spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:59:16.597Z","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:59:16.603Z","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48"}4{"parentUuid":null,"isSidechain":false,"promptId":"8ec3a20a-8304-4240-8dc7-b3a92044df4f","type":"user","message":{"role":"user","content":"Begin"},"uuid":"e60851a4-da58-4b25-b93d-83b333caa430","timestamp":"2026-10-30T04:59:16.607Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","shapeHash":"d9f495c5525fd789ca52b7d818301169852aa68cc44a6c65099897418fbafc07","timestamp":"2026-10-30T04:59:16.621Z","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/RC0207\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"}],"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/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48"}12{"parentUuid":"b14c0582-fa15-47d9-9b47-47c27b9ecc5d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqANcZUjsm8wNdtd57r","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PhjJcJLsa6KJPF76AdtjQi","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  120 simon-pierreboucher  staff  3840 oct.  30 00:59 ..\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\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"6a96fdf4-06c1-4a29-8c9b-8de6623cd40a","timestamp":"2026-10-30T04:59:18.909Z","toolUseResult":{"stdout":"# 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  120 simon-pierreboucher  staff  3840 oct.  30 00:59 ..\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\n(eval):1: no matches found: out/state/*","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"df8c821c-427f-469d-9c1c-ba6254e57ce0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","hash":"ed4cac81d37ee90378b1c462bc2c6a1ef0d2d33359ca4c69989b53fc01d3a5fe","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PhjJcJLsa6KJPF76AdtjQi","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.\nmonth,gdp_level,gdp_yoy,emp_level,emp_yoy\n1998-01-01,1251276.0,4.2359,13864.8,2.4298\n1998-02-01,1263285.0,4.5780,13914.5,2.7803\n1998-03-01,1267784.0,4.9633,13949.4,2.7974\n1998-04-01,1268479.0,4.3134,13977.6,2.9946\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\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\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}{\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}","is_error":false}]},"uuid":"abf1717d-f911-4a19-8f1c-4affb7f7b32b","timestamp":"2026-10-30T04:59:20.900Z","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.\nmonth,gdp_level,gdp_yoy,emp_level,emp_yoy\n1998-01-01,1251276.0,4.2359,13864.8,2.4298\n1998-02-01,1263285.0,4.5780,13914.5,2.7803\n1998-03-01,1267784.0,4.9633,13949.4,2.7974\n1998-04-01,1268479.0,4.3134,13977.6,2.9946\n2026-05-01,2363071.0,1.6466,21121.5,0.7013\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\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}{\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\": 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5) month        datetime64[ns]\ngdp_level           float64\ngdp_yoy             float64\nemp_level           float64\nemp_yoy             float64\ndtype: object\nmonth        0\ngdp_level    2\ngdp_yoy      2\nemp_level    0\nemp_yoy      0\ndtype: int64\n0 count    344.000000\nmean      30.436047\nstd        0.819929\nmin       28.000000\n25%       30.000000\n50%       31.000000\n75%       31.000000\nmax       31.000000\nName: month, dtype: float64\n                               month     gdp_level  ...     emp_level     emp_yoy\ncount                            345  3.430000e+02  ...    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06  ...  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  ...  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06  ...  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06  ...  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06  ...  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06  ...  21214.800000   16.088700\nstd                              NaN  3.003681e+05  ...   1919.265534    2.263768\n\n[8 rows x 5 columns]\n4.985325298356713e-05 4.968314845221755e-05\n[Timestamp('2026-08-01 00:00:00'), Timestamp('2026-09-01 00:00:00')]\n[]\n         month  gdp_yoy  emp_yoy\n264 2020-01-01   2.1209   1.5021\n265 2020-02-01   2.6658   1.2507\n266 2020-03-01  -5.6070  -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01  -7.6173  -8.4346\n270 2020-07-01  -5.2884  -6.1707\n271 2020-08-01  -4.3527  -5.2504\n272 2020-09-01  -3.5236  -3.0659\n273 2020-10-01  -3.0057  -2.6894\n274 2020-11-01  -2.3377  -2.3397\n275 2020-12-01  -2.5523  -3.1001\n276 2021-01-01  -2.0402  -4.2313\n277 2021-02-01  -2.0966  -2.9730\n278 2021-03-01   7.0112   4.5121\n279 2021-04-01  18.5426  16.0887\n280 2021-05-01  13.1156  13.4845\n281 2021-06-01   7.7967   8.1571\n282 2021-07-01   5.8090   6.2641\n283 2021-08-01   5.6440   5.2534","is_error":false}]},"uuid":"c52ad5da-cf4f-43cc-aa29-68cb5e7c427b","timestamp":"2026-10-30T04:59:31.224Z","toolUseResult":{"stdout":"(345, 5) month        datetime64[ns]\ngdp_level           float64\ngdp_yoy             float64\nemp_level           float64\nemp_yoy             float64\ndtype: object\nmonth        0\ngdp_level    2\ngdp_yoy      2\nemp_level    0\nemp_yoy      0\ndtype: int64\n0 count    344.000000\nmean      30.436047\nstd        0.819929\nmin       28.000000\n25%       30.000000\n50%       31.000000\n75%       31.000000\nmax       31.000000\nName: month, dtype: float64\n                               month     gdp_level  ...     emp_level     emp_yoy\ncount                            345  3.430000e+02  ...    345.000000  345.000000\nmean   2012-05-01 10:09:23.478260992  1.813643e+06  ...  17454.385797    1.540610\nmin              1998-01-01 00:00:00  1.251276e+06  ...  13864.800000  -15.576500\n25%              2005-03-01 00:00:00  1.586989e+06  ...  16047.100000    1.028600\n50%              2012-05-01 00:00:00  1.788771e+06  ...  17338.600000    1.679100\n75%              2019-07-01 00:00:00  2.056191e+06  ...  18673.800000    2.322000\nmax              2026-09-01 00:00:00  2.371886e+06  ...  21214.800000   16.088700\nstd                              NaN  3.003681e+05  ...   1919.265534    2.263768\n\n[8 rows x 5 columns]\n4.985325298356713e-05 4.968314845221755e-05\n[Timestamp('2026-08-01 00:00:00'), Timestamp('2026-09-01 00:00:00')]\n[]\n         month  gdp_yoy  emp_yoy\n264 2020-01-01   2.1209   1.5021\n265 2020-02-01   2.6658   1.2507\n266 2020-03-01  -5.6070  -4.6200\n267 2020-04-01 -16.2055 -15.5765\n268 2020-05-01 -12.6253 -13.9328\n269 2020-06-01  -7.6173  -8.4346\n270 2020-07-01  -5.2884  -6.1707\n271 2020-08-01  -4.3527  -5.2504\n272 2020-09-01  -3.5236  -3.0659\n273 2020-10-01  -3.0057  -2.6894\n274 2020-11-01  -2.3377  -2.3397\n275 2020-12-01  -2.5523  -3.1001\n276 2021-01-01  -2.0402  -4.2313\n277 2021-02-01  -2.0966  -2.9730\n278 2021-03-01   7.0112   4.5121\n279 2021-04-01  18.5426  16.0887\n280 2021-05-01  13.1156  13.4845\n281 2021-06-01   7.7967   8.1571\n282 2021-07-01   5.8090   6.2641\n283 2021-08-01   5.6440   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> out/state/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, and 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), (b) one chosen model; relative improvement 1 - RMSE_model/RMSE_naive. Meaningful under: current-vintage data (simulated publication timing only, not real time), fixed origin set, squared/absolute loss.\n**Info rules**: at origin t, GDP columns usable only for months <= t-2, employment only for months <= t-1; applies to estimation sample, training targets, model selection, transforms, plug-in values.\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 task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Invalidity**: any use of GDP > t-2 or employment > t-1; missing actuals for targets (last target 2026-07 is published, so OK); tuning the model on the evaluation errors.\nEOF\ncat > out/state/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\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, 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Meaningful under: current-vintage data (simulated publication timing only, not real time), fixed origin set, squared/absolute loss.\n**Info rules**: at origin t, GDP columns usable only for months <= t-2, employment only for months <= t-1; applies to estimation sample, training targets, model selection, transforms, plug-in values.\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 task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Invalidity**: any use of GDP > t-2 or employment > t-1; missing actuals for targets (last target 2026-07 is published, so OK); tuning the model on the evaluation errors.\nEOF\ncat > out/state/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\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}31{"type":"api-request","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","id":"2835572c-214b-41c0-b198-7ed58b2deb7b","timestamp":"2026-10-30T04:59:40.825Z","version":"2.1.314","querySource":"sdk","shapeHash":"d9f495c5525fd789ca52b7d818301169852aa68cc44a6c65099897418fbafc07","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":"1d487533-9399-46db-b578-1111e57ec408","keep":8,"tail":["c2ea0205ee9c7be51d6454ff76143d88b2dae275b105bf6e5b5c9d9241103c6d","03288c5335bb7493de10015c639075a09214b9fef73e55bbe9529586058fadca"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"264d442705fd76a3f6fc54c1d37ef135f7b5eba6ddfb89d9de4c5bed8cab70b6"}32{"parentUuid":"58e4495d-76c6-4ab2-86bd-3b588851173a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqC9WaEmtr5Dyw3x49f","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017R6Vt9DrVB2kQgHekQFEhr","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\nFile: data/canada_gdp_employment.csv\n- SHA-256: f4c8e4976fb5f5f0c326e40bcb4f85b8b22963e5be89e2729e1a430c198f2bed (matches README and bundle.json)\n- 345 data rows x 5 columns: month (date, YYYY-MM-01), gdp_level (float, M chained 2017 $, SAAR), gdp_yoy (%, 4 dp), emp_level (float, thousands), emp_yoy (%, 4 dp)\n- Range 1998-01-01 .. 2026-09-01; monthly, contiguous (no gaps: consecutive diffs 28-31 days); 0 duplicated months.\n- Missing: gdp_level and gdp_yoy missing for 2026-08, 2026-09 only (not yet published) — expected. emp columns complete.\n- yoy recomputation from levels (where 12-month lag exists in file) matches given yoy to <=5e-5 (rounding).\n- Anomaly / doc mismatch: README says gdp_yoy is empty for the first 12 months, but gdp_yoy and emp_yoy are populated for 1998 (presumably computed from pre-1998 levels not in the file). 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: Canadian monthly GDP y/y growth forecasts under publication lags.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"ardl_emp_direct\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))  # pre-specified training exclusion window\nHORIZONS = (1, 3)\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.set_index(\"month\").sort_index()\nassert d.index.is_unique\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nM = lambda ts, k: ts + pd.DateOffset(months=k)\n\ndef design(gdp, emp, h, exclude_covid):\n    \"\"\"Direct regression rows: target y(m) on [1, y(m-h-2), emp(m-h-1)], using only the masked series.\"\"\"\n    rows = []\n    for m in gdp.dropna().index:\n        ml_y, ml_e = M(m, -(h + 2)), M(m, -(h + 1))\n        if ml_y not in gdp.index or ml_e not in emp.index or pd.isna(gdp.get(ml_y)) or pd.isna(emp.get(ml_e)):\n            continue\n        if exclude_covid and any(COVID[0] <= x <= COVID[1] for x in (m, ml_y, ml_e)):\n            continue\n        rows.append((m, gdp[m], gdp[ml_y], emp[ml_e]))\n    return pd.DataFrame(rows, columns=[\"m\", \"y\", \"ylag\", \"elag\"]).set_index(\"m\")\n\ndef fit_forecast(t, h, exclude_covid=True):\n    gdp = d[\"gdp_yoy\"][d.index <= M(t, -2)]          # GDP available: months <= t-2\n    emp = d[\"emp_yoy\"][d.index <= M(t, -1)]          # LFS available: months <= t-1\n    X = design(gdp, emp, h, exclude_covid)\n    A = np.column_stack([np.ones(len(X)), X[\"ylag\"], X[\"elag\"]])\n    beta, *_ = np.linalg.lstsq(A, X[\"y\"].values, rcond=None)\n    x_new = np.array([1.0, gdp[M(t, -2)], emp[M(t, -1)]])\n    return float(x_new @ beta), X, gdp.index.max(), emp.index.max()\n\nrecs, sens, n_last = [], [], {}\nfor t in origins:\n    for h in HORIZONS:\n        tgt = M(t, h)\n        actual = float(d.loc[tgt, \"gdp_yoy\"])\n        assert not np.isnan(actual)\n        last_g = M(t, -2)\n        recs.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(d.loc[last_g, \"gdp_yoy\"]),\n                         actual=actual, model=\"naive\", last_target_date_used=last_g, last_predictor_date_used=pd.NaT))\n        f, X, lg, le = fit_forecast(t, h, True)\n        assert lg <= M(t, -2) and le <= M(t, -1) and X.index.max() <= M(t, -2)\n        recs.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=f, actual=actual, model=MODEL,\n                         last_target_date_used=lg, last_predictor_date_used=le))\n        fs, *_ = fit_forecast(t, h, False)\n        sens.append(dict(h=h, e=fs - actual))\n        if t == origins[-1]:\n            n_last[h] = len(X)\nfc = pd.DataFrame(recs)\nfc[\"error\"] = fc[\"forecast\"] - fc[\"actual\"]\n\ndef rmse(e): return float(np.sqrt(np.mean(np.square(e))))\ndef mae(e): return float(np.mean(np.abs(e)))\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free rectangular lags, Harvey et al. correction.\"\"\"\n    dl = e1 ** 2 - e2 ** 2\n    n = len(dl); dbar = dl.mean(); dc = dl - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        v = g[0] / n\n    stat = dbar / np.sqrt(v) * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    return float(stat), float(2 * stats.t.sf(abs(stat), df=n - 1))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct h-step OLS regression per horizon and origin of gdp_yoy(m) on a constant, gdp_yoy(m-h-2) \"\n                             \"and emp_yoy(m-h-1), estimated on an expanding window from 1998 using only training pairs whose target \"\n                             \"month is <= t-2 (and employment <= t-1), excluding pairs touching 2020-03..2021-12 (COVID); \"\n                             \"forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\"),\n       \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date()),\n       \"last_target_date_available\": str(M(origins[-1], -2).date()),\n       \"last_predictor_date_available\": str(M(origins[-1], -1).date())}\nsub = {}\nfor h in HORIZONS:\n    en = fc[(fc.horizon == h) & (fc.model == \"naive\")].sort_values(\"origin_date\")[\"error\"].values\n    em = fc[(fc.horizon == h) & (fc.model == MODEL)].sort_values(\"origin_date\")[\"error\"].values\n    res[f\"rmse_naive_h{h}\"], res[f\"mae_naive_h{h}\"] = rmse(en), mae(en)\n    res[f\"rmse_model_h{h}\"], res[f\"mae_model_h{h}\"] = rmse(em), mae(em)\n    sub[h] = (en, em)\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\": \"README states gdp_yoy is empty for the first 12 months, but gdp_yoy and emp_yoy are populated from 1998-01 (presumably from pre-1998 levels not in the file).\",\n                 \"handling\": \"Used the provided yoy values as given; they match recomputation from levels to <=5e-5 where verifiable.\"},\n                {\"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published).\",\n                 \"handling\": \"Expected; never needed (last target month 2026-07, last GDP used 2026-02).\"},\n                {\"issue\": \"Extreme COVID-19 swings 2020-03..2021-06 and base effects through 2021 in both series.\",\n                 \"handling\": \"Pre-specified exclusion of training pairs touching 2020-03..2021-12 from the model; sensitivity without exclusion reported (sens_* keys). Naive baseline unaffected.\"},\n                {\"issue\": \"Data are the current vintage (revised), not first releases.\",\n                 \"handling\": \"Exercise simulates publication timing only; no real-time validity claimed.\"}],\n            \"n_input_rows\": {\"canada_gdp_employment.csv\": n_raw},\n            \"n_analysed\": int(n_last[1]), \"n_analysed_last_origin_h3\": int(n_last[3]),\n            \"random_seed\": None})\nfor h in HORIZONS:\n    en, em = sub[h]\n    s, p = dm_test(em, en, h)\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    se = np.array([r[\"e\"] for r in sens if r[\"h\"] == h])\n    res[f\"sens_rmse_model_nocovidexcl_h{h}\"] = rmse(se)\n    res[f\"sens_rel_improvement_rmse_nocovidexcl_h{h}\"] = 1 - rmse(se) / res[f\"rmse_naive_h{h}\"]\nres = {k: (round(v, 10) if isinstance(v, float) else v) for k, v in res.items()}\n\nos.makedirs(OUT, exist_ok=True)\nout = fc.drop(columns=\"error\").copy()\nfor c in [\"origin_date\", \"target_date\", \"last_target_date_used\", \"last_predictor_date_used\"]:\n    out[c] = out[c].dt.strftime(\"%Y-%m-%d\")\nout[\"last_predictor_date_used\"] = out[\"last_predictor_date_used\"].fillna(\"\")\nout[\"forecast\"] = out[\"forecast\"].round(8)\nout.to_csv(f\"{OUT}/forecasts.csv\", index=False)\nwith open(f\"{OUT}/results.json\", \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\n# Exclusion ledger for the last estimation sample (last origin, h=1)\nt = origins[-1]; h = 1\nn1 = int((d.index <= M(t, -2)).sum())\ngdp = d[\"gdp_yoy\"][d.index <= M(t, -2)]; emp = d[\"emp_yoy\"][d.index <= M(t, -1)]\nn2 = len(design(gdp, emp, h, False)); n3 = n_last[1]\nled = pd.DataFrame([\n    (\"1\", \"GDP month <= t-2 at last origin 2026-04 (publication lag); drops 2026-03..2026-09\", n_raw, n1, \"information-availability rule\"),\n    (\"2\", f\"target month needs gdp_yoy(m-{h+2}) and emp_yoy(m-{h+1}) inside the file\", n1, n2, \"lags unavailable for first months of file\"),\n    (\"3\", \"drop pairs with target/regressor month in 2020-03..2021-12\", n2, n3, \"pre-specified COVID outlier exclusion (model only)\")],\n    columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"])\nled.insert(4, \"n_removed\", led.n_before - led.n_after)\nled = led[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]]\nled.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert n_raw - led.n_removed.sum() == res[\"n_analysed\"]\nprint(json.dumps({k: res[k] for k in res if k.startswith((\"rmse\", \"mae\", \"rel\", \"dm\", \"sens\", \"n_an\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":881,"cache_read_input_tokens":12033,"output_tokens":4417,"output_tokens_details":{"thinking_tokens":388},"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":881},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqCnsP4novn8PnGyWsy","requestRef":"ebae9ec5-ece0-4d71-a105-25eefddf7bc5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"26b3dc01-6a38-429c-9766-6b53ee5d6800","timestamp":"2026-10-30T05:00:25.212Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"26b3dc01-6a38-429c-9766-6b53ee5d6800","isSidechain":false,"promptId":"8ec3a20a-8304-4240-8dc7-b3a92044df4f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Hj1Q7dYkLSQaua4UxvmTf6","type":"tool_result","content":"{\n \"rmse_naive_h1\": 0.8136209149,\n \"mae_naive_h1\": 0.6421395833,\n \"rmse_model_h1\": 0.7429391714,\n \"mae_model_h1\": 0.6039917578,\n \"rmse_naive_h3\": 1.1399827161,\n \"mae_naive_h3\": 0.8715583333,\n \"rmse_model_h3\": 0.902300024,\n \"mae_model_h3\": 0.7534546734,\n \"rel_improvement_rmse_h1\": 0.086873066,\n \"rel_improvement_rmse_h3\": 0.2084967507,\n \"n_analysed\": 310,\n \"n_analysed_last_origin_h3\": 306,\n \"dm_stat_h1\": -2.0256610645,\n \"dm_pvalue_h1\": 0.0484981975,\n \"sens_rmse_model_nocovidexcl_h1\": 0.8040049585,\n \"sens_rel_improvement_rmse_nocovidexcl_h1\": 0.0118187183,\n \"dm_stat_h3\": -1.3490597541,\n \"dm_pvalue_h3\": 0.1837816051,\n \"sens_rmse_model_nocovidexcl_h3\": 0.7937029597,\n \"sens_rel_improvement_rmse_nocovidexcl_h3\": 0.3037587777\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,GDP month <= t-2 at last origin 2026-04 (publication lag); drops 2026-03..2026-09,345,338,7,information-availability rule\n2,target month needs gdp_yoy(m-3) and emp_yoy(m-2) inside the file,338,335,3,lags unavailable for first months of file\n3,drop pairs with target/regressor month in 2020-03..2021-12,335,310,25,pre-specified COVID outlier exclusion (model only)\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,\n2022-05-01,2022-06-01,1,4.71344352,5.9571,ardl_emp_direct,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.9335,4.8317,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.39210287,4.8317,ardl_emp_direct,2022-03-01,2022-04-01\n     193 out/forecasts.csv","is_error":false}]},"uuid":"2cd4c355-4c32-4feb-a2a2-8fce0f9c0464","timestamp":"2026-10-30T05:00:27.363Z","toolUseResult":{"stdout":"{\n \"rmse_naive_h1\": 0.8136209149,\n \"mae_naive_h1\": 0.6421395833,\n \"rmse_model_h1\": 0.7429391714,\n \"mae_model_h1\": 0.6039917578,\n \"rmse_naive_h3\": 1.1399827161,\n \"mae_naive_h3\": 0.8715583333,\n \"rmse_model_h3\": 0.902300024,\n \"mae_model_h3\": 0.7534546734,\n \"rel_improvement_rmse_h1\": 0.086873066,\n \"rel_improvement_rmse_h3\": 0.2084967507,\n \"n_analysed\": 310,\n \"n_analysed_last_origin_h3\": 306,\n \"dm_stat_h1\": -2.0256610645,\n \"dm_pvalue_h1\": 0.0484981975,\n \"sens_rmse_model_nocovidexcl_h1\": 0.8040049585,\n \"sens_rel_improvement_rmse_nocovidexcl_h1\": 0.0118187183,\n \"dm_stat_h3\": -1.3490597541,\n \"dm_pvalue_h3\": 0.1837816051,\n \"sens_rmse_model_nocovidexcl_h3\": 0.7937029597,\n \"sens_rel_improvement_rmse_nocovidexcl_h3\": 0.3037587777\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,GDP month <= t-2 at last origin 2026-04 (publication lag); drops 2026-03..2026-09,345,338,7,information-availability rule\n2,target month needs gdp_yoy(m-3) and emp_yoy(m-2) inside the file,338,335,3,lags unavailable for first months of file\n3,drop pairs with target/regressor month in 2020-03..2021-12,335,310,25,pre-specified COVID outlier exclusion (model only)\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,\n2022-05-01,2022-06-01,1,4.71344352,5.9571,ardl_emp_direct,2022-03-01,2022-04-01\n2022-05-01,2022-08-01,3,4.9335,4.8317,naive,2022-03-01,\n2022-05-01,2022-08-01,3,4.39210287,4.8317,ardl_emp_direct,2022-03-01,2022-04-01\n     193 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"26b3dc01-6a38-429c-9766-6b53ee5d6800","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","hash":"5b8d2b328572697693aa8425de01347f852a3023417440ec8b72df38e6961aa0","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Hj1Q7dYkLSQaua4UxvmTf6","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-01: Canadian monthly GDP y/y growth forecasts under publication lags.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nDATA = \"data/canada_gdp_employment.csv\"\nOUT = \"out\"\nMODEL = \"ardl_emp_direct\"\nCOVID = (pd.Timestamp(\"2020-03-01\"), pd.Timestamp(\"2021-12-01\"))  # pre-specified training exclusion window\nHORIZONS = (1, 3)\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.set_index(\"month\").sort_index()\nassert d.index.is_unique\norigins = pd.date_range(\"2022-05-01\", \"2026-04-01\", freq=\"MS\")\nassert len(origins) == 48\nM = lambda ts, k: ts + pd.DateOffset(months=k)\n\ndef design(gdp, emp, h, exclude_covid):\n    \"\"\"Direct regression rows: target y(m) on [1, y(m-h-2), emp(m-h-1)], using only the masked series.\"\"\"\n    rows = []\n    for m in gdp.dropna().index:\n        ml_y, ml_e = M(m, -(h + 2)), M(m, -(h + 1))\n        if ml_y not in gdp.index or ml_e not in emp.index or pd.isna(gdp.get(ml_y)) or pd.isna(emp.get(ml_e)):\n            continue\n        if exclude_covid and any(COVID[0] <= x <= COVID[1] for x in (m, ml_y, ml_e)):\n            continue\n        rows.append((m, gdp[m], gdp[ml_y], emp[ml_e]))\n    return pd.DataFrame(rows, columns=[\"m\", \"y\", \"ylag\", \"elag\"]).set_index(\"m\")\n\ndef fit_forecast(t, h, exclude_covid=True):\n    gdp = d[\"gdp_yoy\"][d.index <= M(t, -2)]          # GDP available: months <= t-2\n    emp = d[\"emp_yoy\"][d.index <= M(t, -1)]          # LFS available: months <= t-1\n    X = design(gdp, emp, h, exclude_covid)\n    A = np.column_stack([np.ones(len(X)), X[\"ylag\"], X[\"elag\"]])\n    beta, *_ = np.linalg.lstsq(A, X[\"y\"].values, rcond=None)\n    x_new = np.array([1.0, gdp[M(t, -2)], emp[M(t, -1)]])\n    return float(x_new @ beta), X, gdp.index.max(), emp.index.max()\n\nrecs, sens, n_last = [], [], {}\nfor t in origins:\n    for h in HORIZONS:\n        tgt = M(t, h)\n        actual = float(d.loc[tgt, \"gdp_yoy\"])\n        assert not np.isnan(actual)\n        last_g = M(t, -2)\n        recs.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=float(d.loc[last_g, \"gdp_yoy\"]),\n                         actual=actual, model=\"naive\", last_target_date_used=last_g, last_predictor_date_used=pd.NaT))\n        f, X, lg, le = fit_forecast(t, h, True)\n        assert lg <= M(t, -2) and le <= M(t, -1) and X.index.max() <= M(t, -2)\n        recs.append(dict(origin_date=t, target_date=tgt, horizon=h, forecast=f, actual=actual, model=MODEL,\n                         last_target_date_used=lg, last_predictor_date_used=le))\n        fs, *_ = fit_forecast(t, h, False)\n        sens.append(dict(h=h, e=fs - actual))\n        if t == origins[-1]:\n            n_last[h] = len(X)\nfc = pd.DataFrame(recs)\nfc[\"error\"] = fc[\"forecast\"] - fc[\"actual\"]\n\ndef rmse(e): return float(np.sqrt(np.mean(np.square(e))))\ndef mae(e): return float(np.mean(np.abs(e)))\n\ndef dm_test(e1, e2, h):\n    \"\"\"Diebold-Mariano (squared loss), HAC variance with h-1 Bartlett-free rectangular lags, Harvey et al. correction.\"\"\"\n    dl = e1 ** 2 - e2 ** 2\n    n = len(dl); dbar = dl.mean(); dc = dl - dbar\n    g = [np.sum(dc[k:] * dc[:n - k]) / n for k in range(h)]\n    v = (g[0] + 2 * sum(g[1:])) / n\n    if v <= 0:\n        v = g[0] / n\n    stat = dbar / np.sqrt(v) * np.sqrt((n + 1 - 2 * h + h * (h - 1) / n) / n)\n    return float(stat), float(2 * stats.t.sf(abs(stat), df=n - 1))\n\nres = {\"task_id\": \"F3-conf-01\", \"model_name\": MODEL,\n       \"model_description\": (\"Direct h-step OLS regression per horizon and origin of gdp_yoy(m) on a constant, gdp_yoy(m-h-2) \"\n                             \"and emp_yoy(m-h-1), estimated on an expanding window from 1998 using only training pairs whose target \"\n                             \"month is <= t-2 (and employment <= t-1), excluding pairs touching 2020-03..2021-12 (COVID); \"\n                             \"forecast plugs in gdp_yoy(t-2) and emp_yoy(t-1).\"),\n       \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date()),\n       \"last_target_date_available\": str(M(origins[-1], -2).date()),\n       \"last_predictor_date_available\": str(M(origins[-1], -1).date())}\nsub = {}\nfor h in HORIZONS:\n    en = fc[(fc.horizon == h) & (fc.model == \"naive\")].sort_values(\"origin_date\")[\"error\"].values\n    em = fc[(fc.horizon == h) & (fc.model == MODEL)].sort_values(\"origin_date\")[\"error\"].values\n    res[f\"rmse_naive_h{h}\"], res[f\"mae_naive_h{h}\"] = rmse(en), mae(en)\n    res[f\"rmse_model_h{h}\"], res[f\"mae_model_h{h}\"] = rmse(em), mae(em)\n    sub[h] = (en, em)\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\": \"README states gdp_yoy is empty for the first 12 months, but gdp_yoy and emp_yoy are populated from 1998-01 (presumably from pre-1998 levels not in the file).\",\n                 \"handling\": \"Used the provided yoy values as given; they match recomputation from levels to <=5e-5 where verifiable.\"},\n                {\"issue\": \"gdp_level/gdp_yoy missing for 2026-08 and 2026-09 (not yet published).\",\n                 \"handling\": \"Expected; never needed (last target month 2026-07, last GDP used 2026-02).\"},\n                {\"issue\": \"Extreme COVID-19 swings 2020-03..2021-06 and base effects through 2021 in both series.\",\n                 \"handling\": \"Pre-specified exclusion of training pairs touching 2020-03..2021-12 from the model; sensitivity without exclusion reported (sens_* keys). Naive baseline unaffected.\"},\n                {\"issue\": \"Data are the current vintage (revised), not first releases.\",\n                 \"handling\": \"Exercise simulates publication timing only; no real-time validity claimed.\"}],\n            \"n_input_rows\": {\"canada_gdp_employment.csv\": n_raw},\n            \"n_analysed\": int(n_last[1]), \"n_analysed_last_origin_h3\": int(n_last[3]),\n            \"random_seed\": None})\nfor h in HORIZONS:\n    en, em = sub[h]\n    s, p = dm_test(em, en, h)\n    res[f\"dm_stat_h{h}\"], res[f\"dm_pvalue_h{h}\"] = s, p\n    se = np.array([r[\"e\"] for r in sens if r[\"h\"] == h])\n    res[f\"sens_rmse_model_nocovidexcl_h{h}\"] = rmse(se)\n    res[f\"sens_rel_improvement_rmse_nocovidexcl_h{h}\"] = 1 - rmse(se) / res[f\"rmse_naive_h{h}\"]\nres = {k: (round(v, 10) if isinstance(v, float) else v) for k, v in res.items()}\n\nos.makedirs(OUT, exist_ok=True)\nout = fc.drop(columns=\"error\").copy()\nfor c in [\"origin_date\", \"target_date\", \"last_target_date_used\", \"last_predictor_date_used\"]:\n    out[c] = out[c].dt.strftime(\"%Y-%m-%d\")\nout[\"last_predictor_date_used\"] = out[\"last_predictor_date_used\"].fillna(\"\")\nout[\"forecast\"] = out[\"forecast\"].round(8)\nout.to_csv(f\"{OUT}/forecasts.csv\", index=False)\nwith open(f\"{OUT}/results.json\", \"w\") as fh:\n    json.dump(res, fh, indent=2)\n\n# Exclusion ledger for the last estimation sample (last origin, h=1)\nt = origins[-1]; h = 1\nn1 = int((d.index <= M(t, -2)).sum())\ngdp = d[\"gdp_yoy\"][d.index <= M(t, -2)]; emp = d[\"emp_yoy\"][d.index <= M(t, -1)]\nn2 = len(design(gdp, emp, h, False)); n3 = n_last[1]\nled = pd.DataFrame([\n    (\"1\", \"GDP month <= t-2 at last origin 2026-04 (publication lag); drops 2026-03..2026-09\", n_raw, n1, \"information-availability rule\"),\n    (\"2\", f\"target month needs gdp_yoy(m-{h+2}) and emp_yoy(m-{h+1}) inside the file\", n1, n2, \"lags unavailable for first months of file\"),\n    (\"3\", \"drop pairs with target/regressor month in 2020-03..2021-12\", n2, n3, \"pre-specified COVID outlier exclusion (model only)\")],\n    columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"])\nled.insert(4, \"n_removed\", led.n_before - led.n_after)\nled = led[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]]\nled.to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert n_raw - led.n_removed.sum() == res[\"n_analysed\"]\nprint(json.dumps({k: res[k] for k in res if k.startswith((\"rmse\", \"mae\", \"rel\", \"dm\", \"sens\", \"n_an\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && head -5 out/forecasts.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"}}]}}42{"type":"api-request","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","id":"edc6a995-02c6-487a-bdaa-9ace5966d709","timestamp":"2026-10-30T05:00:27.367Z","version":"2.1.314","querySource":"sdk","shapeHash":"d9f495c5525fd789ca52b7d818301169852aa68cc44a6c65099897418fbafc07","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":"ebae9ec5-ece0-4d71-a105-25eefddf7bc5","keep":12,"tail":["5b8d2b328572697693aa8425de01347f852a3023417440ec8b72df38e6961aa0","0a83526e3f1439ef82211634f2a7501d9b3a237b41be21921134b8e6a864cc81"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a0ba36e97d496ad8a52e71cef12ea548abeed999e24354c1baf67cc8edab545b"}43{"parentUuid":"2cd4c355-4c32-4feb-a2a2-8fce0f9c0464","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqFaWKHxKehoGKxwbBp","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS4QYKEAgTGAI4AUIIdGhpbmtpbmcSDGKbY7b2Y3cmgvt/ghoMSHFqIa5DUdEY0VCjIjBMyMOl+V5OXe7tBTdPPcULuGNzwwXRpkWs0+lutjoZ8gDhuWgBx9unVI9SI8aJfgQq/gXuJBi+r6gQ6F57QamARl5LepdEpzS+XxCJd2NXTMQczufhLY/CUwgZV0F1DeYHObb7Kn8SR62KPH7gx2b5ayzqY55MNFabj3V6okWQillBdmfKusDN7d35PBz+tm62c+otV4xgrUF+C8AroqUl/CVVew2O4Nf05FMGRlXM/AKSxVPdAQ14fM+iFNxZT8JuoM6PkQ69NlnPm9Xsffod3ISkiflAPQRPnudCouiGQjczhGbX2vTYPSjRFbaIwxn+JeKvu01sQE441fw9KMlP6SxKGPhsuc/Ud3v0dMYOOssy8Nt9CoTQqaRhDqbkYgGCZXJR9PYuT1w5GHpukwBJHN59DPexMh+YiZQo3mvWVmllwQ7uznNXjyT3NEICgR/GJMLEyVpBqymt6UY9kojvh7nTOySS0Jab6ucq9Q40K5HbAdrQDOXah9pefOnWvSn/+pSfAT2mkYa8l5JEprAUoQa4VUf2Ub57LeXDU0VMgABktkTepdxIxJuMJgvRhM1Hr0WKR1JL2Jb0LwoVwI6zL+9UaZo90We88PdbPeYPITjmuqudIZZ/48pK2hBJB43+/yeGrTJ0uVYrH+gJDLycrRA/R2knbyl/tSlBflPLfY2MS3MrCMxDq3v4UhxAVxJoz7EpqMQbShnpRaFNRnqiS09uIzflQFBZ4U+423Jv+ieMdw80DoxK+4XUyaOMxWEerthAd9XjslMMn8Sf8jDsqt/fAiTvPhNCAqQNPh8tgwFEkmfWuOyyh5HjKxwm98vliErTwsGQ8oQ6qU460BDaAqMQ9JjI2EukDDLVr1HH9WboWm8khkhlqBk4b65MgxAYlhdYQ2bBdiE7uD8HBRsHwDOR8KWxI/uq2hDbLfx8mBRDfC5YB7/8p1sYV7ElsYoFqZpTMlPE6fbeaG9Lzeo75jX4EFe/kCNX6r79je5BOX0ptMwSfZMyGoO202HTnpNBb2fp7pJtj6a3pjJmk6oxFD3KPqeDX0Vsf9aviByrdD1+D7q2u1YLAsnYSn/+nmK3GAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":5292,"cache_read_input_tokens":12914,"output_tokens":835,"output_tokens_details":{"thinking_tokens":49},"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":5292},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":346,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqFaFSJrTUtL7eGpPk8","requestRef":"edc6a995-02c6-487a-bdaa-9ace5966d709","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"578c9928-a0bc-4c05-b3ad-b5fa5d66571b","timestamp":"2026-10-30T05:00:29.040Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}44{"parentUuid":"578c9928-a0bc-4c05-b3ad-b5fa5d66571b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqFaWKHxKehoGKxwbBp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01D4NrzumwruHR1W4hUQW7gH","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 out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo IDENTICAL\n~/wp29/venv/bin/python -c \"\nimport pandas as pd, 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 s,k: s+pd.DateOffset(months=k)\nprint('rows',len(f), f.groupby(['model','horizon']).size().to_dict())\nprint('gdp rule', (f.last_target_date_used<=f.origin_date.apply(lambda x:off(x,-2))).all())\nm=f[f.model!='naive'];print('emp rule',(m.last_predictor_date_used<=m.origin_date.apply(lambda x:off(x,-1))).all(), f[f.model=='naive'].last_predictor_date_used.isna().all())\nprint('target=origin+h',(f.target_date==[off(o,h) for o,h in zip(f.origin_date,f.horizon)]).all())\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual match',(abs(f.actual-d.loc[f.target_date,'gdp_yoy'].values)<1e-9).all())\nprint('pos rmse',all(r[k]>0 for k in r if k.startswith('rmse')), 'rmse>=mae',all(r['rmse_'+a]>=r['mae_'+a] for a in ['naive_h1','naive_h3','model_h1','model_h3']))\nprint('origins',f.origin_date.nunique(),f.origin_date.min().date(),f.origin_date.max().date())\n\"","description":"Reproducibility and consistency 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192 {('ardl_emp_direct', 1): 48, ('ardl_emp_direct', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ngdp rule True\nemp rule True True\ntarget=origin+h True\nactual match True\npos rmse True rmse>=mae True\norigins 48 2022-05-01 2026-04-01","is_error":false}]},"uuid":"cb53fa99-e0d8-4d3f-918a-ad761edfca46","timestamp":"2026-10-30T05:00:36.905Z","toolUseResult":{"stdout":"IDENTICAL\nrows 192 {('ardl_emp_direct', 1): 48, ('ardl_emp_direct', 3): 48, ('naive', 1): 48, ('naive', 3): 48}\ngdp rule True\nemp rule True True\ntarget=origin+h True\nactual match True\npos rmse True rmse>=mae True\norigins 48 2022-05-01 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zip(f.origin_date,f.horizon)]).all())\nd=pd.read_csv('data/canada_gdp_employment.csv',parse_dates=['month']).set_index('month')\nprint('actual match',(abs(f.actual-d.loc[f.target_date,'gdp_yoy'].values)<1e-9).all())\nprint('pos rmse',all(r[k]>0 for k in r if k.startswith('rmse')), 'rmse>=mae',all(r['rmse_'+a]>=r['mae_'+a] for a in ['naive_h1','naive_h3','model_h1','model_h3']))\nprint('origins',f.origin_date.nunique(),f.origin_date.min().date(),f.origin_date.max().date())\n\"","description":"Reproducibility and consistency checks"}}]}}49{"type":"api-request","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","id":"dd138a0b-9928-497e-b2e3-80e02e762373","timestamp":"2026-10-30T05:00:36.909Z","version":"2.1.314","querySource":"sdk","shapeHash":"d9f495c5525fd789ca52b7d818301169852aa68cc44a6c65099897418fbafc07","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":"edc6a995-02c6-487a-bdaa-9ace5966d709","keep":14,"tail":["35c06620e7d53dfdba1a2fda29449e68264c1b33c819cc200241a119d14210a2","e00b780c4acc4f41f14ba4ad5e7a8da302c2aa20108c50a9966d52fb161b5408"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bc736b3e1b1f5a8a767497c39901e73d0eae737146501962aff9246b630b961f"}50{"parentUuid":"cb53fa99-e0d8-4d3f-918a-ad761edfca46","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqGHMDWUXtREvTB9jmj","type":"message","role":"assistant","content":[{"type":"text","text":"Checks pass. Now writing the checks, report, claims, and phase ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":972,"cache_read_input_tokens":18206,"output_tokens":1000,"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":972},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqGH4rHVeekrWk7oJWj","requestRef":"dd138a0b-9928-497e-b2e3-80e02e762373","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6c1d9088-6af4-44c2-a427-51bca54cdcc5","timestamp":"2026-10-30T05:00:38.192Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"6c1d9088-6af4-44c2-a427-51bca54cdcc5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqGHMDWUXtREvTB9jmj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VQF9WbkQbywvkk25oxZzTD","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — all PASS\n1. Clean re-run: deleted results.json/forecasts.csv/exclusions.csv, re-ran analysis.py; byte-identical results.json and forecasts.csv (cmp). PASS\n2. forecasts.csv has 192 rows = 48 origins x 2 horizons x 2 models (48 per cell). PASS\n3. Chronology: last_target_date_used <= origin-2 for all rows; last_predictor_date_used <= origin-1 for model rows; empty for naive. Internal asserts in script also check estimation sample max target month <= t-2. PASS\n4. target_date = origin + h for all rows; actual equals data gdp_yoy of target month for all rows, none missing. PASS\n5. All RMSE > 0; RMSE >= MAE for each model/horizon. PASS\n6. first_origin 2022-05-01 <= last_origin 2026-04-01; last_target_date_available 2026-02-01 and last_predictor_date_available 2026-03-01 precede last origin. PASS\n7. Exclusion ledger: 345 - (7+3+25) = 310 = n_analysed (asserted in script). PASS\n8. Magnitudes: model RMSE (0.74/0.90) of same order as naive (0.81/1.14); naive h3 > naive h1 as expected. PASS\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\",\"schema/missing/duplicates/gaps audited\",\"yoy recomputation check\",\"exclusions.csv sums to n_analysed\"],\"notes\":\"COVID training exclusion pre-specified\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive = gdp_yoy(t-2) computed for 48 origins x 2 horizons; rmse/mae_naive keys written\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\",\"in-loop asserts on info rules\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"chronology checks\",\"row counts\",\"positive RMSE\",\"exclusion ledger sums\"],\"notes\":\"see checks.md\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed (naive + ardl_emp_direct). P4 passed: reproducible, all checks pass.\" >> out/state/log.md","description":"Record checks and update phases"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":972,"cache_read_input_tokens":18206,"output_tokens":1000,"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":972},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqGH4rHVeekrWk7oJWj","requestRef":"dd138a0b-9928-497e-b2e3-80e02e762373","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b3904fc5-a407-4333-ab0c-1a8f389948a9","timestamp":"2026-10-30T05:00:45.924Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"b3904fc5-a407-4333-ab0c-1a8f389948a9","isSidechain":false,"promptId":"8ec3a20a-8304-4240-8dc7-b3a92044df4f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VQF9WbkQbywvkk25oxZzTD","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"55862574-6838-4152-bded-d62f85277031","timestamp":"2026-10-30T05:00:45.957Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b3904fc5-a407-4333-ab0c-1a8f389948a9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","hash":"c827da4620b935f72ca8641bca0107eb847cf64a200bf701156b331c023e6f1a","message":{"role":"assistant","content":[{"type":"text","text":"Checks pass. Now writing the checks, report, claims, and phase ledger."},{"type":"tool_use","id":"toolu_01VQF9WbkQbywvkk25oxZzTD","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — all PASS\n1. Clean re-run: deleted results.json/forecasts.csv/exclusions.csv, re-ran analysis.py; byte-identical results.json and forecasts.csv (cmp). PASS\n2. forecasts.csv has 192 rows = 48 origins x 2 horizons x 2 models (48 per cell). PASS\n3. Chronology: last_target_date_used <= origin-2 for all rows; last_predictor_date_used <= origin-1 for model rows; empty for naive. Internal asserts in script also check estimation sample max target month <= t-2. PASS\n4. target_date = origin + h for all rows; actual equals data gdp_yoy of target month for all rows, none missing. PASS\n5. All RMSE > 0; RMSE >= MAE for each model/horizon. 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PASS\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\",\"schema/missing/duplicates/gaps audited\",\"yoy recomputation check\",\"exclusions.csv sums to n_analysed\"],\"notes\":\"COVID training exclusion pre-specified\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"naive = gdp_yoy(t-2) computed for 48 origins x 2 horizons; rmse/mae_naive keys written\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\",\"in-loop asserts on info rules\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"chronology checks\",\"row counts\",\"positive RMSE\",\"exclusion ledger sums\"],\"notes\":\"see checks.md\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed (naive + ardl_emp_direct). P4 passed: reproducible, all checks pass.\" >> out/state/log.md","description":"Record checks and update phases"}}]}}54{"type":"api-request","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","id":"5eafce4e-2ea5-451c-83ab-c716f4c7ab56","timestamp":"2026-10-30T05:00:45.960Z","version":"2.1.314","querySource":"sdk","shapeHash":"d9f495c5525fd789ca52b7d818301169852aa68cc44a6c65099897418fbafc07","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":"dd138a0b-9928-497e-b2e3-80e02e762373","keep":16,"tail":["c827da4620b935f72ca8641bca0107eb847cf64a200bf701156b331c023e6f1a","5a242e204cb4ffa20e122936543465a4cd84e448b490cab8201a77db867d3058"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c3cdfea7410b3ef8c45763d9628c0ff31bbfac517a83d662a2e2402277619a6f"}55{"parentUuid":"55862574-6838-4152-bded-d62f85277031","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqGwwT67Fs666ZNJi5Q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018CJFRxpGYVtJMFx5BsrfE6","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, 2022-05-01 to 2026-04-01; `n_origins`, `first_origin`, `last_origin`), how well can the year-over-year growth of Canadian monthly real GDP for month t+1 and t+3 be forecast using only data published by then? And does a model that also uses the more timely LFS employment growth beat the naive forecast (the latest published GDP growth, gdp_yoy(t−2))?\n\n## Data and exclusions\n- Input: `data/canada_gdp_employment.csv`: 345 rows (`n_input_rows`), 1998-01 to 2026-09. The SHA-256 matches the README. There are no duplicate months and no gaps. GDP is missing only for 2026-08 and 2026-09, which were not yet published. Full audit: `out/state/data_audit.md`; detected issues: `data_issues`.\n- Publication lags are enforced inside every origin loop. GDP is used only for months ≤ t−2 and employment only for months ≤ t−1. At the last origin, this means GDP up to 2026-02-01 (`last_target_date_available`) and employment up to 2026-03-01 (`last_predictor_date_available`).\n- Exclusions for the last estimation sample (origin 2026-04, h=1; `out/exclusions.csv`):\n  - 7 months removed by the publication-lag rule.\n  - 3 removed because their lags are not in the file.\n  - 25 removed by a COVID-window rule. Before looking at any out-of-sample errors, I decided to drop training pairs whose target or regressor month falls in 2020-03..2021-12.\n  - That leaves 345 − 35 = 310 observations (`n_analysed`); for h=3 it is 306 (`n_analysed_last_origin_h3`).\n- The naive forecast drops no observations.\n\n## Method\n- **Naive:** forecast = gdp_yoy(t−2) for both horizons.\n- **Model `ardl_emp_direct`** (`model_name`, `model_description`): a direct h-step OLS regression, fitted separately for each horizon and origin:\n  - Equation: y(m) = a + b·y(m−h−2) + c·emp_yoy(m−h−1) + e.\n  - Estimation: expanding window from 1998, using only target months ≤ t−2, with the COVID-window exclusion above.\n  - Forecast: plug in y(t−2) and emp_yoy(t−1). This uses the one-month timeliness advantage of the LFS.\n- Model choices were fixed in advance; I did not tune the model on the evaluation errors.\n- Errors are forecast − actual, measured against current-vintage gdp_yoy.\n- No randomness is involved (`random_seed` = null).\n- Replication: `~/wp29/venv/bin/python out/analysis.py`. It regenerates `out/results.json`, `out/forecasts.csv` and `out/exclusions.csv`.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\nAll values are in percentage points, except the last column, which is a fraction.\n\n- The employment-augmented model has lower RMSE and MAE than the naive forecast at both horizons: about 9% lower RMSE at h=1 and about 21% lower at h=3.\n- The naive forecast gets worse as the horizon grows, as expected.\n\n## Uncertainty\nI compared the two models' squared-error losses with a Diebold–Mariano test (Harvey small-sample correction, HAC variance with h−1 lags, n=48):\n- **h=1:** DM = −2.03, p = 0.048 (`dm_stat_h1`, `dm_pvalue_h1`). This is borderline significant at 5%.\n- **h=3:** DM = −1.35, p = 0.18 (`dm_stat_h3`, `dm_pvalue_h3`). This is **not** significant. The overlapping 3-step errors and only 48 origins give limited power.\n\nSensitivity check: keeping the COVID window in the training sample changes the improvement to 0.012 at h=1 (`sens_rel_improvement_rmse_nocovidexcl_h1`) and 0.304 at h=3 (`sens_rel_improvement_rmse_nocovidexcl_h3`). The size of the gain therefore depends noticeably on how the pandemic observations are treated. Only its sign holds in both specifications.\n\n## Limitations and open issues\n- The data are the current (revised) vintage. The exercise simulates publication *timing* only. The errors are not real-time errors, and no real-time validity is claimed.\n- There are 48 origins in a single post-pandemic period (2022–2026), so the evaluation sample is small and the gains are not statistically robust, particularly at h=3.\n- The COVID exclusion window is a judgement call made in advance. The sensitivity results show it matters for how large the gain is.\n- The README says gdp_yoy is empty for the first 12 months, but the file has values from 1998-01. I used the values as given (`data_issues`).\n- Only one model was evaluated, as the task requires. No model search was done.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The employment-augmented direct ARDL model has lower RMSE and MAE than the naive forecast at h=1 (RMSE 0.743 vs 0.814, relative improvement 0.087).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"DM p=0.048, borderline; current-vintage data, timing simulation only.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model has lower RMSE and MAE than naive (RMSE 0.902 vs 1.140, relative improvement 0.208).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Not statistically significant (DM p=0.18).\"},\n {\"id\":\"C3\",\"text\":\"The h=1 improvement is borderline significant and the h=3 improvement is not significant under a Diebold-Mariano test.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"48 origins, overlapping h=3 errors.\"},\n {\"id\":\"C4\",\"text\":\"The size of the gain is sensitive to excluding the COVID period from training (0.012 at h=1 and 0.304 at h=3 without exclusion); only its sign holds in both specifications.\",\"results_keys\":[\"sens_rel_improvement_rmse_nocovidexcl_h1\",\"sens_rel_improvement_rmse_nocovidexcl_h3\"],\"status\":\"supported\",\"caveat\":\"Only two specifications compared.\"},\n {\"id\":\"C5\",\"text\":\"All forecasts respect the publication-lag rules (GDP <= t-2, employment <= t-1); at the last origin these are 2026-02-01 and 2026-03-01.\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Verified via forecasts.csv date columns and in-script asserts.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nassert all(k in r for x in c for k in x['results_keys']); print('claims keys ok')\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report has 6 required sections\",\"claims reference existing results keys (verified)\",\"numbers in report copied from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written. Task complete.\" >> out/state/log.md","description":"Write report, claims, finalize phases"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1015,"cache_read_input_tokens":19178,"output_tokens":3087,"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":1015},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqGwkHaxQKt2NAcyWEs","requestRef":"5eafce4e-2ea5-451c-83ab-c716f4c7ab56","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8a391508-1f96-4c73-9d73-b336ffb53a05","timestamp":"2026-10-30T05:01:12.147Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"8a391508-1f96-4c73-9d73-b336ffb53a05","isSidechain":false,"promptId":"8ec3a20a-8304-4240-8dc7-b3a92044df4f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018CJFRxpGYVtJMFx5BsrfE6","type":"tool_result","content":"claims keys ok\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues","is_error":false}]},"uuid":"532caf99-e2ad-478f-aefb-991e1348f6ef","timestamp":"2026-10-30T05:01:12.200Z","toolUseResult":{"stdout":"claims keys ok\n## Question\n## Data and exclusions\n## Method\n## Results\n## Uncertainty\n## Limitations and open issues","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"8a391508-1f96-4c73-9d73-b336ffb53a05","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0207","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"d750aef6-83b9-444f-bd18-c7550e1c5e48","hash":"c6e82437a48e4a3108df6e0657d103490ff070fa50aeec40f52e7157b4a57610","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_018CJFRxpGYVtJMFx5BsrfE6","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, 2022-05-01 to 2026-04-01; `n_origins`, `first_origin`, `last_origin`), how well can the year-over-year growth of Canadian monthly real GDP for month t+1 and t+3 be forecast using only data published by then? And does a model that also uses the more timely LFS employment growth beat the naive forecast (the latest published GDP growth, gdp_yoy(t−2))?\n\n## Data and exclusions\n- Input: `data/canada_gdp_employment.csv`: 345 rows (`n_input_rows`), 1998-01 to 2026-09. The SHA-256 matches the README. There are no duplicate months and no gaps. GDP is missing only for 2026-08 and 2026-09, which were not yet published. Full audit: `out/state/data_audit.md`; detected issues: `data_issues`.\n- Publication lags are enforced inside every origin loop. GDP is used only for months ≤ t−2 and employment only for months ≤ t−1. At the last origin, this means GDP up to 2026-02-01 (`last_target_date_available`) and employment up to 2026-03-01 (`last_predictor_date_available`).\n- Exclusions for the last estimation sample (origin 2026-04, h=1; `out/exclusions.csv`):\n  - 7 months removed by the publication-lag rule.\n  - 3 removed because their lags are not in the file.\n  - 25 removed by a COVID-window rule. Before looking at any out-of-sample errors, I decided to drop training pairs whose target or regressor month falls in 2020-03..2021-12.\n  - That leaves 345 − 35 = 310 observations (`n_analysed`); for h=3 it is 306 (`n_analysed_last_origin_h3`).\n- The naive forecast drops no observations.\n\n## Method\n- **Naive:** forecast = gdp_yoy(t−2) for both horizons.\n- **Model `ardl_emp_direct`** (`model_name`, `model_description`): a direct h-step OLS regression, fitted separately for each horizon and origin:\n  - Equation: y(m) = a + b·y(m−h−2) + c·emp_yoy(m−h−1) + e.\n  - Estimation: expanding window from 1998, using only target months ≤ t−2, with the COVID-window exclusion above.\n  - Forecast: plug in y(t−2) and emp_yoy(t−1). This uses the one-month timeliness advantage of the LFS.\n- Model choices were fixed in advance; I did not tune the model on the evaluation errors.\n- Errors are forecast − actual, measured against current-vintage gdp_yoy.\n- No randomness is involved (`random_seed` = null).\n- Replication: `~/wp29/venv/bin/python out/analysis.py`. It regenerates `out/results.json`, `out/forecasts.csv` and `out/exclusions.csv`.\n\n## Results\n| horizon | RMSE naive | MAE naive | RMSE model | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| h=1 | 0.814 (`rmse_naive_h1`) | 0.642 (`mae_naive_h1`) | 0.743 (`rmse_model_h1`) | 0.604 (`mae_model_h1`) | 0.087 (`rel_improvement_rmse_h1`) |\n| h=3 | 1.140 (`rmse_naive_h3`) | 0.872 (`mae_naive_h3`) | 0.902 (`rmse_model_h3`) | 0.753 (`mae_model_h3`) | 0.208 (`rel_improvement_rmse_h3`) |\n\nAll values are in percentage points, except the last column, which is a fraction.\n\n- The employment-augmented model has lower RMSE and MAE than the naive forecast at both horizons: about 9% lower RMSE at h=1 and about 21% lower at h=3.\n- The naive forecast gets worse as the horizon grows, as expected.\n\n## Uncertainty\nI compared the two models' squared-error losses with a Diebold–Mariano test (Harvey small-sample correction, HAC variance with h−1 lags, n=48):\n- **h=1:** DM = −2.03, p = 0.048 (`dm_stat_h1`, `dm_pvalue_h1`). This is borderline significant at 5%.\n- **h=3:** DM = −1.35, p = 0.18 (`dm_stat_h3`, `dm_pvalue_h3`). This is **not** significant. The overlapping 3-step errors and only 48 origins give limited power.\n\nSensitivity check: keeping the COVID window in the training sample changes the improvement to 0.012 at h=1 (`sens_rel_improvement_rmse_nocovidexcl_h1`) and 0.304 at h=3 (`sens_rel_improvement_rmse_nocovidexcl_h3`). The size of the gain therefore depends noticeably on how the pandemic observations are treated. Only its sign holds in both specifications.\n\n## Limitations and open issues\n- The data are the current (revised) vintage. The exercise simulates publication *timing* only. The errors are not real-time errors, and no real-time validity is claimed.\n- There are 48 origins in a single post-pandemic period (2022–2026), so the evaluation sample is small and the gains are not statistically robust, particularly at h=3.\n- The COVID exclusion window is a judgement call made in advance. The sensitivity results show it matters for how large the gain is.\n- The README says gdp_yoy is empty for the first 12 months, but the file has values from 1998-01. I used the values as given (`data_issues`).\n- Only one model was evaluated, as the task requires. No model search was done.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The employment-augmented direct ARDL model has lower RMSE and MAE than the naive forecast at h=1 (RMSE 0.743 vs 0.814, relative improvement 0.087).\",\"results_keys\":[\"rmse_model_h1\",\"rmse_naive_h1\",\"mae_model_h1\",\"mae_naive_h1\",\"rel_improvement_rmse_h1\"],\"status\":\"supported\",\"caveat\":\"DM p=0.048, borderline; current-vintage data, timing simulation only.\"},\n {\"id\":\"C2\",\"text\":\"At h=3 the model has lower RMSE and MAE than naive (RMSE 0.902 vs 1.140, relative improvement 0.208).\",\"results_keys\":[\"rmse_model_h3\",\"rmse_naive_h3\",\"mae_model_h3\",\"mae_naive_h3\",\"rel_improvement_rmse_h3\"],\"status\":\"supported\",\"caveat\":\"Not statistically significant (DM p=0.18).\"},\n {\"id\":\"C3\",\"text\":\"The h=1 improvement is borderline significant and the h=3 improvement is not significant under a Diebold-Mariano test.\",\"results_keys\":[\"dm_stat_h1\",\"dm_pvalue_h1\",\"dm_stat_h3\",\"dm_pvalue_h3\"],\"status\":\"supported\",\"caveat\":\"48 origins, overlapping h=3 errors.\"},\n {\"id\":\"C4\",\"text\":\"The size of the gain is sensitive to excluding the COVID period from training (0.012 at h=1 and 0.304 at h=3 without exclusion); only its sign holds in both specifications.\",\"results_keys\":[\"sens_rel_improvement_rmse_nocovidexcl_h1\",\"sens_rel_improvement_rmse_nocovidexcl_h3\"],\"status\":\"supported\",\"caveat\":\"Only two specifications compared.\"},\n {\"id\":\"C5\",\"text\":\"All forecasts respect the publication-lag rules (GDP <= t-2, employment <= t-1); at the last origin these are 2026-02-01 and 2026-03-01.\",\"results_keys\":[\"last_target_date_available\",\"last_predictor_date_available\",\"last_origin\"],\"status\":\"supported\",\"caveat\":\"Verified via forecasts.csv date columns and in-script asserts.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json'));c=json.load(open('out/claims.json'))\nassert all(k in r for x in c for k in x['results_keys']); print('claims keys ok')\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report has 6 required sections\",\"claims reference existing results keys (verified)\",\"numbers in report copied from results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written. 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All files the task requires are written. A clean re-run of `out/analysis.py` reproduced the results and forecasts byte for byte, and all consistency checks passed.\n\n**Model:** a regression fitted separately for each horizon at every origin. It forecasts GDP growth from the latest published GDP growth (month t−2) and the latest employment growth (month t−1). The estimation window grows over time and only uses data that would have been published at each origin.\n\n**Results over the 48 origins** (errors in percentage points; improvement is 1 − model RMSE / naive RMSE):\n\n| Horizon | RMSE naive | RMSE model | MAE naive | MAE model | RMSE improvement |\n|---|---|---|---|---|---|\n| 1 month | 0.814 | 0.743 | 0.642 | 0.604 | 8.7% |\n| 3 months | 1.140 | 0.902 | 0.872 | 0.753 | 20.8% |\n\n- **Significance:** a Diebold–Mariano test comparing the two forecasts gives p = 0.048 at one month and p = 0.18 at three months.\n- **Pandemic sensitivity:** before looking at any results, I removed March 2020 to December 2021 from the model's training data. If those months are kept, the improvement becomes 1.2% at one month and 30.4% at three months. So the model wins under both choices, but how much it wins depends on this decision.\n- **Timing rules:** every forecast uses GDP only up to month t−2 and employment only up to month t−1. Checks on the forecast file confirm this.\n\n**Limitations:**\n- The data are today's revised figures, so this simulates when numbers were published, not what they said at the time. These are not real-time errors.\n- 48 origins from 2022–2026 is a small sample, and only one model was tested.\n- The data README says the growth columns are empty for the first 12 months, but the file has values from January 1998. 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