723853,694696,4.111399\r\n2025-02-01,2025-03-31,722708,700932,3.059439\r\n2025-03-01,2025-04-30,722025,688248,4.791053\r\n2025-04-01,2025-05-31,724131,688629,5.026965\r\n2025-05-01,2025-06-30,715417,692635,3.236242\r\n2025-06-01,2025-07-31,720106,692922,3.848098\r\n2025-07-01,2025-08-31,726283,698909,3.84192\r\n2025-08-01,2025-09-30,732010,697157,4.878354\r\n2025-09-01,2025-11-30,733258,703285,4.17354\r\n2025-10-01,2025-12-31,732633,708070,3.410194\r\n2025-11-01,2026-01-31,735904,712175,3.277601\r\n2025-12-01,2026-02-28,734967,717547,2.398715\r\n2026-01-01,2026-03-31,733537,711095,3.1072\r\n2026-02-01,2026-04-30,739772,711575,3.886121\r\n2026-03-01,2026-04-30,752063,723350,3.89269\r\n2026-04-01,2026-05-31,757085,721903,4.758475\r\n2026-05-01,2026-06-30,763705,714568,6.650342\r\n2026-06-01,2026-07-31,768553,720164,6.503056\r\n2026-07-01,2026-08-31,763602,727176,4.887817\r\n2026-08-01,2026-09-30,737763,700218,5.223092\r\n2026-09-01,2026-10-29,745021,700948,6.09787\r\nPAYEMS_vintage_2025-10-31.csv     1041 observation_date,PAYEMS_20251031 2025-07-01,159518 2025-08-01,159540 \nPAYEMS_vintage_2025-11-30.csv     1042 observation_date,PAYEMS_20251130 2025-08-01,159507 2025-09-01,159626 \nPAYEMS_vintage_2025-12-31.csv     1044 observation_date,PAYEMS_20251231 2025-10-01,159488 2025-11-01,159552 \nPAYEMS_vintage_2026-01-31.csv     1045 observation_date,PAYEMS_20260131 2025-11-01,159476 2025-12-01,159526 \nPAYEMS_vintage_2026-02-28.csv     1046 observation_date,PAYEMS_20260228 2025-12-01,158497 2026-01-01,158627 \nPAYEMS_vintage_2026-03-31.csv     1047 observation_date,PAYEMS_20260331 2026-01-01,158558 2026-02-01,158466 \nPAYEMS_vintage_2026-04-30.csv     1048 observation_date,PAYEMS_20260430 2026-02-01,158459 2026-03-01,158637 \nPAYEMS_vintage_2026-05-31.csv     1049 observation_date,PAYEMS_20260531 2026-03-01,158621 2026-04-01,158736 \nPAYEMS_vintage_2026-06-30.csv     1050 observation_date,PAYEMS_20260630 2026-04-01,158829 2026-05-01,159001 \nPAYEMS_vintage_2026-07-31.csv     1051 observation_date,PAYEMS_20260731 2026-05-01,158927 2026-06-01,158984 \nRSAFS_vintage_2025-10-31.csv      405 observation_date,RSAFS_20251031 2025-07-01,727414 2025-08-01,732010 \nRSAFS_vintage_2025-11-30.csv      406 observation_date,RSAFS_20251130 2025-08-01,732059 2025-09-01,733258 \nRSAFS_vintage_2025-12-31.csv      407 observation_date,RSAFS_20251231 2025-09-01,732444 2025-10-01,732633 \nRSAFS_vintage_2026-01-31.csv      408 observation_date,RSAFS_20260131 2025-10-01,731412 2025-11-01,735904 \nRSAFS_vintage_2026-02-28.csv      409 observation_date,RSAFS_20260228 2025-11-01,735085 2025-12-01,734967 \nRSAFS_vintage_2026-03-31.csv      410 observation_date,RSAFS_20260331 2025-12-01,734685 2026-01-01,733537 \nRSAFS_vintage_2026-04-30.csv      412 observation_date,RSAFS_20260430 2026-02-01,739772 2026-03-01,752063 \nRSAFS_vintage_2026-05-31.csv      413 observation_date,RSAFS_20260531 2026-03-01,753370 2026-04-01,757085 \nRSAFS_vintage_2026-06-30.csv      414 observation_date,RSAFS_20260630 2026-04-01,757036 2026-05-01,763705 \nRSAFS_vintage_2026-07-31.csv      415 observation_date,RSAFS_20260731 2026-05-01,766876 2026-06-01,768553 \nobservation_date,UMCSENT\n1952-11-01,86.2\n1952-12-01,\n2026-07-01,55.2\n2026-08-01,51.7\n2026-09-01,48.1\n2026-07-01,729915\n2026-08-01,739322\n2026-09-01,745021\n      29 first_release.csv\n     418 RSAFS_current.csv\n     888 UMCSENT.csv\n    1335 total","is_error":false}]},"uuid":"ec04ef00-3a16-4f12-9b82-f96a78c89a9b","timestamp":"2026-10-30T05:34:01.066Z","toolUseResult":{"stdout":"# Data provenance — T1-forecast\n\nAll observed series were retrieved by the task designer on 2026-10-30 (between 04:00 and 04:01 UTC) from the Federal Reserve Bank of St. Louis FRED and ALFRED services, exactly as served (CSV, one header line, `observation_date` in YYYY-MM-DD, empty value field where the source reports no value). No value was altered. Row counts are data rows (lines after the header).\n\n| file | content | URL | retrieved (UTC) | data rows | SHA-256 |\n|---|---|---|---|---|---|\n| `vintages/PAYEMS_vintage_2025-10-31.csv` | PAYEMS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 1040 | d4b4c909ed321fbf8021c385beb1512ac000009e22bb20bc27c791a248d33c60 |\n| `vintages/PAYEMS_vintage_2025-11-30.csv` | PAYEMS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 1041 | a05ef7fc50cfc339207788f9d4205a619717bc44a335488d7790ae823bdb7b77 |\n| `vintages/PAYEMS_vintage_2025-12-31.csv` | PAYEMS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 1043 | 829dae087af61916e87994220a3e5ebf5853fa43cc163b3798715603ec8bb031 |\n| `vintages/PAYEMS_vintage_2026-01-31.csv` | PAYEMS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 1044 | 8fd01c5d2740e869458721a66b81bb2e247085e1e60cb93f3cb06bedf01885d7 |\n| `vintages/PAYEMS_vintage_2026-02-28.csv` | PAYEMS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 1045 | 4a913e67ac5ffbb4d127f03f6f10ce0647998779629579617dabf1344d072db3 |\n| `vintages/PAYEMS_vintage_2026-03-31.csv` | PAYEMS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 1046 | 919de86b07be90466e19844b54482ef8a3f6c8da7f9fe2a3ef3af0d02923c92e |\n| `vintages/PAYEMS_vintage_2026-04-30.csv` | PAYEMS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 1047 | 06a8d663d9aaa61c6d5bda15d2893b10fd127b1d64fa16d47335341f62431c7a |\n| `vintages/PAYEMS_vintage_2026-05-31.csv` | PAYEMS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 1048 | e2a152e874139acdebfc1715ffff9c1e5320f639f1443fff976b389724a5932d |\n| `vintages/PAYEMS_vintage_2026-06-30.csv` | PAYEMS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 1049 | 19fbc6cd3547ffad1e22f32ac76f91758859804654b77622a4f42eac2d4e286a |\n| `vintages/PAYEMS_vintage_2026-07-31.csv` | PAYEMS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=PAYEMS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 1050 | 3a3a6603fcc31c6eb2ee3293143888ab3440bbe3a962565eb39711b92019df41 |\n| `vintages/RSAFS_vintage_2025-10-31.csv` | RSAFS as published on 2025-10-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-10-31` | 2026-10-30T04:00Z | 404 | 3ecb0832a391e7261145af41a7a951f823df3a08d476162721022a0922d37e36 |\n| `vintages/RSAFS_vintage_2025-11-30.csv` | RSAFS as published on 2025-11-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-11-30` | 2026-10-30T04:00Z | 405 | 4a683d85374690f1e73d66c4f890156d0d32cd8b27d5d67b72582b9a0dd825a4 |\n| `vintages/RSAFS_vintage_2025-12-31.csv` | RSAFS as published on 2025-12-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2025-12-31` | 2026-10-30T04:00Z | 406 | 436df4b5509b1dfe61ea11b8f98dce27f012ff6b0255eb194eea564ad7e880dd |\n| `vintages/RSAFS_vintage_2026-01-31.csv` | RSAFS as published on 2026-01-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-01-31` | 2026-10-30T04:00Z | 407 | b92ab6a5c1dbdc5653e85c56fdb7dc0646f1fca7227ed6e64f1424e17446aa9e |\n| `vintages/RSAFS_vintage_2026-02-28.csv` | RSAFS as published on 2026-02-28 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-02-28` | 2026-10-30T04:00Z | 408 | 2af0a3aaeeba81efddb2613d326e3f96b5a1c88c41eb30ad384ab3cc4b289a77 |\n| `vintages/RSAFS_vintage_2026-03-31.csv` | RSAFS as published on 2026-03-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-03-31` | 2026-10-30T04:00Z | 409 | 0316fdf238d55d9f109b0be85e8c2d2eb00aee57016c0328d39405b349a8815f |\n| `vintages/RSAFS_vintage_2026-04-30.csv` | RSAFS as published on 2026-04-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-04-30` | 2026-10-30T04:00Z | 411 | 02dcc697a1db34753194974b1481dbd6a3ffcfe333864850e5ddd0e3202d2b8f |\n| `vintages/RSAFS_vintage_2026-05-31.csv` | RSAFS as published on 2026-05-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-05-31` | 2026-10-30T04:00Z | 412 | 8c3fe3530213589635ad6bb086f78ed613c459bf435c4558a5c51c597e19d87b |\n| `vintages/RSAFS_vintage_2026-06-30.csv` | RSAFS as published on 2026-06-30 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-06-30` | 2026-10-30T04:00Z | 413 | 2f103157f4a9b7a3c90c63af2033b9570d5e334205878714bbd35c8b304d7524 |\n| `vintages/RSAFS_vintage_2026-07-31.csv` | RSAFS as published on 2026-07-31 | `https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=RSAFS&vintage_date=2026-07-31` | 2026-10-30T04:00Z | 414 | 52c04a8fce2effa727f3ef267eaee04e13aef20f8aaabc982277d08fc5b8e5b2 |\n| `RSAFS_current.csv` | RSAFS (current vintage, all revisions to date) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=RSAFS` | 2026-10-30T04:00Z | 417 | 627d8bb386ac80f4aede3a9496b63040df584875fb766d455b08ebd84311468f |\n| `UMCSENT.csv` | UMCSENT (current vintage) | `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UMCSENT` | 2026-10-30T04:00Z | 887 | 3076277f9f21f4bd3dc77fd68add84d417e5cce23f3adb96dc70313b3ad2dd46 |\n| `first_release.csv` | derived by the task designer from the month-end ALFRED vintages of RSAFS 2024-06-30 … 2026-10-29 (29 vintages; the 19 not shipped here were used only to date the first release of each month) | (derived) | 2026-10-30T04:00Z | 28 | 1d1ae8b3cacf7799aaefc7e0a969b3886e7979c36fce02ccf77d7c79fb7343e4 |\n\n## Series\n- **RSAFS** — Advance Retail Sales: Retail Trade and Food Services, Total. Source: U.S. Census Bureau, Advance Monthly Sales for Retail and Food Services (via FRED). Units: millions of dollars, seasonally adjusted, monthly, 1992-01 onward. The advance estimate of a month is normally published in the middle of the following month and is revised in the two following monthly releases and in annual revisions; a month-end ALFRED vintage therefore normally contains the months up to the month before the vintage date. The vintage grid in this bundle shows departures from this pattern (e.g. the vintages of 2025-10-31 and 2026-03-31 end two months before their date; the vintage of 2026-04-30 adds two months at once), which reflect the actual publication calendar; the task rules refer to the last month present in each vintage, not to the calendar.\n- **PAYEMS** — All Employees, Total Nonfarm. Source: U.S. Bureau of Labor Statistics, Current Employment Statistics (via FRED). Units: thousands of persons, seasonally adjusted, monthly, 1939-01 onward. Published in the Employment Situation release on the first Friday (normally) of the following month and revised in the two following releases and in annual benchmark revisions; the ALFRED vintages dated as the RSAFS vintages give the data available at each origin.\n- **UMCSENT** — University of Michigan: Consumer Sentiment, index 1966Q1 = 100, monthly (quarterly before 1978; empty fields where no survey was conducted). Source: University of Michigan, Surveys of Consumers (via FRED). The preliminary reading of a month is published in the middle of the month and the final reading at the end of the month; the series is not revised afterwards. The current FRED vintage is provided; at a month-end origin the final reading of the origin month is available (verified on the ALFRED vintages of 2025-10-31, 2026-03-31 and 2026-07-31, which contain the origin month).\n- **first_release.csv** — the first month-end ALFRED vintage in which each target month appears, the level printed there, the level of the month twelve months earlier in the same vintage, and the year-over-year growth 100·ln(ratio) (6 decimals). \"First release\" is defined at month-end granularity: for RSAFS the advance estimate is the only release of a month that can appear in the month-end vintage of the following month, so the value equals the advance estimate except where the publication calendar was disrupted (then it is the first published value as of the first month-end on which it existed).\n\n## Terms\nFRED/ALFRED data are provided by the Federal Reserve Bank of St. Louis under its terms of use (https://fred.stlouisfed.org/legal/); the underlying series are U.S. government statistics (Census Bureau, BLS: public domain) and the University of Michigan Surveys of Consumers index (redistributed by FRED with permission; cite \"University of Michigan, University of Michigan: Consumer Sentiment [UMCSENT], retrieved from FRED\"). The data are reproduced here unchanged for research use.\n{\n  \"task_id\": \"T1-forecast\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\", \"out/table1.csv\", \"out/figure1.png\"],\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}, \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_snaive_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_snaive_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ar1_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_ar1_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ar1_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_ar1_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false}, \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"dm_stat_h1\": {\"type\": \"number\", \"nullable\": false}, \"dm_pvalue_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"dm_stat_h3\": {\"type\": \"number\", \"nullable\": false}, \"dm_pvalue_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"conclusion_code_h1\": {\"type\": \"string\", \"nullable\": false}, \"conclusion_code_h3\": {\"type\": \"string\", \"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\", \"forecasts_csv_chronology\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_snaive_h1\", \"rmse_snaive_h3\", \"rmse_ar1_h1\", \"rmse_ar1_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n  \"forecasts_csv\": {\"path\": \"out/forecasts.csv\", \"origin_col\": \"origin_date\", \"target_col\": \"target_date\", \"vintage_col\": \"vintage_used\", \"rule\": \"vintage_used <= origin_date < target_date\", \"expected_rows\": 80},\n  \"analysis_timeout_s\": 600,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\ntarget_month,first_release_vintage,level_first_release,level_prev_year_same_vintage,yoy_first_release\r\n2024-06-01,2024-07-31,704324,688630,2.253436\r\n2024-07-01,2024-08-31,709668,691250,2.62957\r\n2024-08-01,2024-09-30,710773,695940,2.108966\r\n2024-09-01,2024-10-31,714358,702160,1.722294\r\n2024-10-01,2024-11-30,718867,698956,2.808857\r\n2024-11-01,2024-12-31,724609,698105,3.726268\r\n2024-12-01,2025-01-31,729191,701672,3.846964\r\n2025-01-01,2025-02-28,723853,694696,4.111399\r\n2025-02-01,2025-03-31,722708,700932,3.059439\r\n2025-03-01,2025-04-30,722025,688248,4.791053\r\n2025-04-01,2025-05-31,724131,688629,5.026965\r\n2025-05-01,2025-06-30,715417,692635,3.236242\r\n2025-06-01,2025-07-31,720106,692922,3.848098\r\n2025-07-01,2025-08-31,726283,698909,3.84192\r\n2025-08-01,2025-09-30,732010,697157,4.878354\r\n2025-09-01,2025-11-30,733258,703285,4.17354\r\n2025-10-01,2025-12-31,732633,708070,3.410194\r\n2025-11-01,2026-01-31,735904,712175,3.277601\r\n2025-12-01,2026-02-28,734967,717547,2.398715\r\n2026-01-01,2026-03-31,733537,711095,3.1072\r\n2026-02-01,2026-04-30,739772,711575,3.886121\r\n2026-03-01,2026-04-30,752063,723350,3.89269\r\n2026-04-01,2026-05-31,757085,721903,4.758475\r\n2026-05-01,2026-06-30,763705,714568,6.650342\r\n2026-06-01,2026-07-31,768553,720164,6.503056\r\n2026-07-01,2026-08-31,763602,727176,4.887817\r\n2026-08-01,2026-09-30,737763,700218,5.223092\r\n2026-09-01,2026-10-29,745021,700948,6.09787\r\nPAYEMS_vintage_2025-10-31.csv     1041 observation_date,PAYEMS_20251031 2025-07-01,159518 2025-08-01,159540 \nPAYEMS_vintage_2025-11-30.csv     1042 observation_date,PAYEMS_20251130 2025-08-01,159507 2025-09-01,159626 \nPAYEMS_vintage_2025-12-31.csv     1044 observation_date,PAYEMS_20251231 2025-10-01,159488 2025-11-01,159552 \nPAYEMS_vintage_2026-01-31.csv     1045 observation_date,PAYEMS_20260131 2025-11-01,159476 2025-12-01,159526 \nPAYEMS_vintage_2026-02-28.csv     1046 observation_date,PAYEMS_20260228 2025-12-01,158497 2026-01-01,158627 \nPAYEMS_vintage_2026-03-31.csv     1047 observation_date,PAYEMS_20260331 2026-01-01,158558 2026-02-01,158466 \nPAYEMS_vintage_2026-04-30.csv     1048 observation_date,PAYEMS_20260430 2026-02-01,158459 2026-03-01,158637 \nPAYEMS_vintage_2026-05-31.csv     1049 observation_date,PAYEMS_20260531 2026-03-01,158621 2026-04-01,158736 \nPAYEMS_vintage_2026-06-30.csv     1050 observation_date,PAYEMS_20260630 2026-04-01,158829 2026-05-01,159001 \nPAYEMS_vintage_2026-07-31.csv     1051 observation_date,PAYEMS_20260731 2026-05-01,158927 2026-06-01,158984 \nRSAFS_vintage_2025-10-31.csv      405 observation_date,RSAFS_20251031 2025-07-01,727414 2025-08-01,732010 \nRSAFS_vintage_2025-11-30.csv      406 observation_date,RSAFS_20251130 2025-08-01,732059 2025-09-01,733258 \nRSAFS_vintage_2025-12-31.csv      407 observation_date,RSAFS_20251231 2025-09-01,732444 2025-10-01,732633 \nRSAFS_vintage_2026-01-31.csv      408 observation_date,RSAFS_20260131 2025-10-01,731412 2025-11-01,735904 \nRSAFS_vintage_2026-02-28.csv      409 observation_date,RSAFS_20260228 2025-11-01,735085 2025-12-01,734967 \nRSAFS_vintage_2026-03-31.csv      410 observation_date,RSAFS_20260331 2025-12-01,734685 2026-01-01,733537 \nRSAFS_vintage_2026-04-30.csv      412 observation_date,RSAFS_20260430 2026-02-01,739772 2026-03-01,752063 \nRSAFS_vintage_2026-05-31.csv      413 observation_date,RSAFS_20260531 2026-03-01,753370 2026-04-01,757085 \nRSAFS_vintage_2026-06-30.csv      414 observation_date,RSAFS_20260630 2026-04-01,757036 2026-05-01,763705 \nRSAFS_vintage_2026-07-31.csv      415 observation_date,RSAFS_20260731 2026-05-01,766876 2026-06-01,768553 \nobservation_date,UMCSENT\n1952-11-01,86.2\n1952-12-01,\n2026-07-01,55.2\n2026-08-01,51.7\n2026-09-01,48.1\n2026-07-01,729915\n2026-08-01,739322\n2026-09-01,745021\n      29 first_release.csv\n     418 RSAFS_current.csv\n     888 UMCSENT.csv\n    1335 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Let me look at the data."},{"type":"tool_use","id":"toolu_018qi5s1W7cHZD3hkRx8oBZr","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; cat data/first_release.csv; cd data/vintages; for f in *; do echo \"$f $(wc -l <$f) $(head -1 $f) $(tail -2 $f|tr '\\n' ' ')\"; done; cd ..; head -3 UMCSENT.csv; tail -3 UMCSENT.csv; tail -3 RSAFS_current.csv; wc -l *.csv","description":"Inspect data files and gate 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — T1-forecast\n\n**Question.** In real time (only data published at each of 10 month-end origins 2025-10-31 … 2026-07-31), how accurately can US retail & food services sales (RSAFS) year-over-year log growth g_m = 100·ln(L_m/L_{m-12}) be forecast at h = 1 and h = 3 months beyond the last published month last(V), and does my own model beat the naive (no-change) forecast, judged against the first-release YoY of each target month?\n\n**Estimands** (per h ∈ {1,3}, n = 10 origins):\n(a) RMSE, MAE of naive, seasonal naive, AR(1), and my model vs `yoy_first_release`;\n(b) 1 − RMSE_model/RMSE_naive;\n(c) DM statistic model vs naive, squared loss, d_t = e²_naive − e²_model, Bartlett LRV with truncation lag h−1 (no HLN correction), two-sided normal p-value;\n(d) mean over the 10 h=1 target months of |g_current − yoy_first_release|.\nMeaningful under: the first-release file correctly identifies the first published value; forecasts strictly use vintage-V information; n=10 means DM asymptotics are only indicative.\n\n**Outputs.** out/forecasts.csv (80 rows; origin_date,target_date,horizon,forecast,actual_first_release,model,vintage_used), out/results.json (keys as in TASK.md table), out/table1.csv (model,horizon,rmse,mae,n), out/figure1.png, out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json; plus protocol files out/state/*, out/exclusions.csv.\n\n**Information rules.** At origin V: only RSAFS vintage V, PAYEMS vintage V, UMCSENT months ≤ month(V). 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RSAFS_current only for estimand (d). first_release.csv only as evaluation actuals. vintage_used ≤ origin_date < target_date.\n\n**Invalidity / impossibility.** Missing first-release actual for any target; using current/future data in forecasts; tuning the model on the evaluation actuals (look-ahead model selection); non-positive LRV making DM undefined (would be reported).\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n \"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0) — T1-forecast\n\n**Question.** In real time (only data published at each of 10 month-end origins 2025-10-31 … 2026-07-31), how accurately can US retail & food services sales (RSAFS) year-over-year log growth g_m = 100·ln(L_m/L_{m-12}) be forecast at h = 1 and h = 3 months beyond the last published month last(V), and does my own model beat the naive (no-change) forecast, judged against the first-release YoY of each target month?\n\n**Estimands** (per h ∈ {1,3}, n = 10 origins):\n(a) RMSE, MAE of naive, seasonal naive, AR(1), and my model vs `yoy_first_release`;\n(b) 1 − RMSE_model/RMSE_naive;\n(c) DM statistic model vs naive, squared loss, d_t = e²_naive − e²_model, Bartlett LRV with truncation lag h−1 (no HLN correction), two-sided normal p-value;\n(d) mean over the 10 h=1 target months of |g_current − yoy_first_release|.\nMeaningful under: the first-release file correctly identifies the first published value; 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> /tmp/audit.py <<'EOF'\nimport pandas as pd, numpy as np, hashlib, glob, os\nfiles = sorted(glob.glob('data/*.csv'))+sorted(glob.glob('data/vintages/*.csv'))\nreadme=open('data/README.md').read()\nfor f in files:\n    h=hashlib.sha256(open(f,'rb').read()).hexdigest()\n    d=pd.read_csv(f); c=d.columns[-1] if 'first' not in f else None\n    dates=pd.to_datetime(d.iloc[:,0])\n    ms=dates.dt.to_period('M'); gaps=(ms.diff().dropna().apply(lambda x:x.n)!=1).sum()\n    print(f\"{f}|{h[:12]}|readme_match={h in readme}|rows={len(d)}|cols={d.shape[1]}|{dates.min().date()}..{dates.max().date()}|NA={d.isna().sum().sum()}|dupdates={dates.duplicated().sum()}|gaps={gaps}\")\n# NA in UMCSENT since 1978\nu=pd.read_csv('data/UMCSENT.csv',parse_dates=[0]); print('UMCSENT NA after 1978:',u[u.observation_date>='1978-01-01'].UMCSENT.isna().sum())\n# revision between consecutive vintages\nprev=None\nfor f in sorted(glob.glob('data/vintages/RSAFS*')):\n    d=pd.read_csv(f,parse_dates=[0]).set_index('observation_date').iloc[:,0]\n    if prev is not None:\n        j=d.index.intersection(prev.index); r=(d[j]/prev[j]-1)*100\n        print(os.path.basename(f),'n_changed',(r.abs()>1e-9).sum(),'max|rev|%',round(r.abs().max(),3),'first changed',r[r.abs()>1e-9].index.min())\n    prev=d\ncur=pd.read_csv('data/RSAFS_current.csv',parse_dates=[0]).set_index('observation_date').iloc[:,0]\nj=cur.index.intersection(prev.index); r=(cur[j]/prev[j]-1)*100\nprint('current vs 2026-07-31 n_changed',(r.abs()>1e-9).sum(),'max',r.abs().max(), r[r.abs()>1e-9].index.min())\nprint(cur['2025-06':])\nEOF\n~/wp29/venv/bin/python /tmp/audit.py","description":"Audit data files: hashes, schema, gaps, 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code 1\nTraceback (most recent call last):\n  File \"/tmp/audit.py\", line 3, in <module>\n    readme=open('data/README.md').read()\n           ~~~~^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'data/README.md'","is_error":true,"tool_use_id":"toolu_0124zVxLjCiqufYgiqWv8eBd"}]},"uuid":"3234faea-4988-42df-908f-587b0a117991","timestamp":"2026-10-30T05:34:36.110Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/tmp/audit.py\", line 3, in <module>\n    readme=open('data/README.md').read()\n           ~~~~^^^^^^^^^^^^^^^^^^\nFileNotFoundError: [Errno 2] No such file or directory: 'data/README.md'","sandboxed":false,"sourceToolAssistantUUID":"a5c6d412-89da-484f-adf8-8017f1ffe4c2","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010/data/out/state","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"a6c31d5e5006c417a9068c71f57be58f0b32e688c776100d9f11fa651d52a769","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_0124zVxLjCiqufYgiqWv8eBd","name":"Bash","input":{"command":"cat > /tmp/audit.py <<'EOF'\nimport pandas as pd, numpy as np, hashlib, glob, os\nfiles = sorted(glob.glob('data/*.csv'))+sorted(glob.glob('data/vintages/*.csv'))\nreadme=open('data/README.md').read()\nfor f in files:\n    h=hashlib.sha256(open(f,'rb').read()).hexdigest()\n    d=pd.read_csv(f); c=d.columns[-1] if 'first' not in f else None\n    dates=pd.to_datetime(d.iloc[:,0])\n    ms=dates.dt.to_period('M'); gaps=(ms.diff().dropna().apply(lambda x:x.n)!=1).sum()\n    print(f\"{f}|{h[:12]}|readme_match={h in readme}|rows={len(d)}|cols={d.shape[1]}|{dates.min().date()}..{dates.max().date()}|NA={d.isna().sum().sum()}|dupdates={dates.duplicated().sum()}|gaps={gaps}\")\n# NA in UMCSENT since 1978\nu=pd.read_csv('data/UMCSENT.csv',parse_dates=[0]); print('UMCSENT NA after 1978:',u[u.observation_date>='1978-01-01'].UMCSENT.isna().sum())\n# revision between consecutive vintages\nprev=None\nfor f in sorted(glob.glob('data/vintages/RSAFS*')):\n    d=pd.read_csv(f,parse_dates=[0]).set_index('observation_date').iloc[:,0]\n    if prev is not None:\n        j=d.index.intersection(prev.index); r=(d[j]/prev[j]-1)*100\n        print(os.path.basename(f),'n_changed',(r.abs()>1e-9).sum(),'max|rev|%',round(r.abs().max(),3),'first changed',r[r.abs()>1e-9].index.min())\n    prev=d\ncur=pd.read_csv('data/RSAFS_current.csv',parse_dates=[0]).set_index('observation_date').iloc[:,0]\nj=cur.index.intersection(prev.index); r=(cur[j]/prev[j]-1)*100\nprint('current vs 2026-07-31 n_changed',(r.abs()>1e-9).sum(),'max',r.abs().max(), r[r.abs()>1e-9].index.min())\nprint(cur['2025-06':])\nEOF\n~/wp29/venv/bin/python /tmp/audit.py","description":"Audit data files: hashes, schema, gaps, revisions"}}]}}34{"type":"api-request","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","id":"8b7abf64-7cb4-4950-a92a-e8bce78ef801","timestamp":"2026-10-30T05:34:36.114Z","version":"2.1.314","querySource":"sdk","shapeHash":"81de0bee269150a4a5258c71378fcacd4dd07a9cce8c76ff7b371b779847bd10","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":"c29def51-c236-49c6-91ec-e4a0283cc26e","keep":8,"tail":["a6c31d5e5006c417a9068c71f57be58f0b32e688c776100d9f11fa651d52a769","841d8860e6970d87e8bd1db2970a64f16a89a1cb7a10c40a70aa495c1a75626d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4107e1aec576db98e28af7c7e75cc6600a22ec4a7b8da476da41a3ecc4c96d42"}35{"parentUuid":"3234faea-4988-42df-908f-587b0a117991","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsratbraS7c1Fem3c4h","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016AwQhxNeA6u2ytXveKELSW","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RT0010 && ~/wp29/venv/bin/python /tmp/audit.py","description":"Run audit from workspace root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1067,"cache_read_input_tokens":20372,"output_tokens":127,"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":1067},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsrabjNWEB8tcEm8K4h","requestRef":"8b7abf64-7cb4-4950-a92a-e8bce78ef801","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"73ddfdc8-2ed3-4c8f-9506-9b9b1990a007","timestamp":"2026-10-30T05:34:37.924Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010/data/out/state","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"73ddfdc8-2ed3-4c8f-9506-9b9b1990a007","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016AwQhxNeA6u2ytXveKELSW","type":"tool_result","content":"data/RSAFS_current.csv|627d8bb386ac|readme_match=True|rows=417|cols=2|1992-01-01..2026-09-01|NA=0|dupdates=0|gaps=0\ndata/UMCSENT.csv|3076277f9f21|readme_match=True|rows=887|cols=2|1952-11-01..2026-09-01|NA=210|dupdates=0|gaps=0\ndata/first_release.csv|1d1ae8b3cacf|readme_match=True|rows=28|cols=5|2024-06-01..2026-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-10-31.csv|d4b4c909ed32|readme_match=True|rows=1040|cols=2|1939-01-01..2025-08-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-11-30.csv|a05ef7fc50cf|readme_match=True|rows=1041|cols=2|1939-01-01..2025-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-12-31.csv|829dae087af6|readme_match=True|rows=1043|cols=2|1939-01-01..2025-11-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-01-31.csv|8fd01c5d2740|readme_match=True|rows=1044|cols=2|1939-01-01..2025-12-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-02-28.csv|4a913e67ac5f|readme_match=True|rows=1045|cols=2|1939-01-01..2026-01-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-03-31.csv|919de86b07be|readme_match=True|rows=1046|cols=2|1939-01-01..2026-02-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-04-30.csv|06a8d663d9aa|readme_match=True|rows=1047|cols=2|1939-01-01..2026-03-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-05-31.csv|e2a152e87413|readme_match=True|rows=1048|cols=2|1939-01-01..2026-04-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-06-30.csv|19fbc6cd3547|readme_match=True|rows=1049|cols=2|1939-01-01..2026-05-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-07-31.csv|3a3a6603fcc3|readme_match=True|rows=1050|cols=2|1939-01-01..2026-06-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-10-31.csv|3ecb0832a391|readme_match=True|rows=404|cols=2|1992-01-01..2025-08-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-11-30.csv|4a683d853746|readme_match=True|rows=405|cols=2|1992-01-01..2025-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-12-31.csv|436df4b5509b|readme_match=True|rows=406|cols=2|1992-01-01..2025-10-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-01-31.csv|b92ab6a5c1db|readme_match=True|rows=407|cols=2|1992-01-01..2025-11-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-02-28.csv|2af0a3aaeeba|readme_match=True|rows=408|cols=2|1992-01-01..2025-12-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-03-31.csv|0316fdf238d5|readme_match=True|rows=409|cols=2|1992-01-01..2026-01-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-04-30.csv|02dcc697a1db|readme_match=True|rows=411|cols=2|1992-01-01..2026-03-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-05-31.csv|8c3fe3530213|readme_match=True|rows=412|cols=2|1992-01-01..2026-04-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-06-30.csv|2f103157f4a9|readme_match=True|rows=413|cols=2|1992-01-01..2026-05-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-07-31.csv|52c04a8fce2e|readme_match=True|rows=414|cols=2|1992-01-01..2026-06-01|NA=0|dupdates=0|gaps=0\nUMCSENT NA after 1978: 0\nRSAFS_vintage_2025-11-30.csv n_changed 4 max|rev|% 0.043 first changed 2024-08-01 00:00:00\nRSAFS_vintage_2025-12-31.csv n_changed 4 max|rev|% 0.111 first changed 2024-09-01 00:00:00\nRSAFS_vintage_2026-01-31.csv n_changed 4 max|rev|% 0.167 first changed 2024-10-01 00:00:00\nRSAFS_vintage_2026-02-28.csv n_changed 4 max|rev|% 0.111 first changed 2024-11-01 00:00:00\nRSAFS_vintage_2026-03-31.csv n_changed 4 max|rev|% 0.051 first changed 2024-12-01 00:00:00\nRSAFS_vintage_2026-04-30.csv n_changed 5 max|rev|% 0.132 first changed 2025-01-01 00:00:00\nRSAFS_vintage_2026-05-31.csv n_changed 4 max|rev|% 0.204 first changed 2025-03-01 00:00:00\nRSAFS_vintage_2026-06-30.csv n_changed 4 max|rev|% 0.214 first changed 2025-04-01 00:00:00\nRSAFS_vintage_2026-07-31.csv n_changed 4 max|rev|% 0.415 first changed 2025-05-01 00:00:00\ncurrent vs 2026-07-31 n_changed 138 max 4.639237632277804 2015-01-01 00:00:00\nobservation_date\n2025-06-01    691883\n2025-07-01    698280\n2025-08-01    700407\n2025-09-01    700948\n2025-10-01    700790\n2025-11-01    705453\n2025-12-01    706264\n2026-01-01    701860\n2026-02-01    707959\n2026-03-01    719700\n2026-04-01    723921\n2026-05-01    731415\n2026-06-01    732898\n2026-07-01    729915\n2026-08-01    739322\n2026-09-01    745021\nName: RSAFS, dtype: int64","is_error":false}]},"uuid":"9a3f3b01-7bb2-4219-9947-d37b09a9d71c","timestamp":"2026-10-30T05:34:38.250Z","toolUseResult":{"stdout":"data/RSAFS_current.csv|627d8bb386ac|readme_match=True|rows=417|cols=2|1992-01-01..2026-09-01|NA=0|dupdates=0|gaps=0\ndata/UMCSENT.csv|3076277f9f21|readme_match=True|rows=887|cols=2|1952-11-01..2026-09-01|NA=210|dupdates=0|gaps=0\ndata/first_release.csv|1d1ae8b3cacf|readme_match=True|rows=28|cols=5|2024-06-01..2026-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-10-31.csv|d4b4c909ed32|readme_match=True|rows=1040|cols=2|1939-01-01..2025-08-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-11-30.csv|a05ef7fc50cf|readme_match=True|rows=1041|cols=2|1939-01-01..2025-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2025-12-31.csv|829dae087af6|readme_match=True|rows=1043|cols=2|1939-01-01..2025-11-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-01-31.csv|8fd01c5d2740|readme_match=True|rows=1044|cols=2|1939-01-01..2025-12-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-02-28.csv|4a913e67ac5f|readme_match=True|rows=1045|cols=2|1939-01-01..2026-01-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-03-31.csv|919de86b07be|readme_match=True|rows=1046|cols=2|1939-01-01..2026-02-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-04-30.csv|06a8d663d9aa|readme_match=True|rows=1047|cols=2|1939-01-01..2026-03-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-05-31.csv|e2a152e87413|readme_match=True|rows=1048|cols=2|1939-01-01..2026-04-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-06-30.csv|19fbc6cd3547|readme_match=True|rows=1049|cols=2|1939-01-01..2026-05-01|NA=0|dupdates=0|gaps=0\ndata/vintages/PAYEMS_vintage_2026-07-31.csv|3a3a6603fcc3|readme_match=True|rows=1050|cols=2|1939-01-01..2026-06-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-10-31.csv|3ecb0832a391|readme_match=True|rows=404|cols=2|1992-01-01..2025-08-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-11-30.csv|4a683d853746|readme_match=True|rows=405|cols=2|1992-01-01..2025-09-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2025-12-31.csv|436df4b5509b|readme_match=True|rows=406|cols=2|1992-01-01..2025-10-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-01-31.csv|b92ab6a5c1db|readme_match=True|rows=407|cols=2|1992-01-01..2025-11-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-02-28.csv|2af0a3aaeeba|readme_match=True|rows=408|cols=2|1992-01-01..2025-12-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-03-31.csv|0316fdf238d5|readme_match=True|rows=409|cols=2|1992-01-01..2026-01-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-04-30.csv|02dcc697a1db|readme_match=True|rows=411|cols=2|1992-01-01..2026-03-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-05-31.csv|8c3fe3530213|readme_match=True|rows=412|cols=2|1992-01-01..2026-04-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-06-30.csv|2f103157f4a9|readme_match=True|rows=413|cols=2|1992-01-01..2026-05-01|NA=0|dupdates=0|gaps=0\ndata/vintages/RSAFS_vintage_2026-07-31.csv|52c04a8fce2e|readme_match=True|rows=414|cols=2|1992-01-01..2026-06-01|NA=0|dupdates=0|gaps=0\nUMCSENT NA after 1978: 0\nRSAFS_vintage_2025-11-30.csv n_changed 4 max|rev|% 0.043 first changed 2024-08-01 00:00:00\nRSAFS_vintage_2025-12-31.csv n_changed 4 max|rev|% 0.111 first changed 2024-09-01 00:00:00\nRSAFS_vintage_2026-01-31.csv n_changed 4 max|rev|% 0.167 first changed 2024-10-01 00:00:00\nRSAFS_vintage_2026-02-28.csv n_changed 4 max|rev|% 0.111 first changed 2024-11-01 00:00:00\nRSAFS_vintage_2026-03-31.csv n_changed 4 max|rev|% 0.051 first changed 2024-12-01 00:00:00\nRSAFS_vintage_2026-04-30.csv n_changed 5 max|rev|% 0.132 first changed 2025-01-01 00:00:00\nRSAFS_vintage_2026-05-31.csv n_changed 4 max|rev|% 0.204 first changed 2025-03-01 00:00:00\nRSAFS_vintage_2026-06-30.csv n_changed 4 max|rev|% 0.214 first changed 2025-04-01 00:00:00\nRSAFS_vintage_2026-07-31.csv n_changed 4 max|rev|% 0.415 first changed 2025-05-01 00:00:00\ncurrent vs 2026-07-31 n_changed 138 max 4.639237632277804 2015-01-01 00:00:00\nobservation_date\n2025-06-01    691883\n2025-07-01    698280\n2025-08-01    700407\n2025-09-01    700948\n2025-10-01    700790\n2025-11-01    705453\n2025-12-01    706264\n2026-01-01    701860\n2026-02-01    707959\n2026-03-01    719700\n2026-04-01    723921\n2026-05-01    731415\n2026-06-01    732898\n2026-07-01    729915\n2026-08-01    739322\n2026-09-01    745021\nName: RSAFS, dtype: 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No original data file was touched (SHA-256 of all inputs re-verified against README afterwards: all match).\n- 2026-10-30 P1 audit run (out/state/audit_raw.txt). Decision (made BEFORE computing any evaluation error): my model = \"base-effect-aware random walk with drift on log levels\" (rw_drift_yoy): ln L_{last+j} = ln L_last + j*mu, mu = mean monthly log change over the 36 months ending last(V) in vintage V; YoY forecast = 100*(lnLhat_T - ln L_{T-12}) with L_{T-12} from vintage V. Uses RSAFS vintage V only; PAYEMS/UMCSENT not used (avoids specification search on 10 origins). No tuning on evaluation sample.\nEOF\ngrep -c readme_match=True out/state/audit_raw.txt","description":"Save audit, log incident and model decision"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":285,"cache_read_input_tokens":25304,"output_tokens":509,"output_tokens_details":{"thinking_tokens":23},"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":285},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsss8BMq4dJb2gaTTjb","requestRef":"83b5ec77-5c18-4d82-aea0-e016b96e1592","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"80979b2a-06a6-4d95-b046-01794cccfe62","timestamp":"2026-10-30T05:34:58.374Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"80979b2a-06a6-4d95-b046-01794cccfe62","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0111JhHaRLUbz24FPd1kZim8","type":"tool_result","content":"23","is_error":false}]},"uuid":"6833f69d-0b03-4120-ad82-be2535716bd9","timestamp":"2026-10-30T05:34:58.696Z","toolUseResult":{"stdout":"23","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"80979b2a-06a6-4d95-b046-01794cccfe62","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"a588f180cc742c5c66318974e9eb64164cae23fdafa6554e2a71766d9a35f550","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0111JhHaRLUbz24FPd1kZim8","name":"Bash","input":{"command":"~/wp29/venv/bin/python /tmp/audit.py > out/state/audit_raw.txt && cat >> out/state/log.md <<'EOF'\n- 2026-10-30 Incident: a shell `cd` mistake created data/out/state/ (contract/phases/log) inside data/; moved to out/state/ and removed data/out immediately. No original data file was touched (SHA-256 of all inputs re-verified against README afterwards: all match).\n- 2026-10-30 P1 audit run (out/state/audit_raw.txt). Decision (made BEFORE computing any evaluation error): my model = \"base-effect-aware random walk with drift on log levels\" (rw_drift_yoy): ln L_{last+j} = ln L_last + j*mu, mu = mean monthly log change over the 36 months ending last(V) in vintage V; YoY forecast = 100*(lnLhat_T - ln L_{T-12}) with L_{T-12} from vintage V. Uses RSAFS vintage V only; PAYEMS/UMCSENT not used (avoids specification search on 10 origins). 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writes out/results.json, out/forecasts.csv, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nD, O = \"data\", \"out\"\nORIGINS = [\"2025-10-31\", \"2025-11-30\", \"2025-12-31\", \"2026-01-31\", \"2026-02-28\",\n           \"2026-03-31\", \"2026-04-30\", \"2026-05-31\", \"2026-06-30\", \"2026-07-31\"]\nHORIZONS = [1, 3]\nMODELS = [\"naive\", \"snaive\", \"ar1\", \"model\"]\nDRIFT_WINDOW = 36  # months, fixed a priori\nn_input_rows = {}\n\ndef read_series(path):\n    df = pd.read_csv(path)\n    n_input_rows[os.path.relpath(path, D)] = int(len(df))\n    s = df.set_index(pd.to_datetime(df.iloc[:, 0])).iloc[:, 1]\n    s.index = s.index.to_period(\"M\")\n    return s.astype(float)\n\ndef yoy(level):\n    l = np.log(level)\n    return (100 * (l - l.shift(12))).dropna()\n\nfr = pd.read_csv(os.path.join(D, \"first_release.csv\"))\nn_input_rows[\"first_release.csv\"] = int(len(fr))\nfr[\"tm\"] = pd.to_datetime(fr.target_month).dt.to_period(\"M\")\nactual = fr.set_index(\"tm\")[\"yoy_first_release\"]\ncur = read_series(os.path.join(D, \"RSAFS_current.csv\"))  # used ONLY for estimand (d)\n# Predictor files read for audit row counts only (the model does not use them)\nfor o in ORIGINS:\n    read_series(os.path.join(D, \"vintages\", f\"PAYEMS_vintage_{o}.csv\"))\nread_series(os.path.join(D, \"UMCSENT.csv\"))\n\nrows, data_issues, design = [], [], []\nfor o in ORIGINS:\n    L = read_series(os.path.join(D, \"vintages\", f\"RSAFS_vintage_{o}.csv\"))\n    assert L.index.is_monotonic_increasing and not L.index.duplicated().any() and L.notna().all()\n    last = L.index[-1]\n    vmonth = pd.Period(o, \"M\")\n    g = yoy(L)\n    # AR(1) on g over 1993-02 <= m <= last\n    y = g[(g.index >= pd.Period(\"1993-02\", \"M\"))]\n    x = g.shift(1).reindex(y.index)\n    X = np.column_stack([np.ones(len(y)), x.values]); \n    b = np.linalg.lstsq(X, y.values, rcond=None)[0]\n    # model: random walk with drift on log level, base effect from vintage\n    dl = np.log(L).diff().dropna()\n    mu = dl.iloc[-DRIFT_WINDOW:].mean()\n    design.append({\"origin\": o, \"last_month\": str(last), \"delay_months\": int((vmonth - last).n),\n                   \"ar1_c\": b[0], \"ar1_phi\": b[1], \"drift_pct_per_month\": 100 * mu})\n    for h in HORIZONS:\n        T = last + h\n        assert T > vmonth - 2  # target after last published month\n        f_naive = g[last]\n        f_snaive = g[T - 12]\n        f = g[last]\n        for _ in range(h):\n            f = b[0] + b[1] * f\n        f_ar1 = f\n        f_model = 100 * (np.log(L[last]) + h * mu - np.log(L[T - 12]))\n        a = float(actual[T])\n        for m, fc in zip(MODELS, [f_naive, f_snaive, f_ar1, f_model]):\n            rows.append({\"origin_date\": o, \"target_date\": T.to_timestamp().strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(float(fc), 10), \"actual_first_release\": a,\n                         \"model\": m, \"vintage_used\": o})\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(O, \"forecasts.csv\"), index=False)\nfc[\"e\"] = fc.forecast - fc.actual_first_release\n\nres = {\"task_id\": \"T1-forecast\", \"model_name\": \"rw_drift_yoy\",\n       \"model_description\": (\"Base-effect-aware random walk with drift on log levels. At origin V, using only the RSAFS vintage \"\n           \"dated V: mu = mean of the last 36 monthly log changes of the level ending at last(V); the level forecast is \"\n           \"ln L_{last+h} = ln L_last + h*mu, and the YoY forecast is 100*(ln L_{last+h} - ln L_{T-12}), where L_{T-12} \"\n           \"(the base month, already published) is taken from vintage V. The 36-month window and the specification were fixed \"\n           \"before any forecast error was computed; no parameter is tuned on the evaluation sample. PAYEMS and UMCSENT are not \"\n           \"used, so their availability alignment is not an issue; RSAFS_current and first_release are never used to forecast.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\ntable = []\nfor m in MODELS:\n    for h in HORIZONS:\n        e = fc[(fc.model == m) & (fc.horizon == h)].e.values\n        rmse, mae = float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n        res[f\"rmse_{m}_h{h}\"], res[f\"mae_{m}_h{h}\"] = rmse, mae\n        table.append({\"model\": m, \"horizon\": h, \"rmse\": rmse, \"mae\": mae, \"n\": len(e)})\npd.DataFrame(table).to_csv(os.path.join(O, \"table1.csv\"), index=False)\n\ndef dm(d, h):\n    n = len(d); dc = d - d.mean()\n    lrv = np.sum(dc * dc) / n\n    for k in range(1, h):\n        lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n    stat = d.mean() / np.sqrt(lrv / n)\n    return float(stat), float(2 * (1 - norm.cdf(abs(stat)))), float(lrv)\n\ndm_extra = {}\nfor h in HORIZONS:\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)].e.values\n    em = fc[(fc.model == \"model\") & (fc.horizon == h)].e.values\n    d = en ** 2 - em ** 2\n    s, p, lrv = dm(d, h)\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    better = \"model_better\" if res[f\"rmse_model_h{h}\"] < res[f\"rmse_naive_h{h}\"] else \"naive_better\"\n    res[f\"conclusion_code_h{h}\"] = better + (\"_significant\" if p < 0.05 else \"_not_significant\")\n    dm_extra[f\"h{h}\"] = {\"mean_loss_diff\": float(d.mean()), \"lrv\": lrv,\n                         \"n_origins_model_lower_sq_error\": int((d > 0).sum())}\n\n# (d) revisions: h=1 targets\ngcur = yoy(cur)\nt1 = pd.to_datetime(fc[(fc.horizon == 1) & (fc.model == \"naive\")].target_date).dt.to_period(\"M\")\nrev = [abs(gcur[t] - actual[t]) for t in t1]\nres[\"mean_abs_revision_h1_targets\"] = float(np.mean(rev))\n\ndelays = {x[\"origin\"]: x[\"delay_months\"] for x in design}\ndata_issues = [\n    {\"issue\": \"Irregular RSAFS publication timing: last(V) is two months before the origin month at 2025-10-31 (last 2025-08) and \"\n              \"2026-03-31 (last 2026-01); the 2026-04-30 vintage adds two months at once (2026-02 and 2026-03). Other origins: one month.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar; so two origins (2025-10-31, 2026-03-31) forecast months \"\n                 \"that are 1 resp. 3 months after last(V) but closer to the origin date; target 2026-02 is never an h=1 target and \"\n                 \"some target months appear twice at a given horizon or not at all.\"},\n    {\"issue\": \"PAYEMS vintage 2025-10-31 ends in 2025-08 and vintage 2025-12-31 jumps to 2025-11 (missing employment releases); \"\n              \"PAYEMS vintage 2026-02-28 contains a benchmark revision (level about 1 million lower).\",\n     \"handling\": \"PAYEMS is not used by the model; noted for completeness.\"},\n    {\"issue\": \"UMCSENT has 210 empty values, all before 1978 (survey not monthly).\",\n     \"handling\": \"UMCSENT not used by the model; no imputation.\"},\n    {\"issue\": \"RSAFS_current reflects a large benchmark revision (levels changed for 138 months since 2015-01, up to about 4.6%) relative \"\n              \"to the 2026-07-31 vintage; first_release.csv levels for 2026-08 and 2026-09 come from post-benchmark vintages while \"\n              \"earlier target months come from pre-benchmark vintages.\",\n     \"handling\": \"Forecasts never use RSAFS_current; actuals are taken as given from first_release.csv (YoY is a within-vintage ratio, \"\n                 \"so level shifts largely cancel); the revision estimand (d) reflects these benchmark changes.\"},\n    {\"issue\": \"Monthly vintages revise past levels (up to 0.4% between consecutive vintages in the evaluation period).\",\n     \"handling\": \"Each forecast uses only its own vintage; actual is the first release, so errors include revision noise in the base month.\"},\n]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": data_issues,\n            \"n_input_rows\": dict(sorted(n_input_rows.items())),\n            \"n_analysed\": int(((fc.model == \"model\")).sum()), \"random_seed\": None,\n            \"real_time_design\": design, \"dm_details\": dm_extra,\n            \"abs_revision_h1_by_target\": {str(t): float(r) for t, r in zip(t1, rev)}})\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, default=float)\n\n# exclusion ledger\nn_fr = len(fr); used = sorted(set(pd.to_datetime(fc.target_date).dt.to_period(\"M\")))\npd.DataFrame([\n    {\"step\": 1, \"rule\": \"candidate forecast-actual pairs = 10 origins x 2 horizons x 4 models\", \"n_before\": 80, \"n_after\": 80,\n     \"n_removed\": 0, \"rationale\": \"all targets last(V)+h have a first-release actual; nothing excluded\"},\n    {\"step\": 2, \"rule\": \"pairs for own model (evaluated for RMSE/MAE/DM)\", \"n_before\": 80, \"n_after\": 20, \"n_removed\": 60,\n     \"rationale\": \"baseline rows (60) are evaluated separately in table1; n_analysed counts model pairs only\"},\n]).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\n# figure\nfig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=True)\nfor ax, h in zip(axes, HORIZONS):\n    sub = fc[fc.horizon == h]\n    a = fr[(fr.tm >= pd.Period(\"2025-06\", \"M\"))]\n    ax.plot(a.tm.dt.to_timestamp(), a.yoy_first_release, \"k-o\", ms=3, label=\"first-release actual\")\n    for m, st in [(\"naive\", \"s--\"), (\"model\", \"^-\"), (\"ar1\", \"x:\")]:\n        s = sub[sub.model == m]\n        ax.plot(pd.to_datetime(s.target_date), s.forecast, st, label=m)\n    ax.set_title(f\"h = {h}\"); ax.set_ylabel(\"YoY growth, %\"); ax.tick_params(axis=\"x\", rotation=45)\naxes[0].legend(fontsize=8)\nfig.suptitle(\"RSAFS YoY growth: real-time forecasts vs first release\")\nfig.tight_layout(); fig.savefig(os.path.join(O, \"figure1.png\"), dpi=120)\n\nfor k in [\"rmse_naive_h1\", \"rmse_model_h1\", \"rmse_naive_h3\", \"rmse_model_h3\", \"dm_stat_h1\", \"dm_pvalue_h1\",\n          \"dm_stat_h3\", \"dm_pvalue_h3\", \"conclusion_code_h1\", \"conclusion_code_h3\", \"mean_abs_revision_h1_targets\"]:\n    print(k, res[k])\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(json.dumps(r['real_time_design'],indent=0));print(r['dm_details'],r['abs_revision_h1_by_target'])\"","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":515,"cache_read_input_tokens":25589,"output_tokens":4871,"output_tokens_details":{"thinking_tokens":17},"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":515},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXstF8kevh14FHnTM9c5","requestRef":"9c0336a8-5902-4788-85ae-57624ce7f9c6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b48f18b2-978e-48ff-b9f4-b259fd1aac17","timestamp":"2026-10-30T05:35:37.629Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}64{"parentUuid":"b48f18b2-978e-48ff-b9f4-b259fd1aac17","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QjTQ3yhT76QrM9CwkZEuCc","type":"tool_result","content":"rmse_naive_h1 0.9968743518549719\nrmse_model_h1 0.5524251036176803\nrmse_naive_h3 1.420040373740657\nrmse_model_h3 1.0538489260007893\ndm_stat_h1 2.3021200358532323\ndm_pvalue_h1 0.021328403816257557\ndm_stat_h3 1.864485101676419\ndm_pvalue_h3 0.062253611839883316\nconclusion_code_h1 model_better_significant\nconclusion_code_h3 model_better_not_significant\nmean_abs_revision_h1_targets 0.5482198028389015\nmodel,horizon,rmse,mae,n\nnaive,1,0.9968743518549719,0.8487221879899998,10\nnaive,3,1.420040373740657,1.2004486337699998,10\nsnaive,1,1.6745428991300964,1.30666987625,10\nsnaive,3,1.3160788989366212,1.06586869147,10\nar1,1,0.9405244111134986,0.7976510573200001,10\nar1,3,1.1871178147503472,0.9526895961500002,10\nmodel,1,0.5524251036176803,0.49702333686000005,10\nmodel,3,1.0538489260007893,0.7471097183499998,10\n[\n{\n\"origin\": \"2025-10-31\",\n\"last_month\": \"2025-08\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7245195130966369,\n\"ar1_phi\": 0.8398849523075356,\n\"drift_pct_per_month\": 0.2726829270850958\n},\n{\n\"origin\": \"2025-11-30\",\n\"last_month\": \"2025-09\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7230146850375044,\n\"ar1_phi\": 0.8398593258796581,\n\"drift_pct_per_month\": 0.2845906397826731\n},\n{\n\"origin\": \"2025-12-31\",\n\"last_month\": \"2025-10\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7207145937397748,\n\"ar1_phi\": 0.8398988453218994,\n\"drift_pct_per_month\": 0.2537575944004525\n},\n{\n\"origin\": \"2026-01-31\",\n\"last_month\": \"2025-11\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7197147078017161,\n\"ar1_phi\": 0.839929613490428,\n\"drift_pct_per_month\": 0.3046705275529021\n},\n{\n\"origin\": \"2026-02-28\",\n\"last_month\": \"2025-12\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7161329757524207,\n\"ar1_phi\": 0.840103885059219,\n\"drift_pct_per_month\": 0.33373500370165543\n},\n{\n\"origin\": \"2026-03-31\",\n\"last_month\": \"2026-01\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7173993115367638,\n\"ar1_phi\": 0.8400215083855623,\n\"drift_pct_per_month\": 0.20948125443051147\n},\n{\n\"origin\": \"2026-04-30\",\n\"last_month\": \"2026-03\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7192001736723118,\n\"ar1_phi\": 0.8399036982121751,\n\"drift_pct_per_month\": 0.34146193100709826\n},\n{\n\"origin\": \"2026-05-31\",\n\"last_month\": \"2026-04\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7216117313802095,\n\"ar1_phi\": 0.8398320190588064,\n\"drift_pct_per_month\": 0.33739105093039984\n},\n{\n\"origin\": \"2026-06-30\",\n\"last_month\": \"2026-05\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7265261438714329,\n\"ar1_phi\": 0.8398016339053244,\n\"drift_pct_per_month\": 0.3474913588867226\n},\n{\n\"origin\": \"2026-07-31\",\n\"last_month\": \"2026-06\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7268024118912031,\n\"ar1_phi\": 0.839871473088339,\n\"drift_pct_per_month\": 0.3518484635199215\n}\n]\n{'h1': {'mean_loss_diff': 0.6885849782792655, 'lrv': 0.8946623454191901, 'n_origins_model_lower_sq_error': 8}, 'h3': {'mean_loss_diff': 0.9059171042204879, 'lrv': 2.3607993886369596, 'n_origins_model_lower_sq_error': 8}} {'2025-09': 0.5502167925401169, '2025-10': 0.6641763167215546, '2025-11': 0.43891998227811113, '2025-12': 0.32862681655441595, '2026-01': 0.9756759757612712, '2026-02': 0.4099556308938235, '2026-04': 0.7481728291013168, '2026-05': 0.16533992600872516, '2026-06': 0.7440888135517616, '2026-07': 0.4570249449779187}","is_error":false}]},"uuid":"a06dec8f-9d2f-40c6-bfbd-afe1ee353e1e","timestamp":"2026-10-30T05:35:38.746Z","toolUseResult":{"stdout":"rmse_naive_h1 0.9968743518549719\nrmse_model_h1 0.5524251036176803\nrmse_naive_h3 1.420040373740657\nrmse_model_h3 1.0538489260007893\ndm_stat_h1 2.3021200358532323\ndm_pvalue_h1 0.021328403816257557\ndm_stat_h3 1.864485101676419\ndm_pvalue_h3 0.062253611839883316\nconclusion_code_h1 model_better_significant\nconclusion_code_h3 model_better_not_significant\nmean_abs_revision_h1_targets 0.5482198028389015\nmodel,horizon,rmse,mae,n\nnaive,1,0.9968743518549719,0.8487221879899998,10\nnaive,3,1.420040373740657,1.2004486337699998,10\nsnaive,1,1.6745428991300964,1.30666987625,10\nsnaive,3,1.3160788989366212,1.06586869147,10\nar1,1,0.9405244111134986,0.7976510573200001,10\nar1,3,1.1871178147503472,0.9526895961500002,10\nmodel,1,0.5524251036176803,0.49702333686000005,10\nmodel,3,1.0538489260007893,0.7471097183499998,10\n[\n{\n\"origin\": \"2025-10-31\",\n\"last_month\": \"2025-08\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7245195130966369,\n\"ar1_phi\": 0.8398849523075356,\n\"drift_pct_per_month\": 0.2726829270850958\n},\n{\n\"origin\": \"2025-11-30\",\n\"last_month\": \"2025-09\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7230146850375044,\n\"ar1_phi\": 0.8398593258796581,\n\"drift_pct_per_month\": 0.2845906397826731\n},\n{\n\"origin\": \"2025-12-31\",\n\"last_month\": \"2025-10\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7207145937397748,\n\"ar1_phi\": 0.8398988453218994,\n\"drift_pct_per_month\": 0.2537575944004525\n},\n{\n\"origin\": \"2026-01-31\",\n\"last_month\": \"2025-11\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7197147078017161,\n\"ar1_phi\": 0.839929613490428,\n\"drift_pct_per_month\": 0.3046705275529021\n},\n{\n\"origin\": \"2026-02-28\",\n\"last_month\": \"2025-12\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7161329757524207,\n\"ar1_phi\": 0.840103885059219,\n\"drift_pct_per_month\": 0.33373500370165543\n},\n{\n\"origin\": \"2026-03-31\",\n\"last_month\": \"2026-01\",\n\"delay_months\": 2,\n\"ar1_c\": 0.7173993115367638,\n\"ar1_phi\": 0.8400215083855623,\n\"drift_pct_per_month\": 0.20948125443051147\n},\n{\n\"origin\": \"2026-04-30\",\n\"last_month\": \"2026-03\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7192001736723118,\n\"ar1_phi\": 0.8399036982121751,\n\"drift_pct_per_month\": 0.34146193100709826\n},\n{\n\"origin\": \"2026-05-31\",\n\"last_month\": \"2026-04\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7216117313802095,\n\"ar1_phi\": 0.8398320190588064,\n\"drift_pct_per_month\": 0.33739105093039984\n},\n{\n\"origin\": \"2026-06-30\",\n\"last_month\": \"2026-05\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7265261438714329,\n\"ar1_phi\": 0.8398016339053244,\n\"drift_pct_per_month\": 0.3474913588867226\n},\n{\n\"origin\": \"2026-07-31\",\n\"last_month\": \"2026-06\",\n\"delay_months\": 1,\n\"ar1_c\": 0.7268024118912031,\n\"ar1_phi\": 0.839871473088339,\n\"drift_pct_per_month\": 0.3518484635199215\n}\n]\n{'h1': {'mean_loss_diff': 0.6885849782792655, 'lrv': 0.8946623454191901, 'n_origins_model_lower_sq_error': 8}, 'h3': {'mean_loss_diff': 0.9059171042204879, 'lrv': 2.3607993886369596, 'n_origins_model_lower_sq_error': 8}} {'2025-09': 0.5502167925401169, '2025-10': 0.6641763167215546, '2025-11': 0.43891998227811113, '2025-12': 0.32862681655441595, '2026-01': 0.9756759757612712, '2026-02': 0.4099556308938235, '2026-04': 0.7481728291013168, '2026-05': 0.16533992600872516, '2026-06': 0.7440888135517616, '2026-07': 0.4570249449779187}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b48f18b2-978e-48ff-b9f4-b259fd1aac17","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}65{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"b3fbc2ffb1fcc10c1f0be676098a9adadb06c6914eb439b52d8d04feb254aaf3","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01QjTQ3yhT76QrM9CwkZEuCc","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"T1-forecast: real-time forecasting of US retail-sales YoY growth.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy.stats import norm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nD, O = \"data\", \"out\"\nORIGINS = [\"2025-10-31\", \"2025-11-30\", \"2025-12-31\", \"2026-01-31\", \"2026-02-28\",\n           \"2026-03-31\", \"2026-04-30\", \"2026-05-31\", \"2026-06-30\", \"2026-07-31\"]\nHORIZONS = [1, 3]\nMODELS = [\"naive\", \"snaive\", \"ar1\", \"model\"]\nDRIFT_WINDOW = 36  # months, fixed a priori\nn_input_rows = {}\n\ndef read_series(path):\n    df = pd.read_csv(path)\n    n_input_rows[os.path.relpath(path, D)] = int(len(df))\n    s = df.set_index(pd.to_datetime(df.iloc[:, 0])).iloc[:, 1]\n    s.index = s.index.to_period(\"M\")\n    return s.astype(float)\n\ndef yoy(level):\n    l = np.log(level)\n    return (100 * (l - l.shift(12))).dropna()\n\nfr = pd.read_csv(os.path.join(D, \"first_release.csv\"))\nn_input_rows[\"first_release.csv\"] = int(len(fr))\nfr[\"tm\"] = pd.to_datetime(fr.target_month).dt.to_period(\"M\")\nactual = fr.set_index(\"tm\")[\"yoy_first_release\"]\ncur = read_series(os.path.join(D, \"RSAFS_current.csv\"))  # used ONLY for estimand (d)\n# Predictor files read for audit row counts only (the model does not use them)\nfor o in ORIGINS:\n    read_series(os.path.join(D, \"vintages\", f\"PAYEMS_vintage_{o}.csv\"))\nread_series(os.path.join(D, \"UMCSENT.csv\"))\n\nrows, data_issues, design = [], [], []\nfor o in ORIGINS:\n    L = read_series(os.path.join(D, \"vintages\", f\"RSAFS_vintage_{o}.csv\"))\n    assert L.index.is_monotonic_increasing and not L.index.duplicated().any() and L.notna().all()\n    last = L.index[-1]\n    vmonth = pd.Period(o, \"M\")\n    g = yoy(L)\n    # AR(1) on g over 1993-02 <= m <= last\n    y = g[(g.index >= pd.Period(\"1993-02\", \"M\"))]\n    x = g.shift(1).reindex(y.index)\n    X = np.column_stack([np.ones(len(y)), x.values]); \n    b = np.linalg.lstsq(X, y.values, rcond=None)[0]\n    # model: random walk with drift on log level, base effect from vintage\n    dl = np.log(L).diff().dropna()\n    mu = dl.iloc[-DRIFT_WINDOW:].mean()\n    design.append({\"origin\": o, \"last_month\": str(last), \"delay_months\": int((vmonth - last).n),\n                   \"ar1_c\": b[0], \"ar1_phi\": b[1], \"drift_pct_per_month\": 100 * mu})\n    for h in HORIZONS:\n        T = last + h\n        assert T > vmonth - 2  # target after last published month\n        f_naive = g[last]\n        f_snaive = g[T - 12]\n        f = g[last]\n        for _ in range(h):\n            f = b[0] + b[1] * f\n        f_ar1 = f\n        f_model = 100 * (np.log(L[last]) + h * mu - np.log(L[T - 12]))\n        a = float(actual[T])\n        for m, fc in zip(MODELS, [f_naive, f_snaive, f_ar1, f_model]):\n            rows.append({\"origin_date\": o, \"target_date\": T.to_timestamp().strftime(\"%Y-%m-%d\"),\n                         \"horizon\": h, \"forecast\": round(float(fc), 10), \"actual_first_release\": a,\n                         \"model\": m, \"vintage_used\": o})\nfc = pd.DataFrame(rows)\nfc.to_csv(os.path.join(O, \"forecasts.csv\"), index=False)\nfc[\"e\"] = fc.forecast - fc.actual_first_release\n\nres = {\"task_id\": \"T1-forecast\", \"model_name\": \"rw_drift_yoy\",\n       \"model_description\": (\"Base-effect-aware random walk with drift on log levels. At origin V, using only the RSAFS vintage \"\n           \"dated V: mu = mean of the last 36 monthly log changes of the level ending at last(V); the level forecast is \"\n           \"ln L_{last+h} = ln L_last + h*mu, and the YoY forecast is 100*(ln L_{last+h} - ln L_{T-12}), where L_{T-12} \"\n           \"(the base month, already published) is taken from vintage V. The 36-month window and the specification were fixed \"\n           \"before any forecast error was computed; no parameter is tuned on the evaluation sample. PAYEMS and UMCSENT are not \"\n           \"used, so their availability alignment is not an issue; RSAFS_current and first_release are never used to forecast.\"),\n       \"n_origins\": len(ORIGINS), \"first_origin\": ORIGINS[0], \"last_origin\": ORIGINS[-1]}\ntable = []\nfor m in MODELS:\n    for h in HORIZONS:\n        e = fc[(fc.model == m) & (fc.horizon == h)].e.values\n        rmse, mae = float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n        res[f\"rmse_{m}_h{h}\"], res[f\"mae_{m}_h{h}\"] = rmse, mae\n        table.append({\"model\": m, \"horizon\": h, \"rmse\": rmse, \"mae\": mae, \"n\": len(e)})\npd.DataFrame(table).to_csv(os.path.join(O, \"table1.csv\"), index=False)\n\ndef dm(d, h):\n    n = len(d); dc = d - d.mean()\n    lrv = np.sum(dc * dc) / n\n    for k in range(1, h):\n        lrv += 2 * (1 - k / h) * np.sum(dc[k:] * dc[:-k]) / n\n    stat = d.mean() / np.sqrt(lrv / n)\n    return float(stat), float(2 * (1 - norm.cdf(abs(stat)))), float(lrv)\n\ndm_extra = {}\nfor h in HORIZONS:\n    en = fc[(fc.model == \"naive\") & (fc.horizon == h)].e.values\n    em = fc[(fc.model == \"model\") & (fc.horizon == h)].e.values\n    d = en ** 2 - em ** 2\n    s, p, lrv = dm(d, h)\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    better = \"model_better\" if res[f\"rmse_model_h{h}\"] < res[f\"rmse_naive_h{h}\"] else \"naive_better\"\n    res[f\"conclusion_code_h{h}\"] = better + (\"_significant\" if p < 0.05 else \"_not_significant\")\n    dm_extra[f\"h{h}\"] = {\"mean_loss_diff\": float(d.mean()), \"lrv\": lrv,\n                         \"n_origins_model_lower_sq_error\": int((d > 0).sum())}\n\n# (d) revisions: h=1 targets\ngcur = yoy(cur)\nt1 = pd.to_datetime(fc[(fc.horizon == 1) & (fc.model == \"naive\")].target_date).dt.to_period(\"M\")\nrev = [abs(gcur[t] - actual[t]) for t in t1]\nres[\"mean_abs_revision_h1_targets\"] = float(np.mean(rev))\n\ndelays = {x[\"origin\"]: x[\"delay_months\"] for x in design}\ndata_issues = [\n    {\"issue\": \"Irregular RSAFS publication timing: last(V) is two months before the origin month at 2025-10-31 (last 2025-08) and \"\n              \"2026-03-31 (last 2026-01); the 2026-04-30 vintage adds two months at once (2026-02 and 2026-03). Other origins: one month.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar; so two origins (2025-10-31, 2026-03-31) forecast months \"\n                 \"that are 1 resp. 3 months after last(V) but closer to the origin date; target 2026-02 is never an h=1 target and \"\n                 \"some target months appear twice at a given horizon or not at all.\"},\n    {\"issue\": \"PAYEMS vintage 2025-10-31 ends in 2025-08 and vintage 2025-12-31 jumps to 2025-11 (missing employment releases); \"\n              \"PAYEMS vintage 2026-02-28 contains a benchmark revision (level about 1 million lower).\",\n     \"handling\": \"PAYEMS is not used by the model; noted for completeness.\"},\n    {\"issue\": \"UMCSENT has 210 empty values, all before 1978 (survey not monthly).\",\n     \"handling\": \"UMCSENT not used by the model; no imputation.\"},\n    {\"issue\": \"RSAFS_current reflects a large benchmark revision (levels changed for 138 months since 2015-01, up to about 4.6%) relative \"\n              \"to the 2026-07-31 vintage; first_release.csv levels for 2026-08 and 2026-09 come from post-benchmark vintages while \"\n              \"earlier target months come from pre-benchmark vintages.\",\n     \"handling\": \"Forecasts never use RSAFS_current; actuals are taken as given from first_release.csv (YoY is a within-vintage ratio, \"\n                 \"so level shifts largely cancel); the revision estimand (d) reflects these benchmark changes.\"},\n    {\"issue\": \"Monthly vintages revise past levels (up to 0.4% between consecutive vintages in the evaluation period).\",\n     \"handling\": \"Each forecast uses only its own vintage; actual is the first release, so errors include revision noise in the base month.\"},\n]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None, \"data_issues\": data_issues,\n            \"n_input_rows\": dict(sorted(n_input_rows.items())),\n            \"n_analysed\": int(((fc.model == \"model\")).sum()), \"random_seed\": None,\n            \"real_time_design\": design, \"dm_details\": dm_extra,\n            \"abs_revision_h1_by_target\": {str(t): float(r) for t, r in zip(t1, rev)}})\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2, default=float)\n\n# exclusion ledger\nn_fr = len(fr); used = sorted(set(pd.to_datetime(fc.target_date).dt.to_period(\"M\")))\npd.DataFrame([\n    {\"step\": 1, \"rule\": \"candidate forecast-actual pairs = 10 origins x 2 horizons x 4 models\", \"n_before\": 80, \"n_after\": 80,\n     \"n_removed\": 0, \"rationale\": \"all targets last(V)+h have a first-release actual; nothing excluded\"},\n    {\"step\": 2, \"rule\": \"pairs for own model (evaluated for RMSE/MAE/DM)\", \"n_before\": 80, \"n_after\": 20, \"n_removed\": 60,\n     \"rationale\": \"baseline rows (60) are evaluated separately in table1; n_analysed counts model pairs only\"},\n]).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\n# figure\nfig, axes = plt.subplots(1, 2, figsize=(11, 4), sharey=True)\nfor ax, h in zip(axes, HORIZONS):\n    sub = fc[fc.horizon == h]\n    a = fr[(fr.tm >= pd.Period(\"2025-06\", \"M\"))]\n    ax.plot(a.tm.dt.to_timestamp(), a.yoy_first_release, \"k-o\", ms=3, label=\"first-release actual\")\n    for m, st in [(\"naive\", \"s--\"), (\"model\", \"^-\"), (\"ar1\", \"x:\")]:\n        s = sub[sub.model == m]\n        ax.plot(pd.to_datetime(s.target_date), s.forecast, st, label=m)\n    ax.set_title(f\"h = {h}\"); ax.set_ylabel(\"YoY growth, %\"); ax.tick_params(axis=\"x\", rotation=45)\naxes[0].legend(fontsize=8)\nfig.suptitle(\"RSAFS YoY growth: real-time forecasts vs first release\")\nfig.tight_layout(); fig.savefig(os.path.join(O, \"figure1.png\"), dpi=120)\n\nfor k in [\"rmse_naive_h1\", \"rmse_model_h1\", \"rmse_naive_h3\", \"rmse_model_h3\", \"dm_stat_h1\", \"dm_pvalue_h1\",\n          \"dm_stat_h3\", \"dm_pvalue_h3\", \"conclusion_code_h1\", \"conclusion_code_h3\", \"mean_abs_revision_h1_targets\"]:\n    print(k, res[k])\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv && ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(json.dumps(r['real_time_design'],indent=0));print(r['dm_details'],r['abs_revision_h1_by_target'])\"","description":"Write and run analysis 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out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''    {\"issue\": \"Irregular RSAFS publication timing: last(V) is two months before the origin month at 2025-10-31 (last 2025-08) and \"\n              \"2026-03-31 (last 2026-01); the 2026-04-30 vintage adds two months at once (2026-02 and 2026-03). Other origins: one month.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar; so two origins (2025-10-31, 2026-03-31) forecast months \"\n                 \"that are 1 resp. 3 months after last(V) but closer to the origin date; target 2026-02 is never an h=1 target and \"\n                 \"some target months appear twice at a given horizon or not at all.\"},''',\n'''    {\"issue\": \"Irregular RSAFS publication timing: at the six origins 2025-10-31 ... 2026-03-31 the last published month is two \"\n              \"months before the origin month (e.g. last(2025-10-31) = 2025-08) instead of the usual one; the 2026-04-30 vintage \"\n              \"adds two months at once (2026-02 and 2026-03) and the last four origins have the usual one-month delay.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar. Consequently target 2026-02 is never an h=1 target \"\n                 \"(2026-03 is h=1 for origin 2026-04-30), and for the first six origins the h=1 target month lies entirely before \"\n                 \"the origin date (a 'nowcast/backcast' of an unpublished month). Per-origin last(V) and delay are in real_time_design.\"},''')\ns=s.replace(\"        assert T > vmonth - 2  # target after last published month\\n\",\"        assert T > last and T not in L.index  # target not yet published in vintage V\\n\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -c \"six origins\" out/results.json && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Fix delay description, rerun, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":6554,"cache_read_input_tokens":26104,"output_tokens":1466,"output_tokens_details":{"thinking_tokens":630},"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":6554},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXswCQtJkf1c5PeRfJek","requestRef":"9733d4cb-e6da-4195-975d-be46f02674dd","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d813880f-6b02-4385-baea-cc2a7e40dcfb","timestamp":"2026-10-30T05:35:52.221Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}71{"parentUuid":"d813880f-6b02-4385-baea-cc2a7e40dcfb","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015xpTazaweD74VQxFY8kHyy","type":"tool_result","content":"2\nPGAR GATE pgar_gate/1.0 — FAILED (11 of 65 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] file:out/table1.csv: present\n  [ok] file:out/figure1.png: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_snaive_h1: ok\n  [ok] key:mae_snaive_h1: ok\n  [ok] key:rmse_snaive_h3: ok\n  [ok] key:mae_snaive_h3: ok\n  [ok] key:rmse_ar1_h1: ok\n  [ok] key:mae_ar1_h1: ok\n  [ok] key:rmse_ar1_h3: ok\n  [ok] key:mae_ar1_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:dm_stat_h1: ok\n  [ok] key:dm_pvalue_h1: ok\n  [ok] key:dm_stat_h3: ok\n  [ok] key:dm_pvalue_h3: ok\n  [ok] key:conclusion_code_h1: ok\n  [ok] key:conclusion_code_h3: ok\n  [ok] key:mean_abs_revision_h1_targets: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] positive:rmse_naive_h1: 0.9968743518549719\n  [ok] positive:rmse_naive_h3: 1.420040373740657\n  [ok] positive:rmse_snaive_h1: 1.6745428991300964\n  [ok] positive:rmse_snaive_h3: 1.3160788989366212\n  [ok] positive:rmse_ar1_h1: 0.9405244111134986\n  [ok] positive:rmse_ar1_h3: 1.1871178147503472\n  [ok] positive:rmse_model_h1: 0.5524251036176803\n  [ok] positive:rmse_model_h3: 1.0538489260007893\n  [ok] chronology:first_origin<=last_origin: 2025-10-31 vs 2026-07-31\n  [FAIL] forecasts_chronology: 40 rows with target_date <= origin_date out of 80\n  [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RT0010/out/state/gate_receipts/20261030T053554.295492Z.json\n20261030T053554.295492Z.json","is_error":false}]},"uuid":"6aa4717d-152e-499f-9d13-0bbf60d4fbe4","timestamp":"2026-10-30T05:35:54.307Z","toolUseResult":{"stdout":"2\nPGAR GATE pgar_gate/1.0 — FAILED (11 of 65 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] file:out/table1.csv: present\n  [ok] file:out/figure1.png: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_snaive_h1: ok\n  [ok] key:mae_snaive_h1: ok\n  [ok] key:rmse_snaive_h3: ok\n  [ok] key:mae_snaive_h3: ok\n  [ok] key:rmse_ar1_h1: ok\n  [ok] key:mae_ar1_h1: ok\n  [ok] key:rmse_ar1_h3: ok\n  [ok] key:mae_ar1_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:dm_stat_h1: ok\n  [ok] key:dm_pvalue_h1: ok\n  [ok] key:dm_stat_h3: ok\n  [ok] key:dm_pvalue_h3: ok\n  [ok] key:conclusion_code_h1: ok\n  [ok] key:conclusion_code_h3: ok\n  [ok] key:mean_abs_revision_h1_targets: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] positive:rmse_naive_h1: 0.9968743518549719\n  [ok] positive:rmse_naive_h3: 1.420040373740657\n  [ok] positive:rmse_snaive_h1: 1.6745428991300964\n  [ok] positive:rmse_snaive_h3: 1.3160788989366212\n  [ok] positive:rmse_ar1_h1: 0.9405244111134986\n  [ok] positive:rmse_ar1_h3: 1.1871178147503472\n  [ok] positive:rmse_model_h1: 0.5524251036176803\n  [ok] positive:rmse_model_h3: 1.0538489260007893\n  [ok] chronology:first_origin<=last_origin: 2025-10-31 vs 2026-07-31\n  [FAIL] forecasts_chronology: 40 rows with target_date <= origin_date out of 80\n  [ok] reproducibility: regenerated in 1.0s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RT0010/out/state/gate_receipts/20261030T053554.295492Z.json\n20261030T053554.295492Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d813880f-6b02-4385-baea-cc2a7e40dcfb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}72{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"d3b5efdb595ba3dab618ad6f759f7b5e438b43820a28389040c880b6286401ba","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_015xpTazaweD74VQxFY8kHyy","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''    {\"issue\": \"Irregular RSAFS publication timing: last(V) is two months before the origin month at 2025-10-31 (last 2025-08) and \"\n              \"2026-03-31 (last 2026-01); the 2026-04-30 vintage adds two months at once (2026-02 and 2026-03). Other origins: one month.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar; so two origins (2025-10-31, 2026-03-31) forecast months \"\n                 \"that are 1 resp. 3 months after last(V) but closer to the origin date; target 2026-02 is never an h=1 target and \"\n                 \"some target months appear twice at a given horizon or not at all.\"},''',\n'''    {\"issue\": \"Irregular RSAFS publication timing: at the six origins 2025-10-31 ... 2026-03-31 the last published month is two \"\n              \"months before the origin month (e.g. last(2025-10-31) = 2025-08) instead of the usual one; the 2026-04-30 vintage \"\n              \"adds two months at once (2026-02 and 2026-03) and the last four origins have the usual one-month delay.\",\n     \"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar. Consequently target 2026-02 is never an h=1 target \"\n                 \"(2026-03 is h=1 for origin 2026-04-30), and for the first six origins the h=1 target month lies entirely before \"\n                 \"the origin date (a 'nowcast/backcast' of an unpublished month). Per-origin last(V) and delay are in real_time_design.\"},''')\ns=s.replace(\"        assert T > vmonth - 2  # target after last published month\\n\",\"        assert T > last and T not in L.index  # target not yet published in vintage V\\n\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -c \"six origins\" out/results.json && ~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Fix delay description, rerun, run 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> /tmp/verify.py <<'EOF'\nimport json,hashlib,subprocess,os,glob,numpy as np,pandas as pd\nh0=hashlib.sha256(open('out/results.json','rb').read()).hexdigest()\nfor f in ['results.json','forecasts.csv','table1.csv','figure1.png']: os.remove('out/'+f)\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],check=True,capture_output=True)\nh1=hashlib.sha256(open('out/results.json','rb').read()).hexdigest()\nprint('C1 clean rerun identical results.json:',h0==h1)\nr=json.load(open('out/results.json')); fc=pd.read_csv('out/forecasts.csv'); fr=pd.read_csv('data/first_release.csv')\nprint('C2 rows',len(fc),'unique keys',len(fc.drop_duplicates(['origin_date','horizon','model'])))\nprint('C3 vintage_used==origin all:',(fc.vintage_used==fc.origin_date).all())\n# C4 first release consistency with shipped vintages\nok=True\nfor _,x in fr.iterrows():\n    p=f\"data/vintages/RSAFS_vintage_{x.first_release_vintage}.csv\"\n    if os.path.exists(p):\n        v=pd.read_csv(p,index_col=0).iloc[:,0]\n        ok&= v[x.target_month]==x.level_first_release and v.index[-1]>=x.target_month\n        t12=str(pd.Period(x.target_month[:7],'M')-12)+'-01'; ok&= v[t12]==x.level_prev_year_same_vintage\n    ok&= abs(100*np.log(x.level_first_release/x.level_prev_year_same_vintage)-x.yoy_first_release)<1e-5\nprint('C4 first_release consistent with shipped vintages & formula:',ok)\n# C5 independent recompute of RMSE/MAE and DM\ne=fc.forecast-fc.actual_first_release; fc['e']=e\nbad=0\nfor (m,h),g in fc.groupby(['model','horizon']):\n    bad+=abs(np.sqrt((g.e**2).mean())-r[f'rmse_{m}_h{h}'])>1e-12\nprint('C5 RMSE recompute mismatches:',bad)\nfor h in [1,3]:\n    d=(fc[(fc.model=='naive')&(fc.horizon==h)].e.values**2-fc[(fc.model=='model')&(fc.horizon==h)].e.values**2)\n    n=len(d);dc=d-d.mean();g=[np.dot(dc[k:],dc[:n-k])/n for k in range(3)]\n    lrv=g[0] if h==1 else g[0]+2*(2/3*g[1]+1/3*g[2])\n    print(f'C6 DM h{h} indep',d.mean()/np.sqrt(lrv/n),'stored',r[f'dm_stat_h{h}'],'lrv>0',lrv>0)\n# C7 naive = last yoy of vintage, model base month check\no='2026-04-30';v=pd.read_csv(f'data/vintages/RSAFS_vintage_{o}.csv',index_col=0).iloc[:,0]\nprint('C7 naive check',fc[(fc.origin_date==o)&(fc.model=='naive')].forecast.values, 100*np.log(v.iloc[-1]/v.iloc[-13]))\nprint('C8 rel impr', r['rel_improvement_rmse_h1'],1-r['rmse_model_h1']/r['rmse_naive_h1'])\nprint('C9 MAE<=RMSE all', all(r[f'mae_{m}_h{h}']<=r[f'rmse_{m}_h{h}'] for m in ['naive','snaive','ar1','model'] for h in [1,3]))\nprint('C10 targets all > last(V):', all(pd.Period(t[:7],'M')>pd.Period(x['last_month'],'M') for x in r['real_time_design'] for t in fc[fc.origin_date==x['origin']].target_date))\ncur=pd.read_csv('data/RSAFS_current.csv',index_col=0).iloc[:,0]\nt='2026-01-01';tp='2025-01-01';print('C11 revision check 2026-01',abs(100*np.log(cur[t]/cur[tp])-fr.set_index('target_month').yoy_first_release[t]), r['abs_revision_h1_by_target']['2026-01'])\nEOF\n~/wp29/venv/bin/python /tmp/verify.py","description":"Run verification checks incl. clean 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clean rerun identical results.json: True\nC2 rows 80 unique keys 80\nC3 vintage_used==origin all: True\nC4 first_release consistent with shipped vintages & formula: True\nC5 RMSE recompute mismatches: 0\nC6 DM h1 indep 2.3021200358532323 stored 2.3021200358532323 lrv>0 True\nC6 DM h3 indep 1.864485101676419 stored 1.864485101676419 lrv>0 True\nC7 naive check [3.8926898 3.8926898] 3.892689796092228\nC8 rel impr 0.44584279594541254 0.44584279594541254\nC9 MAE<=RMSE all True\nC10 targets all > last(V): True\nC11 revision check 2026-01 0.9756759757612237 0.9756759757612712","is_error":false}]},"uuid":"2b653684-45fc-46b7-aaca-43c13f164941","timestamp":"2026-10-30T05:36:19.063Z","toolUseResult":{"stdout":"C1 clean rerun identical results.json: True\nC2 rows 80 unique keys 80\nC3 vintage_used==origin all: True\nC4 first_release consistent with shipped vintages & formula: True\nC5 RMSE recompute mismatches: 0\nC6 DM h1 indep 2.3021200358532323 stored 2.3021200358532323 lrv>0 True\nC6 DM h3 indep 1.864485101676419 stored 1.864485101676419 lrv>0 True\nC7 naive check [3.8926898 3.8926898] 3.892689796092228\nC8 rel impr 0.44584279594541254 0.44584279594541254\nC9 MAE<=RMSE all True\nC10 targets all > last(V): True\nC11 revision check 2026-01 0.9756759757612237 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> /tmp/verify.py <<'EOF'\nimport json,hashlib,subprocess,os,glob,numpy as np,pandas as pd\nh0=hashlib.sha256(open('out/results.json','rb').read()).hexdigest()\nfor f in ['results.json','forecasts.csv','table1.csv','figure1.png']: os.remove('out/'+f)\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],check=True,capture_output=True)\nh1=hashlib.sha256(open('out/results.json','rb').read()).hexdigest()\nprint('C1 clean rerun identical results.json:',h0==h1)\nr=json.load(open('out/results.json')); fc=pd.read_csv('out/forecasts.csv'); fr=pd.read_csv('data/first_release.csv')\nprint('C2 rows',len(fc),'unique keys',len(fc.drop_duplicates(['origin_date','horizon','model'])))\nprint('C3 vintage_used==origin all:',(fc.vintage_used==fc.origin_date).all())\n# C4 first release consistency with shipped vintages\nok=True\nfor _,x in fr.iterrows():\n    p=f\"data/vintages/RSAFS_vintage_{x.first_release_vintage}.csv\"\n    if os.path.exists(p):\n        v=pd.read_csv(p,index_col=0).iloc[:,0]\n        ok&= v[x.target_month]==x.level_first_release and v.index[-1]>=x.target_month\n        t12=str(pd.Period(x.target_month[:7],'M')-12)+'-01'; ok&= v[t12]==x.level_prev_year_same_vintage\n    ok&= abs(100*np.log(x.level_first_release/x.level_prev_year_same_vintage)-x.yoy_first_release)<1e-5\nprint('C4 first_release consistent with shipped vintages & formula:',ok)\n# C5 independent recompute of RMSE/MAE and DM\ne=fc.forecast-fc.actual_first_release; fc['e']=e\nbad=0\nfor (m,h),g in fc.groupby(['model','horizon']):\n    bad+=abs(np.sqrt((g.e**2).mean())-r[f'rmse_{m}_h{h}'])>1e-12\nprint('C5 RMSE recompute mismatches:',bad)\nfor h in [1,3]:\n    d=(fc[(fc.model=='naive')&(fc.horizon==h)].e.values**2-fc[(fc.model=='model')&(fc.horizon==h)].e.values**2)\n    n=len(d);dc=d-d.mean();g=[np.dot(dc[k:],dc[:n-k])/n for k in range(3)]\n    lrv=g[0] if h==1 else g[0]+2*(2/3*g[1]+1/3*g[2])\n    print(f'C6 DM h{h} indep',d.mean()/np.sqrt(lrv/n),'stored',r[f'dm_stat_h{h}'],'lrv>0',lrv>0)\n# C7 naive = last yoy of vintage, model base month check\no='2026-04-30';v=pd.read_csv(f'data/vintages/RSAFS_vintage_{o}.csv',index_col=0).iloc[:,0]\nprint('C7 naive check',fc[(fc.origin_date==o)&(fc.model=='naive')].forecast.values, 100*np.log(v.iloc[-1]/v.iloc[-13]))\nprint('C8 rel impr', r['rel_improvement_rmse_h1'],1-r['rmse_model_h1']/r['rmse_naive_h1'])\nprint('C9 MAE<=RMSE all', all(r[f'mae_{m}_h{h}']<=r[f'rmse_{m}_h{h}'] for m in ['naive','snaive','ar1','model'] for h in [1,3]))\nprint('C10 targets all > last(V):', all(pd.Period(t[:7],'M')>pd.Period(x['last_month'],'M') for x in r['real_time_design'] for t in fc[fc.origin_date==x['origin']].target_date))\ncur=pd.read_csv('data/RSAFS_current.csv',index_col=0).iloc[:,0]\nt='2026-01-01';tp='2025-01-01';print('C11 revision check 2026-01',abs(100*np.log(cur[t]/cur[tp])-fr.set_index('target_month').yoy_first_release[t]), r['abs_revision_h1_by_target']['2026-01'])\nEOF\n~/wp29/venv/bin/python /tmp/verify.py","description":"Run verification checks incl. clean 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(2 levels on the left, 1 on the right)","is_error":true,"tool_use_id":"toolu_01G2SnYYo85eHU6rYBjccFiU"}]},"uuid":"f8f2f4b4-4554-4cbe-8e52-30c12d0b380e","timestamp":"2026-10-30T05:36:30.955Z","toolUseResult":"Error: Exit code 1\nC1 clean rerun identical results.json: True\nC2 rows 80 unique keys 80\nTraceback (most recent call last):\n  File \"<string>\", line 2, in <module>\n    import pandas as pd; fc=pd.read_csv('out/forecasts.csv'); print(fc[fc.model.isin(['naive','model'])].pivot_table(index=['horizon','origin_date','target_date'],columns='model',values=['forecast']).join(fc.groupby(['horizon','origin_date','target_date']).actual_first_release.first()).round(2))\n                                                                    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/frame.py\", line 10784, in join\n    return merge(\n        self,\n    ...<7 lines>...\n        validate=validate,\n    )\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/reshape/merge.py\", line 170, in merge\n    op = _MergeOperation(\n        left_df,\n    ...<10 lines>...\n        validate=validate,\n    )\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/reshape/merge.py\", line 784, in __init__\n    raise MergeError(msg)\npandas.errors.MergeError: Not allowed to merge between different levels. 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Re-run verification on the stable script:"},{"type":"tool_use","id":"toolu_01G2SnYYo85eHU6rYBjccFiU","name":"Bash","input":{"command":"~/wp29/venv/bin/python out/state/verify.py > out/state/verify_output.txt; cat out/state/verify_output.txt | head -2; ~/wp29/venv/bin/python -c \"\nimport pandas as pd; fc=pd.read_csv('out/forecasts.csv'); print(fc[fc.model.isin(['naive','model'])].pivot_table(index=['horizon','origin_date','target_date'],columns='model',values=['forecast']).join(fc.groupby(['horizon','origin_date','target_date']).actual_first_release.first()).round(2))\"","description":"Re-verify and view forecasts"}}]}}90{"type":"api-request","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","id":"952df9f9-aeeb-4585-aa58-3e57877fa9b4","timestamp":"2026-10-30T05:36:30.959Z","version":"2.1.314","querySource":"sdk","shapeHash":"81de0bee269150a4a5258c71378fcacd4dd07a9cce8c76ff7b371b779847bd10","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":"33a4807a-cc1f-480c-a692-c8d513644b62","keep":28,"tail":["678efe5d8356fb6c806f3653e5917ad915285ce501ddeb139acd2a7cd8641fef","75e726e27e09b11d89c2084e7dd55d1b19f8e55bba6e5e751cb9e386228868e0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",29,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"df8bc9c2cf480223956bd174053b32c5eba23b54955ff21323c1398d6d047092"}91{"parentUuid":"f8f2f4b4-4554-4cbe-8e52-30c12d0b380e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt13uH7swqwb3zoKJzk","type":"message","role":"assistant","content":[{"type":"text","text":"Not important; the verification passed. Now write state files (audit, checks, phases) and the report."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1031,"cache_read_input_tokens":39158,"output_tokens":216,"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":1031},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXt13cQVtYk3vgf98w1p","requestRef":"952df9f9-aeeb-4585-aa58-3e57877fa9b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"019ec315-4026-4ff4-8d94-a56814888c18","timestamp":"2026-10-30T05:36:32.472Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}92{"parentUuid":"019ec315-4026-4ff4-8d94-a56814888c18","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt13uH7swqwb3zoKJzk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XPoRH3PRFsPdiVTJrK52Dn","name":"Bash","input":{"command":"cat out/state/verify_output.txt; cat out/results.json | ~/wp29/venv/bin/python -c \"import json,sys;r=json.load(sys.stdin);[print(k,round(v,4) if isinstance(v,float) else v) for k,v in r.items() if k not in('data_issues','real_time_design','model_description')]\"","description":"Show verification and results values"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1031,"cache_read_input_tokens":39158,"output_tokens":216,"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":1031},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt13cQVtYk3vgf98w1p","requestRef":"952df9f9-aeeb-4585-aa58-3e57877fa9b4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1d718294-4a32-47a3-939e-c4d3f82a36ed","timestamp":"2026-10-30T05:36:33.715Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}93{"parentUuid":"1d718294-4a32-47a3-939e-c4d3f82a36ed","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XPoRH3PRFsPdiVTJrK52Dn","type":"tool_result","content":"C1 clean rerun identical results.json: True\nC2 rows 80 unique keys 80\nC3 vintage_used==origin all: True\nC4 first_release consistent with shipped vintages & formula: True\nC5 RMSE recompute mismatches: 0\nC6 DM h1 indep 2.3021200358532323 stored 2.3021200358532323 lrv>0 True\nC6 DM h3 indep 1.864485101676419 stored 1.864485101676419 lrv>0 True\nC7 naive check [3.8926898 3.8926898] 3.892689796092228\nC8 rel impr 0.44584279594541254 0.44584279594541254\nC9 MAE<=RMSE all True\nC10 targets all > last(V): True\nC11 revision check 2026-01 0.9756759757612237 0.9756759757612712\ntask_id T1-forecast\nmodel_name rw_drift_yoy\nn_origins 10\nfirst_origin 2025-10-31\nlast_origin 2026-07-31\nrmse_naive_h1 0.9969\nmae_naive_h1 0.8487\nrmse_naive_h3 1.42\nmae_naive_h3 1.2004\nrmse_snaive_h1 1.6745\nmae_snaive_h1 1.3067\nrmse_snaive_h3 1.3161\nmae_snaive_h3 1.0659\nrmse_ar1_h1 0.9405\nmae_ar1_h1 0.7977\nrmse_ar1_h3 1.1871\nmae_ar1_h3 0.9527\nrmse_model_h1 0.5524\nmae_model_h1 0.497\nrmse_model_h3 1.0538\nmae_model_h3 0.7471\nrel_improvement_rmse_h1 0.4458\ndm_stat_h1 2.3021\ndm_pvalue_h1 0.0213\nconclusion_code_h1 model_better_significant\nrel_improvement_rmse_h3 0.2579\ndm_stat_h3 1.8645\ndm_pvalue_h3 0.0623\nconclusion_code_h3 model_better_not_significant\nmean_abs_revision_h1_targets 0.5482\nfeasibility complete\nfeasibility_reason None\nn_input_rows {'RSAFS_current.csv': 417, 'UMCSENT.csv': 887, 'first_release.csv': 28, 'vintages/PAYEMS_vintage_2025-10-31.csv': 1040, 'vintages/PAYEMS_vintage_2025-11-30.csv': 1041, 'vintages/PAYEMS_vintage_2025-12-31.csv': 1043, 'vintages/PAYEMS_vintage_2026-01-31.csv': 1044, 'vintages/PAYEMS_vintage_2026-02-28.csv': 1045, 'vintages/PAYEMS_vintage_2026-03-31.csv': 1046, 'vintages/PAYEMS_vintage_2026-04-30.csv': 1047, 'vintages/PAYEMS_vintage_2026-05-31.csv': 1048, 'vintages/PAYEMS_vintage_2026-06-30.csv': 1049, 'vintages/PAYEMS_vintage_2026-07-31.csv': 1050, 'vintages/RSAFS_vintage_2025-10-31.csv': 404, 'vintages/RSAFS_vintage_2025-11-30.csv': 405, 'vintages/RSAFS_vintage_2025-12-31.csv': 406, 'vintages/RSAFS_vintage_2026-01-31.csv': 407, 'vintages/RSAFS_vintage_2026-02-28.csv': 408, 'vintages/RSAFS_vintage_2026-03-31.csv': 409, 'vintages/RSAFS_vintage_2026-04-30.csv': 411, 'vintages/RSAFS_vintage_2026-05-31.csv': 412, 'vintages/RSAFS_vintage_2026-06-30.csv': 413, 'vintages/RSAFS_vintage_2026-07-31.csv': 414}\nn_analysed 20\nrandom_seed None\ndm_details {'h1': {'mean_loss_diff': 0.6885849782792655, 'lrv': 0.8946623454191901, 'n_origins_model_lower_sq_error': 8, 'sign_test_pvalue_two_sided': 0.109375}, 'h3': {'mean_loss_diff': 0.9059171042204879, 'lrv': 2.3607993886369596, 'n_origins_model_lower_sq_error': 8, 'sign_test_pvalue_two_sided': 0.109375}}\nabs_revision_h1_by_target {'2025-09': 0.5502167925401169, '2025-10': 0.6641763167215546, '2025-11': 0.43891998227811113, '2025-12': 0.32862681655441595, '2026-01': 0.9756759757612712, '2026-02': 0.4099556308938235, '2026-04': 0.7481728291013168, '2026-05': 0.16533992600872516, '2026-06': 0.7440888135517616, '2026-07': 0.4570249449779187}","is_error":false}]},"uuid":"51d1441b-75c6-42a9-9508-5838be5c36cd","timestamp":"2026-10-30T05:36:33.764Z","toolUseResult":{"stdout":"C1 clean rerun identical results.json: True\nC2 rows 80 unique keys 80\nC3 vintage_used==origin all: True\nC4 first_release consistent with shipped vintages & formula: True\nC5 RMSE recompute mismatches: 0\nC6 DM h1 indep 2.3021200358532323 stored 2.3021200358532323 lrv>0 True\nC6 DM h3 indep 1.864485101676419 stored 1.864485101676419 lrv>0 True\nC7 naive check [3.8926898 3.8926898] 3.892689796092228\nC8 rel impr 0.44584279594541254 0.44584279594541254\nC9 MAE<=RMSE all True\nC10 targets all > last(V): True\nC11 revision check 2026-01 0.9756759757612237 0.9756759757612712\ntask_id T1-forecast\nmodel_name rw_drift_yoy\nn_origins 10\nfirst_origin 2025-10-31\nlast_origin 2026-07-31\nrmse_naive_h1 0.9969\nmae_naive_h1 0.8487\nrmse_naive_h3 1.42\nmae_naive_h3 1.2004\nrmse_snaive_h1 1.6745\nmae_snaive_h1 1.3067\nrmse_snaive_h3 1.3161\nmae_snaive_h3 1.0659\nrmse_ar1_h1 0.9405\nmae_ar1_h1 0.7977\nrmse_ar1_h3 1.1871\nmae_ar1_h3 0.9527\nrmse_model_h1 0.5524\nmae_model_h1 0.497\nrmse_model_h3 1.0538\nmae_model_h3 0.7471\nrel_improvement_rmse_h1 0.4458\ndm_stat_h1 2.3021\ndm_pvalue_h1 0.0213\nconclusion_code_h1 model_better_significant\nrel_improvement_rmse_h3 0.2579\ndm_stat_h3 1.8645\ndm_pvalue_h3 0.0623\nconclusion_code_h3 model_better_not_significant\nmean_abs_revision_h1_targets 0.5482\nfeasibility complete\nfeasibility_reason None\nn_input_rows {'RSAFS_current.csv': 417, 'UMCSENT.csv': 887, 'first_release.csv': 28, 'vintages/PAYEMS_vintage_2025-10-31.csv': 1040, 'vintages/PAYEMS_vintage_2025-11-30.csv': 1041, 'vintages/PAYEMS_vintage_2025-12-31.csv': 1043, 'vintages/PAYEMS_vintage_2026-01-31.csv': 1044, 'vintages/PAYEMS_vintage_2026-02-28.csv': 1045, 'vintages/PAYEMS_vintage_2026-03-31.csv': 1046, 'vintages/PAYEMS_vintage_2026-04-30.csv': 1047, 'vintages/PAYEMS_vintage_2026-05-31.csv': 1048, 'vintages/PAYEMS_vintage_2026-06-30.csv': 1049, 'vintages/PAYEMS_vintage_2026-07-31.csv': 1050, 'vintages/RSAFS_vintage_2025-10-31.csv': 404, 'vintages/RSAFS_vintage_2025-11-30.csv': 405, 'vintages/RSAFS_vintage_2025-12-31.csv': 406, 'vintages/RSAFS_vintage_2026-01-31.csv': 407, 'vintages/RSAFS_vintage_2026-02-28.csv': 408, 'vintages/RSAFS_vintage_2026-03-31.csv': 409, 'vintages/RSAFS_vintage_2026-04-30.csv': 411, 'vintages/RSAFS_vintage_2026-05-31.csv': 412, 'vintages/RSAFS_vintage_2026-06-30.csv': 413, 'vintages/RSAFS_vintage_2026-07-31.csv': 414}\nn_analysed 20\nrandom_seed None\ndm_details {'h1': {'mean_loss_diff': 0.6885849782792655, 'lrv': 0.8946623454191901, 'n_origins_model_lower_sq_error': 8, 'sign_test_pvalue_two_sided': 0.109375}, 'h3': {'mean_loss_diff': 0.9059171042204879, 'lrv': 2.3607993886369596, 'n_origins_model_lower_sq_error': 8, 'sign_test_pvalue_two_sided': 0.109375}}\nabs_revision_h1_by_target {'2025-09': 0.5502167925401169, '2025-10': 0.6641763167215546, '2025-11': 0.43891998227811113, '2025-12': 0.32862681655441595, '2026-01': 0.9756759757612712, '2026-02': 0.4099556308938235, '2026-04': 0.7481728291013168, '2026-05': 0.16533992600872516, '2026-06': 0.7440888135517616, '2026-07': 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Now write state files (audit, checks, phases) and the report."},{"type":"tool_use","id":"toolu_01XPoRH3PRFsPdiVTJrK52Dn","name":"Bash","input":{"command":"cat out/state/verify_output.txt; cat out/results.json | ~/wp29/venv/bin/python -c \"import json,sys;r=json.load(sys.stdin);[print(k,round(v,4) if isinstance(v,float) else v) for k,v in r.items() if k not in('data_issues','real_time_design','model_description')]\"","description":"Show verification and results 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Consequently target 2026-02 is never an h=1 target \"\n                 \"(2026-03 is h=1 for origin 2026-04-30), and'''\nassert old in s\ns=s.replace(old,'''\"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar. Consequently month 2026-03 is never an h=1 target \"\n                 \"(h=1 targets: 2026-02 from origin 2026-03-31, then 2026-04 from origin 2026-04-30), and''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/state/verify.py > /dev/null; ~/wp29/venv/bin/python out/state/verify.py > out/state/verify_output.txt; head -1 out/state/verify_output.txt","description":"Correct target-month description and re-verify"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1969,"cache_read_input_tokens":40189,"output_tokens":594,"output_tokens_details":{"thinking_tokens":171},"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":1969},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt1FtbSn8shpVTC4Kmh","requestRef":"e3469bd7-ef56-4df0-b718-645d2975d64e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4d2be401-d7f9-40de-801c-4bc621cb6d3a","timestamp":"2026-10-30T05:36:39.047Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}98{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"4d2be401-d7f9-40de-801c-4bc621cb6d3a","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0"}99{"type":"atis-latch","atis":"","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0"}100{"parentUuid":"4d2be401-d7f9-40de-801c-4bc621cb6d3a","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Vxn64YmG4me5Ez7CwpvprY","type":"tool_result","content":"C1 clean rerun identical results.json: True","is_error":false}]},"uuid":"759442ec-60c3-48c8-8492-5aea0fb1dc21","timestamp":"2026-10-30T05:36:41.559Z","toolUseResult":{"stdout":"C1 clean rerun identical results.json: True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4d2be401-d7f9-40de-801c-4bc621cb6d3a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}101{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"97e607b07916f0fc642fd5a0dbc5622b50e0739b1cfe1051b75062435c1c5766","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Vxn64YmG4me5Ez7CwpvprY","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\nold='''\"handling\": \"Targets defined as last(V)+h per TASK.md, not by calendar. 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No missing values in RSAFS/PAYEMS vintages, RSAFS_current, first_release.\n- Publication timing (RSAFS): last(V) = 2025-08, 2025-09, 2025-10, 2025-11, 2025-12, 2026-01 for origins 2025-10-31 … 2026-03-31 (two-month delay), then 2026-03, 2026-04, 2026-05, 2026-06 for 2026-04-30 … 2026-07-31 (one-month delay; 2026-04-30 adds two months at once).\n- PAYEMS: vintage 2025-10-31 ends 2025-08; 2025-12-31 jumps from 2025-09 to 2025-11; benchmark revision visible in 2026-02-28 vintage (≈ −1 million).\n- Revisions: consecutive RSAFS vintages revise ~4 past months each (max 0.4% in level). RSAFS_current differs from the 2026-07-31 vintage on 138 months since 2015-01 (max 4.6% in level) — an annual/benchmark revision. first_release.csv levels for 2026-08/2026-09 are post-benchmark (prior-year level ≈700k vs ≈730k before).\n- first_release.csv: for every row whose first_release_vintage is one of the 10 shipped vintages, level and prior-year level match the vintage exactly, the month is the last row, and yoy = 100·ln(ratio) to 1e-5 (verify.py C4).\n\n## Availability-rule handling\n- RSAFS_current: used only for estimand (d) (in analysis.py it is read and used only in the revision block).\n- first_release.csv: used only as evaluation actuals.\n- UMCSENT current vintage: not used by any forecast (model uses RSAFS vintage V only). PAYEMS: not used.\n- No input violates the rules as used.\nEOF\n} > data_audit.md\ncat > checks.md <<'EOF'\n# Verification checks (P4)\nScript: out/state/verify.py; output: out/state/verify_output.txt.\n\n| id | check | outcome |\n|---|---|---|\n| C1 | Delete results/forecasts/table/figure, re-run analysis.py, SHA-256 of results.json identical | PASS |\n| C2 | forecasts.csv has 80 rows, unique (origin,horizon,model) | PASS |\n| C3 | vintage_used == origin_date for all rows (no future vintage) | PASS |\n| C4 | first_release.csv consistent with shipped vintages and YoY formula | PASS |\n| C5 | RMSE recomputed independently from forecasts.csv equals results.json | PASS (0 mismatches) |\n| C6 | DM statistics recomputed independently with explicit Bartlett weights (h=3: 2/3, 1/3); LRV > 0 | PASS (2.3021, 1.8645) |\n| C7 | naive forecast equals last YoY of vintage (spot check 2026-04-30) | PASS |\n| C8 | rel_improvement = 1 − RMSE_model/RMSE_naive | PASS |\n| C9 | MAE ≤ RMSE for all model/horizon pairs | PASS |\n| C10 | every target month > last(V) and not published in vintage V | PASS (also asserted in analysis.py) |\n| C11 | revision for 2026-01 recomputed by hand from RSAFS_current | PASS |\n| C12 | Magnitudes: model RMSE (0.55/1.05) below naive (1.00/1.42) and in the same order of magnitude as the baselines; snaive worst at h=1 as expected for a 12-month-old value | PASS (plausible) |\n| C13 | Gate `forecasts_chronology` (rule origin_date < target_date) | FAIL — structural: all 40 h=1 rows have target_date (YYYY-MM-01 of last(V)+1) ≤ month-end origin_date. This follows from TASK.md's definition target = last(V)+h with target_date formatted YYYY-MM-01; for the six two-month-delay origins the h=1 target month is entirely before the origin date. No date convention compatible with TASK.md satisfies the rule. Information validity is not affected: the target month is not present in vintage V (C10) and vintage_used ≤ origin_date (C3). Reported, not \"fixed\", because altering targets would violate TASK.md. |\nEOF\necho ok","description":"Write data audit and checks docs"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":614,"cache_read_input_tokens":42158,"output_tokens":1815,"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":614},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt1pvT9YGLZihH7oGzH","requestRef":"2277e0c7-c3b4-4af5-bb18-bd9e7734a5ba","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1599cc18-c31c-4451-96a0-dfd5af7a69eb","timestamp":"2026-10-30T05:36:58.417Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}105{"parentUuid":"1599cc18-c31c-4451-96a0-dfd5af7a69eb","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012LyPXiqTv9nhxu65J5UBqH","type":"tool_result","content":"ok","is_error":false}]},"uuid":"e239b7cc-f0da-4589-8ffb-39dc85cb30c6","timestamp":"2026-10-30T05:36:58.447Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1599cc18-c31c-4451-96a0-dfd5af7a69eb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010/out/state","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}106{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"5629d6dd47f981de2091d5c748ede63ad29a07954918059eb36bb01340ded34c","message":{"role":"assistant","content":[{"type":"text","text":"Now the state documents."},{"type":"tool_use","id":"toolu_012LyPXiqTv9nhxu65J5UBqH","name":"Bash","input":{"command":"cd out/state && { echo \"# Data audit (P1)\"; echo; echo \"Raw output of the audit script: audit_raw.txt (file | sha256 prefix | matches README | rows | cols | date range | NA count | duplicate dates | month gaps).\"; echo '```'; cat audit_raw.txt; echo '```'; cat <<'EOF'\n\n## Findings\n- All 23 input files: SHA-256 matches `data/README.md`; row counts match README; no duplicate dates, no gaps in the monthly index; dates are first-of-month.\n- Units: RSAFS millions USD SA; PAYEMS thousands of persons SA; UMCSENT index 1966Q1=100.\n- Missing values: only UMCSENT (210 empty fields, all before 1978). No missing values in RSAFS/PAYEMS vintages, RSAFS_current, first_release.\n- Publication timing (RSAFS): last(V) = 2025-08, 2025-09, 2025-10, 2025-11, 2025-12, 2026-01 for origins 2025-10-31 … 2026-03-31 (two-month delay), then 2026-03, 2026-04, 2026-05, 2026-06 for 2026-04-30 … 2026-07-31 (one-month delay; 2026-04-30 adds two months at once).\n- PAYEMS: vintage 2025-10-31 ends 2025-08; 2025-12-31 jumps from 2025-09 to 2025-11; benchmark revision visible in 2026-02-28 vintage (≈ −1 million).\n- Revisions: consecutive RSAFS vintages revise ~4 past months each (max 0.4% in level). RSAFS_current differs from the 2026-07-31 vintage on 138 months since 2015-01 (max 4.6% in level) — an annual/benchmark revision. first_release.csv levels for 2026-08/2026-09 are post-benchmark (prior-year level ≈700k vs ≈730k before).\n- first_release.csv: for every row whose first_release_vintage is one of the 10 shipped vintages, level and prior-year level match the vintage exactly, the month is the last row, and yoy = 100·ln(ratio) to 1e-5 (verify.py C4).\n\n## Availability-rule handling\n- RSAFS_current: used only for estimand (d) (in analysis.py it is read and used only in the revision block).\n- first_release.csv: used only as evaluation actuals.\n- UMCSENT current vintage: not used by any forecast (model uses RSAFS vintage V only). PAYEMS: not used.\n- No input violates the rules as used.\nEOF\n} > data_audit.md\ncat > checks.md <<'EOF'\n# Verification checks (P4)\nScript: out/state/verify.py; output: out/state/verify_output.txt.\n\n| id | check | outcome |\n|---|---|---|\n| C1 | Delete results/forecasts/table/figure, re-run analysis.py, SHA-256 of results.json identical | PASS |\n| C2 | forecasts.csv has 80 rows, unique (origin,horizon,model) | PASS |\n| C3 | vintage_used == origin_date for all rows (no future vintage) | PASS |\n| C4 | first_release.csv consistent with shipped vintages and YoY formula | PASS |\n| C5 | RMSE recomputed independently from forecasts.csv equals results.json | PASS (0 mismatches) |\n| C6 | DM statistics recomputed independently with explicit Bartlett weights (h=3: 2/3, 1/3); LRV > 0 | PASS (2.3021, 1.8645) |\n| C7 | naive forecast equals last YoY of vintage (spot check 2026-04-30) | PASS |\n| C8 | rel_improvement = 1 − RMSE_model/RMSE_naive | PASS |\n| C9 | MAE ≤ RMSE for all model/horizon pairs | PASS |\n| C10 | every target month > last(V) and not published in vintage V | PASS (also asserted in analysis.py) |\n| C11 | revision for 2026-01 recomputed by hand from RSAFS_current | PASS |\n| C12 | Magnitudes: model RMSE (0.55/1.05) below naive (1.00/1.42) and in the same order of magnitude as the baselines; snaive worst at h=1 as expected for a 12-month-old value | PASS (plausible) |\n| C13 | Gate `forecasts_chronology` (rule origin_date < target_date) | FAIL — structural: all 40 h=1 rows have target_date (YYYY-MM-01 of last(V)+1) ≤ month-end origin_date. This follows from TASK.md's definition target = last(V)+h with target_date formatted YYYY-MM-01; for the six two-month-delay origins the h=1 target month is entirely before the origin date. No date convention compatible with TASK.md satisfies the rule. Information validity is not affected: the target month is not present in vintage V (C10) and vintage_used ≤ origin_date (C3). Reported, not \"fixed\", because altering targets would violate TASK.md. |\nEOF\necho ok","description":"Write data audit and checks docs"}}]}}107{"type":"api-request","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","id":"9ff1728e-06b1-4750-8d38-4a813b099515","timestamp":"2026-10-30T05:36:58.450Z","version":"2.1.314","querySource":"sdk","shapeHash":"81de0bee269150a4a5258c71378fcacd4dd07a9cce8c76ff7b371b779847bd10","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":"2277e0c7-c3b4-4af5-bb18-bd9e7734a5ba","keep":34,"tail":["5629d6dd47f981de2091d5c748ede63ad29a07954918059eb36bb01340ded34c","fed302fdf10a860597b9b6af50346ce73d79741a5d8d95ef55ab9a777898295a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",35,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a0441fdc51d84f1ff0f40b1dc288f025a2c14a7525eed8f0052b0adc96a82253"}108{"parentUuid":"e239b7cc-f0da-4589-8ffb-39dc85cb30c6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt35QcPUJANSw1uS7hw","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":1822,"cache_read_input_tokens":42772,"output_tokens":4497,"output_tokens_details":{"thinking_tokens":64},"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":1822},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":457,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXt359FFs22yrMtUauSz","requestRef":"9ff1728e-06b1-4750-8d38-4a813b099515","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1e48c380-8020-4c85-a4d4-d71d77e57b44","timestamp":"2026-10-30T05:37:00.399Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010/out/state","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}109{"parentUuid":"1e48c380-8020-4c85-a4d4-d71d77e57b44","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt35QcPUJANSw1uS7hw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01J24QPwFhuA9CrmJ27RZPmZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Real-time forecasting of US retail-sales YoY growth from ALFRED vintages (T1-forecast)\n\n## Question\nUsing only the data published at each of 10 month-end forecast origins (2025-10-31 … 2026-07-31; `first_origin`, `last_origin`, `n_origins`), how well can the year-over-year log growth of US retail and food services sales (RSAFS), g_m = 100·ln(L_m/L_{m−12}), be forecast 1 and 3 months beyond the last published month, and does a model of my own beat the naive no-change forecast when errors are measured against the first-release YoY of each target month?\n\n## Data and exclusions\n- Inputs: 10 RSAFS and 10 PAYEMS ALFRED vintages (one per origin), UMCSENT (current vintage), RSAFS_current, first_release.csv. Row counts are in `n_input_rows`; every SHA-256 matched `data/README.md` (out/state/data_audit.md).\n- **Real-time design.** The forecast at origin V uses only the RSAFS vintage dated V (`vintage_used` = `origin_date` for all 80 rows of out/forecasts.csv). Targets are last(V)+h, where last(V) is the last month in that vintage. Publication delays seen in the vintages: at the six origins 2025-10-31 … 2026-03-31 the last published month is two months before the origin month (last(V) = 2025-08 … 2026-01). The 2026-04-30 vintage adds two months at once (2026-02, 2026-03), and the last four origins have the usual one-month delay (last(V) = 2026-03 … 2026-06). Per-origin last(V) and delay are stored in `real_time_design`. Because of this, month 2026-03 is never an h=1 target, and for the first six origins the h=1 \"forecast\" is really a backcast of a month that had already ended but was not yet published.\n- Actuals: `yoy_first_release` from first_release.csv. Where the first-release vintage is one of the shipped vintages, its values match that vintage exactly (check C4).\n- RSAFS_current is used only for the revision estimand. PAYEMS and UMCSENT are read (for row counts) but are not used by any forecast.\n- Exclusions (out/exclusions.csv): none. All 80 forecast–actual pairs (10 origins × 2 horizons × 4 models) have an actual. `n_analysed` = 20 model pairs (10 per horizon).\n- Data issues found, with how each was handled, are listed in `data_issues`: irregular publication timing; PAYEMS gaps and a benchmark revision; pre-1978 UMCSENT gaps; a benchmark revision in RSAFS_current that also affects the first-release levels of 2026-08 and 2026-09; and routine monthly revisions.\n\n## Method\n- Baselines, computed from vintage V as TASK.md specifies: **naive** = g_{last(V)}; **seasonal naive** = g_{T−12}; **AR(1)** = OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **Own model, `model_name` = rw_drift_yoy** (see `model_description`). This is a base-effect-aware random walk with drift on log levels. The drift μ is the mean monthly log change over the 36 months ending at last(V) in vintage V. The forecast is ĝ_T = 100·(ln L_last + h·μ − ln L_{T−12}), where the base-month level L_{T−12} is already published in vintage V. The point of the model is that YoY growth is a 12-month sum: 12−h of its monthly components are already known at the origin, and only h months need forecasting. The naive forecast instead implicitly assumes that the month dropping out of the 12-month window equals the month coming in. I fixed the specification and the 36-month window before computing any forecast error (out/state/log.md). Nothing was tuned on the evaluation sample, and no alternatives were tried.\n- Evaluation: RMSE and MAE over the 10 origins per horizon. Diebold–Mariano test of model vs naive: d_t = e²_naive − e²_model, with Bartlett long-run variance at truncation lag h−1 (h=3 weights 2/3, 1/3), no HLN factor, two-sided normal p-value. As an additional distribution-free measure, I report an exact two-sided sign test on the sign of d_t (`dm_details`).\n- Revision estimand: the mean over the 10 h=1 target months of |g^current_T − yoy_first_release_T|.\n- No randomness is used (`random_seed` = null). out/analysis.py regenerates every output deterministically.\n\n## Results\nRMSE / MAE in percentage points of YoY growth (out/table1.csv):\n\n| model | h=1 RMSE | h=1 MAE | h=3 RMSE | h=3 MAE |\n|---|---|---|---|---|\n| naive | 0.997 (`rmse_naive_h1`) | 0.849 (`mae_naive_h1`) | 1.420 (`rmse_naive_h3`) | 1.200 (`mae_naive_h3`) |\n| seasonal naive | 1.675 (`rmse_snaive_h1`) | 1.307 (`mae_snaive_h1`) | 1.316 (`rmse_snaive_h3`) | 1.066 (`mae_snaive_h3`) |\n| AR(1) | 0.941 (`rmse_ar1_h1`) | 0.798 (`mae_ar1_h1`) | 1.187 (`rmse_ar1_h3`) | 0.953 (`mae_ar1_h3`) |\n| rw_drift_yoy | 0.552 (`rmse_model_h1`) | 0.497 (`mae_model_h1`) | 1.054 (`rmse_model_h3`) | 0.747 (`mae_model_h3`) |\n\n- The model has the lowest RMSE and MAE of the four methods at both horizons.\n- Its RMSE is 44.6% lower than the naive RMSE at h=1 (`rel_improvement_rmse_h1` = 0.446) and 25.8% lower at h=3 (`rel_improvement_rmse_h3` = 0.258).\n- AR(1) beats naive only modestly. Seasonal naive is the worst method at h=1, but it beats naive at h=3.\n- Diebold–Mariano, model vs naive:\n  - h=1: DM = 2.30, p = 0.021 (`dm_stat_h1`, `dm_pvalue_h1`), giving `conclusion_code_h1` = model_better_significant.\n  - h=3: DM = 1.86, p = 0.062 (`dm_stat_h3`, `dm_pvalue_h3`), giving `conclusion_code_h3` = model_better_not_significant.\n- Revisions: the first-release YoY of the 10 h=1 target months differs from today's published YoY by 0.548 pp on average (`mean_abs_revision_h1_targets`; per-month values in `abs_revision_h1_by_target`, range 0.17–0.98). This is the same order of magnitude as the model's h=1 RMSE, so a substantial part of what a \"forecast error\" against first release measures is measurement noise in the first release relative to today's data.\n- Figure: out/figure1.png shows first-release actuals and the naive, AR(1) and model forecasts by target month, one panel per horizon.\n\n## Uncertainty\n- There are only n = 10 origins per horizon, and at h=3 the errors from neighbouring origins overlap. The DM statistic relies on asymptotic normality and on a long-run variance estimated from 10 points. With samples this small, DM tests without the HLN correction are known to over-reject, so the h=1 p-value of 0.021 (`dm_pvalue_h1`) should be read as indicative, not conclusive.\n- A distribution-free check is weaker. The model had a lower squared error than naive at 8 of 10 origins at each horizon, and the exact two-sided sign-test p-value is 0.109 for both (`dm_details`). This is not significant at 5%.\n- At h=3 the DM p-value of 0.062 (`dm_pvalue_h3`) is above 5%.\n- The evaluation window covers one short period (target months 2025-09 to 2026-09). It includes an acceleration of YoY growth to about 6.5% in 2026-05 and 2026-06, and it contains publication disruptions. The RMSE ranking could differ in other periods.\n- I computed no bootstrap interval, since 10 dependent observations would make one unreliable.\n\n## Limitations and open issues\n- **Gate conflict (structural, reported, not fixed).** The output-contract gate's `forecasts_chronology` rule (origin_date < target_date) fails for all 40 h=1 rows. TASK.md defines the target as last(V)+h and formats `target_date` as YYYY-MM-01. With month-end origins, an h=1 target month always starts before the origin date, and for the six two-month-delay origins it has ended entirely before it. No date format that respects TASK.md can satisfy this rule, so I kept the TASK.md definition (TASK.md is authoritative). Information validity is unaffected: no target month is present in the vintage used, and vintage_used ≤ origin_date for every row (out/state/checks.md, C3, C10, C13). The latest gate receipt therefore reports passed = false on this single check only.\n- With n = 10 origins, all significance statements are fragile (see Uncertainty).\n- The model is univariate. PAYEMS and UMCSENT were not used, so I cannot say whether they would help. I deliberately ran no specification search on 10 origins.\n- The actuals mix pre- and post-benchmark vintages (2026-08 and 2026-09 come from post-benchmark vintages). YoY ratios within a vintage largely cancel level shifts, but this is a possible source of extra error at the last h=3 targets.\n- The two-month publication delays at six origins mean the h=1 results partly measure backcasting, not forecasting, ability.\n- Workflow incident: a shell-directory slip briefly created a folder `data/out/` (containing only my own state notes). I removed it at once. No input file was modified, and all input SHA-256 values were re-verified afterwards (out/state/log.md).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The own model (rw_drift_yoy) has lower RMSE and MAE than all three baselines at h=1 and h=3.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rmse_snaive_h1\",\"rmse_ar1_h1\",\"rmse_model_h3\",\"rmse_naive_h3\",\"rmse_snaive_h3\",\"rmse_ar1_h3\",\"mae_model_h1\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Only 10 origins in one short period.\"},\n {\"id\":\"C2\",\"text\":\"Relative to naive, the model reduces RMSE by about 45% at h=1 and 26% at h=3.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Point estimates; sampling uncertainty is large.\"},\n {\"id\":\"C3\",\"text\":\"At h=1 the DM test rejects equal accuracy at 5% (model better); at h=3 it does not.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"conclusion_code_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\",\"conclusion_code_h3\"],\"status\":\"supported\",\"caveat\":\"Asymptotic DM without HLN correction on n=10 likely over-rejects; exact sign test p=0.109 at both horizons (dm_details) is not significant.\"},\n {\"id\":\"C4\",\"text\":\"The mean absolute revision between first-release and current YoY of the h=1 target months is about 0.55 pp, comparable to the model's h=1 RMSE.\",\"results_keys\":[\"mean_abs_revision_h1_targets\",\"rmse_model_h1\"],\"status\":\"supported\",\"caveat\":\"Current vintage includes a benchmark revision.\"},\n {\"id\":\"C5\",\"text\":\"AR(1) improves only modestly on naive, and seasonal naive is worst at h=1 but beats naive at h=3.\",\"results_keys\":[\"rmse_ar1_h1\",\"rmse_ar1_h3\",\"rmse_snaive_h1\",\"rmse_snaive_h3\",\"rmse_naive_h1\",\"rmse_naive_h3\"],\"status\":\"supported\",\"caveat\":\"10 origins.\"},\n {\"id\":\"C6\",\"text\":\"Publication delays: six origins (2025-10-31 to 2026-03-31) had a two-month delay, the last four a one-month delay.\",\"results_keys\":[\"real_time_design\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\necho done","description":"Write report and claims 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> out/report.md <<'EOF'\n# Real-time forecasting of US retail-sales YoY growth from ALFRED vintages (T1-forecast)\n\n## Question\nUsing only the data published at each of 10 month-end forecast origins (2025-10-31 … 2026-07-31; `first_origin`, `last_origin`, `n_origins`), how well can the year-over-year log growth of US retail and food services sales (RSAFS), g_m = 100·ln(L_m/L_{m−12}), be forecast 1 and 3 months beyond the last published month, and does a model of my own beat the naive no-change forecast when errors are measured against the first-release YoY of each target month?\n\n## Data and exclusions\n- Inputs: 10 RSAFS and 10 PAYEMS ALFRED vintages (one per origin), UMCSENT (current vintage), RSAFS_current, first_release.csv. Row counts are in `n_input_rows`; every SHA-256 matched `data/README.md` (out/state/data_audit.md).\n- **Real-time design.** The forecast at origin V uses only the RSAFS vintage dated V (`vintage_used` = `origin_date` for all 80 rows of out/forecasts.csv). Targets are last(V)+h, where last(V) is the last month in that vintage. Publication delays seen in the vintages: at the six origins 2025-10-31 … 2026-03-31 the last published month is two months before the origin month (last(V) = 2025-08 … 2026-01). The 2026-04-30 vintage adds two months at once (2026-02, 2026-03), and the last four origins have the usual one-month delay (last(V) = 2026-03 … 2026-06). Per-origin last(V) and delay are stored in `real_time_design`. Because of this, month 2026-03 is never an h=1 target, and for the first six origins the h=1 \"forecast\" is really a backcast of a month that had already ended but was not yet published.\n- Actuals: `yoy_first_release` from first_release.csv. Where the first-release vintage is one of the shipped vintages, its values match that vintage exactly (check C4).\n- RSAFS_current is used only for the revision estimand. PAYEMS and UMCSENT are read (for row counts) but are not used by any forecast.\n- Exclusions (out/exclusions.csv): none. All 80 forecast–actual pairs (10 origins × 2 horizons × 4 models) have an actual. `n_analysed` = 20 model pairs (10 per horizon).\n- Data issues found, with how each was handled, are listed in `data_issues`: irregular publication timing; PAYEMS gaps and a benchmark revision; pre-1978 UMCSENT gaps; a benchmark revision in RSAFS_current that also affects the first-release levels of 2026-08 and 2026-09; and routine monthly revisions.\n\n## Method\n- Baselines, computed from vintage V as TASK.md specifies: **naive** = g_{last(V)}; **seasonal naive** = g_{T−12}; **AR(1)** = OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **Own model, `model_name` = rw_drift_yoy** (see `model_description`). This is a base-effect-aware random walk with drift on log levels. The drift μ is the mean monthly log change over the 36 months ending at last(V) in vintage V. The forecast is ĝ_T = 100·(ln L_last + h·μ − ln L_{T−12}), where the base-month level L_{T−12} is already published in vintage V. The point of the model is that YoY growth is a 12-month sum: 12−h of its monthly components are already known at the origin, and only h months need forecasting. The naive forecast instead implicitly assumes that the month dropping out of the 12-month window equals the month coming in. I fixed the specification and the 36-month window before computing any forecast error (out/state/log.md). Nothing was tuned on the evaluation sample, and no alternatives were tried.\n- Evaluation: RMSE and MAE over the 10 origins per horizon. Diebold–Mariano test of model vs naive: d_t = e²_naive − e²_model, with Bartlett long-run variance at truncation lag h−1 (h=3 weights 2/3, 1/3), no HLN factor, two-sided normal p-value. As an additional distribution-free measure, I report an exact two-sided sign test on the sign of d_t (`dm_details`).\n- Revision estimand: the mean over the 10 h=1 target months of |g^current_T − yoy_first_release_T|.\n- No randomness is used (`random_seed` = null). out/analysis.py regenerates every output deterministically.\n\n## Results\nRMSE / MAE in percentage points of YoY growth (out/table1.csv):\n\n| model | h=1 RMSE | h=1 MAE | h=3 RMSE | h=3 MAE |\n|---|---|---|---|---|\n| naive | 0.997 (`rmse_naive_h1`) | 0.849 (`mae_naive_h1`) | 1.420 (`rmse_naive_h3`) | 1.200 (`mae_naive_h3`) |\n| seasonal naive | 1.675 (`rmse_snaive_h1`) | 1.307 (`mae_snaive_h1`) | 1.316 (`rmse_snaive_h3`) | 1.066 (`mae_snaive_h3`) |\n| AR(1) | 0.941 (`rmse_ar1_h1`) | 0.798 (`mae_ar1_h1`) | 1.187 (`rmse_ar1_h3`) | 0.953 (`mae_ar1_h3`) |\n| rw_drift_yoy | 0.552 (`rmse_model_h1`) | 0.497 (`mae_model_h1`) | 1.054 (`rmse_model_h3`) | 0.747 (`mae_model_h3`) |\n\n- The model has the lowest RMSE and MAE of the four methods at both horizons.\n- Its RMSE is 44.6% lower than the naive RMSE at h=1 (`rel_improvement_rmse_h1` = 0.446) and 25.8% lower at h=3 (`rel_improvement_rmse_h3` = 0.258).\n- AR(1) beats naive only modestly. Seasonal naive is the worst method at h=1, but it beats naive at h=3.\n- Diebold–Mariano, model vs naive:\n  - h=1: DM = 2.30, p = 0.021 (`dm_stat_h1`, `dm_pvalue_h1`), giving `conclusion_code_h1` = model_better_significant.\n  - h=3: DM = 1.86, p = 0.062 (`dm_stat_h3`, `dm_pvalue_h3`), giving `conclusion_code_h3` = model_better_not_significant.\n- Revisions: the first-release YoY of the 10 h=1 target months differs from today's published YoY by 0.548 pp on average (`mean_abs_revision_h1_targets`; per-month values in `abs_revision_h1_by_target`, range 0.17–0.98). This is the same order of magnitude as the model's h=1 RMSE, so a substantial part of what a \"forecast error\" against first release measures is measurement noise in the first release relative to today's data.\n- Figure: out/figure1.png shows first-release actuals and the naive, AR(1) and model forecasts by target month, one panel per horizon.\n\n## Uncertainty\n- There are only n = 10 origins per horizon, and at h=3 the errors from neighbouring origins overlap. The DM statistic relies on asymptotic normality and on a long-run variance estimated from 10 points. With samples this small, DM tests without the HLN correction are known to over-reject, so the h=1 p-value of 0.021 (`dm_pvalue_h1`) should be read as indicative, not conclusive.\n- A distribution-free check is weaker. The model had a lower squared error than naive at 8 of 10 origins at each horizon, and the exact two-sided sign-test p-value is 0.109 for both (`dm_details`). This is not significant at 5%.\n- At h=3 the DM p-value of 0.062 (`dm_pvalue_h3`) is above 5%.\n- The evaluation window covers one short period (target months 2025-09 to 2026-09). It includes an acceleration of YoY growth to about 6.5% in 2026-05 and 2026-06, and it contains publication disruptions. The RMSE ranking could differ in other periods.\n- I computed no bootstrap interval, since 10 dependent observations would make one unreliable.\n\n## Limitations and open issues\n- **Gate conflict (structural, reported, not fixed).** The output-contract gate's `forecasts_chronology` rule (origin_date < target_date) fails for all 40 h=1 rows. TASK.md defines the target as last(V)+h and formats `target_date` as YYYY-MM-01. With month-end origins, an h=1 target month always starts before the origin date, and for the six two-month-delay origins it has ended entirely before it. No date format that respects TASK.md can satisfy this rule, so I kept the TASK.md definition (TASK.md is authoritative). Information validity is unaffected: no target month is present in the vintage used, and vintage_used ≤ origin_date for every row (out/state/checks.md, C3, C10, C13). The latest gate receipt therefore reports passed = false on this single check only.\n- With n = 10 origins, all significance statements are fragile (see Uncertainty).\n- The model is univariate. PAYEMS and UMCSENT were not used, so I cannot say whether they would help. I deliberately ran no specification search on 10 origins.\n- The actuals mix pre- and post-benchmark vintages (2026-08 and 2026-09 come from post-benchmark vintages). YoY ratios within a vintage largely cancel level shifts, but this is a possible source of extra error at the last h=3 targets.\n- The two-month publication delays at six origins mean the h=1 results partly measure backcasting, not forecasting, ability.\n- Workflow incident: a shell-directory slip briefly created a folder `data/out/` (containing only my own state notes). I removed it at once. No input file was modified, and all input SHA-256 values were re-verified afterwards (out/state/log.md).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The own model (rw_drift_yoy) has lower RMSE and MAE than all three baselines at h=1 and h=3.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rmse_snaive_h1\",\"rmse_ar1_h1\",\"rmse_model_h3\",\"rmse_naive_h3\",\"rmse_snaive_h3\",\"rmse_ar1_h3\",\"mae_model_h1\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Only 10 origins in one short period.\"},\n {\"id\":\"C2\",\"text\":\"Relative to naive, the model reduces RMSE by about 45% at h=1 and 26% at h=3.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Point estimates; sampling uncertainty is large.\"},\n {\"id\":\"C3\",\"text\":\"At h=1 the DM test rejects equal accuracy at 5% (model better); at h=3 it does 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without HLN correction on n=10 likely over-rejects; exact sign test p=0.109 at both horizons (dm_details) is not significant.\"},\n {\"id\":\"C4\",\"text\":\"The mean absolute revision between first-release and current YoY of the h=1 target months is about 0.55 pp, comparable to the model's h=1 RMSE.\",\"results_keys\":[\"mean_abs_revision_h1_targets\",\"rmse_model_h1\"],\"status\":\"supported\",\"caveat\":\"Current vintage includes a benchmark revision.\"},\n {\"id\":\"C5\",\"text\":\"AR(1) improves only modestly on naive, and seasonal naive is worst at h=1 but beats naive at h=3.\",\"results_keys\":[\"rmse_ar1_h1\",\"rmse_ar1_h3\",\"rmse_snaive_h1\",\"rmse_snaive_h3\",\"rmse_naive_h1\",\"rmse_naive_h3\"],\"status\":\"supported\",\"caveat\":\"10 origins.\"},\n {\"id\":\"C6\",\"text\":\"Publication delays: six origins (2025-10-31 to 2026-03-31) had a two-month delay, the last four a one-month delay.\",\"results_keys\":[\"real_time_design\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write claims ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":288,"cache_read_input_tokens":49136,"output_tokens":1039,"output_tokens_details":{"thinking_tokens":34},"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":288},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt67TULSyBSyzQiEeMP","requestRef":"4eceb65c-d583-4136-bbf7-a17e74b508fb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a607c4a4-73c9-4e74-85ef-0aa95a202626","timestamp":"2026-10-30T05:37:47.519Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}122{"parentUuid":"a607c4a4-73c9-4e74-85ef-0aa95a202626","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01586ta7svA1DPgPP3UM9LQf","type":"tool_result","content":"ok","is_error":false}]},"uuid":"2cfa74e2-86cf-46b6-a8c0-c2559ea2004c","timestamp":"2026-10-30T05:37:47.567Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a607c4a4-73c9-4e74-85ef-0aa95a202626","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}123{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"d2d294ed4f6a119f830f27edc55accf63abcf6d543fa8356aa5a36b4dfb16fdf","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01586ta7svA1DPgPP3UM9LQf","name":"Bash","input":{"command":"cat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The own model (rw_drift_yoy) has lower RMSE and MAE than all three baselines at h=1 and h=3.\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"rmse_snaive_h1\",\"rmse_ar1_h1\",\"rmse_model_h3\",\"rmse_naive_h3\",\"rmse_snaive_h3\",\"rmse_ar1_h3\",\"mae_model_h1\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Only 10 origins in one short period.\"},\n {\"id\":\"C2\",\"text\":\"Relative to naive, the model reduces RMSE by about 45% at h=1 and 26% at h=3.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Point estimates; sampling uncertainty is large.\"},\n {\"id\":\"C3\",\"text\":\"At h=1 the DM test rejects equal accuracy at 5% (model better); at h=3 it does not.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"conclusion_code_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\",\"conclusion_code_h3\"],\"status\":\"supported\",\"caveat\":\"Asymptotic DM without HLN correction on n=10 likely over-rejects; exact sign test p=0.109 at both horizons (dm_details) is not significant.\"},\n {\"id\":\"C4\",\"text\":\"The mean absolute revision between first-release and current YoY of the h=1 target months is about 0.55 pp, comparable to the model's h=1 RMSE.\",\"results_keys\":[\"mean_abs_revision_h1_targets\",\"rmse_model_h1\"],\"status\":\"supported\",\"caveat\":\"Current vintage includes a benchmark revision.\"},\n {\"id\":\"C5\",\"text\":\"AR(1) improves only modestly on naive, and seasonal naive is worst at h=1 but beats naive at h=3.\",\"results_keys\":[\"rmse_ar1_h1\",\"rmse_ar1_h3\",\"rmse_snaive_h1\",\"rmse_snaive_h3\",\"rmse_naive_h1\",\"rmse_naive_h3\"],\"status\":\"supported\",\"caveat\":\"10 origins.\"},\n {\"id\":\"C6\",\"text\":\"Publication delays: six origins (2025-10-31 to 2026-03-31) had a two-month delay, the last four a one-month delay.\",\"results_keys\":[\"real_time_design\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'));print('ok')\"","description":"Write claims ledger"}}]}}124{"type":"api-request","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","id":"c96f4533-d565-4903-ad69-d342b5572d61","timestamp":"2026-10-30T05:37:47.571Z","version":"2.1.314","querySource":"sdk","shapeHash":"81de0bee269150a4a5258c71378fcacd4dd07a9cce8c76ff7b371b779847bd10","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":"4eceb65c-d583-4136-bbf7-a17e74b508fb","keep":40,"tail":["d2d294ed4f6a119f830f27edc55accf63abcf6d543fa8356aa5a36b4dfb16fdf","d595191d2abfad3c9b164bc1ebcadfec228e68040e5d903083a1fe5d0bbe1e1a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",41,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b96223f85872951e2aa3a5acf81c4483b1d5f855cc05579e0c4e6ebf87697ac8"}125{"parentUuid":"2cfa74e2-86cf-46b6-a8c0-c2559ea2004c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt6hSPJpv2WRVQmymCt","type":"message","role":"assistant","content":[{"type":"text","text":"Now the report (via the Bash heredoc from root):"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1017,"cache_read_input_tokens":49424,"output_tokens":3587,"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":1017},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXt6h9XGSaRK7FZXz4yJ","requestRef":"c96f4533-d565-4903-ad69-d342b5572d61","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cbd305a3-e6d0-437d-86cf-9c97193fdf8f","timestamp":"2026-10-30T05:37:48.811Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}126{"parentUuid":"cbd305a3-e6d0-437d-86cf-9c97193fdf8f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXt6hSPJpv2WRVQmymCt","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0116tawyLbJM8Qqp3mLjQ1ZR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Real-time forecasting of US retail-sales YoY growth from ALFRED vintages (T1-forecast)\n\n## Question\nUsing only the data published at each of 10 month-end forecast origins (2025-10-31 … 2026-07-31; `first_origin`, `last_origin`, `n_origins`), how well can the year-over-year log growth of US retail and food services sales (RSAFS), g_m = 100·ln(L_m/L_{m−12}), be forecast 1 and 3 months beyond the last published month, and does a model of my own beat the naive no-change forecast when errors are measured against the first-release YoY of each target month?\n\n## Data and exclusions\n- Inputs: 10 RSAFS and 10 PAYEMS ALFRED vintages (one per origin), UMCSENT (current vintage), RSAFS_current, first_release.csv. Row counts are in `n_input_rows`; every SHA-256 matched `data/README.md` (out/state/data_audit.md).\n- **Real-time design.** The forecast at origin V uses only the RSAFS vintage dated V (`vintage_used` = `origin_date` for all 80 rows of out/forecasts.csv). Targets are last(V)+h, where last(V) is the last month in that vintage. Publication delays seen in the vintages: at the six origins 2025-10-31 … 2026-03-31 the last published month is two months before the origin month (last(V) = 2025-08 … 2026-01). The 2026-04-30 vintage adds two months at once (2026-02, 2026-03), and the last four origins have the usual one-month delay (last(V) = 2026-03 … 2026-06). Per-origin last(V) and delay are stored in `real_time_design`. Because of this, month 2026-03 is never an h=1 target, and for the first six origins the h=1 \"forecast\" is really a backcast of a month that had already ended but was not yet published.\n- Actuals: `yoy_first_release` from first_release.csv. Where the first-release vintage is one of the shipped vintages, its values match that vintage exactly (check C4).\n- RSAFS_current is used only for the revision estimand. PAYEMS and UMCSENT are read (for row counts) but are not used by any forecast.\n- Exclusions (out/exclusions.csv): none. All 80 forecast–actual pairs (10 origins × 2 horizons × 4 models) have an actual. `n_analysed` = 20 model pairs (10 per horizon).\n- Data issues found, with how each was handled, are listed in `data_issues`: irregular publication timing; PAYEMS gaps and a benchmark revision; pre-1978 UMCSENT gaps; a benchmark revision in RSAFS_current that also affects the first-release levels of 2026-08 and 2026-09; and routine monthly revisions.\n\n## Method\n- Baselines, computed from vintage V as TASK.md specifies: **naive** = g_{last(V)}; **seasonal naive** = g_{T−12}; **AR(1)** = OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **Own model, `model_name` = rw_drift_yoy** (see `model_description`). This is a base-effect-aware random walk with drift on log levels. The drift μ is the mean monthly log change over the 36 months ending at last(V) in vintage V. The forecast is ĝ_T = 100·(ln L_last + h·μ − ln L_{T−12}), where the base-month level L_{T−12} is already published in vintage V. The point of the model is that YoY growth is a 12-month sum: 12−h of its monthly components are already known at the origin, and only h months need forecasting. The naive forecast instead implicitly assumes that the month dropping out of the 12-month window equals the month coming in. I fixed the specification and the 36-month window before computing any forecast error (out/state/log.md). Nothing was tuned on the evaluation sample, and no alternatives were tried.\n- Evaluation: RMSE and MAE over the 10 origins per horizon. Diebold–Mariano test of model vs naive: d_t = e²_naive − e²_model, with Bartlett long-run variance at truncation lag h−1 (h=3 weights 2/3, 1/3), no HLN factor, two-sided normal p-value. As an additional distribution-free measure, I report an exact two-sided sign test on the sign of d_t (`dm_details`).\n- Revision estimand: the mean over the 10 h=1 target months of |g^current_T − yoy_first_release_T|.\n- No randomness is used (`random_seed` = null). out/analysis.py regenerates every output deterministically.\n\n## Results\nRMSE / MAE in percentage points of YoY growth (out/table1.csv):\n\n| model | h=1 RMSE | h=1 MAE | h=3 RMSE | h=3 MAE |\n|---|---|---|---|---|\n| naive | 0.997 (`rmse_naive_h1`) | 0.849 (`mae_naive_h1`) | 1.420 (`rmse_naive_h3`) | 1.200 (`mae_naive_h3`) |\n| seasonal naive | 1.675 (`rmse_snaive_h1`) | 1.307 (`mae_snaive_h1`) | 1.316 (`rmse_snaive_h3`) | 1.066 (`mae_snaive_h3`) |\n| AR(1) | 0.941 (`rmse_ar1_h1`) | 0.798 (`mae_ar1_h1`) | 1.187 (`rmse_ar1_h3`) | 0.953 (`mae_ar1_h3`) |\n| rw_drift_yoy | 0.552 (`rmse_model_h1`) | 0.497 (`mae_model_h1`) | 1.054 (`rmse_model_h3`) | 0.747 (`mae_model_h3`) |\n\n- The model has the lowest RMSE and MAE of the four methods at both horizons.\n- Its RMSE is 44.6% lower than the naive RMSE at h=1 (`rel_improvement_rmse_h1` = 0.446) and 25.8% lower at h=3 (`rel_improvement_rmse_h3` = 0.258).\n- AR(1) beats naive only modestly. Seasonal naive is the worst method at h=1, but it beats naive at h=3.\n- Diebold–Mariano, model vs naive:\n  - h=1: DM = 2.30, p = 0.021 (`dm_stat_h1`, `dm_pvalue_h1`), giving `conclusion_code_h1` = model_better_significant.\n  - h=3: DM = 1.86, p = 0.062 (`dm_stat_h3`, `dm_pvalue_h3`), giving `conclusion_code_h3` = model_better_not_significant.\n- Revisions: the first-release YoY of the 10 h=1 target months differs from today's published YoY by 0.548 pp on average (`mean_abs_revision_h1_targets`; per-month values in `abs_revision_h1_by_target`, range 0.17–0.98). This is the same order of magnitude as the model's h=1 RMSE, so a substantial part of what a \"forecast error\" against first release measures is measurement noise in the first release relative to today's data.\n- Figure: out/figure1.png shows first-release actuals and the naive, AR(1) and model forecasts by target month, one panel per horizon.\n\n## Uncertainty\n- There are only n = 10 origins per horizon, and at h=3 the errors from neighbouring origins overlap. The DM statistic relies on asymptotic normality and on a long-run variance estimated from 10 points. With samples this small, DM tests without the HLN correction are known to over-reject, so the h=1 p-value of 0.021 (`dm_pvalue_h1`) should be read as indicative, not conclusive.\n- A distribution-free check is weaker. The model had a lower squared error than naive at 8 of 10 origins at each horizon, and the exact two-sided sign-test p-value is 0.109 for both (`dm_details`). This is not significant at 5%.\n- At h=3 the DM p-value of 0.062 (`dm_pvalue_h3`) is above 5%.\n- The evaluation window covers one short period (target months 2025-09 to 2026-09). It includes an acceleration of YoY growth to about 6.5% in 2026-05 and 2026-06, and it contains publication disruptions. The RMSE ranking could differ in other periods.\n- I computed no bootstrap interval, since 10 dependent observations would make one unreliable.\n\n## Limitations and open issues\n- **Gate conflict (structural, reported, not fixed).** The output-contract gate's `forecasts_chronology` rule (origin_date < target_date) fails for all 40 h=1 rows. TASK.md defines the target as last(V)+h and formats `target_date` as YYYY-MM-01. With month-end origins, an h=1 target month always starts before the origin date, and for the six two-month-delay origins it has ended entirely before it. No date format that respects TASK.md can satisfy this rule, so I kept the TASK.md definition (TASK.md is authoritative). Information validity is unaffected: no target month is present in the vintage used, and vintage_used ≤ origin_date for every row (out/state/checks.md, C3, C10, C13). The latest gate receipt therefore reports passed = false on this single check only.\n- With n = 10 origins, all significance statements are fragile (see Uncertainty).\n- The model is univariate. PAYEMS and UMCSENT were not used, so I cannot say whether they would help. I deliberately ran no specification search on 10 origins.\n- The actuals mix pre- and post-benchmark vintages (2026-08 and 2026-09 come from post-benchmark vintages). YoY ratios within a vintage largely cancel level shifts, but this is a possible source of extra error at the last h=3 targets.\n- The two-month publication delays at six origins mean the h=1 results partly measure backcasting, not forecasting, ability.\n- Workflow incident: a shell-directory slip briefly created a folder `data/out/` (containing only my own state notes). I removed it at once. No input file was modified, and all input SHA-256 values were re-verified afterwards (out/state/log.md).\nEOF\nwc -l out/report.md","description":"Write report"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1017,"cache_read_input_tokens":49424,"output_tokens":3587,"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":1017},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXt6h9XGSaRK7FZXz4yJ","requestRef":"c96f4533-d565-4903-ad69-d342b5572d61","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"426ce908-1a7d-429c-b5e0-3f049dadd950","timestamp":"2026-10-30T05:38:11.119Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}127{"parentUuid":"426ce908-1a7d-429c-b5e0-3f049dadd950","isSidechain":false,"promptId":"638bff5e-d6a2-42cf-aa08-19291c10cdad","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0116tawyLbJM8Qqp3mLjQ1ZR","type":"tool_result","content":"      53 out/report.md","is_error":false}]},"uuid":"a52ce8e1-3579-4c6c-99f4-ae070d367948","timestamp":"2026-10-30T05:38:11.151Z","toolUseResult":{"stdout":"      53 out/report.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"426ce908-1a7d-429c-b5e0-3f049dadd950","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}128{"type":"api-request-blob","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","hash":"0fcb70451947f52250d949e4a6523eb69744e37b5602ea9df5c02c8febb34839","message":{"role":"assistant","content":[{"type":"text","text":"Now the report (via the Bash heredoc from root):"},{"type":"tool_use","id":"toolu_0116tawyLbJM8Qqp3mLjQ1ZR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Real-time forecasting of US retail-sales YoY growth from ALFRED vintages (T1-forecast)\n\n## Question\nUsing only the data published at each of 10 month-end forecast origins (2025-10-31 … 2026-07-31; `first_origin`, `last_origin`, `n_origins`), how well can the year-over-year log growth of US retail and food services sales (RSAFS), g_m = 100·ln(L_m/L_{m−12}), be forecast 1 and 3 months beyond the last published month, and does a model of my own beat the naive no-change forecast when errors are measured against the first-release YoY of each target month?\n\n## Data and exclusions\n- Inputs: 10 RSAFS and 10 PAYEMS ALFRED vintages (one per origin), UMCSENT (current vintage), RSAFS_current, first_release.csv. Row counts are in `n_input_rows`; every SHA-256 matched `data/README.md` (out/state/data_audit.md).\n- **Real-time design.** The forecast at origin V uses only the RSAFS vintage dated V (`vintage_used` = `origin_date` for all 80 rows of out/forecasts.csv). Targets are last(V)+h, where last(V) is the last month in that vintage. Publication delays seen in the vintages: at the six origins 2025-10-31 … 2026-03-31 the last published month is two months before the origin month (last(V) = 2025-08 … 2026-01). The 2026-04-30 vintage adds two months at once (2026-02, 2026-03), and the last four origins have the usual one-month delay (last(V) = 2026-03 … 2026-06). Per-origin last(V) and delay are stored in `real_time_design`. Because of this, month 2026-03 is never an h=1 target, and for the first six origins the h=1 \"forecast\" is really a backcast of a month that had already ended but was not yet published.\n- Actuals: `yoy_first_release` from first_release.csv. Where the first-release vintage is one of the shipped vintages, its values match that vintage exactly (check C4).\n- RSAFS_current is used only for the revision estimand. PAYEMS and UMCSENT are read (for row counts) but are not used by any forecast.\n- Exclusions (out/exclusions.csv): none. All 80 forecast–actual pairs (10 origins × 2 horizons × 4 models) have an actual. `n_analysed` = 20 model pairs (10 per horizon).\n- Data issues found, with how each was handled, are listed in `data_issues`: irregular publication timing; PAYEMS gaps and a benchmark revision; pre-1978 UMCSENT gaps; a benchmark revision in RSAFS_current that also affects the first-release levels of 2026-08 and 2026-09; and routine monthly revisions.\n\n## Method\n- Baselines, computed from vintage V as TASK.md specifies: **naive** = g_{last(V)}; **seasonal naive** = g_{T−12}; **AR(1)** = OLS of g_m on a constant and g_{m−1} for 1993-02 ≤ m ≤ last(V), iterated h steps.\n- **Own model, `model_name` = rw_drift_yoy** (see `model_description`). This is a base-effect-aware random walk with drift on log levels. The drift μ is the mean monthly log change over the 36 months ending at last(V) in vintage V. The forecast is ĝ_T = 100·(ln L_last + h·μ − ln L_{T−12}), where the base-month level L_{T−12} is already published in vintage V. The point of the model is that YoY growth is a 12-month sum: 12−h of its monthly components are already known at the origin, and only h months need forecasting. The naive forecast instead implicitly assumes that the month dropping out of the 12-month window equals the month coming in. I fixed the specification and the 36-month window before computing any forecast error (out/state/log.md). Nothing was tuned on the evaluation sample, and no alternatives were tried.\n- Evaluation: RMSE and MAE over the 10 origins per horizon. Diebold–Mariano test of model vs naive: d_t = e²_naive − e²_model, with Bartlett long-run variance at truncation lag h−1 (h=3 weights 2/3, 1/3), no HLN factor, two-sided normal p-value. As an additional distribution-free measure, I report an exact two-sided sign test on the sign of d_t (`dm_details`).\n- Revision estimand: the mean over the 10 h=1 target months of |g^current_T − yoy_first_release_T|.\n- No randomness is used (`random_seed` = null). out/analysis.py regenerates every output deterministically.\n\n## Results\nRMSE / MAE in percentage points of YoY growth (out/table1.csv):\n\n| model | h=1 RMSE | h=1 MAE | h=3 RMSE | h=3 MAE |\n|---|---|---|---|---|\n| naive | 0.997 (`rmse_naive_h1`) | 0.849 (`mae_naive_h1`) | 1.420 (`rmse_naive_h3`) | 1.200 (`mae_naive_h3`) |\n| seasonal naive | 1.675 (`rmse_snaive_h1`) | 1.307 (`mae_snaive_h1`) | 1.316 (`rmse_snaive_h3`) | 1.066 (`mae_snaive_h3`) |\n| AR(1) | 0.941 (`rmse_ar1_h1`) | 0.798 (`mae_ar1_h1`) | 1.187 (`rmse_ar1_h3`) | 0.953 (`mae_ar1_h3`) |\n| rw_drift_yoy | 0.552 (`rmse_model_h1`) | 0.497 (`mae_model_h1`) | 1.054 (`rmse_model_h3`) | 0.747 (`mae_model_h3`) |\n\n- The model has the lowest RMSE and MAE of the four methods at both horizons.\n- Its RMSE is 44.6% lower than the naive RMSE at h=1 (`rel_improvement_rmse_h1` = 0.446) and 25.8% lower at h=3 (`rel_improvement_rmse_h3` = 0.258).\n- AR(1) beats naive only modestly. Seasonal naive is the worst method at h=1, but it beats naive at h=3.\n- Diebold–Mariano, model vs naive:\n  - h=1: DM = 2.30, p = 0.021 (`dm_stat_h1`, `dm_pvalue_h1`), giving `conclusion_code_h1` = model_better_significant.\n  - h=3: DM = 1.86, p = 0.062 (`dm_stat_h3`, `dm_pvalue_h3`), giving `conclusion_code_h3` = model_better_not_significant.\n- Revisions: the first-release YoY of the 10 h=1 target months differs from today's published YoY by 0.548 pp on average (`mean_abs_revision_h1_targets`; per-month values in `abs_revision_h1_by_target`, range 0.17–0.98). This is the same order of magnitude as the model's h=1 RMSE, so a substantial part of what a \"forecast error\" against first release measures is measurement noise in the first release relative to today's data.\n- Figure: out/figure1.png shows first-release actuals and the naive, AR(1) and model forecasts by target month, one panel per horizon.\n\n## Uncertainty\n- There are only n = 10 origins per horizon, and at h=3 the errors from neighbouring origins overlap. The DM statistic relies on asymptotic normality and on a long-run variance estimated from 10 points. With samples this small, DM tests without the HLN correction are known to over-reject, so the h=1 p-value of 0.021 (`dm_pvalue_h1`) should be read as indicative, not conclusive.\n- A distribution-free check is weaker. The model had a lower squared error than naive at 8 of 10 origins at each horizon, and the exact two-sided sign-test p-value is 0.109 for both (`dm_details`). This is not significant at 5%.\n- At h=3 the DM p-value of 0.062 (`dm_pvalue_h3`) is above 5%.\n- The evaluation window covers one short period (target months 2025-09 to 2026-09). It includes an acceleration of YoY growth to about 6.5% in 2026-05 and 2026-06, and it contains publication disruptions. The RMSE ranking could differ in other periods.\n- I computed no bootstrap interval, since 10 dependent observations would make one unreliable.\n\n## Limitations and open issues\n- **Gate conflict (structural, reported, not fixed).** The output-contract gate's `forecasts_chronology` rule (origin_date < target_date) fails for all 40 h=1 rows. TASK.md defines the target as last(V)+h and formats `target_date` as YYYY-MM-01. With month-end origins, an h=1 target month always starts before the origin date, and for the six two-month-delay origins it has ended entirely before it. No date format that respects TASK.md can satisfy this rule, so I kept the TASK.md definition (TASK.md is authoritative). Information validity is unaffected: no target month is present in the vintage used, and vintage_used ≤ origin_date for every row (out/state/checks.md, C3, C10, C13). The latest gate receipt therefore reports passed = false on this single check only.\n- With n = 10 origins, all significance statements are fragile (see Uncertainty).\n- The model is univariate. PAYEMS and UMCSENT were not used, so I cannot say whether they would help. I deliberately ran no specification search on 10 origins.\n- The actuals mix pre- and post-benchmark vintages (2026-08 and 2026-09 come from post-benchmark vintages). YoY ratios within a vintage largely cancel level shifts, but this is a possible source of extra error at the last h=3 targets.\n- The two-month publication delays at six origins mean the h=1 results partly measure backcasting, not forecasting, ability.\n- Workflow incident: a shell-directory slip briefly created a folder `data/out/` (containing only my own state notes). I removed it at once. No input file was modified, and all input SHA-256 values were re-verified afterwards (out/state/log.md).\nEOF\nwc -l out/report.md","description":"Write 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about 6% (h=1) and 16% (h=3) in RMSE, and/' out/claims.json\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np={\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of 23 inputs computed, all match README\",\"row/col counts, date ranges, NA, duplicates, gaps recorded (data_audit.md)\",\"exclusion ledger written (0 removed of 80 pairs)\",\"availability: RSAFS_current only for (d); UMCSENT/PAYEMS unused\"],\"notes\":\"data/out accidentally created and removed; inputs re-verified\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"naive, snaive, ar1 RMSE/MAE computed under required keys\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes all outputs\",\"model fixed a priori (log.md)\",\"no randomness; random_seed null\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"C1 clean rerun identical SHA-256\",\"C2-C12 consistency checks pass (checks.md)\",\"C13 gate forecasts_chronology FAIL: structural conflict with TASK.md target definition, documented\",\"gate run 1 receipt out/state/gate_receipts/20261030T053554.295492Z.json (failed: report/claims missing + chronology)\"],\"notes\":\"Passed with one documented structural gate failure\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json written, keys exist\",\"final gate run recorded below\"],\"notes\":\"\"}}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Update phases ledger and run 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Not altering TASK.md-defined targets.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | head -1; ls data","description":"Record final receipt and re-run 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GATE pgar_gate/1.0 — FAILED (1 of 65 checks failed)\nfirst_release.csv\nREADME.md\nRSAFS_current.csv\nUMCSENT.csv\nvintages","is_error":false}]},"uuid":"0117dd5b-4e6f-4ee2-94d8-54ef4b1b166d","timestamp":"2026-10-30T05:38:37.480Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (1 of 65 checks 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errors are percentage points of year-over-year growth, measured against the first-release value):\n\n| model | RMSE h=1 | RMSE h=3 |\n|---|---|---|\n| naive | 0.997 | 1.420 |\n| seasonal naive | 1.675 | 1.316 |\n| AR(1) | 0.941 | 1.187 |\n| my model | 0.552 | 1.054 |\n\n- **My model** is a random walk with drift on log levels. It forecasts only the last h monthly changes, because the other 12−h months of the year-over-year figure are already published at the origin. I fixed it before computing any forecast error. It uses only the retail-sales vintage of each origin, not the employment or sentiment series.\n- **Gain over naive:** RMSE is 44.6% lower at h=1 and 25.8% lower at h=3.\n- **Diebold–Mariano test:** at h=1, DM = 2.30 with p = 0.021, so the model is significantly better at 5%. At h=3, DM = 1.86 with p = 0.062, so it is better but not significant.\n- **Caveat on significance:** with only 10 origins this test tends to over-reject. A simpler sign test finds the model better at 8 of 10 origins at each horizon, with p = 0.109, which is not significant.\n- **Revisions:** today's growth figures differ from the first-release ones by 0.548 points on average for the h=1 target months. That is about the size of the model's h=1 error, so part of what counts as forecast error here is noise in the first release.\n- **Publication delays:** at the first six origins the last published month was two months old, not one. For those origins the h=1 \"forecast\" is really an estimate of a month that had already ended but wasn't yet published.\n\n**The gate failure:** 64 of 65 checks pass. The one that fails requires every target date to fall after its origin date. All 40 h=1 rows break that rule, because `TASK.md` defines the target as the last published month + h, written as the first day of the month, and the origins are month-ends. No date format that follows `TASK.md` passes it. The forecasts still use only data available at each origin (no target month is in the vintage used). 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message came through as just \"The\" for the third time, so I can't tell what you're asking. The text seems to be cut off before it sends.\n\nI checked whether the separate reviewer's file `out/review/review.json` exists yet: it doesn't, so there are no review issues to address. Nothing has changed since my last update. All outputs are in `out/`, and the gate passes 64 of 65 checks. The one failure is the target-date check, which can't pass without departing from how `TASK.md` defines targets.\n\nIf typing a full message isn't working, a single keyword is enough:\n- **\"gate\"**: make the gate pass by changing the target dates, even though that departs from `TASK.md`.\n- **\"review\"**: deal with reviewer issues once the file appears.\n- **\"done\"**: no further action needed."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":171,"cache_read_input_tokens":58425,"output_tokens":255,"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":171},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXtCUgr3oXTa72hLZYaW","requestRef":"af06167a-acd5-480d-acaa-9264bfab7835","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"379d3614-50eb-4822-818a-a3c7ca4c3316","timestamp":"2026-10-30T05:39:09.518Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RT0010","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","version":"2.1.314","gitBranch":"HEAD"}186{"type":"last-prompt","lastPrompt":"The","leafUuid":"379d3614-50eb-4822-818a-a3c7ca4c3316","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0"}187{"type":"cost-state","sessionId":"721ea5bf-1b50-4bde-86b2-8b03078b40c0","totalCostUSD":1.1483454000000002,"totalAPIDuration":297436,"totalAPIDurationWithoutRetries":297414,"totalToolDuration":13856,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":311598,"startTime":1793338433575,"modelUsage":{"claude-opus-5-5":{"inputTokens":68,"outputTokens":31945,"thinkingTokens":5065,"cacheReadInputTokens":1118567,"cacheCreationInputTokens":57092,"cacheCreationEphemeral5mInputTokens":57092,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":1.1483454000000002}},"hasUnknownModelCost":false}188