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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The estimands are the out-of-sample RMSE and MAE, over a fixed set of 48 rolling (expanding-window) origins and horizons h ∈ {1, 3}, of (a) the naive forecast and (b) one model of your choice, and the relative improvement 1 − RMSE_model / RMSE_naive per horizon.\n\n## Inputs\n- `data/canada_gdp_employment.csv` — observed data (Statistics Canada, current vintage, retrieved 2026-10-30 UTC via the Web Data Service; see `data/README.md` for vectors, URL, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `gdp_level` (monthly real GDP at basic prices, chained 2017 dollars, SA, millions), `gdp_yoy` (target: year-over-year growth of `gdp_level`, percent, 4 decimals), `emp_level` (LFS employment, SA, thousands), `emp_yoy` (predictor: year-over-year employment growth, percent, 4 decimals). 345 monthly rows, 1998-01 to 2026-09; GDP columns are empty after the last published GDP month (2026-07), employment runs to 2026-09.\n\n## Information-availability rules\n- Forecast origins: the 48 months t = 2022-05-01, 2022-06-01, …, 2026-04-01 (identified by their `month` value). The origin is the **end** of month t. Horizons h ∈ {1, 3}; the target of origin t at horizon h is `gdp_yoy` of month t + h.\n- Publication lags (documented in `data/README.md`): monthly GDP for a reference month is published about two months after the end of that month; LFS employment for a reference month is published in the first/second week of the following month. Therefore, at origin t, **the GDP columns are available only for months ≤ t − 2 and the employment columns only for months ≤ t − 1**. A forecast made at origin t (its estimation sample, its model selection, its transformations and the values plugged into the forecast) may use only these observations. Training targets must also be observed at the origin.\n- Naive baseline: for both horizons, the forecast is the most recent GDP growth value available at the origin, i.e. `gdp_yoy` of month t − 2.\n- The errors are computed on the data as given (current vintage); the exercise simulates publication timing, not historical first-release values — do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), columns: `origin_date` (YYYY-MM-DD, the `month` of the origin), `target_date`, `horizon` (integer), `forecast`, `actual` (the `gdp_yoy` value of the target month), `model` (`naive` or the value of `model_name`), `last_target_date_used` (YYYY-MM-DD: the latest `gdp_yoy` month entering the forecast, estimation included), `last_predictor_date_used` (YYYY-MM-DD: the latest `emp_yoy` month used, or empty if the model does not use employment). Expected: 48 × 2 × 2 = 192 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-01\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, predictors, estimation sample, method) |\n| `n_origins` | integer | — | 48 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `last_target_date_available` | string | date | latest `gdp_yoy` month available at the last origin (2026-04-01) under the rules |\n| `last_predictor_date_available` | string | date | latest `emp_yoy` month available at the last origin under the rules |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"canada_gdp_employment.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations in the last estimation sample |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\ncanada_gdp_employment.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01NW11EKz4Tys2TqLpe49z9j"}]},"uuid":"0c395112-a817-4e5c-9d0a-4cd21b292cfe","timestamp":"2026-10-30T04:59:41.133Z","toolUseResult":"Error: Exit code 1\n# F3-conf-01 — Forecasting Canadian monthly GDP growth under publication lags\n\n## Scientific question\nAt the end of each month, how well can the year-over-year growth of Canadian monthly real GDP one and three months ahead be forecast from the information that is actually published at that time, and does a model that also uses the (more timely) Labour Force Survey employment growth beat the naive forecast? The estimands are the out-of-sample RMSE and MAE, over a fixed set of 48 rolling (expanding-window) origins and horizons h ∈ {1, 3}, of (a) the naive forecast and (b) one model of your choice, and the relative improvement 1 − RMSE_model / RMSE_naive per horizon.\n\n## Inputs\n- `data/canada_gdp_employment.csv` — observed data (Statistics Canada, current vintage, retrieved 2026-10-30 UTC via the Web Data Service; see `data/README.md` for vectors, URL, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `gdp_level` (monthly real GDP at basic prices, chained 2017 dollars, SA, millions), `gdp_yoy` (target: year-over-year growth of `gdp_level`, percent, 4 decimals), `emp_level` (LFS employment, SA, thousands), `emp_yoy` (predictor: year-over-year employment growth, percent, 4 decimals). 345 monthly rows, 1998-01 to 2026-09; GDP columns are empty after the last published GDP month (2026-07), employment runs to 2026-09.\n\n## Information-availability rules\n- Forecast origins: the 48 months t = 2022-05-01, 2022-06-01, …, 2026-04-01 (identified by their `month` value). The origin is the **end** of month t. Horizons h ∈ {1, 3}; the target of origin t at horizon h is `gdp_yoy` of month t + h.\n- Publication lags (documented in `data/README.md`): monthly GDP for a reference month is published about two months after the end of that month; LFS employment for a reference month is published in the first/second week of the following month. Therefore, at origin t, **the GDP columns are available only for months ≤ t − 2 and the employment columns only for months ≤ t − 1**. A forecast made at origin t (its estimation sample, its model selection, its transformations and the values plugged into the forecast) may use only these observations. Training targets must also be observed at the origin.\n- Naive baseline: for both horizons, the forecast is the most recent GDP growth value available at the origin, i.e. `gdp_yoy` of month t − 2.\n- The errors are computed on the data as given (current vintage); the exercise simulates publication timing, not historical first-release values — do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — the forecast ledger, one row per (origin, horizon, model), columns: `origin_date` (YYYY-MM-DD, the `month` of the origin), `target_date`, `horizon` (integer), `forecast`, `actual` (the `gdp_yoy` value of the target month), `model` (`naive` or the value of `model_name`), `last_target_date_used` (YYYY-MM-DD: the latest `gdp_yoy` month entering the forecast, estimation included), `last_predictor_date_used` (YYYY-MM-DD: the latest `emp_yoy` month used, or empty if the model does not use employment). Expected: 48 × 2 × 2 = 192 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-01\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, predictors, estimation sample, method) |\n| `n_origins` | integer | — | 48 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `last_target_date_available` | string | date | latest `gdp_yoy` month available at the last origin (2026-04-01) under the rules |\n| `last_predictor_date_available` | string | date | latest `emp_yoy` month available at the last origin under the rules |\n| `rmse_naive_h1`, `mae_naive_h1`, `rmse_naive_h3`, `mae_naive_h3` | number | percentage points | naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h3`, `mae_model_h3` | number | percentage points | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h3` | number | fraction | 1 − rmse_model / rmse_naive |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | — | {\"canada_gdp_employment.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | observations in the last estimation sample |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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Last available month: 2026-07 (the value for a reference month is published by Statistics Canada about two months after the end of that month — e.g. July 2026 GDP was published on 2026-09-30).\n- `gdp_yoy` — target series: year-over-year growth of `gdp_level`, 100 × (level_m / level_{m-12} − 1), rounded to 4 decimals (percent). Empty for the first 12 months.\n- `emp_level` — vector v2062811, table 14-10-0287-01: Employment, both sexes, 15 years and over, seasonally adjusted, thousands of persons, Labour Force Survey, monthly. Last available month: 2026-09 (published in the first or second week of the following month).\n- `emp_yoy` — predictor series: year-over-year growth of `emp_level`, same formula, percent, 4 decimals.\n\n**Publication lags (information rule used in TASK.md)**: at the end of month t, `gdp_*` is known for months ≤ t − 2 and `emp_*` for months ≤ t − 1. Both series are the current vintage as of retrieval (monthly GDP and LFS employment are revised after first publication); the file is therefore a final-vintage dataset and the exercise simulates only the publication *timing*, not the historical first-release values. No real-time (vintage) validity is claimed.\n{\n  \"task_id\": \"F3-conf-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"model_name\": {\"type\": \"string\", \"nullable\": false},\n    \"model_description\": {\"type\": \"string\", \"nullable\": false},\n    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_target_date_available\": {\"type\": \"string\", \"nullable\": false},\n    \"last_predictor_date_available\": {\"type\": \"string\", \"nullable\": false},\n    \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_model_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_rmse_h3\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"], [\"last_target_date_available\", \"last_origin\"], [\"last_predictor_date_available\", \"last_origin\"]],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\nmonth,gdp_level,gdp_yoy,emp_level,emp_yoy\n1998-01-01,1251276.0,4.2359,13864.8,2.4298\n1998-02-01,1263285.0,4.5780,13914.5,2.7803\n1998-03-01,1267784.0,4.9633,13949.4,2.7974\n1998-04-01,1268479.0,4.3134,13977.6,2.9946\n2026-02-01,2343731.0,0.7697,21037.3,0.2468\n2026-03-01,2340120.0,0.4175,21051.4,0.4155\n2026-04-01,2355221.0,1.1488,21033.7,0.3196\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\n2026-06-01,2371886.0,2.0909,21139.7,0.4705\n2026-07-01,2371771.0,1.4203,21214.8,0.9349\n2026-08-01,,,21173.1,1.0331\n2026-09-01,,,21104.8,0.4517","is_error":false}]},"uuid":"7263643e-7707-4059-8558-00ecaffa4aa6","timestamp":"2026-10-30T04:59:42.942Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-01\n\n## canada_gdp_employment.csv\nOne row per month (`month`, YYYY-MM-01), 1998-01 to 2026-09-01 (345 data rows), built from two Statistics Canada vectors retrieved through the Web Data Service:\n\n- **Endpoint**: `POST https://www150.statcan.gc.ca/t1/wds/rest/getDataFromVectorsAndLatestNPeriods` with body `[{\"vectorId\":65201210,\"latestN\":400},{\"vectorId\":2062811,\"latestN\":400}]`\n- **Retrieved (UTC)**: 2026-10-30T03:25:10Z\n- **SHA-256 of this file**: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed\n- **License**: Statistics Canada Open Licence (https://www.statcan.gc.ca/en/reference/licence).\n\nColumns:\n- `gdp_level` — vector v65201210, table 36-10-0434-01: Gross domestic product at basic prices, all industries, chained (2017) dollars, seasonally adjusted at annual rates, millions of dollars, monthly. Last available month: 2026-07 (the value for a reference month is published by Statistics Canada about two months after the end of that month — e.g. July 2026 GDP was published on 2026-09-30).\n- `gdp_yoy` — target series: year-over-year growth of `gdp_level`, 100 × (level_m / level_{m-12} − 1), rounded to 4 decimals (percent). Empty for the first 12 months.\n- `emp_level` — vector v2062811, table 14-10-0287-01: Employment, both sexes, 15 years and over, seasonally adjusted, thousands of persons, Labour Force Survey, monthly. Last available month: 2026-09 (published in the first or second week of the following month).\n- `emp_yoy` — predictor series: year-over-year growth of `emp_level`, same formula, percent, 4 decimals.\n\n**Publication lags (information rule used in TASK.md)**: at the end of month t, `gdp_*` is known for months ≤ t − 2 and `emp_*` for months ≤ t − 1. Both series are the current vintage as of retrieval (monthly GDP and LFS employment are revised after first publication); the file is therefore a final-vintage dataset and the exercise simulates only the publication *timing*, not the historical first-release values. No real-time (vintage) validity is claimed.\n{\n  \"task_id\": \"F3-conf-01\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"model_name\": {\"type\": \"string\", \"nullable\": false},\n    \"model_description\": {\"type\": \"string\", \"nullable\": false},\n    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_target_date_available\": {\"type\": \"string\", \"nullable\": false},\n    \"last_predictor_date_available\": {\"type\": \"string\", \"nullable\": false},\n    \"rmse_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive_h1\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_naive_h3\": {\"type\": \"number\", \"nullable\": 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\"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive_h1\", \"rmse_naive_h3\", \"rmse_model_h1\", \"rmse_model_h3\"],\n  \"chronology_pairs\": [[\"first_origin\", \"last_origin\"], [\"last_target_date_available\", \"last_origin\"], [\"last_predictor_date_available\", \"last_origin\"]],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 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 data/canada_gdp_employment.csv\n2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f  data/README.md\n(345, 5) 1998-01-01 00:00:00 2026-09-01 00:00:00 0\nmonth        0\ngdp_level    2\ngdp_yoy      2\nemp_level    0\nemp_yoy      0\ndtype: int64\nTrue\ngdp recompute maxdiff 4.985325298356713e-05 emp 4.968314845221755e-05\n(12, 5)                                month     gdp_level  ...     emp_level     emp_yoy\ncount                            345  3.430000e+02  ...    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06  ...  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  ...  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06  ...  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06  ...  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06  ...  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06  ...  21214.800000   16.088700\nstd                              NaN  3.003681e+05  ...   1919.265534    2.263768\n\n[8 rows x 5 columns]\n         month  gdp_yoy  emp_yoy\n264 2020-01-01   2.1209   1.5021\n265 2020-02-01   2.6658   1.2507\n266 2020-03-01  -5.6070  -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01  -7.6173  -8.4346\n270 2020-07-01  -5.2884  -6.1707\n271 2020-08-01  -4.3527  -5.2504\n272 2020-09-01  -3.5236  -3.0659\n273 2020-10-01  -3.0057  -2.6894\n274 2020-11-01  -2.3377  -2.3397\n275 2020-12-01  -2.5523  -3.1001\n276 2021-01-01  -2.0402  -4.2313\n277 2021-02-01  -2.0966  -2.9730\n278 2021-03-01   7.0112   4.5121\n279 2021-04-01  18.5426  16.0887\n280 2021-05-01  13.1156  13.4845\n281 2021-06-01   7.7967   8.1571\n282 2021-07-01   5.8090   6.2641\n283 2021-08-01   5.6440   5.2534","is_error":false}]},"uuid":"5f3dc3bd-df83-4eb2-a1ca-9872163cb30b","timestamp":"2026-10-30T04:59:54.221Z","toolUseResult":{"stdout":"f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed  data/canada_gdp_employment.csv\n2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f  data/README.md\n(345, 5) 1998-01-01 00:00:00 2026-09-01 00:00:00 0\nmonth        0\ngdp_level    2\ngdp_yoy      2\nemp_level    0\nemp_yoy      0\ndtype: int64\nTrue\ngdp recompute maxdiff 4.985325298356713e-05 emp 4.968314845221755e-05\n(12, 5)                                month     gdp_level  ...     emp_level     emp_yoy\ncount                            345  3.430000e+02  ...    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06  ...  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  ...  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06  ...  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06  ...  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06  ...  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06  ...  21214.800000   16.088700\nstd                              NaN  3.003681e+05  ...   1919.265534    2.263768\n\n[8 rows x 5 columns]\n         month  gdp_yoy  emp_yoy\n264 2020-01-01   2.1209   1.5021\n265 2020-02-01   2.6658   1.2507\n266 2020-03-01  -5.6070  -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01  -7.6173  -8.4346\n270 2020-07-01  -5.2884  -6.1707\n271 2020-08-01  -4.3527  -5.2504\n272 2020-09-01  -3.5236  -3.0659\n273 2020-10-01  -3.0057  -2.6894\n274 2020-11-01  -2.3377  -2.3397\n275 2020-12-01  -2.5523  -3.1001\n276 2021-01-01  -2.0402  -4.2313\n277 2021-02-01  -2.0966  -2.9730\n278 2021-03-01   7.0112   4.5121\n279 2021-04-01  18.5426  16.0887\n280 2021-05-01  13.1156  13.4845\n281 2021-06-01   7.7967   8.1571\n282 2021-07-01   5.8090   6.2641\n283 2021-08-01   5.6440   5.2534","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"780263f8-3c24-4215-85c0-9bab7c0511c5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","hash":"9c58a0ecbba2678423105c18559c705af515ef95e326698f81ddfb1b3c5293db","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01NpLqozoVFfCiEmsxeMZEah","name":"Bash","input":{"command":"mkdir -p out/state; shasum -a 256 data/*; ~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month'])\nprint(d.shape, d.month.min(), d.month.max(), d.month.duplicated().sum())\nprint(d.isna().sum())\nprint((d.month.diff().dt.days.dropna().between(28,31)).all())\ng=100*(d.gdp_level/d.gdp_level.shift(12)-1); e=100*(d.emp_level/d.emp_level.shift(12)-1)\nprint('gdp recompute maxdiff',(g-d.gdp_yoy).abs().max(),'emp',(e-d.emp_yoy).abs().max())\nprint(d[d.gdp_yoy.notna()&g.isna()].shape, d.describe())\nprint(d[(d.month>='2020-01-01')&(d.month<='2021-08-01')][['month','gdp_yoy','emp_yoy']].to_string())\n\"","description":"Hash and audit the data 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-p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: pseudo-real-time forecasts of Canadian monthly GDP yoy growth.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))  # training exclusion window\nMODEL = \"adl_emp_direct\"\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.set_index(\"month\").sort_index()\nmonths = d.index\npos = {m: i for i, m in enumerate(months)}\n\ndef shift(m, k):\n    return m + pd.DateOffset(months=k)\n\ndef in_covid(m):\n    return COVID[0] <= m <= COVID[1]\n\ndef training_rows(t, h):\n    \"\"\"Direct regression rows indexed by base month m: target y[m+k], regressors y[m], e[m+1], k=h+2.\n    Only data available at end of month t: gdp months <= t-2, emp months <= t-1.\"\"\"\n    k = h + 2\n    gcut, ecut = shift(t, -2), shift(t, -1)\n    log = []\n    m_all = [m for m in months if not np.isnan(d.loc[m, \"gdp_yoy\"])]\n    rows = list(m_all)\n    log.append((\"A\", \"base month has non-missing gdp_yoy in file\", n_raw, len(rows)))\n    rows = [m for m in rows if m <= gcut]\n    log.append((\"B\", f\"base month gdp available at origin (<= t-2 = {gcut.date()})\", log[-1][3], len(rows)))\n    rows = [m for m in rows if shift(m, k) <= gcut and shift(m, 1) <= ecut and shift(m, k) in pos]\n    log.append((\"C\", f\"training target month m+{k} observed at origin (<= {gcut.date()})\", log[-1][3], len(rows)))\n    rows = [m for m in rows if not (in_covid(m) or in_covid(shift(m, 1)) or in_covid(shift(m, k)))]\n    log.append((\"D\", \"drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\", log[-1][3], len(rows)))\n    return rows, log\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nrecs = []\nlast_log = {}\nfor t in origins:\n    gcut, ecut = shift(t, -2), shift(t, -1)\n    y_now = d.loc[gcut, \"gdp_yoy\"]; e_now = d.loc[ecut, \"emp_yoy\"]\n    assert not np.isnan(y_now) and not np.isnan(e_now)\n    for h in (1, 3):\n        k = h + 2\n        target = shift(t, h)\n        actual = float(d.loc[target, \"gdp_yoy\"])\n        recs.append(dict(origin_date=t.date().isoformat(), target_date=target.date().isoformat(), horizon=h,\n                         forecast=float(y_now), actual=actual, model=\"naive\",\n                         last_target_date_used=gcut.date().isoformat(), last_predictor_date_used=\"\"))\n        rows, log = training_rows(t, h)\n        Y = np.array([d.loc[shift(m, k), \"gdp_yoy\"] for m in rows])\n        X = np.column_stack([np.ones(len(rows)), [d.loc[m, \"gdp_yoy\"] for m in rows],\n                             [d.loc[shift(m, 1), \"emp_yoy\"] for m in rows]])\n        beta, *_ = np.linalg.lstsq(X, Y, rcond=None)\n        fc = float(beta @ np.array([1.0, y_now, e_now]))\n        last_tgt = max(max(shift(m, k) for m in rows), gcut)\n        last_emp = max(max(shift(m, 1) for m in rows), ecut)\n        assert last_tgt <= gcut and last_emp <= ecut\n        recs.append(dict(origin_date=t.date().isoformat(), target_date=target.date().isoformat(), horizon=h,\n                         forecast=fc, actual=actual, model=MODEL,\n                         last_target_date_used=last_tgt.date().isoformat(),\n                         last_predictor_date_used=last_emp.date().isoformat()))\n        if t == origins[-1]:\n            last_log[h] = (log, len(rows))\n\nfc = pd.DataFrame(recs)\nfc.to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\ndef err(model, h):\n    s = fc[(fc.model == model) & (fc.horizon == h)]\n    e = s.forecast - s.actual\n    return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct OLS regression per horizon h of gdp_yoy[m+h+2] on a constant, gdp_yoy[m] and emp_yoy[m+1], \"\n                             \"re-estimated at each origin t on an expanding sample from 1998 using only pairs observed at t \"\n                             \"(gdp <= t-2, emp <= t-1), excluding pairs touching 2020-03..2021-12; forecast plugs in gdp_yoy[t-2] and emp_yoy[t-1].\"),\n       \"n_origins\": int(len(origins)), \"first_origin\": origins[0].date().isoformat(),\n       \"last_origin\": origins[-1].date().isoformat(),\n       \"last_target_date_available\": shift(origins[-1], -2).date().isoformat(),\n       \"last_predictor_date_available\": shift(origins[-1], -1).date().isoformat()}\nfor model, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n    for h in (1, 3):\n        r, a = err(model, h)\n        res[f\"rmse_{tag}_h{h}\"] = r; res[f\"mae_{tag}_h{h}\"] = a\nfor h in (1, 3):\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n            \"data_issues\": [\n                {\"issue\": \"gdp_level/gdp_yoy empty for 2026-08 and 2026-09 (not yet published)\",\n                 \"handling\": \"never needed: latest GDP month used is 2026-02 (t-2 at last origin); latest target is 2026-07.\"},\n                {\"issue\": \"README says gdp_yoy/emp_yoy are empty for the first 12 months, but the file has values for 1998-01..1998-12 that cannot be recomputed from the levels in the file\",\n                 \"handling\": \"used as given (values for 1999 onward match 100*(L_m/L_{m-12}-1) to <5e-5); noted as a documentation inconsistency.\"},\n                {\"issue\": \"pandemic outliers 2020-03..2021-12 (gdp_yoy from -16.2 to +18.5 percent) would dominate OLS estimates\",\n                 \"handling\": \"training pairs whose regressor or target month falls in 2020-03..2021-12 are excluded (rule fixed a priori; window ends before the first origin's information set).\"},\n                {\"issue\": \"current-vintage data, not historical first releases\",\n                 \"handling\": \"exercise simulates publication timing only; no real-time validity claimed.\"}],\n            \"n_input_rows\": {\"canada_gdp_employment.csv\": int(n_raw)},\n            \"n_analysed\": int(last_log[1][1]), \"random_seed\": None})\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger: last origin, h=1 estimation sample (n_analysed); h=3 shown for reference\nex = []\nfor h in (1, 3):\n    for step, rule, nb, na in last_log[h][0]:\n        ex.append(dict(step=f\"h{h}_{step}\", rule=f\"[origin 2026-04-01, h={h}] {rule}\", n_before=nb, n_after=na,\n                       n_removed=nb - na, rationale=\"information-availability rule\" if step in \"BC\" else\n                       (\"missing value (unpublished GDP)\" if step == \"A\" else \"pandemic outliers excluded from estimation\")))\npd.DataFrame(ex).to_csv(\"out/exclusions.csv\", index=False)\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; head -5 out/forecasts.csv; wc -l out/forecasts.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2088,"cache_read_input_tokens":8883,"output_tokens":3786,"output_tokens_details":{"thinking_tokens":580},"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":2088},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqD7dVQWr6wZ1HjLrvk","requestRef":"1d9ad080-07a5-4657-ae9b-52cb248ae65b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"305f3b02-015d-48f4-8f02-17a15fe23f8d","timestamp":"2026-10-30T05:00:24.986Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}30{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"305f3b02-015d-48f4-8f02-17a15fe23f8d","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b"}31{"type":"atis-latch","atis":"","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b"}32{"parentUuid":"305f3b02-015d-48f4-8f02-17a15fe23f8d","isSidechain":false,"promptId":"041004c5-850b-4853-8ff7-381653fb1fa5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LYCehZijp1akb335mXAcmm","type":"tool_result","content":"{\n \"n_origins\": 48,\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825,\n \"n_input_rows\": {\n  \"canada_gdp_employment.csv\": 345\n },\n \"n_analysed\": 310\n}\nstep,rule,n_before,n_after,n_removed,rationale\nh1_A,\"[origin 2026-04-01, h=1] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh1_B,\"[origin 2026-04-01, h=1] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh1_C,\"[origin 2026-04-01, h=1] training target month m+3 observed at origin (<= 2026-02-01)\",338,335,3,information-availability rule\nh1_D,\"[origin 2026-04-01, h=1] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",335,310,25,pandemic outliers excluded from estimation\nh3_A,\"[origin 2026-04-01, h=3] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh3_B,\"[origin 2026-04-01, h=3] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh3_C,\"[origin 2026-04-01, h=3] training target month m+5 observed at origin (<= 2026-02-01)\",338,333,5,information-availability rule\nh3_D,\"[origin 2026-04-01, h=3] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",333,306,27,pandemic outliers excluded from estimation\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.933500,5.957100,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.713444,5.957100,adl_emp_direct,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.933500,4.831700,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.392103,4.831700,adl_emp_direct,2022-03-01,2022-04-01\n     193 out/forecasts.csv","is_error":false}]},"uuid":"338aa5f7-5a12-4369-bf9d-91c1313edf31","timestamp":"2026-10-30T05:00:27.151Z","toolUseResult":{"stdout":"{\n \"n_origins\": 48,\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h1\": 0.7429391714223801,\n \"mae_model_h1\": 0.6039917578066155,\n \"rmse_model_h3\": 0.9023000239646665,\n \"mae_model_h3\": 0.7534546734385598,\n \"rel_improvement_rmse_h1\": 0.08687306602134803,\n \"rel_improvement_rmse_h3\": 0.20849675065004825,\n \"n_input_rows\": {\n  \"canada_gdp_employment.csv\": 345\n },\n \"n_analysed\": 310\n}\nstep,rule,n_before,n_after,n_removed,rationale\nh1_A,\"[origin 2026-04-01, h=1] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh1_B,\"[origin 2026-04-01, h=1] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh1_C,\"[origin 2026-04-01, h=1] training target month m+3 observed at origin (<= 2026-02-01)\",338,335,3,information-availability rule\nh1_D,\"[origin 2026-04-01, h=1] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",335,310,25,pandemic outliers excluded from estimation\nh3_A,\"[origin 2026-04-01, h=3] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh3_B,\"[origin 2026-04-01, h=3] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh3_C,\"[origin 2026-04-01, h=3] training target month m+5 observed at origin (<= 2026-02-01)\",338,333,5,information-availability rule\nh3_D,\"[origin 2026-04-01, h=3] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",333,306,27,pandemic outliers excluded from estimation\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.933500,5.957100,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.713444,5.957100,adl_emp_direct,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.933500,4.831700,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.392103,4.831700,adl_emp_direct,2022-03-01,2022-04-01\n     193 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"305f3b02-015d-48f4-8f02-17a15fe23f8d","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","hash":"098912d638b2b9a2058b3917610173d1f6e4cf3797d7cb5b17a8d288ecee458b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01LYCehZijp1akb335mXAcmm","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: pseudo-real-time forecasts of Canadian monthly GDP yoy growth.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))  # training exclusion window\nMODEL = \"adl_emp_direct\"\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.set_index(\"month\").sort_index()\nmonths = d.index\npos = {m: i for i, m in enumerate(months)}\n\ndef shift(m, k):\n    return m + pd.DateOffset(months=k)\n\ndef in_covid(m):\n    return COVID[0] <= m <= COVID[1]\n\ndef training_rows(t, h):\n    \"\"\"Direct regression rows indexed by base month m: target y[m+k], regressors y[m], e[m+1], k=h+2.\n    Only data available at end of month t: gdp months <= t-2, emp months <= t-1.\"\"\"\n    k = h + 2\n    gcut, ecut = shift(t, -2), shift(t, -1)\n    log = []\n    m_all = [m for m in months if not np.isnan(d.loc[m, \"gdp_yoy\"])]\n    rows = list(m_all)\n    log.append((\"A\", \"base month has non-missing gdp_yoy in file\", n_raw, len(rows)))\n    rows = [m for m in rows if m <= gcut]\n    log.append((\"B\", f\"base month gdp available at origin (<= t-2 = {gcut.date()})\", log[-1][3], len(rows)))\n    rows = [m for m in rows if shift(m, k) <= gcut and shift(m, 1) <= ecut and shift(m, k) in pos]\n    log.append((\"C\", f\"training target month m+{k} observed at origin (<= {gcut.date()})\", log[-1][3], len(rows)))\n    rows = [m for m in rows if not (in_covid(m) or in_covid(shift(m, 1)) or in_covid(shift(m, k)))]\n    log.append((\"D\", \"drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\", log[-1][3], len(rows)))\n    return rows, log\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nrecs = []\nlast_log = {}\nfor t in origins:\n    gcut, ecut = shift(t, -2), shift(t, -1)\n    y_now = d.loc[gcut, \"gdp_yoy\"]; e_now = d.loc[ecut, \"emp_yoy\"]\n    assert not np.isnan(y_now) and not np.isnan(e_now)\n    for h in (1, 3):\n        k = h + 2\n        target = shift(t, h)\n        actual = float(d.loc[target, \"gdp_yoy\"])\n        recs.append(dict(origin_date=t.date().isoformat(), target_date=target.date().isoformat(), horizon=h,\n                         forecast=float(y_now), actual=actual, model=\"naive\",\n                         last_target_date_used=gcut.date().isoformat(), last_predictor_date_used=\"\"))\n        rows, log = training_rows(t, h)\n        Y = np.array([d.loc[shift(m, k), \"gdp_yoy\"] for m in rows])\n        X = np.column_stack([np.ones(len(rows)), [d.loc[m, \"gdp_yoy\"] for m in rows],\n                             [d.loc[shift(m, 1), \"emp_yoy\"] for m in rows]])\n        beta, *_ = np.linalg.lstsq(X, Y, rcond=None)\n        fc = float(beta @ np.array([1.0, y_now, e_now]))\n        last_tgt = max(max(shift(m, k) for m in rows), gcut)\n        last_emp = max(max(shift(m, 1) for m in rows), ecut)\n        assert last_tgt <= gcut and last_emp <= ecut\n        recs.append(dict(origin_date=t.date().isoformat(), target_date=target.date().isoformat(), horizon=h,\n                         forecast=fc, actual=actual, model=MODEL,\n                         last_target_date_used=last_tgt.date().isoformat(),\n                         last_predictor_date_used=last_emp.date().isoformat()))\n        if t == origins[-1]:\n            last_log[h] = (log, len(rows))\n\nfc = pd.DataFrame(recs)\nfc.to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\ndef err(model, h):\n    s = fc[(fc.model == model) & (fc.horizon == h)]\n    e = s.forecast - s.actual\n    return float(np.sqrt((e ** 2).mean())), float(e.abs().mean())\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct OLS regression per horizon h of gdp_yoy[m+h+2] on a constant, gdp_yoy[m] and emp_yoy[m+1], \"\n                             \"re-estimated at each origin t on an expanding sample from 1998 using only pairs observed at t \"\n                             \"(gdp <= t-2, emp <= t-1), excluding pairs touching 2020-03..2021-12; forecast plugs in gdp_yoy[t-2] and emp_yoy[t-1].\"),\n       \"n_origins\": int(len(origins)), \"first_origin\": origins[0].date().isoformat(),\n       \"last_origin\": origins[-1].date().isoformat(),\n       \"last_target_date_available\": shift(origins[-1], -2).date().isoformat(),\n       \"last_predictor_date_available\": shift(origins[-1], -1).date().isoformat()}\nfor model, tag in ((\"naive\", \"naive\"), (MODEL, \"model\")):\n    for h in (1, 3):\n        r, a = err(model, h)\n        res[f\"rmse_{tag}_h{h}\"] = r; res[f\"mae_{tag}_h{h}\"] = a\nfor h in (1, 3):\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n            \"data_issues\": [\n                {\"issue\": \"gdp_level/gdp_yoy empty for 2026-08 and 2026-09 (not yet published)\",\n                 \"handling\": \"never needed: latest GDP month used is 2026-02 (t-2 at last origin); latest target is 2026-07.\"},\n                {\"issue\": \"README says gdp_yoy/emp_yoy are empty for the first 12 months, but the file has values for 1998-01..1998-12 that cannot be recomputed from the levels in the file\",\n                 \"handling\": \"used as given (values for 1999 onward match 100*(L_m/L_{m-12}-1) to <5e-5); noted as a documentation inconsistency.\"},\n                {\"issue\": \"pandemic outliers 2020-03..2021-12 (gdp_yoy from -16.2 to +18.5 percent) would dominate OLS estimates\",\n                 \"handling\": \"training pairs whose regressor or target month falls in 2020-03..2021-12 are excluded (rule fixed a priori; window ends before the first origin's information set).\"},\n                {\"issue\": \"current-vintage data, not historical first releases\",\n                 \"handling\": \"exercise simulates publication timing only; no real-time validity claimed.\"}],\n            \"n_input_rows\": {\"canada_gdp_employment.csv\": int(n_raw)},\n            \"n_analysed\": int(last_log[1][1]), \"random_seed\": None})\nwith open(\"out/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\n# exclusion ledger: last origin, h=1 estimation sample (n_analysed); h=3 shown for reference\nex = []\nfor h in (1, 3):\n    for step, rule, nb, na in last_log[h][0]:\n        ex.append(dict(step=f\"h{h}_{step}\", rule=f\"[origin 2026-04-01, h={h}] {rule}\", n_before=nb, n_after=na,\n                       n_removed=nb - na, rationale=\"information-availability rule\" if step in \"BC\" else\n                       (\"missing value (unpublished GDP)\" if step == \"A\" else \"pandemic outliers excluded from estimation\")))\npd.DataFrame(ex).to_csv(\"out/exclusions.csv\", index=False)\nprint(json.dumps({k: v for k, v in res.items() if k.startswith((\"rmse\", \"mae\", \"rel\", \"n_\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv; head -5 out/forecasts.csv; wc -l out/forecasts.csv","description":"Write and run analysis script"}}]}}34{"type":"api-request","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","id":"29deb3dd-65b6-4e2f-be37-b9c203cb8643","timestamp":"2026-10-30T05:00:27.153Z","version":"2.1.314","querySource":"sdk","shapeHash":"3253bbc274cb158d50e4c3f3d7f1ba5a165e5ec71c440a72f530112e420d0cfe","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":"1d9ad080-07a5-4657-ae9b-52cb248ae65b","keep":8,"tail":["098912d638b2b9a2058b3917610173d1f6e4cf3797d7cb5b17a8d288ecee458b","1e82987b08021e016cfcfc7db342d7c1bd355d281236f9ede1bd2a9e17f0b663"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8b7c59b564764122e378bd525c8c97c512e2e04d7d3413d42676783315cc7e9a"}35{"parentUuid":"338aa5f7-5a12-4369-bf9d-91c1313edf31","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqFYhvhsaxK6ZVFcV7X","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":4824,"cache_read_input_tokens":10971,"output_tokens":873,"output_tokens_details":{"thinking_tokens":204},"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":4824},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1837,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqFYR4dfcDyB6EdbFKx","requestRef":"29deb3dd-65b6-4e2f-be37-b9c203cb8643","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d5a80d71-a8d5-40be-b471-666f9e3760db","timestamp":"2026-10-30T05:00:30.144Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"d5a80d71-a8d5-40be-b471-666f9e3760db","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqFYhvhsaxK6ZVFcV7X","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016wPocGCfaJCEeTaL4BAMgw","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace(\"\"\"ex = []\nfor h in (1, 3):\"\"\",\"\"\"ex = []\nfor h in (1,):\"\"\")\ns=s.replace(\"\"\"with open(\"out/results.json\", \"w\") as f:\"\"\",\"\"\"# Diebold-Mariano test (squared loss), Newey-West variance with h+1 lags (overlap of k=h+2-step errors)\nfrom scipy import stats\nfor h in (1, 3):\n    a = fc[(fc.model == \"naive\") & (fc.horizon == h)].reset_index(drop=True)\n    b = fc[(fc.model == MODEL) & (fc.horizon == h)].reset_index(drop=True)\n    dl = ((a.forecast - a.actual) ** 2 - (b.forecast - b.actual) ** 2).to_numpy()\n    n = len(dl); dc = dl - dl.mean(); L = h + 1\n    v = dc @ dc / n + 2 * sum((1 - j / (L + 1)) * (dc[j:] @ dc[:-j]) / n for j in range(1, L + 1))\n    stat = dl.mean() / np.sqrt(v / n)\n    res[f\"dm_stat_h{h}\"] = float(stat)\n    res[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * (1 - stats.t.cdf(abs(stat), n - 1)))\nwith open(\"out/results.json\", \"w\") as f:\"\"\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py | tail -3; grep dm out/results.json; cat out/exclusions.csv","description":"Add DM test, restrict exclusion ledger to h=1"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4824,"cache_read_input_tokens":10971,"output_tokens":873,"output_tokens_details":{"thinking_tokens":204},"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":4824},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqFYR4dfcDyB6EdbFKx","requestRef":"29deb3dd-65b6-4e2f-be37-b9c203cb8643","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c6bf1964-3f25-4915-924c-f5db1c1694cb","timestamp":"2026-10-30T05:00:34.856Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"c6bf1964-3f25-4915-924c-f5db1c1694cb","isSidechain":false,"promptId":"041004c5-850b-4853-8ff7-381653fb1fa5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016wPocGCfaJCEeTaL4BAMgw","type":"tool_result","content":" },\n \"n_analysed\": 310\n}\n  \"dm_stat_h1\": 1.4092110099855293,\n  \"dm_pvalue_two_sided_h1\": 0.16535513441717664,\n  \"dm_stat_h3\": 1.5242598000021463,\n  \"dm_pvalue_two_sided_h3\": 0.13414500432933552\nstep,rule,n_before,n_after,n_removed,rationale\nh1_A,\"[origin 2026-04-01, h=1] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh1_B,\"[origin 2026-04-01, h=1] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh1_C,\"[origin 2026-04-01, h=1] training target month m+3 observed at origin (<= 2026-02-01)\",338,335,3,information-availability rule\nh1_D,\"[origin 2026-04-01, h=1] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",335,310,25,pandemic outliers excluded from estimation","is_error":false}]},"uuid":"78a3ae95-89f1-460a-84c1-4120ec0e14aa","timestamp":"2026-10-30T05:00:37.365Z","toolUseResult":{"stdout":" },\n \"n_analysed\": 310\n}\n  \"dm_stat_h1\": 1.4092110099855293,\n  \"dm_pvalue_two_sided_h1\": 0.16535513441717664,\n  \"dm_stat_h3\": 1.5242598000021463,\n  \"dm_pvalue_two_sided_h3\": 0.13414500432933552\nstep,rule,n_before,n_after,n_removed,rationale\nh1_A,\"[origin 2026-04-01, h=1] base month has non-missing gdp_yoy in file\",345,343,2,missing value (unpublished GDP)\nh1_B,\"[origin 2026-04-01, h=1] base month gdp available at origin (<= t-2 = 2026-02-01)\",343,338,5,information-availability rule\nh1_C,\"[origin 2026-04-01, h=1] training target month m+3 observed at origin (<= 2026-02-01)\",338,335,3,information-availability rule\nh1_D,\"[origin 2026-04-01, h=1] drop rows whose regressor or target month lies in 2020-03..2021-12 (pandemic)\",335,310,25,pandemic outliers excluded from estimation","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c6bf1964-3f25-4915-924c-f5db1c1694cb","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","hash":"5f131780a38000dbf38f2ac7cb700bc92e9c3d775d87c41639802fb084d3b58c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_016wPocGCfaJCEeTaL4BAMgw","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace(\"\"\"ex = []\nfor h in (1, 3):\"\"\",\"\"\"ex = []\nfor h in (1,):\"\"\")\ns=s.replace(\"\"\"with open(\"out/results.json\", \"w\") as f:\"\"\",\"\"\"# Diebold-Mariano test (squared loss), Newey-West variance with h+1 lags (overlap of k=h+2-step errors)\nfrom scipy import stats\nfor h in (1, 3):\n    a = fc[(fc.model == \"naive\") & (fc.horizon == h)].reset_index(drop=True)\n    b = fc[(fc.model == MODEL) & (fc.horizon == h)].reset_index(drop=True)\n    dl = ((a.forecast - a.actual) ** 2 - (b.forecast - b.actual) ** 2).to_numpy()\n    n = len(dl); dc = dl - dl.mean(); L = h + 1\n    v = dc @ dc / n + 2 * sum((1 - j / (L + 1)) * (dc[j:] @ dc[:-j]) / n for j in range(1, L + 1))\n    stat = dl.mean() / np.sqrt(v / n)\n    res[f\"dm_stat_h{h}\"] = float(stat)\n    res[f\"dm_pvalue_two_sided_h{h}\"] = float(2 * (1 - stats.t.cdf(abs(stat), n - 1)))\nwith open(\"out/results.json\", \"w\") as f:\"\"\")\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py | tail -3; grep dm out/results.json; cat out/exclusions.csv","description":"Add DM test, restrict exclusion ledger to h=1"}}]}}39{"type":"api-request","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","id":"85b822d2-f661-4050-9fe5-1ed2b5658f86","timestamp":"2026-10-30T05:00:37.368Z","version":"2.1.314","querySource":"sdk","shapeHash":"3253bbc274cb158d50e4c3f3d7f1ba5a165e5ec71c440a72f530112e420d0cfe","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":"29deb3dd-65b6-4e2f-be37-b9c203cb8643","keep":10,"tail":["5f131780a38000dbf38f2ac7cb700bc92e9c3d775d87c41639802fb084d3b58c","106ffd1a4aa1852a3de46b7ae6c47c4661798685cf1ec7aedce152f04656fc91"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"911b5033d7e769360d4887b752af11335def65a0883187dbdf605aaea5dee994"}40{"parentUuid":"78a3ae95-89f1-460a-84c1-4120ec0e14aa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqGJNF5tVxUEEpN1izh","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification checks, then state files."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1266,"cache_read_input_tokens":15795,"output_tokens":956,"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":1266},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqGJ6rxcA49K9Xsvh4M","requestRef":"85b822d2-f661-4050-9fe5-1ed2b5658f86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c5dce4a5-9215-4ee8-8ff2-7b950add18fc","timestamp":"2026-10-30T05:00:38.315Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"c5dce4a5-9215-4ee8-8ff2-7b950add18fc","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqGJNF5tVxUEEpN1izh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CyfCXL5tKC8JCEzUbtmDsV","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo REPRO_OK\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nf=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date','last_target_date_used','last_predictor_date_used'])\nr=json.load(open('out/results.json'))\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\no=f.origin_date\nprint('target rule', ((f.last_target_date_used<=o-pd.DateOffset(months=2))).all())\nm=f.model!='naive'\nprint('emp rule', (f.last_predictor_date_used[m]<=o[m]-pd.DateOffset(months=1)).all(), f.last_predictor_date_used[~m].isna().all())\nprint('target date', (f.target_date==o+pd.to_timedelta(0)+f.horizon.apply(lambda h: pd.DateOffset(months=h)) if False else True))\nprint('targets', all(f.target_date[i]==o[i]+pd.DateOffset(months=int(f.horizon[i])) for i in range(len(f))))\nprint('actual match', np.allclose(f.actual, d.loc[f.target_date,'gdp_yoy'].values))\nnv=f[~m]; print('naive=y[t-2]', np.allclose(nv.forecast, d.loc[nv.origin_date-pd.DateOffset(months=2),'gdp_yoy'].values))\nprint('rel', r['rel_improvement_rmse_h1']==1-r['rmse_model_h1']/r['rmse_naive_h1'], 'rmse>=mae', all(r[f'rmse_{a}_h{h}']>=r[f'mae_{a}_h{h}'] for a in ['naive','model'] for h in [1,3]))\ne=pd.read_csv('out/exclusions.csv'); print('ledger', 345-e.n_removed.sum()==r['n_analysed'], (e.n_before-e.n_after==e.n_removed).all())\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1266,"cache_read_input_tokens":15795,"output_tokens":956,"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":1266},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqGJ6rxcA49K9Xsvh4M","requestRef":"85b822d2-f661-4050-9fe5-1ed2b5658f86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0c65ce2d-c802-408a-9f2f-23aefa659d25","timestamp":"2026-10-30T05:00:44.915Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}42{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"0c65ce2d-c802-408a-9f2f-23aefa659d25","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b"}43{"type":"atis-latch","atis":"","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b"}44{"parentUuid":"0c65ce2d-c802-408a-9f2f-23aefa659d25","isSidechain":false,"promptId":"041004c5-850b-4853-8ff7-381653fb1fa5","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CyfCXL5tKC8JCEzUbtmDsV","type":"tool_result","content":"REPRO_OK\nrows 192 {('adl_emp_direct', 1): 48, ('adl_emp_direct', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget rule True\nemp rule True True\ntarget date True\ntargets True\nactual match True\nnaive=y[t-2] True\nrel True rmse>=mae True\nledger True True","is_error":false}]},"uuid":"2cf4844c-cd87-40ca-b0a3-e0db45003a9b","timestamp":"2026-10-30T05:00:47.620Z","toolUseResult":{"stdout":"REPRO_OK\nrows 192 {('adl_emp_direct', 1): 48, ('adl_emp_direct', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget rule True\nemp rule True True\ntarget date True\ntargets True\nactual match True\nnaive=y[t-2] True\nrel True rmse>=mae True\nledger True True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0c65ce2d-c802-408a-9f2f-23aefa659d25","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}45{"type":"api-request-blob","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","hash":"38968922578602633e095aae92b601943fe13c8fbc5babdadbac65bdfb1e8b40","message":{"role":"assistant","content":[{"type":"text","text":"Now verification checks, then state files."},{"type":"tool_use","id":"toolu_01CyfCXL5tKC8JCEzUbtmDsV","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo REPRO_OK\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, json, numpy as np\nf=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date','last_target_date_used','last_predictor_date_used'])\nr=json.load(open('out/results.json'))\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\no=f.origin_date\nprint('target rule', ((f.last_target_date_used<=o-pd.DateOffset(months=2))).all())\nm=f.model!='naive'\nprint('emp rule', (f.last_predictor_date_used[m]<=o[m]-pd.DateOffset(months=1)).all(), f.last_predictor_date_used[~m].isna().all())\nprint('target date', (f.target_date==o+pd.to_timedelta(0)+f.horizon.apply(lambda h: pd.DateOffset(months=h)) if False else True))\nprint('targets', all(f.target_date[i]==o[i]+pd.DateOffset(months=int(f.horizon[i])) for i in range(len(f))))\nprint('actual match', np.allclose(f.actual, d.loc[f.target_date,'gdp_yoy'].values))\nnv=f[~m]; print('naive=y[t-2]', np.allclose(nv.forecast, d.loc[nv.origin_date-pd.DateOffset(months=2),'gdp_yoy'].values))\nprint('rel', r['rel_improvement_rmse_h1']==1-r['rmse_model_h1']/r['rmse_naive_h1'], 'rmse>=mae', all(r[f'rmse_{a}_h{h}']>=r[f'mae_{a}_h{h}'] for a in ['naive','model'] for h in [1,3]))\ne=pd.read_csv('out/exclusions.csv'); print('ledger', 345-e.n_removed.sum()==r['n_analysed'], (e.n_before-e.n_after==e.n_removed).all())\nEOF","description":"Reproducibility and consistency checks"}}]}}46{"type":"api-request","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","id":"c2faf4e7-6e4b-4f10-9c15-af9273078926","timestamp":"2026-10-30T05:00:47.623Z","version":"2.1.314","querySource":"sdk","shapeHash":"3253bbc274cb158d50e4c3f3d7f1ba5a165e5ec71c440a72f530112e420d0cfe","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":"85b822d2-f661-4050-9fe5-1ed2b5658f86","keep":12,"tail":["38968922578602633e095aae92b601943fe13c8fbc5babdadbac65bdfb1e8b40","95554a8a518d5329ad9fd3e28780181b8c7c1771fa6e89923b06a9262b3e2409"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0fbb1efef09d999b981f1ab3704c1f77c4d369073032088a2840c9b9269aab53"}47{"parentUuid":"2cf4844c-cd87-40ca-b0a3-e0db45003a9b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqH4ETe4o1U26rWEt4W","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":1084,"cache_read_input_tokens":17061,"output_tokens":2441,"output_tokens_details":{"thinking_tokens":39},"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":1084},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":438,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqH3wMnjH8TPGV9Fmn9","requestRef":"c2faf4e7-6e4b-4f10-9c15-af9273078926","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b508178c-1292-4ff6-852d-beaf3f61f7b3","timestamp":"2026-10-30T05:00:48.873Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0208/out/state","sessionId":"016a6a4d-7cd8-4dde-8f0b-b4c72da0ae3b","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"b508178c-1292-4ff6-852d-beaf3f61f7b3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqH4ETe4o1U26rWEt4W","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V7JvmuMQMZmtemXYEix2sf","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** At the end of month t (48 origins 2022-05..2026-04), how accurately can Canadian monthly real GDP yoy growth for month t+h (h=1,3) be forecast using only data published by then, and does a model adding LFS employment yoy growth beat the naive forecast (gdp_yoy[t-2])?\n**Estimands.** Out-of-sample RMSE and MAE (pp) over the 48 origins per horizon for naive and one model; relative improvement 1 - RMSE_model/RMSE_naive. Meaningful assuming current-vintage data are an acceptable stand-in (timing simulation only, no real-time vintage claim) and the 48-origin window is representative.\n**Outputs.** out/forecasts.csv (192 rows; origin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json.\n**Information rules.** At origin t: GDP columns only for months <= t-2, employment only for months <= t-1; applies to estimation sample, selection, transforms, plug-in values; training targets must be observed at t.\n**Invalidity.** Any use of gdp > t-2 or emp > t-1; missing actuals for targets (latest target 2026-07 is available, so feasible); claiming real-time validity.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit (P1)\n- data/canada_gdp_employment.csv SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README). data/README.md SHA-256 2000e2e6...fa2f.\n- 345 rows x 5 cols (month, gdp_level, gdp_yoy, emp_level, emp_yoy); months 1998-01-01..2026-09-01, consecutive, no duplicates.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only 2026-08, 2026-09 (unpublished). emp complete.\n- Recomputed yoy from levels (1999+): max abs diff 5.0e-5 for both (rounding). 1998 yoy values are present although README says first 12 months empty; cannot be recomputed from file -> used as given, flagged in data_issues.\n- Anomalies: pandemic swings 2020-03..2021-12 (gdp_yoy min -16.21 in 2020-04, max 18.54 in 2021-04; emp_yoy -15.58/+16.09). Handled by excluding those months from model estimation (decided a priori; window precedes first origin's information set 2022-03).\n- Availability: the file contains data after each origin's cut-off (it is the current vintage). Handling: the script slices by date per origin (gdp <= t-2, emp <= t-1) and asserts it; ledger columns last_*_date_used document it.\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json/forecasts.csv, re-ran) byte-identical | PASS |\n| forecasts.csv has 192 rows, 48 per (model,horizon) | PASS |\n| last_target_date_used <= origin-2 months for all rows | PASS |\n| last_predictor_date_used <= origin-1 month for model rows; empty for naive | PASS |\n| target_date = origin + h months; actual equals data gdp_yoy of target month | PASS |\n| naive forecast equals gdp_yoy[t-2] | PASS |\n| rel_improvement = 1 - rmse_model/rmse_naive; RMSE >= MAE; RMSE > 0 | PASS |\n| exclusions.csv: 345 - sum(n_removed) = n_analysed (310), n_before-n_after=n_removed | PASS |\n| Magnitude vs baseline: model RMSE 0.74/0.90 vs naive 0.81/1.14 pp, same order | PASS |\n| Chronology: first_origin<last_origin, last_target(2026-02)<last_origin, last_predictor(2026-03)<last_origin | PASS |\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written; passed.\n- 2026-10-30 P1 data audited (hash matches); decision: exclude pandemic months 2020-03..2021-12 from estimation; passed.\n- 2026-10-30 P2 naive baseline computed (rmse_naive_h1/h3); passed.\n- 2026-10-30 P3 model adl_emp_direct implemented in out/analysis.py, DM test added; passed.\n- 2026-10-30 P4 reproducibility + consistency checks all pass; gate run.\nEOF","description":"Write contract, audit, checks, 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read; contract.md written\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256 matches README\", \"schema/missing/duplicates/yoy recompute checked\", \"exclusions.csv sums to n_analysed\"], \"notes\": \"see data_audit.md\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive = gdp_yoy[t-2] verified for all 96 naive rows\"], \"notes\": \"rmse_naive_h1, rmse_naive_h3\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py runs end to end, asserts information cut-offs\"], \"notes\": \"no randomness; random_seed null\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"clean re-run byte-identical\", \"consistency checks in checks.md all PASS\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30","description":"Write phase ledger and run 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no such file or directory: out/state/phases.json\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 1 checks failed)\n  [FAIL] gate_spec: gate_spec.json missing\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0208/out/state/out/state/gate_receipts/20261030T050108.485922Z.json","is_error":false}]},"uuid":"df4f6b2e-beb3-4985-bdd5-ded9e7934f41","timestamp":"2026-10-30T05:01:08.491Z","toolUseResult":{"stdout":"(eval):1: no such file or directory: out/state/phases.json\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 1 checks failed)\n  [FAIL] gate_spec: gate_spec.json missing\nreceipt: 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out/state\ncat > contract.md <<'EOF'\n# Contract (P0)\n**Question.** At the end of month t (48 origins 2022-05..2026-04), how accurately can Canadian monthly real GDP yoy growth for month t+h (h=1,3) be forecast using only data published by then, and does a model adding LFS employment yoy growth beat the naive forecast (gdp_yoy[t-2])?\n**Estimands.** Out-of-sample RMSE and MAE (pp) over the 48 origins per horizon for naive and one model; relative improvement 1 - RMSE_model/RMSE_naive. Meaningful assuming current-vintage data are an acceptable stand-in (timing simulation only, no real-time vintage claim) and the 48-origin window is representative.\n**Outputs.** out/forecasts.csv (192 rows; origin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used); out/results.json (task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json.\n**Information rules.** At origin t: GDP columns only for months <= t-2, employment only for months <= t-1; applies to estimation sample, selection, transforms, plug-in values; training targets must be observed at t.\n**Invalidity.** Any use of gdp > t-2 or emp > t-1; missing actuals for targets (latest target 2026-07 is available, so feasible); claiming real-time validity.\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit (P1)\n- data/canada_gdp_employment.csv SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README). data/README.md SHA-256 2000e2e6...fa2f.\n- 345 rows x 5 cols (month, gdp_level, gdp_yoy, emp_level, emp_yoy); months 1998-01-01..2026-09-01, consecutive, no duplicates.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only 2026-08, 2026-09 (unpublished). emp complete.\n- Recomputed yoy from levels (1999+): max abs diff 5.0e-5 for both (rounding). 1998 yoy values are present although README says first 12 months empty; cannot be recomputed from file -> used as given, flagged in data_issues.\n- Anomalies: pandemic swings 2020-03..2021-12 (gdp_yoy min -16.21 in 2020-04, max 18.54 in 2021-04; emp_yoy -15.58/+16.09). Handled by excluding those months from model estimation (decided a priori; window precedes first origin's information set 2022-03).\n- Availability: the file contains data after each origin's cut-off (it is the current vintage). Handling: the script slices by date per origin (gdp <= t-2, emp <= t-1) and asserts it; ledger columns last_*_date_used document it.\nEOF\ncat > checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json/forecasts.csv, re-ran) byte-identical | PASS |\n| forecasts.csv has 192 rows, 48 per (model,horizon) | PASS |\n| last_target_date_used <= origin-2 months for all rows | PASS |\n| last_predictor_date_used <= origin-1 month for model rows; empty for naive | PASS |\n| target_date = origin + h months; actual equals data gdp_yoy of target month | PASS |\n| naive forecast equals gdp_yoy[t-2] | PASS |\n| rel_improvement = 1 - rmse_model/rmse_naive; RMSE >= MAE; RMSE > 0 | PASS |\n| exclusions.csv: 345 - sum(n_removed) = n_analysed (310), n_before-n_after=n_removed | PASS |\n| Magnitude vs baseline: model RMSE 0.74/0.90 vs naive 0.81/1.14 pp, same order | PASS |\n| Chronology: first_origin<last_origin, last_target(2026-02)<last_origin, last_predictor(2026-03)<last_origin | PASS |\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 contract written; passed.\n- 2026-10-30 P1 data audited (hash matches); decision: exclude pandemic months 2020-03..2021-12 from estimation; passed.\n- 2026-10-30 P2 naive baseline computed (rmse_naive_h1/h3); passed.\n- 2026-10-30 P3 model adl_emp_direct implemented in out/analysis.py, DM test added; passed.\n- 2026-10-30 P4 reproducibility + consistency checks all pass; gate run.\nEOF","description":"Write contract, audit, checks, log"}},{"type":"tool_use","id":"toolu_01Kn4sAjtnvz86FEmiwsSRKa","name":"Bash","input":{"command":"cat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"TASK.md read; contract.md written\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"SHA-256 matches README\", \"schema/missing/duplicates/yoy recompute checked\", \"exclusions.csv sums to n_analysed\"], \"notes\": \"see data_audit.md\"},\n \"P2\": 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 [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] chronology:first_origin<=last_origin: 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ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 4 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2022-05-01 vs 2026-04-01\n  [ok] chronology:last_target_date_available<=last_origin: 2026-02-01 vs 2026-04-01\n  [ok] chronology:last_predictor_date_available<=last_origin: 2026-03-01 vs 2026-04-01\n  [ok] reproducibility: regenerated in 2.4s; identical within tolerance\nreceipt: 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0.595224\nnaive          1        1.515326  0.617812  0.482247  0.599391  0.656317\n               3        2.122848  0.855457  0.693185  0.828809  0.940238","is_error":false}]},"uuid":"3ea4bf9c-e618-47ff-9790-85ae759aea4c","timestamp":"2026-10-30T05:01:18.443Z","toolUseResult":{"stdout":"{\n  \"task_id\": \"F3-conf-01\",\n  \"model_name\": \"adl_emp_direct\",\n  \"model_description\": \"Direct OLS regression per horizon h of gdp_yoy[m+h+2] on a constant, gdp_yoy[m] and emp_yoy[m+1], re-estimated at each origin t on an expanding sample from 1998 using only pairs observed at t (gdp <= t-2, emp <= t-1), excluding pairs touching 2020-03..2021-12; forecast plugs in gdp_yoy[t-2] and emp_yoy[t-1].\",\n  \"n_origins\": 48,\n  \"first_origin\": \"2022-05-01\",\n  \"last_origin\": \"2026-04-01\",\n  \"last_target_date_available\": \"2026-02-01\",\n  \"last_predictor_date_available\": \"2026-03-01\",\n  \"rmse_naive_h1\": 0.813620914876824,\n  \"mae_naive_h1\": 0.6421395833333333,\n  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> out/report.md <<'EOF'\n# F3-conf-01 — Forecasting Canadian monthly GDP yoy growth under publication lags\n\n## Question\nAt the end of each month t (48 origins, `first_origin` = 2022-05-01 to `last_origin` = 2026-04-01; `n_origins`), forecast year-over-year growth of Canadian monthly real GDP for months t+1 and t+3 using only data published by then (GDP up to t−2, LFS employment up to t−1). Compare the naive forecast (gdp_yoy of t−2) with a model that also uses employment growth, using RMSE, MAE and 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\nOne file, `data/canada_gdp_employment.csv`: 345 rows (`n_input_rows`), 1998-01 to 2026-09. Its SHA-256 matches `data/README.md` (details in `out/state/data_audit.md`). GDP is missing for 2026-08 and 2026-09 because it was not yet published. These months are never needed. The latest GDP month used anywhere is 2026-02 (`last_target_date_available`), and the latest target month is 2026-07. Data issues and how each was handled are listed in `data_issues`:\n- The README says the first 12 yoy values are empty, but the file has values for 1998. They were used as given.\n- The pandemic months are extreme outliers, with gdp_yoy ranging from −16.2 % to +18.5 %. Any training pair whose regressor or target month falls in 2020-03..2021-12 was excluded from estimation. This rule was fixed before any forecasts were computed, and the excluded window ends before the information set of the first origin begins.\n\nAt every origin the script cuts the data by date (GDP ≤ t−2, employment ≤ t−1) and checks the cut with assertions. `out/exclusions.csv` gives the exclusion ledger for the last estimation sample (origin 2026-04-01, h=1): 345 → 343 after dropping missing GDP, → 338 after keeping only GDP available at the origin, → 335 after keeping only training targets observed at the origin, → 310 after the pandemic exclusion. That leaves `n_analysed` = 310. The h=3 sample at the same origin has 306 rows, because it loses two more training targets and two more pandemic-touching pairs.\n\n## Method\n- **Naive:** the forecast for both horizons is gdp_yoy[t−2].\n- **Model `adl_emp_direct`** (`model_name`, `model_description`): a direct OLS regression estimated separately for each horizon. It regresses gdp_yoy[m+h+2] on a constant, gdp_yoy[m] and emp_yoy[m+1]. The step of h+2 reflects the two-month GDP publication lag.\n  - It is re-estimated at each origin on an expanding sample starting in 1998, using only pairs whose months were all published at t.\n  - The forecast plugs in gdp_yoy[t−2] and emp_yoy[t−1].\n  - The specification was fixed in advance; no model selection was done on the evaluation period.\n  - The method has no randomness (`random_seed` = null).\n- Each forecast's `last_target_date_used` and `last_predictor_date_used` are recorded in `out/forecasts.csv` (192 rows) and checked to be ≤ t−2 and ≤ t−1 respectively.\n- Errors are computed against the current-vintage gdp_yoy.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\nAll errors are in percentage points. Over the 48 origins, the model has lower RMSE and MAE than the naive forecast at both horizons: about 9 % lower RMSE at h=1 and 21 % lower at h=3.\n\n## Uncertainty\n- **Diebold–Mariano test.** This compares squared-error losses, using a Newey–West variance with h+1 lags (the overlapping errors are h+2 steps ahead) and a t(47) reference distribution. It gives a statistic of 1.41 with two-sided p = 0.165 at h=1 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`), and 1.52 with p = 0.134 at h=3 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`).\n- **Interpretation.** The improvements are **not statistically significant** at conventional levels with only 48 overlapping origins.\n- **Where the gain comes from.** A diagnostic breakdown by origin year, computed from `out/forecasts.csv` but not stored in results.json, shows that a large part of the gain comes from the 2022 origins, when post-pandemic growth was reverting quickly.\n\n## Limitations and open issues\n- The data are the current vintage, and both GDP and LFS are revised after first release. The exercise simulates publication timing only and makes no claim of real-time validity.\n- The model combines mean reversion (an intercept and a coefficient on lagged GDP below one) with employment growth. I did not run a separate model without employment, so this exercise **does not show** how much of the gain is due to employment specifically.\n- Results depend on the a-priori pandemic exclusion window and on a short evaluation period of 48 overlapping origins.\n- The README and the data disagree about whether yoy values are present for 1998. The 1998 values were used as given.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over the 48 origins the adl_emp_direct model has lower RMSE and MAE than the naive forecast at h=1 (relative RMSE improvement about 0.087).\", \"results_keys\": [\"rmse_model_h1\", \"rmse_naive_h1\", \"mae_model_h1\", \"mae_naive_h1\", \"rel_improvement_rmse_h1\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data; DM test not significant (p=0.165).\"},\n {\"id\": \"C2\", \"text\": \"At h=3 the model has lower RMSE and MAE than the naive forecast (relative RMSE improvement about 0.208).\", \"results_keys\": [\"rmse_model_h3\", \"rmse_naive_h3\", \"mae_model_h3\", \"mae_naive_h3\", \"rel_improvement_rmse_h3\"], \"status\": \"supported\", \"caveat\": \"DM test not significant (p=0.134); gain concentrated in 2022 origins.\"},\n {\"id\": \"C3\", \"text\": \"The improvements are not statistically significant at conventional levels according to Diebold-Mariano tests.\", \"results_keys\": [\"dm_stat_h1\", \"dm_pvalue_two_sided_h1\", \"dm_stat_h3\", \"dm_pvalue_two_sided_h3\"], \"status\": \"supported\", \"caveat\": \"Small sample, HAC variance with h+1 lags.\"},\n {\"id\": \"C4\", \"text\": \"All forecasts respect the publication-lag rules (GDP <= t-2, employment <= t-1).\", \"results_keys\": [\"last_target_date_available\", \"last_predictor_date_available\", \"last_origin\"], \"status\": \"supported\", \"caveat\": \"Verified row by row in out/forecasts.csv; timing simulation only, not real-time vintages.\"},\n {\"id\": \"C5\", \"text\": \"The share of the gain attributable to employment growth specifically (vs. mean reversion) is not established.\", \"results_keys\": [\"model_description\"], \"status\": \"not_evaluated\", \"caveat\": \"No no-employment comparison model was run.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\",\"gate run receipt out/state/gate_receipts/20261030T050115.542497Z.json: contract keys, exclusion ledger, chronology, reproducibility ok; remaining failures were P5 artefacts not yet written\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections written, all numbers cite results keys\",\"claims.json written\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T050115). 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Compare the naive forecast (gdp_yoy of t−2) with a model that also uses employment growth, using RMSE, MAE and 1 − RMSE_model/RMSE_naive.\n\n## Data and exclusions\nOne file, `data/canada_gdp_employment.csv`: 345 rows (`n_input_rows`), 1998-01 to 2026-09. Its SHA-256 matches `data/README.md` (details in `out/state/data_audit.md`). GDP is missing for 2026-08 and 2026-09 because it was not yet published. These months are never needed. The latest GDP month used anywhere is 2026-02 (`last_target_date_available`), and the latest target month is 2026-07. Data issues and how each was handled are listed in `data_issues`:\n- The README says the first 12 yoy values are empty, but the file has values for 1998. They were used as given.\n- The pandemic months are extreme outliers, with gdp_yoy ranging from −16.2 % to +18.5 %. Any training pair whose regressor or target month falls in 2020-03..2021-12 was excluded from estimation. This rule was fixed before any forecasts were computed, and the excluded window ends before the information set of the first origin begins.\n\nAt every origin the script cuts the data by date (GDP ≤ t−2, employment ≤ t−1) and checks the cut with assertions. `out/exclusions.csv` gives the exclusion ledger for the last estimation sample (origin 2026-04-01, h=1): 345 → 343 after dropping missing GDP, → 338 after keeping only GDP available at the origin, → 335 after keeping only training targets observed at the origin, → 310 after the pandemic exclusion. That leaves `n_analysed` = 310. The h=3 sample at the same origin has 306 rows, because it loses two more training targets and two more pandemic-touching pairs.\n\n## Method\n- **Naive:** the forecast for both horizons is gdp_yoy[t−2].\n- **Model `adl_emp_direct`** (`model_name`, `model_description`): a direct OLS regression estimated separately for each horizon. It regresses gdp_yoy[m+h+2] on a constant, gdp_yoy[m] and emp_yoy[m+1]. The step of h+2 reflects the two-month GDP publication lag.\n  - It is re-estimated at each origin on an expanding sample starting in 1998, using only pairs whose months were all published at t.\n  - The forecast plugs in gdp_yoy[t−2] and emp_yoy[t−1].\n  - The specification was fixed in advance; no model selection was done on the evaluation period.\n  - The method has no randomness (`random_seed` = null).\n- Each forecast's `last_target_date_used` and `last_predictor_date_used` are recorded in `out/forecasts.csv` (192 rows) and checked to be ≤ t−2 and ≤ t−1 respectively.\n- Errors are computed against the current-vintage gdp_yoy.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\nAll errors are in percentage points. Over the 48 origins, the model has lower RMSE and MAE than the naive forecast at both horizons: about 9 % lower RMSE at h=1 and 21 % lower at h=3.\n\n## Uncertainty\n- **Diebold–Mariano test.** This compares squared-error losses, using a Newey–West variance with h+1 lags (the overlapping errors are h+2 steps ahead) and a t(47) reference distribution. It gives a statistic of 1.41 with two-sided p = 0.165 at h=1 (`dm_stat_h1`, `dm_pvalue_two_sided_h1`), and 1.52 with p = 0.134 at h=3 (`dm_stat_h3`, `dm_pvalue_two_sided_h3`).\n- **Interpretation.** The improvements are **not statistically significant** at conventional levels with only 48 overlapping origins.\n- **Where the gain comes from.** A diagnostic breakdown by origin year, computed from `out/forecasts.csv` but not stored in results.json, shows that a large part of the gain comes from the 2022 origins, when post-pandemic growth was reverting quickly.\n\n## Limitations and open issues\n- The data are the current vintage, and both GDP and LFS are revised after first release. The exercise simulates publication timing only and makes no claim of real-time validity.\n- The model combines mean reversion (an intercept and a coefficient on lagged GDP below one) with employment growth. I did not run a separate model without employment, so this exercise **does not show** how much of the gain is due to employment specifically.\n- Results depend on the a-priori pandemic exclusion window and on a short evaluation period of 48 overlapping origins.\n- The README and the data disagree about whether yoy values are present for 1998. The 1998 values were used as given.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over the 48 origins the adl_emp_direct model has lower RMSE and MAE than the naive forecast at h=1 (relative RMSE improvement about 0.087).\", \"results_keys\": [\"rmse_model_h1\", \"rmse_naive_h1\", \"mae_model_h1\", \"mae_naive_h1\", \"rel_improvement_rmse_h1\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data; DM test not significant (p=0.165).\"},\n {\"id\": \"C2\", \"text\": \"At h=3 the model has lower RMSE and MAE than the naive forecast (relative RMSE improvement about 0.208).\", \"results_keys\": [\"rmse_model_h3\", \"rmse_naive_h3\", \"mae_model_h3\", \"mae_naive_h3\", \"rel_improvement_rmse_h3\"], \"status\": \"supported\", \"caveat\": \"DM test not significant (p=0.134); gain concentrated in 2022 origins.\"},\n {\"id\": \"C3\", \"text\": \"The improvements are not statistically significant at conventional levels according to Diebold-Mariano tests.\", \"results_keys\": [\"dm_stat_h1\", \"dm_pvalue_two_sided_h1\", \"dm_stat_h3\", \"dm_pvalue_two_sided_h3\"], \"status\": \"supported\", \"caveat\": \"Small sample, HAC variance with h+1 lags.\"},\n {\"id\": \"C4\", \"text\": \"All forecasts respect the publication-lag rules (GDP <= t-2, employment <= t-1).\", \"results_keys\": [\"last_target_date_available\", \"last_predictor_date_available\", \"last_origin\"], \"status\": \"supported\", \"caveat\": \"Verified row by row in out/forecasts.csv; timing simulation only, not real-time vintages.\"},\n {\"id\": \"C5\", \"text\": \"The share of the gain attributable to employment growth specifically (vs. mean reversion) is not established.\", \"results_keys\": [\"model_description\"], \"status\": \"not_evaluated\", \"caveat\": \"No no-employment comparison model was run.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"consistency checks in checks.md all PASS\",\"gate run receipt out/state/gate_receipts/20261030T050115.542497Z.json: contract keys, exclusion ledger, chronology, reproducibility ok; remaining failures were P5 artefacts not yet written\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections written, all numbers cite results keys\",\"claims.json written\",\"final gate run (see receipts)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T050115). 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The model that adds employment growth beats the naive forecast at both horizons, but the improvement is not statistically significant. The final gate run passed with 0 of 43 checks failing, and re-running `out/analysis.py` from a clean state reproduces the results exactly.\n\n**Model:** `adl_emp_direct`, a separate regression for each horizon. It predicts GDP growth h+2 months ahead from a constant, the latest published GDP growth (month t−2) and the latest employment growth (month t−1). It is re-estimated at every forecast date using only data published by then. I left out of estimation any period touching March 2020 to December 2021, because the pandemic swings in GDP growth (−16% to +18.5%) would dominate the fit. I fixed that rule before computing any forecasts.\n\n**Results over the 48 forecast dates (percentage points):**\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | RMSE improvement |\n|---|---|---|---|---|---|\n| 1 month | 0.814 | 0.743 | 0.642 | 0.604 | 8.7% |\n| 3 months | 1.140 | 0.902 | 0.872 | 0.753 | 20.8% |\n\n- **Significance:** a Diebold–Mariano test comparing the two forecasts gives p = 0.165 at one month and p = 0.134 at three months, so the gains could be chance with only 48 overlapping forecasts.\n- **Where the gain comes from:** much of it comes from the 2022 forecast dates, when growth was falling back quickly after the pandemic.\n- **Publication timing:** I checked every row of the forecast file: no forecast uses GDP later than t−2 or employment later than t−1.\n\n**Limitations:**\n- **Revised data:** the data are today's revised figures, so this simulates publication timing only, not what forecasters actually saw at the time.\n- **Employment's share of the gain:** the model mixes a pull back toward average growth with the employment signal. I didn't run a version without employment, so it isn't shown how much of the gain comes from employment specifically.\n- **Pandemic exclusion:** the results depend on the chosen pandemic exclusion window.\n- **1998 values:** the data README says the first 12 months of growth values should be empty, but the file has values for 1998. They can't be recomputed from the file's own levels, so I used them as given and flagged this in the report.\n\nEverything is in `out/`. `report.md` is the write-up, `results.json` holds the numbers, `forecasts.csv` has all 192 forecasts, and `analysis.py` regenerates them. 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