spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:01:40.734Z","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:01:40.740Z","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}4{"parentUuid":null,"isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":"Begin"},"uuid":"a8ef16d4-756c-4388-b8ea-a72eb2adc83f","timestamp":"2026-10-30T05:01:40.744Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","timestamp":"2026-10-30T05:01:40.759Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0214\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"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 user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}12{"parentUuid":"8643d71a-33ff-4a04-bc82-a8a4b68dd488","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqMCeqN8QSgEgXo3AdB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AnBTJu1vKkwmk8jz518xdf","name":"Bash","input":{"command":"cat TASK.md; 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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.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  125 simon-pierreboucher  staff  4000 oct.  30 01:01 ..\n-rw-r--r--    1 simon-pierreboucher  staff   525 oct.  29 23:41 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:37 data\n-rw-r--r--    1 simon-pierreboucher  staff  2216 oct.  29 23:38 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  5459 oct.  29 23:38 TASK.md\ndata:\ntotal 40\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:37 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  14907 oct.  29 23:37 canada_gdp_employment.csv\n-rw-r--r--  1 simon-pierreboucher  staff   2064 oct.  29 23:37 README.md","is_error":true,"tool_use_id":"toolu_01AnBTJu1vKkwmk8jz518xdf"}]},"uuid":"179c4db9-6925-472f-b4fe-14026b5ac6bd","timestamp":"2026-10-30T05:01:45.887Z","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). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x    6 simon-pierreboucher  staff   192 oct.  29 23:41 .\ndrwxr-xr-x  125 simon-pierreboucher  staff  4000 oct.  30 01:01 ..\n-rw-r--r--    1 simon-pierreboucher  staff   525 oct.  29 23:41 bundle.json\ndrwxr-xr-x    4 simon-pierreboucher  staff   128 oct.  29 23:37 data\n-rw-r--r--    1 simon-pierreboucher  staff  2216 oct.  29 23:38 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  5459 oct.  29 23:38 TASK.md\ndata:\ntotal 40\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:37 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:41 ..\n-rw-r--r--  1 simon-pierreboucher  staff  14907 oct.  29 23:37 canada_gdp_employment.csv\n-rw-r--r--  1 simon-pierreboucher  staff   2064 oct.  29 23:37 README.md","sandboxed":false,"sourceToolAssistantUUID":"e693c37d-ccf5-4bb8-b0ec-427b37f56087","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"d2296e07dc9a16fbee90f24be2d37c5bc7d98723a73f230cf850ca431623eac6","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01AnBTJu1vKkwmk8jz518xdf","name":"Bash","input":{"command":"cat TASK.md; 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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}\n{\n  \"task_id\": \"F3-conf-01\",\n  \"family\": \"F3\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:04.958026+00:00\",\n  \"files\": {\n    \"TASK.md\": \"b1385cbc4e56444c3aeb8f948062b345ed14ef346ab2fad744a9f5a6549fad0e\",\n    \"data/README.md\": \"2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f\",\n    \"data/canada_gdp_employment.csv\": \"f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed\",\n    \"gate_spec.json\": \"240492f4e6d528fdf529ad5d19987f04aac9f15e90c60902f6e847967524f3f1\"\n  }\n}month,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\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\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\nf4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed  data/canada_gdp_employment.csv\n2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f  data/README.md","is_error":false}]},"uuid":"12972758-2caa-41e6-b362-fd2d9f9a0eb6","timestamp":"2026-10-30T05:01:47.459Z","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\": 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}\n{\n  \"task_id\": \"F3-conf-01\",\n  \"family\": \"F3\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:41:04.958026+00:00\",\n  \"files\": {\n    \"TASK.md\": \"b1385cbc4e56444c3aeb8f948062b345ed14ef346ab2fad744a9f5a6549fad0e\",\n    \"data/README.md\": \"2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f\",\n    \"data/canada_gdp_employment.csv\": \"f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed\",\n    \"gate_spec.json\": \"240492f4e6d528fdf529ad5d19987f04aac9f15e90c60902f6e847967524f3f1\"\n  }\n}month,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\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\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\nf4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed  data/canada_gdp_employment.csv\n2000e2e64c856f80d0781666df479d4ebfc63cb09ce41d9db99c04ca2b92fa2f  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-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())\nfor v in ['gdp','emp']:\n  r=100*(d[v+'_level']/d[v+'_level'].shift(12)-1)\n  diff=(r.round(4)-d[v+'_yoy']).abs()\n  print(v, diff.max(), (diff>1e-3).sum()); print(d.loc[diff>1e-3,['month',v+'_level',v+'_yoy']].assign(rec=r[diff>1e-3]).to_string())\nprint(d.describe().to_string())\nprint(d[d.month.between('2020-01-01','2021-06-01')][['month','gdp_yoy','emp_yoy']].to_string())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2601,"cache_read_input_tokens":6717,"output_tokens":487,"output_tokens_details":{"thinking_tokens":85},"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":2601},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqMTwLo3GH1F95z7QsK","requestRef":"c6685693-bed9-4cd7-abea-6fd8db587866","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c77b978b-cf7e-4b12-b3a8-94badc42ae8a","timestamp":"2026-10-30T05:01:53.389Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"c77b978b-cf7e-4b12-b3a8-94badc42ae8a","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012nd4d33fMCT1WsiCAXPT1T","type":"tool_result","content":"(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 0.0 0\nEmpty DataFrame\nColumns: [month, gdp_level, gdp_yoy, rec]\nIndex: []\nemp 0.0 0\nEmpty DataFrame\nColumns: [month, emp_level, emp_yoy, rec]\nIndex: []\n                               month     gdp_level     gdp_yoy     emp_level     emp_yoy\ncount                            345  3.430000e+02  343.000000    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06    2.330479  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  -16.205500  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06    1.519850  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06    2.473300  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06    3.464450  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06   18.542600  21214.800000   16.088700\nstd                              NaN  3.003681e+05    2.672409   1919.265534    2.263768\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","is_error":false}]},"uuid":"53608c52-bd77-490d-934f-5e8c65d8e3c3","timestamp":"2026-10-30T05:01:53.671Z","toolUseResult":{"stdout":"(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 0.0 0\nEmpty DataFrame\nColumns: [month, gdp_level, gdp_yoy, rec]\nIndex: []\nemp 0.0 0\nEmpty DataFrame\nColumns: [month, emp_level, emp_yoy, rec]\nIndex: []\n                               month     gdp_level     gdp_yoy     emp_level     emp_yoy\ncount                            345  3.430000e+02  343.000000    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06    2.330479  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  -16.205500  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06    1.519850  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06    2.473300  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06    3.464450  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06   18.542600  21214.800000   16.088700\nstd                              NaN  3.003681e+05    2.672409   1919.265534    2.263768\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   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-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())\nfor v in ['gdp','emp']:\n  r=100*(d[v+'_level']/d[v+'_level'].shift(12)-1)\n  diff=(r.round(4)-d[v+'_yoy']).abs()\n  print(v, diff.max(), (diff>1e-3).sum()); 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question**: At end of month t (48 origins 2022-05..2026-04), how accurately can Canadian monthly real GDP y/y growth for month t+h (h=1,3) be forecast using only data published by then; does a model adding LFS employment y/y growth beat the naive forecast?\n**Estimands**: out-of-sample RMSE and MAE (pp) per horizon for (a) naive = gdp_yoy(t-2) and (b) one chosen model; relative improvement 1 - RMSE_model/RMSE_naive per horizon. Meaningful under: current-vintage data (simulates publication timing only, not first releases); fixed origin set; expanding window.\n**Information rules**: at origin t, GDP columns usable only for months <= t-2; employment only <= t-1. Applies to estimation sample, model selection, transforms, plug-in values; training targets must be observed (<= t-2).\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 (keys per TASK.md table); out/report.md (6 sections); out/analysis.py; out/claims.json; plus state files, exclusions.csv.\n**Invalid/impossible if**: any forecast uses data beyond the availability limits; targets missing for some origin (last target 2026-04+3=2026-07, available); data hash mismatch.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract (P0)\n**Question**: At end of month t (48 origins 2022-05..2026-04), how accurately can Canadian monthly real GDP y/y growth for month t+h (h=1,3) be forecast using only data published by then; does a model adding LFS employment y/y growth beat the naive forecast?\n**Estimands**: out-of-sample RMSE and MAE (pp) per horizon for (a) naive = gdp_yoy(t-2) and (b) one chosen model; relative improvement 1 - RMSE_model/RMSE_naive per horizon. Meaningful under: current-vintage data (simulates publication timing only, not first releases); fixed origin set; expanding window.\n**Information rules**: at origin t, GDP columns usable only for months <= t-2; employment only <= t-1. Applies to estimation sample, model selection, transforms, plug-in values; training targets must be observed (<= t-2).\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 (keys per TASK.md table); out/report.md (6 sections); out/analysis.py; out/claims.json; plus state files, exclusions.csv.\n**Invalid/impossible if**: any forecast uses data beyond the availability limits; targets missing for some origin (last target 2026-04+3=2026-07, available); data hash mismatch.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md"}}]}}31{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"3d5d0a6b-d935-441d-99cf-6ac24b220d33","timestamp":"2026-10-30T05:02:08.003Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"79249a87-38c0-4a73-89ed-7271d96fb492","keep":8,"tail":["c91fbb35940eaeb29ce16de9415a3bf30e12f8eb374e13730380e2cd96527113","9692c361568c4b6edc94b3b8d9999a4c601df5a06a590e2b40899e5760e1814d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"26054965955bf41ef281ff11ef66d8d6e8ff878e15cca13efb559bc24a7ef2ef"}32{"parentUuid":"426c0913-9834-4128-b970-c2e22174929e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqNyyPwh6tsWLo3cqzn","type":"message","role":"assistant","content":[{"type":"text","text":"Now the data audit and analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1405,"cache_read_input_tokens":10703,"output_tokens":894,"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":1405},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqNyhH5XK53qCwpkmpP","requestRef":"3d5d0a6b-d935-441d-99cf-6ac24b220d33","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"80be0ace-afb1-4767-baad-bcd5ae1b5144","timestamp":"2026-10-30T05:02:09.161Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214/out/state","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"80be0ace-afb1-4767-baad-bcd5ae1b5144","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqNyyPwh6tsWLo3cqzn","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015UfU7t7PHcSVJbgM1Ekjs9","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv — SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README and bundle.json). data/README.md SHA-256 2000e2e6...fa2f (matches bundle).\n- 345 data rows x 5 columns (month, gdp_level, gdp_yoy, emp_level, emp_yoy). Months 1998-01-01..2026-09-01, contiguous monthly, no duplicate months.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only for 2026-08, 2026-09 (not yet published, expected). No other missing values.\n- Consistency: gdp_yoy and emp_yoy recomputed from levels (100*(L_m/L_{m-12}-1), rounded 4dp) match exactly for all months from 1999-01 (max abs diff 0.0).\n- Anomaly: README says yoy is \"empty for the first 12 months\", but gdp_yoy and emp_yoy are populated for 1998-01..1998-12 (presumably computed from 1997 levels not in the file). Cannot be verified from file; values used as given (only affect early training samples).\n- Anomaly: COVID-19 period: gdp_yoy ranges -16.2 (2020-04) to +18.5 (2021-04); emp_yoy -15.6 to +16.1. Extreme outliers relative to the rest of the sample (sd ~2.7). Handling: training observations whose target or regressor months fall in 2020-03..2021-06 are excluded from model estimation (pre-specified, not tuned on test errors); naive unaffected; all test targets (2022-06..2026-07) lie outside this window.\n- Availability: file contains data beyond each origin (current vintage up to 2026-09). Handling: analysis.py slices per origin (GDP <= t-2, emp <= t-1) and records last dates used per forecast; checks assert the rule.\n- Vintage: final-vintage data, so exercise is pseudo-real-time (timing only).\nEOF\necho \"- 2026-10-30 P1: audit written; decision: exclude COVID 2020-03..2021-06 from estimation samples.\" >> out/state/log.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1405,"cache_read_input_tokens":10703,"output_tokens":894,"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":1405},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqNyhH5XK53qCwpkmpP","requestRef":"3d5d0a6b-d935-441d-99cf-6ac24b220d33","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b084947f-8bc2-48e3-9d21-653f17344a95","timestamp":"2026-10-30T05:02:16.595Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214/out/state","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"b084947f-8bc2-48e3-9d21-653f17344a95","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\n(eval):1: no such file or directory: out/state/data_audit.md\n(eval):13: no such file or directory: out/state/log.md","is_error":true,"tool_use_id":"toolu_015UfU7t7PHcSVJbgM1Ekjs9"}]},"uuid":"9dd0321b-d17e-4ec7-99db-aacfae62ef5c","timestamp":"2026-10-30T05:02:16.626Z","toolUseResult":"Error: Exit code 1\n(eval):1: no such file or directory: out/state/data_audit.md\n(eval):13: no such file or directory: out/state/log.md","sandboxed":false,"sourceToolAssistantUUID":"b084947f-8bc2-48e3-9d21-653f17344a95","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214/out/state","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"1f9e98fb2930539c7984d2fdc98ca79fd355d847c5247351660b3cc05a70e635","message":{"role":"assistant","content":[{"type":"text","text":"Now the data audit and analysis script."},{"type":"tool_use","id":"toolu_015UfU7t7PHcSVJbgM1Ekjs9","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv — SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README and bundle.json). data/README.md SHA-256 2000e2e6...fa2f (matches bundle).\n- 345 data rows x 5 columns (month, gdp_level, gdp_yoy, emp_level, emp_yoy). Months 1998-01-01..2026-09-01, contiguous monthly, no duplicate months.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only for 2026-08, 2026-09 (not yet published, expected). No other missing values.\n- Consistency: gdp_yoy and emp_yoy recomputed from levels (100*(L_m/L_{m-12}-1), rounded 4dp) match exactly for all months from 1999-01 (max abs diff 0.0).\n- Anomaly: README says yoy is \"empty for the first 12 months\", but gdp_yoy and emp_yoy are populated for 1998-01..1998-12 (presumably computed from 1997 levels not in the file). Cannot be verified from file; values used as given (only affect early training samples).\n- Anomaly: COVID-19 period: gdp_yoy ranges -16.2 (2020-04) to +18.5 (2021-04); emp_yoy -15.6 to +16.1. Extreme outliers relative to the rest of the sample (sd ~2.7). Handling: training observations whose target or regressor months fall in 2020-03..2021-06 are excluded from model estimation (pre-specified, not tuned on test errors); naive unaffected; all test targets (2022-06..2026-07) lie outside this window.\n- Availability: file contains data beyond each origin (current vintage up to 2026-09). Handling: analysis.py slices per origin (GDP <= t-2, emp <= t-1) and records last dates used per forecast; checks assert the rule.\n- Vintage: final-vintage data, so exercise is pseudo-real-time (timing only).\nEOF\necho \"- 2026-10-30 P1: audit written; decision: exclude COVID 2020-03..2021-06 from estimation samples.\" >> out/state/log.md"}}]}}36{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"d98c5582-7795-448b-b833-c49cffcd527f","timestamp":"2026-10-30T05:02:16.629Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"3d5d0a6b-d935-441d-99cf-6ac24b220d33","keep":10,"tail":["1f9e98fb2930539c7984d2fdc98ca79fd355d847c5247351660b3cc05a70e635","350faa3a791dff18499974f8f9617814fee3cf3ebd0c0cc91fdb6b822ba2bcff"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"24915ea06436e79ec9b0e1bb70637188b515d7c18fb01742bb32389c059c511e"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9dd0321b-d17e-4ec7-99db-aacfae62ef5c","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}38{"type":"atis-latch","atis":"","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}39{"parentUuid":"9dd0321b-d17e-4ec7-99db-aacfae62ef5c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqPcsg6VBHcaQZCjWsF","type":"message","role":"assistant","content":[{"type":"text","text":"Shell cwd changed; retrying from workspace root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":956,"cache_read_input_tokens":12108,"output_tokens":929,"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":956},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqPcaZBr16pP2gJ4Xrz","requestRef":"d98c5582-7795-448b-b833-c49cffcd527f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9a4962cd-92d4-4c17-b1a4-fff6904ca84e","timestamp":"2026-10-30T05:02:17.957Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214/out/state","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"9a4962cd-92d4-4c17-b1a4-fff6904ca84e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqPcsg6VBHcaQZCjWsF","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0185DU6XtAtMTYek8W4MJ3Rf","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0214 && cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv — SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README and bundle.json). data/README.md SHA-256 2000e2e6...fa2f (matches bundle).\n- 345 data rows x 5 columns (month, gdp_level, gdp_yoy, emp_level, emp_yoy). Months 1998-01-01..2026-09-01, contiguous monthly, no duplicate months.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only for 2026-08, 2026-09 (not yet published, expected). No other missing values.\n- Consistency: gdp_yoy and emp_yoy recomputed from levels (100*(L_m/L_{m-12}-1), rounded 4dp) match exactly for all months from 1999-01 (max abs diff 0.0).\n- Anomaly: README says yoy is \"empty for the first 12 months\", but gdp_yoy and emp_yoy are populated for 1998-01..1998-12 (presumably computed from 1997 levels not in the file). Cannot be verified from file; values used as given (only affect early training samples).\n- Anomaly: COVID-19 period: gdp_yoy ranges -16.2 (2020-04) to +18.5 (2021-04); emp_yoy -15.6 to +16.1. Extreme outliers relative to the rest of the sample (sd ~2.7). Handling: training observations whose target or regressor months fall in 2020-03..2021-06 are excluded from model estimation (pre-specified, not tuned on test errors); naive unaffected; all test targets (2022-06..2026-07) lie outside this window.\n- Availability: file contains data beyond each origin (current vintage up to 2026-09). Handling: analysis.py slices per origin (GDP <= t-2, emp <= t-1) and records last dates used per forecast; checks assert the rule.\n- Vintage: final-vintage data, so exercise is pseudo-real-time (timing only).\nEOF\necho \"- 2026-10-30 P1: audit written; decision: exclude COVID 2020-03..2021-06 from estimation samples.\" >> out/state/log.md"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":956,"cache_read_input_tokens":12108,"output_tokens":929,"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":956},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqPcaZBr16pP2gJ4Xrz","requestRef":"d98c5582-7795-448b-b833-c49cffcd527f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"459a1e93-95e4-4530-9f9c-856dbe1d15e0","timestamp":"2026-10-30T05:02:23.620Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214/out/state","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"459a1e93-95e4-4530-9f9c-856dbe1d15e0","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0185DU6XtAtMTYek8W4MJ3Rf","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"219604be-39de-4e52-b59b-50e137c79afe","timestamp":"2026-10-30T05:02:23.645Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"459a1e93-95e4-4530-9f9c-856dbe1d15e0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"e42472b00a100bcbbbc031b419df974c705a228a40b7609294685857e20a341c","message":{"role":"assistant","content":[{"type":"text","text":"Shell cwd changed; retrying from workspace root."},{"type":"tool_use","id":"toolu_0185DU6XtAtMTYek8W4MJ3Rf","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0214 && cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv — SHA-256 f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README and bundle.json). data/README.md SHA-256 2000e2e6...fa2f (matches bundle).\n- 345 data rows x 5 columns (month, gdp_level, gdp_yoy, emp_level, emp_yoy). Months 1998-01-01..2026-09-01, contiguous monthly, no duplicate months.\n- Units: gdp_level chained 2017 $ millions SAAR; emp_level thousands SA; *_yoy percent (4 dp).\n- Missing: gdp_level/gdp_yoy missing only for 2026-08, 2026-09 (not yet published, expected). No other missing values.\n- Consistency: gdp_yoy and emp_yoy recomputed from levels (100*(L_m/L_{m-12}-1), rounded 4dp) match exactly for all months from 1999-01 (max abs diff 0.0).\n- Anomaly: README says yoy is \"empty for the first 12 months\", but gdp_yoy and emp_yoy are populated for 1998-01..1998-12 (presumably computed from 1997 levels not in the file). Cannot be verified from file; values used as given (only affect early training samples).\n- Anomaly: COVID-19 period: gdp_yoy ranges -16.2 (2020-04) to +18.5 (2021-04); emp_yoy -15.6 to +16.1. Extreme outliers relative to the rest of the sample (sd ~2.7). Handling: training observations whose target or regressor months fall in 2020-03..2021-06 are excluded from model estimation (pre-specified, not tuned on test errors); naive unaffected; all test targets (2022-06..2026-07) lie outside this window.\n- Availability: file contains data beyond each origin (current vintage up to 2026-09). Handling: analysis.py slices per origin (GDP <= t-2, emp <= t-1) and records last dates used per forecast; checks assert the rule.\n- Vintage: final-vintage data, so exercise is pseudo-real-time (timing only).\nEOF\necho \"- 2026-10-30 P1: audit written; decision: exclude COVID 2020-03..2021-06 from estimation samples.\" >> 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: pseudo-real-time forecasts of Canadian monthly GDP y/y growth.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"ardl_emp\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-06-01\"))\nHORIZONS = [1, 3]\n\nd = pd.read_csv(DATA, parse_dates=[\"month\"]).sort_values(\"month\").reset_index(drop=True)\nn_raw = len(d)\nassert not d.month.duplicated().any()\nmonths = list(d.month)\nidx = {m: i for i, m in enumerate(months)}\ny = d.gdp_yoy.to_numpy(float); e = d.emp_yoy.to_numpy(float)\n\ndef in_covid(i):\n    return COVID[0] <= months[i] <= COVID[1]\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nfmt = lambda m: pd.Timestamp(m).strftime(\"%Y-%m-%d\")\n\ndef train_rows(t, h):\n    \"\"\"Training pairs for origin index t, horizon h: target y[s+h], regressors y[s-2], e[s-1].\n    Constraint: s+h <= t-2 (target observed), s-1 <= t-1, s-2 >= 0; all values non-missing.\"\"\"\n    rows, n_covid = [], 0\n    for s in range(2, t - 2 - h + 1):\n        if np.isnan(y[s + h]) or np.isnan(y[s - 2]) or np.isnan(e[s - 1]):\n            continue\n        if in_covid(s + h) or in_covid(s - 2) or in_covid(s - 1):\n            n_covid += 1; continue\n        rows.append(s)\n    return rows, n_covid\n\nrecs, last_sample = [], {}\nfor o in origins:\n    t = idx[o]\n    gdp_last, emp_last = t - 2, t - 1\n    for h in HORIZONS:\n        actual = y[t + h]\n        assert not np.isnan(actual)\n        # naive\n        recs.append(dict(origin_date=fmt(o), target_date=fmt(months[t + h]), horizon=h,\n                         forecast=y[gdp_last], actual=actual, model=\"naive\",\n                         last_target_date_used=fmt(months[gdp_last]), last_predictor_date_used=\"\"))\n        # direct ARDL: y_{s+h} = a + b*y_{s-2} + c*e_{s-1}\n        rows, n_cov = train_rows(t, h)\n        S = np.array(rows)\n        X = np.column_stack([np.ones(len(S)), y[S - 2], e[S - 1]])\n        Y = y[S + h]\n        beta, *_ = np.linalg.lstsq(X, Y, rcond=None)\n        fc = float(beta @ np.array([1.0, y[gdp_last], e[emp_last]]))\n        lt = max(S.max() + h, gdp_last); lp = max(S.max() - 1, emp_last)\n        assert lt <= t - 2 and lp <= t - 1\n        recs.append(dict(origin_date=fmt(o), target_date=fmt(months[t + h]), horizon=h,\n                         forecast=fc, actual=actual, model=MODEL,\n                         last_target_date_used=fmt(months[lt]), last_predictor_date_used=fmt(months[lp])))\n        if o == origins[-1]:\n            last_sample[h] = dict(n=len(S), n_covid=n_cov, first=fmt(months[S.min() + h]), last=fmt(months[S.max() + h]),\n                                  beta=[float(b) for b in beta])\n\nf = pd.DataFrame(recs)\nf[\"forecast\"] = f.forecast.round(6); f[\"error\"] = f.forecast - f.actual\nf.drop(columns=\"error\").to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False)\n\ndef m(model, h):\n    err = f[(f.model == model) & (f.horizon == h)].error.to_numpy()\n    assert len(err) == 48\n    return float(np.sqrt(np.mean(err ** 2))), float(np.mean(np.abs(err)))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct h-step OLS regression of gdp_yoy(t+h) on a constant, the latest available gdp_yoy (t-2) \"\n                             \"and the latest available LFS emp_yoy (t-1), re-estimated at each origin on an expanding window \"\n                             \"starting 1998 using only pairs whose target is observed at the origin, excluding pairs touching 2020-03..2021-06 (COVID).\"),\n       \"n_origins\": int(len(origins)), \"first_origin\": fmt(origins[0]), \"last_origin\": fmt(origins[-1]),\n       \"last_target_date_available\": fmt(months[idx[origins[-1]] - 2]),\n       \"last_predictor_date_available\": fmt(months[idx[origins[-1]] - 1])}\nfor h in HORIZONS:\n    rn, an = m(\"naive\", h); rm, am = m(MODEL, h)\n    res.update({f\"rmse_naive_h{h}\": rn, f\"mae_naive_h{h}\": an, f\"rmse_model_h{h}\": rm, f\"mae_model_h{h}\": am})\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": \"gdp_yoy/emp_yoy populated for 1998-01..1998-12 although README says empty for first 12 months (1997 levels not in file; cannot be verified)\",\n         \"handling\": \"used as given; only affects the earliest training observations\"},\n        {\"issue\": \"GDP columns missing for 2026-08 and 2026-09 (not yet published)\", \"handling\": \"expected; never needed (latest target 2026-07)\"},\n        {\"issue\": \"COVID-19 extreme y/y swings 2020-03..2021-06 (gdp_yoy -16.2 to +18.5)\",\n         \"handling\": \"training pairs whose target or regressor month is in 2020-03..2021-06 excluded from model estimation; naive unaffected\"},\n        {\"issue\": \"data are current vintage, not first releases\", \"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_sample[1][\"n\"]),\n    \"n_analysed_h3\": int(last_sample[3][\"n\"]),\n    \"last_estimation_sample\": {f\"h{h}\": last_sample[h] for h in HORIZONS},\n    \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\n# exclusion ledger for the last estimation sample (origin 2026-04-01, h=1)\nt = idx[origins[-1]]; h = 1\nn_avail = t - 2 + 1  # rows with month <= t-2 (GDP available)\nn_pairs = len(range(2, t - 2 - h + 1))\nls = last_sample[1]\nex = pd.DataFrame([\n    dict(step=1, rule=\"keep months <= last_origin-2 (2026-02) with GDP published at last origin\", n_before=n_raw, n_after=n_avail,\n         n_removed=n_raw - n_avail, rationale=\"information-availability rule (GDP <= t-2)\"),\n    dict(step=2, rule=\"form direct h=1 training pairs (need y(s-2), target y(s+1) <= t-2)\", n_before=n_avail, n_after=n_pairs,\n         n_removed=n_avail - n_pairs, rationale=\"lag/lead construction loses rows at sample edges\"),\n    dict(step=3, rule=\"drop pairs whose target or regressor month is in 2020-03..2021-06\", n_before=n_pairs, n_after=ls[\"n\"],\n         n_removed=n_pairs - ls[\"n\"], rationale=\"COVID outliers would dominate OLS; pre-specified\")])\nassert ex.n_removed.sum() == n_raw - ls[\"n\"]\nex.to_csv(os.path.join(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"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":944,"cache_read_input_tokens":13064,"output_tokens":3515,"output_tokens_details":{"thinking_tokens":287},"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":944},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqQ8bqakc8TknZcBMxc","requestRef":"e1a82b45-f3cb-4da7-8cd0-2750743116e9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"67c71de8-b4ba-4a00-8a57-edcd092ced3f","timestamp":"2026-10-30T05:02:51.312Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"67c71de8-b4ba-4a00-8a57-edcd092ced3f","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Uy39TTgBKg855JxB9JqVS4","type":"tool_result","content":"{\n \"n_origins\": 48,\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_model_h1\": 0.7412347469524387,\n \"mae_model_h1\": 0.6030098541666667,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h3\": 0.9466798110600257,\n \"mae_model_h3\": 0.7957363958333333,\n \"rel_improvement_rmse_h1\": 0.08896792916802532,\n \"rel_improvement_rmse_h3\": 0.16956652261227145,\n \"n_input_rows\": {\n  \"canada_gdp_employment.csv\": 345\n },\n \"n_analysed\": 316,\n \"n_analysed_h3\": 312\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,keep months <= last_origin-2 (2026-02) with GDP published at last origin,345,338,7,information-availability rule (GDP <= t-2)\n2,\"form direct h=1 training pairs (need y(s-2), target y(s+1) <= t-2)\",338,335,3,lag/lead construction loses rows at sample edges\n3,drop pairs whose target or regressor month is in 2020-03..2021-06,335,316,19,COVID outliers would dominate OLS; pre-specified\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.9335,5.9571,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.733948,5.9571,ardl_emp,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.9335,4.8317,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.46323,4.8317,ardl_emp,2022-03-01,2022-04-01\n     193 out/forecasts.csv","is_error":false}]},"uuid":"20358c78-45ef-461b-a39a-28afd2c09903","timestamp":"2026-10-30T05:02:51.658Z","toolUseResult":{"stdout":"{\n \"n_origins\": 48,\n \"rmse_naive_h1\": 0.813620914876824,\n \"mae_naive_h1\": 0.6421395833333333,\n \"rmse_model_h1\": 0.7412347469524387,\n \"mae_model_h1\": 0.6030098541666667,\n \"rmse_naive_h3\": 1.1399827160604965,\n \"mae_naive_h3\": 0.8715583333333333,\n \"rmse_model_h3\": 0.9466798110600257,\n \"mae_model_h3\": 0.7957363958333333,\n \"rel_improvement_rmse_h1\": 0.08896792916802532,\n \"rel_improvement_rmse_h3\": 0.16956652261227145,\n \"n_input_rows\": {\n  \"canada_gdp_employment.csv\": 345\n },\n \"n_analysed\": 316,\n \"n_analysed_h3\": 312\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,keep months <= last_origin-2 (2026-02) with GDP published at last origin,345,338,7,information-availability rule (GDP <= t-2)\n2,\"form direct h=1 training pairs (need y(s-2), target y(s+1) <= t-2)\",338,335,3,lag/lead construction loses rows at sample edges\n3,drop pairs whose target or regressor month is in 2020-03..2021-06,335,316,19,COVID outliers would dominate OLS; pre-specified\norigin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used\n2022-05-01,2022-06-01,1,4.9335,5.9571,naive,2022-03-01,\n2022-05-01,2022-06-01,1,4.733948,5.9571,ardl_emp,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.9335,4.8317,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.46323,4.8317,ardl_emp,2022-03-01,2022-04-01\n     193 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"67c71de8-b4ba-4a00-8a57-edcd092ced3f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"0305f159beede82efa977fadd5afa8c83e015f8760d4995db10bcc07efde17d9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Uy39TTgBKg855JxB9JqVS4","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: pseudo-real-time forecasts of Canadian monthly GDP y/y growth.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads data/ only; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"ardl_emp\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-06-01\"))\nHORIZONS = [1, 3]\n\nd = pd.read_csv(DATA, parse_dates=[\"month\"]).sort_values(\"month\").reset_index(drop=True)\nn_raw = len(d)\nassert not d.month.duplicated().any()\nmonths = list(d.month)\nidx = {m: i for i, m in enumerate(months)}\ny = d.gdp_yoy.to_numpy(float); e = d.emp_yoy.to_numpy(float)\n\ndef in_covid(i):\n    return COVID[0] <= months[i] <= COVID[1]\n\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nfmt = lambda m: pd.Timestamp(m).strftime(\"%Y-%m-%d\")\n\ndef train_rows(t, h):\n    \"\"\"Training pairs for origin index t, horizon h: target y[s+h], regressors y[s-2], e[s-1].\n    Constraint: s+h <= t-2 (target observed), s-1 <= t-1, s-2 >= 0; all values non-missing.\"\"\"\n    rows, n_covid = [], 0\n    for s in range(2, t - 2 - h + 1):\n        if np.isnan(y[s + h]) or np.isnan(y[s - 2]) or np.isnan(e[s - 1]):\n            continue\n        if in_covid(s + h) or in_covid(s - 2) or in_covid(s - 1):\n            n_covid += 1; continue\n        rows.append(s)\n    return rows, n_covid\n\nrecs, last_sample = [], {}\nfor o in origins:\n    t = idx[o]\n    gdp_last, emp_last = t - 2, t - 1\n    for h in HORIZONS:\n        actual = y[t + h]\n        assert not np.isnan(actual)\n        # naive\n        recs.append(dict(origin_date=fmt(o), target_date=fmt(months[t + h]), horizon=h,\n                         forecast=y[gdp_last], actual=actual, model=\"naive\",\n                         last_target_date_used=fmt(months[gdp_last]), last_predictor_date_used=\"\"))\n        # direct ARDL: y_{s+h} = a + b*y_{s-2} + c*e_{s-1}\n        rows, n_cov = train_rows(t, h)\n        S = np.array(rows)\n        X = np.column_stack([np.ones(len(S)), y[S - 2], e[S - 1]])\n        Y = y[S + h]\n        beta, *_ = np.linalg.lstsq(X, Y, rcond=None)\n        fc = float(beta @ np.array([1.0, y[gdp_last], e[emp_last]]))\n        lt = max(S.max() + h, gdp_last); lp = max(S.max() - 1, emp_last)\n        assert lt <= t - 2 and lp <= t - 1\n        recs.append(dict(origin_date=fmt(o), target_date=fmt(months[t + h]), horizon=h,\n                         forecast=fc, actual=actual, model=MODEL,\n                         last_target_date_used=fmt(months[lt]), last_predictor_date_used=fmt(months[lp])))\n        if o == origins[-1]:\n            last_sample[h] = dict(n=len(S), n_covid=n_cov, first=fmt(months[S.min() + h]), last=fmt(months[S.max() + h]),\n                                  beta=[float(b) for b in beta])\n\nf = pd.DataFrame(recs)\nf[\"forecast\"] = f.forecast.round(6); f[\"error\"] = f.forecast - f.actual\nf.drop(columns=\"error\").to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False)\n\ndef m(model, h):\n    err = f[(f.model == model) & (f.horizon == h)].error.to_numpy()\n    assert len(err) == 48\n    return float(np.sqrt(np.mean(err ** 2))), float(np.mean(np.abs(err)))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct h-step OLS regression of gdp_yoy(t+h) on a constant, the latest available gdp_yoy (t-2) \"\n                             \"and the latest available LFS emp_yoy (t-1), re-estimated at each origin on an expanding window \"\n                             \"starting 1998 using only pairs whose target is observed at the origin, excluding pairs touching 2020-03..2021-06 (COVID).\"),\n       \"n_origins\": int(len(origins)), \"first_origin\": fmt(origins[0]), \"last_origin\": fmt(origins[-1]),\n       \"last_target_date_available\": fmt(months[idx[origins[-1]] - 2]),\n       \"last_predictor_date_available\": fmt(months[idx[origins[-1]] - 1])}\nfor h in HORIZONS:\n    rn, an = m(\"naive\", h); rm, am = m(MODEL, h)\n    res.update({f\"rmse_naive_h{h}\": rn, f\"mae_naive_h{h}\": an, f\"rmse_model_h{h}\": rm, f\"mae_model_h{h}\": am})\nfor h in HORIZONS:\n    res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_naive_h{h}\"]\nres.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": \"gdp_yoy/emp_yoy populated for 1998-01..1998-12 although README says empty for first 12 months (1997 levels not in file; cannot be verified)\",\n         \"handling\": \"used as given; only affects the earliest training observations\"},\n        {\"issue\": \"GDP columns missing for 2026-08 and 2026-09 (not yet published)\", \"handling\": \"expected; never needed (latest target 2026-07)\"},\n        {\"issue\": \"COVID-19 extreme y/y swings 2020-03..2021-06 (gdp_yoy -16.2 to +18.5)\",\n         \"handling\": \"training pairs whose target or regressor month is in 2020-03..2021-06 excluded from model estimation; naive unaffected\"},\n        {\"issue\": \"data are current vintage, not first releases\", \"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_sample[1][\"n\"]),\n    \"n_analysed_h3\": int(last_sample[3][\"n\"]),\n    \"last_estimation_sample\": {f\"h{h}\": last_sample[h] for h in HORIZONS},\n    \"random_seed\": None})\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\n# exclusion ledger for the last estimation sample (origin 2026-04-01, h=1)\nt = idx[origins[-1]]; h = 1\nn_avail = t - 2 + 1  # rows with month <= t-2 (GDP available)\nn_pairs = len(range(2, t - 2 - h + 1))\nls = last_sample[1]\nex = pd.DataFrame([\n    dict(step=1, rule=\"keep months <= last_origin-2 (2026-02) with GDP published at last origin\", n_before=n_raw, n_after=n_avail,\n         n_removed=n_raw - n_avail, rationale=\"information-availability rule (GDP <= t-2)\"),\n    dict(step=2, rule=\"form direct h=1 training pairs (need y(s-2), target y(s+1) <= t-2)\", n_before=n_avail, n_after=n_pairs,\n         n_removed=n_avail - n_pairs, rationale=\"lag/lead construction loses rows at sample edges\"),\n    dict(step=3, rule=\"drop pairs whose target or regressor month is in 2020-03..2021-06\", n_before=n_pairs, n_after=ls[\"n\"],\n         n_removed=n_pairs - ls[\"n\"], rationale=\"COVID outliers would dominate OLS; pre-specified\")])\nassert ex.n_removed.sum() == n_raw - ls[\"n\"]\nex.to_csv(os.path.join(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"}}]}}48{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"666f7c91-6025-41a1-9ee4-11bd851eca6d","timestamp":"2026-10-30T05:02:51.663Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"e1a82b45-f3cb-4da7-8cd0-2750743116e9","keep":14,"tail":["0305f159beede82efa977fadd5afa8c83e015f8760d4995db10bcc07efde17d9","7dad0114a2456b90c88ef0a9c4329716ac9b59fdb92c5e98984701e89e19da90"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"126cf94e16959e7fd847c5aaef5d54e4648bc59ffebb11427460f46fafaf3c5f"}49{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"20358c78-45ef-461b-a39a-28afd2c09903","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}50{"type":"atis-latch","atis":"","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}51{"parentUuid":"20358c78-45ef-461b-a39a-28afd2c09903","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSCfDtRjTqQ3p9VjNE","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":4201,"cache_read_input_tokens":14008,"output_tokens":861,"output_tokens_details":{"thinking_tokens":154},"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":4201},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1418,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqSCNrTsoAu5kyn61H3","requestRef":"666f7c91-6025-41a1-9ee4-11bd851eca6d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4df0bb74-b245-438b-bbf6-4056ce2fc36c","timestamp":"2026-10-30T05:02:55.486Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"4df0bb74-b245-438b-bbf6-4056ce2fc36c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSCfDtRjTqQ3p9VjNE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01BbFnimT2KiaemJ7tLNiKxZ","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''res.update({\"feasibility\"''','''from scipy import stats\nfor h in HORIZONS:\n    en = f[(f.model == \"naive\") & (f.horizon == h)].error.to_numpy(); em = f[(f.model == MODEL) & (f.horizon == h)].error.to_numpy()\n    dl = en ** 2 - em ** 2; T = len(dl); L = h + 1  # forecast gap is h+2 months -> MA(h+1) overlap\n    u = dl - dl.mean(); lrv = u @ u / T\n    for k in range(1, L + 1):\n        lrv += 2 * (1 - k / (L + 1)) * (u[k:] @ u[:-k]) / T\n    dm = dl.mean() / np.sqrt(lrv / T)\n    hln = dm * np.sqrt((T + 1 - 2 * (h + 2) + (h + 2) * (h + 1) / T) / T)  # Harvey-Leybourne-Newbold correction\n    res[f\"dm_stat_h{h}\"] = float(hln); res[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(hln), T - 1))\nres.update({\"feasibility\"''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -E \"dm_|beta\" -A0 out/results.json; ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(r['dm_stat_h1'],r['dm_pvalue_h1'],r['dm_stat_h3'],r['dm_pvalue_h3']);print(r['last_estimation_sample'])\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4201,"cache_read_input_tokens":14008,"output_tokens":861,"output_tokens_details":{"thinking_tokens":154},"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":4201},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqSCNrTsoAu5kyn61H3","requestRef":"666f7c91-6025-41a1-9ee4-11bd851eca6d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8b9c522a-45aa-43cd-82da-39607ea97042","timestamp":"2026-10-30T05:03:00.499Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"8b9c522a-45aa-43cd-82da-39607ea97042","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01BbFnimT2KiaemJ7tLNiKxZ","type":"tool_result","content":"  \"dm_stat_h1\": 1.3308886605073662,\n  \"dm_pvalue_h1\": 0.1896462756025693,\n  \"dm_stat_h3\": 1.2563329714548979,\n  \"dm_pvalue_h3\": 0.2152041190128537,\n      \"beta\": [\n      \"beta\": [\n1.3308886605073662 0.1896462756025693 1.2563329714548979 0.2152041190128537\n{'h1': {'n': 316, 'n_covid': 19, 'first': '1998-04-01', 'last': '2026-02-01', 'beta': [0.2922715731600751, 0.815370883958882, 0.0641950219125709]}, 'h3': {'n': 312, 'n_covid': 21, 'first': '1998-06-01', 'last': '2026-02-01', 'beta': [0.6321209864459353, 0.6522533186565527, 0.08582716491416294]}}","is_error":false}]},"uuid":"35d27e71-af5f-4a33-912c-beac7c832a5c","timestamp":"2026-10-30T05:03:01.277Z","toolUseResult":{"stdout":"  \"dm_stat_h1\": 1.3308886605073662,\n  \"dm_pvalue_h1\": 0.1896462756025693,\n  \"dm_stat_h3\": 1.2563329714548979,\n  \"dm_pvalue_h3\": 0.2152041190128537,\n      \"beta\": [\n      \"beta\": [\n1.3308886605073662 0.1896462756025693 1.2563329714548979 0.2152041190128537\n{'h1': {'n': 316, 'n_covid': 19, 'first': '1998-04-01', 'last': '2026-02-01', 'beta': [0.2922715731600751, 0.815370883958882, 0.0641950219125709]}, 'h3': {'n': 312, 'n_covid': 21, 'first': '1998-06-01', 'last': '2026-02-01', 'beta': [0.6321209864459353, 0.6522533186565527, 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- <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''res.update({\"feasibility\"''','''from scipy import stats\nfor h in HORIZONS:\n    en = f[(f.model == \"naive\") & (f.horizon == h)].error.to_numpy(); em = f[(f.model == MODEL) & (f.horizon == h)].error.to_numpy()\n    dl = en ** 2 - em ** 2; T = len(dl); L = h + 1  # forecast gap is h+2 months -> MA(h+1) overlap\n    u = dl - dl.mean(); lrv = u @ u / T\n    for k in range(1, L + 1):\n        lrv += 2 * (1 - k / (L + 1)) * (u[k:] @ u[:-k]) / T\n    dm = dl.mean() / np.sqrt(lrv / T)\n    hln = dm * np.sqrt((T + 1 - 2 * (h + 2) + (h + 2) * (h + 1) / T) / T)  # Harvey-Leybourne-Newbold correction\n    res[f\"dm_stat_h{h}\"] = float(hln); res[f\"dm_pvalue_h{h}\"] = float(2 * stats.t.sf(abs(hln), T - 1))\nres.update({\"feasibility\"''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && grep -E \"dm_|beta\" -A0 out/results.json; ~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(r['dm_stat_h1'],r['dm_pvalue_h1'],r['dm_stat_h3'],r['dm_pvalue_h3']);print(r['last_estimation_sample'])\""}}]}}55{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"93ed4917-3c81-47a6-94c9-e23163cbdf93","timestamp":"2026-10-30T05:03:01.280Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"666f7c91-6025-41a1-9ee4-11bd851eca6d","keep":16,"tail":["27da4b9516ef891849097c257dfdfa69b800c7c17bc3fb1424a453bc246a3473","030b0dd0f1b59aaa2c97af26803b428890cd7b6fd7f7cf3fc6a75d38ce7dc652"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"00bac39a2f5d5920aecae357747b821d398c020c4042ba7f48e74eeed9de49b2"}56{"parentUuid":"35d27e71-af5f-4a33-912c-beac7c832a5c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSuoz32Gcw5Y7Yf1Fs","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":1143,"cache_read_input_tokens":18209,"output_tokens":1053,"output_tokens_details":{"thinking_tokens":34},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1143},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":385,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqSuWNx1z3Mn6JAvu2D","requestRef":"93ed4917-3c81-47a6-94c9-e23163cbdf93","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b9596221-9801-4943-9bf7-b48de91808bd","timestamp":"2026-10-30T05:03:02.852Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"b9596221-9801-4943-9bf7-b48de91808bd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSuoz32Gcw5Y7Yf1Fs","type":"message","role":"assistant","content":[{"type":"text","text":"Now P4 verification."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1143,"cache_read_input_tokens":18209,"output_tokens":1053,"output_tokens_details":{"thinking_tokens":34},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1143},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqSuWNx1z3Mn6JAvu2D","requestRef":"93ed4917-3c81-47a6-94c9-e23163cbdf93","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"512aa8fb-1b35-4ee4-a05b-e0e81b21250a","timestamp":"2026-10-30T05:03:02.853Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"512aa8fb-1b35-4ee4-a05b-e0e81b21250a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqSuoz32Gcw5Y7Yf1Fs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TkCuouFfx1BhwjAp6KouVM","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo REPRO_OK\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np,json\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'))\noff=lambda a,b:(a.dt.year-b.dt.year)*12+(a.dt.month-b.dt.month)\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\nprint('target offset ok',(off(f.target_date,f.origin_date)==f.horizon).all())\nprint('gdp rule ok',(off(f.origin_date,f.last_target_date_used)>=2).all())\nm=f[f.model=='ardl_emp'];print('emp rule ok',(off(m.origin_date,m.last_predictor_date_used)>=1).all(), f[f.model=='naive'].last_predictor_date_used.isna().all())\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual ok',np.allclose(f.actual,d.loc[f.target_date,'gdp_yoy'].values))\nn=f[f.model=='naive'];print('naive ok',np.allclose(n.forecast,d.loc[n.origin_date-pd.DateOffset(months=2),'gdp_yoy'].values))\nfor h in [1,3]:\n  for mod,k in [('naive','naive'),('ardl_emp','model')]:\n    e=f[(f.model==mod)&(f.horizon==h)].eval('forecast-actual');assert abs(np.sqrt((e**2).mean())-r[f'rmse_{k}_h{h}'])<1e-12\n  assert abs(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']-r[f'rel_improvement_rmse_h{h}'])<1e-12\n  assert r[f'mae_model_h{h}']<=r[f'rmse_model_h{h}'] and r[f'mae_naive_h{h}']<=r[f'rmse_naive_h{h}']\nprint('metrics recompute ok; MAE<=RMSE ok')\nex=pd.read_csv('out/exclusions.csv');print('excl sum ok',345-ex.n_removed.sum()==r['n_analysed'])\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1143,"cache_read_input_tokens":18209,"output_tokens":1053,"output_tokens_details":{"thinking_tokens":34},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1143},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqSuWNx1z3Mn6JAvu2D","requestRef":"93ed4917-3c81-47a6-94c9-e23163cbdf93","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a099be9f-e212-439c-a4f1-7cc24f627a70","timestamp":"2026-10-30T05:03:09.814Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"a099be9f-e212-439c-a4f1-7cc24f627a70","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TkCuouFfx1BhwjAp6KouVM","type":"tool_result","content":"REPRO_OK\nrows 192 {('ardl_emp', 1): 48, ('ardl_emp', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget offset ok True\ngdp rule ok True\nemp rule ok True True\nactual ok True\nnaive ok True\nmetrics recompute ok; MAE<=RMSE ok\nexcl sum ok True","is_error":false}]},"uuid":"40ae4733-e304-4e7a-a104-0261869e93aa","timestamp":"2026-10-30T05:03:10.861Z","toolUseResult":{"stdout":"REPRO_OK\nrows 192 {('ardl_emp', 1): 48, ('ardl_emp', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ntarget offset ok True\ngdp rule ok True\nemp rule ok True True\nactual ok True\nnaive ok True\nmetrics recompute ok; MAE<=RMSE ok\nexcl sum ok True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a099be9f-e212-439c-a4f1-7cc24f627a70","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"06319d3d5537a7cd18d847e3d5fb7532ac145ec14e6025cad3e1df66a0d5362c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now P4 verification."},{"type":"tool_use","id":"toolu_01TkCuouFfx1BhwjAp6KouVM","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo REPRO_OK\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np,json\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'))\noff=lambda a,b:(a.dt.year-b.dt.year)*12+(a.dt.month-b.dt.month)\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\nprint('target offset ok',(off(f.target_date,f.origin_date)==f.horizon).all())\nprint('gdp rule ok',(off(f.origin_date,f.last_target_date_used)>=2).all())\nm=f[f.model=='ardl_emp'];print('emp rule ok',(off(m.origin_date,m.last_predictor_date_used)>=1).all(), f[f.model=='naive'].last_predictor_date_used.isna().all())\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual ok',np.allclose(f.actual,d.loc[f.target_date,'gdp_yoy'].values))\nn=f[f.model=='naive'];print('naive ok',np.allclose(n.forecast,d.loc[n.origin_date-pd.DateOffset(months=2),'gdp_yoy'].values))\nfor h in [1,3]:\n  for mod,k in [('naive','naive'),('ardl_emp','model')]:\n    e=f[(f.model==mod)&(f.horizon==h)].eval('forecast-actual');assert abs(np.sqrt((e**2).mean())-r[f'rmse_{k}_h{h}'])<1e-12\n  assert abs(1-r[f'rmse_model_h{h}']/r[f'rmse_naive_h{h}']-r[f'rel_improvement_rmse_h{h}'])<1e-12\n  assert r[f'mae_model_h{h}']<=r[f'rmse_model_h{h}'] and r[f'mae_naive_h{h}']<=r[f'rmse_naive_h{h}']\nprint('metrics recompute ok; MAE<=RMSE ok')\nex=pd.read_csv('out/exclusions.csv');print('excl sum ok',345-ex.n_removed.sum()==r['n_analysed'])\n\""}}]}}61{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"a2a64c16-f17d-46fa-93dc-b4e64e8e6470","timestamp":"2026-10-30T05:03:10.864Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"93ed4917-3c81-47a6-94c9-e23163cbdf93","keep":18,"tail":["06319d3d5537a7cd18d847e3d5fb7532ac145ec14e6025cad3e1df66a0d5362c","afc7761e3bd6c048bf29a02ce18d7b2e7ffb5b5b9adce41ae9bbcac2354e5770"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"004517c7cb077a35ef5d6dd57c1e7ccd9cdd2f1b4aaf0550e14c65cd740494b9"}62{"parentUuid":"40ae4733-e304-4e7a-a104-0261869e93aa","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqTckbDpjxziuX5561j","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TTM1vBaXy7hqdjnbEMYgiZ","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run of out/analysis.py (deleted results.json, forecasts.csv, exclusions.csv) reproduces byte-identical results.json and forecasts.csv | PASS |\n| forecasts.csv has 192 rows, 48 per (model, horizon) | PASS |\n| target_date = origin + h months for all rows | PASS |\n| last_target_date_used <= origin − 2 months for all rows (GDP rule; incl. estimation sample) | PASS |\n| last_predictor_date_used <= origin − 1 month for model rows; empty for naive | PASS |\n| actual equals data gdp_yoy of target month | PASS |\n| naive forecast equals gdp_yoy(t−2) | PASS |\n| RMSE/MAE recomputed from ledger match results.json; rel_improvement identity holds | PASS |\n| MAE <= RMSE, all RMSE > 0 | PASS |\n| Exclusion ledger: 345 − (7+3+19) = 316 = n_analysed | PASS |\n| In-script asserts: 48 origins, no missing actuals, lt<=t−2, lp<=t−1 | PASS |\n| Magnitudes: model RMSE (0.74/0.95) below naive (0.81/1.14), same order of magnitude; plausible | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1189,"cache_read_input_tokens":19352,"output_tokens":578,"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":1189},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqTcTyRupMEKkC5LbaB","requestRef":"a2a64c16-f17d-46fa-93dc-b4e64e8e6470","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"20035618-0160-4f5e-b55c-4debd2153f51","timestamp":"2026-10-30T05:03:16.173Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"20035618-0160-4f5e-b55c-4debd2153f51","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TTM1vBaXy7hqdjnbEMYgiZ","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 43 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:last_target_date_available: ok\n  [ok] key:last_predictor_date_available: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] chronology:first_origin<=last_origin: 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 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0214/out/state/gate_receipts/20261030T050316.932194Z.json\n20261030T050316.932194Z.json","is_error":false}]},"uuid":"3bea1eb9-f67f-4764-b0e7-ff80cf78a313","timestamp":"2026-10-30T05:03:16.942Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 43 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:model_name: ok\n  [ok] key:model_description: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:last_target_date_available: ok\n  [ok] key:last_predictor_date_available: ok\n  [ok] key:rmse_naive_h1: ok\n  [ok] key:mae_naive_h1: ok\n  [ok] key:rmse_naive_h3: ok\n  [ok] key:mae_naive_h3: ok\n  [ok] key:rmse_model_h1: ok\n  [ok] key:mae_model_h1: ok\n  [ok] key:rmse_model_h3: ok\n  [ok] key:mae_model_h3: ok\n  [ok] key:rel_improvement_rmse_h1: ok\n  [ok] key:rel_improvement_rmse_h3: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] chronology:first_origin<=last_origin: 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 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0214/out/state/gate_receipts/20261030T050316.932194Z.json\n20261030T050316.932194Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"20035618-0160-4f5e-b55c-4debd2153f51","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}64{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"f1805a49ade94e450e81644580f79fed0bcc5abc1809c59bda74ac78d0c6a5ff","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01TTM1vBaXy7hqdjnbEMYgiZ","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run of out/analysis.py (deleted results.json, forecasts.csv, exclusions.csv) reproduces byte-identical results.json and forecasts.csv | PASS |\n| forecasts.csv has 192 rows, 48 per (model, horizon) | PASS |\n| target_date = origin + h months for all rows | PASS |\n| last_target_date_used <= origin − 2 months for all rows (GDP rule; incl. estimation sample) | PASS |\n| last_predictor_date_used <= origin − 1 month for model rows; empty for naive | PASS |\n| actual equals data gdp_yoy of target month | PASS |\n| naive forecast equals gdp_yoy(t−2) | PASS |\n| RMSE/MAE recomputed from ledger match results.json; rel_improvement identity holds | PASS |\n| MAE <= RMSE, all RMSE > 0 | PASS |\n| Exclusion ledger: 345 − (7+3+19) = 316 = n_analysed | PASS |\n| In-script asserts: 48 origins, no missing actuals, lt<=t−2, lp<=t−1 | PASS |\n| Magnitudes: model RMSE (0.74/0.95) below naive (0.81/1.14), same order of magnitude; plausible | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts"}}]}}65{"type":"api-request","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","id":"6aa7c121-ab87-4f7a-95c9-6e69a3bc225b","timestamp":"2026-10-30T05:03:16.945Z","version":"2.1.314","querySource":"sdk","shapeHash":"082ce90230938627e4a2f493d5c6d77c69a74949a20efb0eaa85ab69e98c1bb4","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":"a2a64c16-f17d-46fa-93dc-b4e64e8e6470","keep":20,"tail":["f1805a49ade94e450e81644580f79fed0bcc5abc1809c59bda74ac78d0c6a5ff","3be11944af587af536e6f7c06ba3891e3488810c8c3398960ac42b902120551c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"e46069632effd39bb88233e0fd4d4a20a8d6ca1d7d1a81e318e3dfc68c219065"}66{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"3bea1eb9-f67f-4764-b0e7-ff80cf78a313","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}67{"type":"atis-latch","atis":"","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4"}68{"parentUuid":"3bea1eb9-f67f-4764-b0e7-ff80cf78a313","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqU4kGtRKFebh1Sp4Hh","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":1575,"cache_read_input_tokens":20541,"output_tokens":3526,"output_tokens_details":{"thinking_tokens":37},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":566,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqU4UebpaSQEBFkKzPq","requestRef":"6aa7c121-ab87-4f7a-95c9-6e69a3bc225b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4095d5d3-0b9a-4aab-bb65-759ef25e7470","timestamp":"2026-10-30T05:03:18.760Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"4095d5d3-0b9a-4aab-bb65-759ef25e7470","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqU4kGtRKFebh1Sp4Hh","type":"message","role":"assistant","content":[{"type":"text","text":"The remaining failures at P4 are all P5 deliverables, as expected. Writing the report and claims next."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1575,"cache_read_input_tokens":20541,"output_tokens":3526,"output_tokens_details":{"thinking_tokens":37},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqU4UebpaSQEBFkKzPq","requestRef":"6aa7c121-ab87-4f7a-95c9-6e69a3bc225b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"296e45ee-1504-4de5-95ae-1689f7fd1613","timestamp":"2026-10-30T05:03:18.760Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"296e45ee-1504-4de5-95ae-1689f7fd1613","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqU4kGtRKFebh1Sp4Hh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RtcN3xsTSmzFHaESgccemZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-01 — Forecasting Canadian monthly GDP y/y 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`), how well can the year-over-year growth of Canadian monthly real GDP for months t+1 and t+3 be forecast with the data published by then? And does a model that adds LFS employment growth (published sooner) beat the naive forecast? GDP can be used only for months ≤ t−2 and employment only for months ≤ t−1. At the last origin this means GDP up to `last_target_date_available` (2026-02-01) and employment up to `last_predictor_date_available` (2026-03-01).\n\n## Data and exclusions\n- One file: `data/canada_gdp_employment.csv`. Its SHA-256 matches the README. It has 345 rows (`n_input_rows`), covers 1998-01 to 2026-09, has no duplicate months and no gaps. GDP is missing only for 2026-08 and 2026-09, which were not yet published.\n- I recomputed both y/y columns from the levels and they match exactly from 1999-01 onward. Data issues are listed in `data_issues`:\n  - The README says the y/y columns are empty for 1998, but the file fills them in. I used those values as given.\n  - The COVID swings (GDP y/y from −16.2 to +18.5 %) are extreme outliers. I removed them from model estimation: any training pair whose target or regressor month falls in 2020-03..2021-06 is dropped. I fixed this rule in advance and did not tune it on test errors. No test target falls in that window, and the naive forecast is unaffected.\n  - The data are the current vintage, not the figures as first published.\n- Exclusion ledger (`out/exclusions.csv`) for the last estimation sample (origin 2026-04, h=1):\n  - 345 rows → 338 available at that origin (GDP ≤ 2026-02)\n  - → 335 lag/lead pairs\n  - → 316 after the COVID exclusion (`n_analysed`). The h=3 equivalent is 312 (`n_analysed_h3`).\n  The sample size grows with each origin.\n\n## Method\n- **Naive**: the forecast for both horizons is gdp_yoy(t−2).\n- **Model `ardl_emp`** (`model_name`, `model_description`): a direct h-step OLS regression, gdp_yoy(s+h) = a + b·gdp_yoy(s−2) + c·emp_yoy(s−1).\n  - It is re-estimated at every origin on an expanding window that starts in 1998.\n  - The window includes only pairs whose target month is ≤ t−2, so every training target was already published at the origin.\n  - The forecast plugs in gdp_yoy(t−2) and emp_yoy(t−1).\n  - There is no model selection or tuning and no randomness (`random_seed` = null).\n- For every forecast, `out/forecasts.csv` records the latest GDP and employment months used, including in estimation. Checks confirm all of them respect the lag rules (`out/state/checks.md`).\n- Accuracy is measured by RMSE and MAE in percentage points over the 48 origins for each horizon.\n\n## Results\n| | h=1 | h=3 |\n|---|---|---|\n| Naive RMSE | 0.814 (`rmse_naive_h1`) | 1.140 (`rmse_naive_h3`) |\n| Naive MAE | 0.642 (`mae_naive_h1`) | 0.872 (`mae_naive_h3`) |\n| Model RMSE | 0.741 (`rmse_model_h1`) | 0.947 (`rmse_model_h3`) |\n| Model MAE | 0.603 (`mae_model_h1`) | 0.796 (`mae_model_h3`) |\n| 1 − RMSE_model/RMSE_naive | 0.089 (`rel_improvement_rmse_h1`) | 0.170 (`rel_improvement_rmse_h3`) |\n\nAt both horizons the employment-augmented model has lower RMSE and MAE than the naive forecast: about 9 % lower RMSE at h=1 and 17 % lower at h=3. Because GDP is published two months late, the effective lead time is h+2 months, which is why errors grow with h. In the last estimation sample (`last_estimation_sample`) the coefficient on employment growth is positive but small (≈0.06 at h=1 and ≈0.09 at h=3). Most of the gain likely comes from the estimated mean reversion (b < 1 plus an intercept) rather than from employment itself. I did not test this separately.\n\n## Uncertainty\nI compared squared-error losses with a Diebold–Mariano test (Newey–West variance with h+1 lags and the Harvey–Leybourne–Newbold small-sample correction). The statistics are 1.33 at h=1 (`dm_stat_h1`, p = 0.19, `dm_pvalue_h1`) and 1.26 at h=3 (`dm_stat_h3`, p = 0.22, `dm_pvalue_h3`). With only 48 overlapping origins, the improvement is **not statistically significant** at conventional levels. The point estimates of the relative improvement are therefore imprecise.\n\n## Limitations and open issues\n- The errors come from current-vintage data: the exercise simulates publication timing, not the values first published, so no real-time validity is claimed.\n- The evaluation covers only 48 origins (2022–2026), a post-COVID period with falling growth. The results may not carry over to other periods.\n- The COVID exclusion window is a judgement call I made in advance. I tested no alternatives, so the model's sensitivity to it is unknown.\n- I evaluated only one model, and did not separate the contribution of employment from that of mean reversion. The claim that employment growth \"helps\" is therefore not established; only \"the model with employment beats the naive forecast in this sample\" is.\n- I could not verify the y/y values for 1998, because the 1997 levels are not in the file.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 48 origins the employment-augmented direct ARDL model has lower RMSE and MAE than the naive forecast at h=1 and h=3.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; post-COVID evaluation window only.\"},\n {\"id\":\"C2\",\"text\":\"Relative RMSE improvement over naive is about 9% at h=1 and 17% at h=3.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Point estimates; imprecise.\"},\n {\"id\":\"C3\",\"text\":\"The improvement is not statistically significant by a HLN-corrected Diebold-Mariano test (p about 0.19 and 0.22).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"Small sample of 48 overlapping errors; DM test has low power.\"},\n {\"id\":\"C4\",\"text\":\"Forecast errors increase with horizon for both methods.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h3\",\"rmse_model_h1\",\"rmse_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C5\",\"text\":\"Under the publication-lag rule the last origin uses GDP through 2026-02 and employment through 2026-03, and the final estimation sample has 316 observations (h=1).\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"Employment growth per se drives the improvement.\",\"results_keys\":[\"last_estimation_sample\"],\"status\":\"not_evaluated\",\"caveat\":\"No ablation without employment was run; small positive coefficient suggests mean reversion may dominate.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"schema, ranges, missing, duplicates audited\",\"yoy recomputed from levels: exact match\",\"exclusion ledger sums verified\"],\"notes\":\"COVID 2020-03..2021-06 excluded from estimation\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive gdp_yoy(t-2) computed for 48 origins x 2 horizons; values verified against data\"],\"notes\":\"rmse_naive_h1/h3 in results.json\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, deterministic, no seed\",\"in-script availability asserts pass\"],\"notes\":\"direct ARDL with emp_yoy(t-1)\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"all checks in checks.md PASS\",\"gate run 20261030T050316.932194Z: only P5 artifacts (report, claims, phase terminal) failing, as expected at P4\"],\"notes\":\"receipt out/state/gate_receipts/20261030T050316.932194Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"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 P2-P4 passed (checks.md; gate run at P4). P5: report and claims written.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1575,"cache_read_input_tokens":20541,"output_tokens":3526,"output_tokens_details":{"thinking_tokens":37},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqU4UebpaSQEBFkKzPq","requestRef":"6aa7c121-ab87-4f7a-95c9-6e69a3bc225b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c7594cdb-3290-4879-87dd-06e592f06e02","timestamp":"2026-10-30T05:03:46.778Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}71{"parentUuid":"c7594cdb-3290-4879-87dd-06e592f06e02","isSidechain":false,"promptId":"b425d176-7d04-4330-a159-c6c1b864a628","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RtcN3xsTSmzFHaESgccemZ","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0214/out/state/gate_receipts/20261030T050347.579534Z.json","is_error":false}]},"uuid":"b5be7156-4657-4731-add0-4ad88914d6e8","timestamp":"2026-10-30T05:03:47.588Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 43 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0214/out/state/gate_receipts/20261030T050347.579534Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c7594cdb-3290-4879-87dd-06e592f06e02","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0214","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","version":"2.1.314","gitBranch":"HEAD"}72{"type":"api-request-blob","sessionId":"9c7cecd0-911e-4d96-b1db-bdb9b49329f4","hash":"5dca5da5c5de851bb2319897406ef984233ca71332e344a1daa8a2a7cb89ca16","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The remaining failures at P4 are all P5 deliverables, as expected. Writing the report and claims next."},{"type":"tool_use","id":"toolu_01RtcN3xsTSmzFHaESgccemZ","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-01 — Forecasting Canadian monthly GDP y/y 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`), how well can the year-over-year growth of Canadian monthly real GDP for months t+1 and t+3 be forecast with the data published by then? And does a model that adds LFS employment growth (published sooner) beat the naive forecast? GDP can be used only for months ≤ t−2 and employment only for months ≤ t−1. At the last origin this means GDP up to `last_target_date_available` (2026-02-01) and employment up to `last_predictor_date_available` (2026-03-01).\n\n## Data and exclusions\n- One file: `data/canada_gdp_employment.csv`. Its SHA-256 matches the README. It has 345 rows (`n_input_rows`), covers 1998-01 to 2026-09, has no duplicate months and no gaps. GDP is missing only for 2026-08 and 2026-09, which were not yet published.\n- I recomputed both y/y columns from the levels and they match exactly from 1999-01 onward. Data issues are listed in `data_issues`:\n  - The README says the y/y columns are empty for 1998, but the file fills them in. I used those values as given.\n  - The COVID swings (GDP y/y from −16.2 to +18.5 %) are extreme outliers. I removed them from model estimation: any training pair whose target or regressor month falls in 2020-03..2021-06 is dropped. I fixed this rule in advance and did not tune it on test errors. No test target falls in that window, and the naive forecast is unaffected.\n  - The data are the current vintage, not the figures as first published.\n- Exclusion ledger (`out/exclusions.csv`) for the last estimation sample (origin 2026-04, h=1):\n  - 345 rows → 338 available at that origin (GDP ≤ 2026-02)\n  - → 335 lag/lead pairs\n  - → 316 after the COVID exclusion (`n_analysed`). The h=3 equivalent is 312 (`n_analysed_h3`).\n  The sample size grows with each origin.\n\n## Method\n- **Naive**: the forecast for both horizons is gdp_yoy(t−2).\n- **Model `ardl_emp`** (`model_name`, `model_description`): a direct h-step OLS regression, gdp_yoy(s+h) = a + b·gdp_yoy(s−2) + c·emp_yoy(s−1).\n  - It is re-estimated at every origin on an expanding window that starts in 1998.\n  - The window includes only pairs whose target month is ≤ t−2, so every training target was already published at the origin.\n  - The forecast plugs in gdp_yoy(t−2) and emp_yoy(t−1).\n  - There is no model selection or tuning and no randomness (`random_seed` = null).\n- For every forecast, `out/forecasts.csv` records the latest GDP and employment months used, including in estimation. Checks confirm all of them respect the lag rules (`out/state/checks.md`).\n- Accuracy is measured by RMSE and MAE in percentage points over the 48 origins for each horizon.\n\n## Results\n| | h=1 | h=3 |\n|---|---|---|\n| Naive RMSE | 0.814 (`rmse_naive_h1`) | 1.140 (`rmse_naive_h3`) |\n| Naive MAE | 0.642 (`mae_naive_h1`) | 0.872 (`mae_naive_h3`) |\n| Model RMSE | 0.741 (`rmse_model_h1`) | 0.947 (`rmse_model_h3`) |\n| Model MAE | 0.603 (`mae_model_h1`) | 0.796 (`mae_model_h3`) |\n| 1 − RMSE_model/RMSE_naive | 0.089 (`rel_improvement_rmse_h1`) | 0.170 (`rel_improvement_rmse_h3`) |\n\nAt both horizons the employment-augmented model has lower RMSE and MAE than the naive forecast: about 9 % lower RMSE at h=1 and 17 % lower at h=3. Because GDP is published two months late, the effective lead time is h+2 months, which is why errors grow with h. In the last estimation sample (`last_estimation_sample`) the coefficient on employment growth is positive but small (≈0.06 at h=1 and ≈0.09 at h=3). Most of the gain likely comes from the estimated mean reversion (b < 1 plus an intercept) rather than from employment itself. I did not test this separately.\n\n## Uncertainty\nI compared squared-error losses with a Diebold–Mariano test (Newey–West variance with h+1 lags and the Harvey–Leybourne–Newbold small-sample correction). The statistics are 1.33 at h=1 (`dm_stat_h1`, p = 0.19, `dm_pvalue_h1`) and 1.26 at h=3 (`dm_stat_h3`, p = 0.22, `dm_pvalue_h3`). With only 48 overlapping origins, the improvement is **not statistically significant** at conventional levels. The point estimates of the relative improvement are therefore imprecise.\n\n## Limitations and open issues\n- The errors come from current-vintage data: the exercise simulates publication timing, not the values first published, so no real-time validity is claimed.\n- The evaluation covers only 48 origins (2022–2026), a post-COVID period with falling growth. The results may not carry over to other periods.\n- The COVID exclusion window is a judgement call I made in advance. I tested no alternatives, so the model's sensitivity to it is unknown.\n- I evaluated only one model, and did not separate the contribution of employment from that of mean reversion. The claim that employment growth \"helps\" is therefore not established; only \"the model with employment beats the naive forecast in this sample\" is.\n- I could not verify the y/y values for 1998, because the 1997 levels are not in the file.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 48 origins the employment-augmented direct ARDL model has lower RMSE and MAE than the naive forecast at h=1 and h=3.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_model_h1\",\"mae_naive_h1\",\"mae_model_h1\",\"rmse_naive_h3\",\"rmse_model_h3\",\"mae_naive_h3\",\"mae_model_h3\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; post-COVID evaluation window only.\"},\n {\"id\":\"C2\",\"text\":\"Relative RMSE improvement over naive is about 9% at h=1 and 17% at h=3.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Point estimates; imprecise.\"},\n {\"id\":\"C3\",\"text\":\"The improvement is not statistically significant by a HLN-corrected Diebold-Mariano test (p about 0.19 and 0.22).\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"Small sample of 48 overlapping errors; DM test has low power.\"},\n {\"id\":\"C4\",\"text\":\"Forecast errors increase with horizon for both methods.\",\"results_keys\":[\"rmse_naive_h1\",\"rmse_naive_h3\",\"rmse_model_h1\",\"rmse_model_h3\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C5\",\"text\":\"Under the publication-lag rule the last origin uses GDP through 2026-02 and employment through 2026-03, and the final estimation sample has 316 observations (h=1).\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"Employment growth per se drives the improvement.\",\"results_keys\":[\"last_estimation_sample\"],\"status\":\"not_evaluated\",\"caveat\":\"No ablation without employment was run; small positive coefficient suggests mean reversion may dominate.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 matches README/bundle\",\"schema, ranges, missing, duplicates audited\",\"yoy recomputed from levels: exact match\",\"exclusion ledger sums verified\"],\"notes\":\"COVID 2020-03..2021-06 excluded from estimation\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive gdp_yoy(t-2) computed for 48 origins x 2 horizons; values verified against data\"],\"notes\":\"rmse_naive_h1/h3 in results.json\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, deterministic, no seed\",\"in-script availability asserts pass\"],\"notes\":\"direct ARDL with emp_yoy(t-1)\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"all checks in checks.md PASS\",\"gate run 20261030T050316.932194Z: only P5 artifacts (report, claims, phase terminal) failing, as expected at P4\"],\"notes\":\"receipt out/state/gate_receipts/20261030T050316.932194Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"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 P2-P4 passed (checks.md; gate run at P4). 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Adding employment growth lowered forecast errors against the naive forecast at both horizons, but the gain is not statistically significant.\n\n**My model (`ardl_emp`):** a regression that predicts GDP growth h months ahead from the latest published GDP growth (month t−2) and the latest employment growth (month t−1). It is re-fitted at each origin using all data published by then, starting from 1998.\n\n| Over 48 origins | h=1 | h=3 |\n|---|---|---|\n| Naive forecast RMSE / MAE | 0.814 / 0.642 | 1.140 / 0.872 |\n| My model RMSE / MAE | 0.741 / 0.603 | 0.947 / 0.796 |\n| Relative RMSE improvement | 0.089 | 0.170 |\n\n- **Significance:** a Diebold–Mariano test comparing the two sets of errors gives p = 0.19 at h=1 and p = 0.22 at h=3. With only 48 overlapping origins, the improvement could be chance.\n- **Publication lags:** every forecast uses only data published by its origin. At the last origin (2026-04) that means GDP through 2026-02 and employment through 2026-03. The last estimation sample has 316 observations.\n- **Reproducibility:** re-running `out/analysis.py` from scratch reproduces identical files.\n\n**Choices and limitations:**\n- **COVID months left out:** I excluded the extreme 2020-03 to 2021-06 swings (GDP growth from −16.2% to +18.5%) from model fitting. I set this rule before seeing any test errors and didn't try alternative windows, so I don't know how sensitive the results are to it. No test target falls in that window, and the naive forecast is unaffected.\n- **Not a true real-time test:** the data are today's revised figures, so this simulates publication timing only, not what forecasters actually saw at the time.\n- **Short test period:** the 48 origins cover 2022–2026 only.\n- **Employment's role is unproven:** I didn't run the model without employment. Its coefficient is small (about 0.06–0.09), so most of the gain may come from the model pulling forecasts back toward the average, not from employment.\n- **Data quirk:** the data README says the growth columns are empty for 1998, but the file fills them in. I used the values as given; they can't be checked because the 1997 levels aren't in the file.\n\nEverything is in `out/`; start with `report.md`. 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