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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimands are the out-of-sample RMSE and MAE of the seasonal-naive forecast and of your model at each horizon, the relative improvement 1 − RMSE_model / RMSE_snaive per horizon, and, at h = 4, the mean loss differential d̄ = mean_t (e²_snaive,t − e²_model,t) with a standard error valid under serial correlation of the loss differential (Diebold–Mariano-type test).\n\n## Inputs\n- `data/cpi_quarterly.csv` — observed data derived from the Bank of Canada Valet series V41690973 (Total CPI, Canada, index 2002 = 100, not seasonally adjusted, monthly; Statistics Canada table 18-10-0004-01); retrieved 2026-10-30 UTC; see `data/README.md` for URL, SHA-256 and terms. Columns: `quarter` (first month of the quarter, YYYY-MM-01), `cpi_avg` (mean of the three monthly index values), `infl_annualised` (target series: 400 × Δln cpi_avg, percent at annual rate, 6 decimals; empty for 1995Q1). 127 complete quarters, 1995Q1 to 2026Q3.\n- `data/cpi_monthly.csv` — the monthly index as served (`month`, `cpi`), 381 rows 1995-01 to 2026-09, provided for provenance; the quarterly file is the input of the analysis.\n\n## Information-availability rules\n- Forecast origins: the 40 quarters t = 2015-10-01, 2016-01-01, …, 2025-07-01 (the `quarter` value). Horizons h ∈ {1, 2, 4}; the target of origin t at horizon h is `infl_annualised` of quarter t + h.\n- A forecast made at origin t may use only quarters dated ≤ t (expanding window: estimation, model selection and transformations included); a quarter's value is treated as known at the end of that quarter.\n- Seasonal-naive baseline: the forecast for the target quarter t + h is the value of `infl_annualised` four quarters before the target, i.e. of quarter t + h − 4 (always ≤ t for h ≤ 4).\n- The h = 4 test: with one forecast per origin, consecutive 4-step-ahead errors overlap, so the loss differential d_t is serially correlated; the standard error of d̄ must be computed with a heteroskedasticity-and-autocorrelation-consistent (long-run variance) estimator with a truncation lag of at least h − 1 = 3 quarters; report the lag used.\n- Errors are computed on the data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, horizon, model), columns `origin_date`, `target_date`, `horizon` (1, 2 or 4), `forecast`, `actual`, `model` (`snaive` or the value of `model_name`). Expected: 40 × 3 × 2 = 240 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-04\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, seasonal terms, direct vs iterated, estimation sample, method) |\n| `n_origins` | integer | — | 40 |\n| `first_origin`, `last_origin` | string | date | first and last origin quarters |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h2`, `mae_snaive_h2`, `rmse_snaive_h4`, `mae_snaive_h4` | number | percentage points (annualised) | seasonal-naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h2`, `mae_model_h2`, `rmse_model_h4`, `mae_model_h4` | number | percentage points (annualised) | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h2`, `rel_improvement_rmse_h4` | number | fraction | 1 − rmse_model / rmse_snaive |\n| `loss_diff_mean_h4` | number | squared percentage points | mean over the 40 origins of e²_snaive − e²_model at h = 4 (positive = model better) |\n| `loss_diff_se_h4` | number | squared percentage points | HAC standard error of `loss_diff_mean_h4` |\n| `hac_lags_h4` | integer | quarters | truncation lag used in the HAC estimator |\n| `hac_kernel_h4` | string | — | kernel used (e.g. \"bartlett\", \"rectangular\") |\n| `dm_stat_h4` | number | — | loss_diff_mean_h4 / loss_diff_se_h4 |\n| `dm_pvalue_h4` | number | — | two-sided p-value of `dm_stat_h4` |\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 | — | {\"cpi_quarterly.csv\": rows read excluding header, \"cpi_monthly.csv\": rows read excluding 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 23:55 .\ndrwxr-xr-x 59 simon-pierreboucher staff 1888 oct. 30 00:23 ..\n-rw-r--r-- 1 simon-pierreboucher staff 613 oct. 29 23:55 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:49 data\n-rw-r--r-- 1 simon-pierreboucher staff 2692 oct. 29 23:51 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 5817 oct. 29 23:51 TASK.md\ndata:\ncpi_monthly.csv\ncpi_quarterly.csv\nREADME.md","is_error":false}]},"uuid":"87519ac4-5570-4513-a2bd-4150db3e8840","timestamp":"2026-10-30T04:23:18.288Z","toolUseResult":{"stdout":"# F3-conf-04 — Multi-horizon forecasts of Canadian quarterly inflation with overlapping targets\n\n## Scientific question\nOver 40 rolling quarterly origins (2015Q4 to 2025Q3), does a time-series model of your choice forecast Canadian quarterly CPI inflation (annualised, not seasonally adjusted) better than the seasonal-naive forecast at horizons of 1, 2 and 4 quarters, and is the difference in squared-error loss at the 4-quarter horizon statistically distinguishable from zero once the dependence created by overlapping multi-step forecast errors is taken into account? The estimands are the out-of-sample RMSE and MAE of the seasonal-naive forecast and of your model at each horizon, the relative improvement 1 − RMSE_model / RMSE_snaive per horizon, and, at h = 4, the mean loss differential d̄ = mean_t (e²_snaive,t − e²_model,t) with a standard error valid under serial correlation of the loss differential (Diebold–Mariano-type test).\n\n## Inputs\n- `data/cpi_quarterly.csv` — observed data derived from the Bank of Canada Valet series V41690973 (Total CPI, Canada, index 2002 = 100, not seasonally adjusted, monthly; Statistics Canada table 18-10-0004-01); retrieved 2026-10-30 UTC; see `data/README.md` for URL, SHA-256 and terms. Columns: `quarter` (first month of the quarter, YYYY-MM-01), `cpi_avg` (mean of the three monthly index values), `infl_annualised` (target series: 400 × Δln cpi_avg, percent at annual rate, 6 decimals; empty for 1995Q1). 127 complete quarters, 1995Q1 to 2026Q3.\n- `data/cpi_monthly.csv` — the monthly index as served (`month`, `cpi`), 381 rows 1995-01 to 2026-09, provided for provenance; the quarterly file is the input of the analysis.\n\n## Information-availability rules\n- Forecast origins: the 40 quarters t = 2015-10-01, 2016-01-01, …, 2025-07-01 (the `quarter` value). Horizons h ∈ {1, 2, 4}; the target of origin t at horizon h is `infl_annualised` of quarter t + h.\n- A forecast made at origin t may use only quarters dated ≤ t (expanding window: estimation, model selection and transformations included); a quarter's value is treated as known at the end of that quarter.\n- Seasonal-naive baseline: the forecast for the target quarter t + h is the value of `infl_annualised` four quarters before the target, i.e. of quarter t + h − 4 (always ≤ t for h ≤ 4).\n- The h = 4 test: with one forecast per origin, consecutive 4-step-ahead errors overlap, so the loss differential d_t is serially correlated; the standard error of d̄ must be computed with a heteroskedasticity-and-autocorrelation-consistent (long-run variance) estimator with a truncation lag of at least h − 1 = 3 quarters; report the lag used.\n- Errors are computed on the data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, horizon, model), columns `origin_date`, `target_date`, `horizon` (1, 2 or 4), `forecast`, `actual`, `model` (`snaive` or the value of `model_name`). Expected: 40 × 3 × 2 = 240 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-04\" |\n| `model_name` | string | — | short name of your model, used in the `model` column |\n| `model_description` | string | — | one-sentence specification (lags, seasonal terms, direct vs iterated, estimation sample, method) |\n| `n_origins` | integer | — | 40 |\n| `first_origin`, `last_origin` | string | date | first and last origin quarters |\n| `rmse_snaive_h1`, `mae_snaive_h1`, `rmse_snaive_h2`, `mae_snaive_h2`, `rmse_snaive_h4`, `mae_snaive_h4` | number | percentage points (annualised) | seasonal-naive errors per horizon |\n| `rmse_model_h1`, `mae_model_h1`, `rmse_model_h2`, `mae_model_h2`, `rmse_model_h4`, `mae_model_h4` | number | percentage points (annualised) | your model's errors per horizon |\n| `rel_improvement_rmse_h1`, `rel_improvement_rmse_h2`, `rel_improvement_rmse_h4` | number | fraction | 1 − rmse_model / rmse_snaive |\n| `loss_diff_mean_h4` | number | squared percentage points | mean over the 40 origins of e²_snaive − e²_model at h = 4 (positive = model better) |\n| `loss_diff_se_h4` | number | squared percentage points | HAC standard error of `loss_diff_mean_h4` |\n| `hac_lags_h4` | integer | quarters | truncation lag used in the HAC estimator |\n| `hac_kernel_h4` | string | — | kernel used (e.g. \"bartlett\", \"rectangular\") |\n| `dm_stat_h4` | number | — | loss_diff_mean_h4 / loss_diff_se_h4 |\n| `dm_pvalue_h4` | number | — | two-sided p-value of `dm_stat_h4` |\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 | — | {\"cpi_quarterly.csv\": rows read excluding header, \"cpi_monthly.csv\": rows read excluding header} |\n| `n_analysed` | integer | — | observations in the last estimation sample |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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published by Statistics Canada, table 18-10-0004-01, redistributed by the Bank of Canada).\n- **Retrieval URL**: https://www.bankofcanada.ca/valet/observations/V41690973/json?start_date=1995-01-01\n- **Retrieved (UTC)**: 2026-10-30T03:25:10Z. Terms: https://www.bankofcanada.ca/terms/\n- **Format**: `month` (YYYY-MM-01), `cpi` (index, one decimal, as served). 381 rows, 1995-01 to 2026-09-01. SHA-256 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2.\n\n## cpi_quarterly.csv\nDerived from the monthly file: `quarter` (first month of the quarter, YYYY-MM-01), `cpi_avg` (arithmetic mean of the three monthly index values, 6 decimals), `infl_annualised` (target series: 400 × (ln cpi_avg_q − ln cpi_avg_{q−1}), percent at annual rate, 6 decimals; empty for 1995Q1). Only complete quarters (three months) are included: 127 rows, 1995Q1 to 2026-07-01. SHA-256 c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea. Because the index is not seasonally adjusted, the quarterly inflation series has a pronounced seasonal pattern.\n\n## Vintage caveat\nThe CPI is not revised after publication (except for the periodic basket updates applied to the index chain by Statistics Canada), so the current vintage is essentially the real-time series; nevertheless the exercise treats a quarter's value as known at the end of that quarter (the actual publication occurs about three weeks after the last month), and no claim of historical real-time performance is made.\n{\n \"task_id\": \"F3-conf-04\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"rmse_snaive_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_snaive_h2\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_snaive_h4\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h4\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h2\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h4\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h4\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h4\": {\"type\": \"number\", \"nullable\": false},\n \"loss_diff_mean_h4\": {\"type\": \"number\", \"nullable\": false},\n \"loss_diff_se_h4\": {\"type\": \"number\", \"nullable\": false},\n \"hac_lags_h4\": {\"type\": \"integer\", \"nullable\": false},\n \"hac_kernel_h4\": {\"type\": \"string\", \"nullable\": false},\n \"dm_stat_h4\": {\"type\": \"number\", \"nullable\": false},\n \"dm_pvalue_h4\": {\"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\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_snaive_h1\", \"rmse_snaive_h2\", \"rmse_snaive_h4\", \"rmse_model_h1\", \"rmse_model_h2\", \"rmse_model_h4\", \"loss_diff_se_h4\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"F3-conf-04\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:55:58.429558+00:00\",\n \"files\": {\n \"TASK.md\": \"3039dc66364cbd8ca986e67cd619f2be53509c6cf77f55bc179583ee85464810\",\n \"data/README.md\": \"8ddc03b23fc4efce8ce697879fbb2ddca04c40ae54eb772a04e9777444098d0a\",\n \"data/cpi_monthly.csv\": \"855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2\",\n \"data/cpi_quarterly.csv\": \"c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea\",\n \"gate_spec.json\": \"3585c0dae94511350806b5f595e10839bd205eb59763dac49deb6a66b1396eab\"\n }\n}==> data/cpi_monthly.csv <==\nmonth,cpi\n1995-01-01,86.6\n1995-02-01,87.0\n1995-03-01,87.2\n1995-04-01,87.5\n\n==> data/cpi_quarterly.csv <==\nquarter,cpi_avg,infl_annualised\n1995-01-01,86.933333,\n1995-04-01,87.633333,3.207961\n1995-07-01,87.800000,0.760023\n1995-10-01,87.833333,0.151831\n==> data/cpi_monthly.csv <==\n2026-07-01,169.9\n2026-08-01,169.8\n2026-09-01,170.0\n\n==> data/cpi_quarterly.csv <==\n2026-01-01,166.100000,2.092560\n2026-04-01,168.866667,6.607773\n2026-07-01,169.900000,2.440232\n 382 data/cpi_monthly.csv\n 128 data/cpi_quarterly.csv\n 510 total\n855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 data/cpi_monthly.csv\nc8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea data/cpi_quarterly.csv\n8ddc03b23fc4efce8ce697879fbb2ddca04c40ae54eb772a04e9777444098d0a data/README.md","is_error":false}]},"uuid":"df30084c-6f74-4485-ba03-6fa5749ea6bb","timestamp":"2026-10-30T04:23:20.551Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-04\n\n## cpi_monthly.csv\n- **Series**: Bank of Canada Valet series **V41690973** — Total CPI (Consumer Price Index, all-items, Canada, index 2002 = 100, **not seasonally adjusted**, monthly; published by Statistics Canada, table 18-10-0004-01, redistributed by the Bank of Canada).\n- **Retrieval URL**: https://www.bankofcanada.ca/valet/observations/V41690973/json?start_date=1995-01-01\n- **Retrieved (UTC)**: 2026-10-30T03:25:10Z. Terms: https://www.bankofcanada.ca/terms/\n- **Format**: `month` (YYYY-MM-01), `cpi` (index, one decimal, as served). 381 rows, 1995-01 to 2026-09-01. SHA-256 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2.\n\n## cpi_quarterly.csv\nDerived from the monthly file: `quarter` (first month of the quarter, YYYY-MM-01), `cpi_avg` (arithmetic mean of the three monthly index values, 6 decimals), `infl_annualised` (target series: 400 × (ln cpi_avg_q − ln cpi_avg_{q−1}), percent at annual rate, 6 decimals; empty for 1995Q1). Only complete quarters (three months) are included: 127 rows, 1995Q1 to 2026-07-01. SHA-256 c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea. Because the index is not seasonally adjusted, the quarterly inflation series has a pronounced seasonal pattern.\n\n## Vintage caveat\nThe CPI is not revised after publication (except for the periodic basket updates applied to the index chain by Statistics Canada), so the current vintage is essentially the real-time series; nevertheless the exercise treats a quarter's value as known at the end of that quarter (the actual publication occurs about three weeks after the last month), and no claim of historical real-time performance is made.\n{\n \"task_id\": \"F3-conf-04\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/forecasts.csv\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"model_name\": {\"type\": \"string\", \"nullable\": false},\n \"model_description\": {\"type\": \"string\", \"nullable\": false},\n \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n \"rmse_snaive_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_snaive_h2\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_snaive_h4\": {\"type\": \"number\", \"nullable\": false}, \"mae_snaive_h4\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h1\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h2\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rmse_model_h4\": {\"type\": \"number\", \"nullable\": false}, \"mae_model_h4\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h1\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h2\": {\"type\": \"number\", \"nullable\": false},\n \"rel_improvement_rmse_h4\": {\"type\": \"number\", \"nullable\": false},\n \"loss_diff_mean_h4\": {\"type\": \"number\", \"nullable\": false},\n \"loss_diff_se_h4\": {\"type\": \"number\", \"nullable\": false},\n \"hac_lags_h4\": {\"type\": \"integer\", \"nullable\": false},\n \"hac_kernel_h4\": {\"type\": \"string\", \"nullable\": false},\n \"dm_stat_h4\": {\"type\": \"number\", \"nullable\": false},\n \"dm_pvalue_h4\": {\"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\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [],\n \"positive_keys\": [\"rmse_snaive_h1\", \"rmse_snaive_h2\", \"rmse_snaive_h4\", \"rmse_model_h1\", \"rmse_model_h2\", \"rmse_model_h4\", \"loss_diff_se_h4\"],\n \"chronology_pairs\": [[\"first_origin\", \"last_origin\"]],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n \"task_id\": \"F3-conf-04\",\n \"family\": \"F3\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:55:58.429558+00:00\",\n \"files\": {\n \"TASK.md\": \"3039dc66364cbd8ca986e67cd619f2be53509c6cf77f55bc179583ee85464810\",\n \"data/README.md\": \"8ddc03b23fc4efce8ce697879fbb2ddca04c40ae54eb772a04e9777444098d0a\",\n \"data/cpi_monthly.csv\": \"855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2\",\n \"data/cpi_quarterly.csv\": \"c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea\",\n \"gate_spec.json\": \"3585c0dae94511350806b5f595e10839bd205eb59763dac49deb6a66b1396eab\"\n }\n}==> data/cpi_monthly.csv <==\nmonth,cpi\n1995-01-01,86.6\n1995-02-01,87.0\n1995-03-01,87.2\n1995-04-01,87.5\n\n==> data/cpi_quarterly.csv <==\nquarter,cpi_avg,infl_annualised\n1995-01-01,86.933333,\n1995-04-01,87.633333,3.207961\n1995-07-01,87.800000,0.760023\n1995-10-01,87.833333,0.151831\n==> data/cpi_monthly.csv <==\n2026-07-01,169.9\n2026-08-01,169.8\n2026-09-01,170.0\n\n==> data/cpi_quarterly.csv <==\n2026-01-01,166.100000,2.092560\n2026-04-01,168.866667,6.607773\n2026-07-01,169.900000,2.440232\n 382 data/cpi_monthly.csv\n 128 data/cpi_quarterly.csv\n 510 total\n855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 data/cpi_monthly.csv\nc8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F3-conf-04\n\n**Question.** Over 40 rolling quarterly origins (2015-10-01 … 2025-07-01), does a chosen time-series model forecast Canadian quarterly CPI inflation (400·Δln of quarterly-average NSA CPI) better than the seasonal-naive forecast at h = 1, 2, 4; and at h = 4 is the mean squared-error loss differential distinguishable from zero using a HAC (long-run variance) SE with truncation lag ≥ 3?\n\n**Estimands.** Out-of-sample RMSE and MAE per horizon for snaive and model; 1 − RMSE_model/RMSE_snaive per h; at h=4, d̄ = mean(e²_snaive − e²_model) over 40 origins, its HAC SE, DM statistic d̄/SE and two-sided p-value. Meaningful under covariance stationarity of the loss differential (DM assumptions); with only 40 overlapping observations, asymptotic normal inference is approximate.\n\n**Outputs.** out/forecasts.csv (240 rows: origin_date, target_date, horizon, forecast, actual, model); out/results.json with all keys in TASK.md table; out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py (regenerates results.json + forecasts.csv deterministically from data/); out/claims.json.\n\n**Information rules.** A forecast at origin t uses only quarters ≤ t (estimation, lag selection, all transformations inside the expanding window). Target = infl_annualised at t+h. Snaive forecast = value at t+h−4. Data as given; no real-time claim.\n\n**Invalidating conditions.** Missing target quarters (last target 2026-07-01 must exist), use of data > t, HAC lag < 3, non-positive SE, row counts ≠ 240.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2949,"cache_read_input_tokens":7133,"output_tokens":2159,"output_tokens_details":{"thinking_tokens":584},"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":2949},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnRQyG9J8PWKU3NM6sT","requestRef":"6f928e75-7a87-4334-a0d5-48036c632f19","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"83a75aed-0753-433c-9b79-6cfc40c12352","timestamp":"2026-10-30T04:23:36.831Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081/out/state","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"83a75aed-0753-433c-9b79-6cfc40c12352","isSidechain":false,"promptId":"c28e28dd-9014-4a65-afa2-bd4ce774e93a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QwqgPKjGpYS1D1jom1ELS1","type":"tool_result","content":"(Bash 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pd, numpy as np\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']); m=pd.read_csv('data/cpi_monthly.csv',parse_dates=['month'])\nprint(q.shape,m.shape); print(q.isna().sum().to_dict(), m.isna().sum().to_dict())\nprint(q.quarter.duplicated().sum(), m.month.duplicated().sum())\nprint((q.quarter.diff().dt.days.dropna().between(89,92)).all(), (m.month.diff().dt.days.dropna().between(28,31)).all())\nprint(q.quarter.dt.month.isin([1,4,7,10]).all())\nm['q']=m.month.dt.to_period('Q'); a=m.groupby('q').cpi.agg(['mean','size'])\nprint('max |cpi_avg diff|', np.abs(a['mean'].values-q.cpi_avg.values).max(), a['size'].min())\ninf=400*np.log(q.cpi_avg).diff(); print('max infl diff', np.abs(inf-q.infl_annualised).max())\nprint(q.infl_annualised.describe())\nq['mo']=q.quarter.dt.month; print(q.groupby('mo').infl_annualised.mean())\nprint(q.infl_annualised.abs().sort_values().tail(5)); print(q.loc[q.infl_annualised.abs().idxmax()])\n\"","description":"Audit data schema, duplicates, derivation"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2949,"cache_read_input_tokens":7133,"output_tokens":2159,"output_tokens_details":{"thinking_tokens":584},"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":2949},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXnRQyG9J8PWKU3NM6sT","requestRef":"6f928e75-7a87-4334-a0d5-48036c632f19","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c1eaf40f-fa5e-4290-82a2-58a4fead9851","timestamp":"2026-10-30T04:23:41.229Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081/out/state","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}27{"parentUuid":"c1eaf40f-fa5e-4290-82a2-58a4fead9851","isSidechain":false,"promptId":"c28e28dd-9014-4a65-afa2-bd4ce774e93a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Em9QdWQpdGjjGoeHCn5yZq","type":"tool_result","content":"(127, 3) (381, 2)\n{'quarter': 0, 'cpi_avg': 0, 'infl_annualised': 1} {'month': 0, 'cpi': 0}\n0 0\nTrue True\nTrue\nmax |cpi_avg diff| 3.33333360913457e-07 3\nmax infl diff 3.325781598340072e-06\ncount 126.000000\nmean 2.127202\nstd 2.385945\nmin -6.037850\n25% 0.667988\n50% 1.971882\n75% 3.503591\nmax 12.149214\nName: infl_annualised, dtype: float64\nmo\n1 2.322092\n4 4.175373\n7 1.658245\n10 0.302154\nName: infl_annualised, dtype: float64\n25 6.697577\n53 8.116732\n108 8.155133\n109 12.149214\n0 NaN\nName: infl_annualised, dtype: float64\nquarter 2022-04-01 00:00:00\ncpi_avg 151.533333\ninfl_annualised 12.149214\nmo 4\nName: 109, dtype: object","is_error":false}]},"uuid":"c3e2c20b-9ae4-45d6-9f6a-125be5598fed","timestamp":"2026-10-30T04:23:41.517Z","toolUseResult":{"stdout":"(127, 3) (381, 2)\n{'quarter': 0, 'cpi_avg': 0, 'infl_annualised': 1} {'month': 0, 'cpi': 0}\n0 0\nTrue True\nTrue\nmax |cpi_avg diff| 3.33333360913457e-07 3\nmax infl diff 3.325781598340072e-06\ncount 126.000000\nmean 2.127202\nstd 2.385945\nmin -6.037850\n25% 0.667988\n50% 1.971882\n75% 3.503591\nmax 12.149214\nName: infl_annualised, dtype: float64\nmo\n1 2.322092\n4 4.175373\n7 1.658245\n10 0.302154\nName: infl_annualised, dtype: float64\n25 6.697577\n53 8.116732\n108 8.155133\n109 12.149214\n0 NaN\nName: infl_annualised, dtype: float64\nquarter 2022-04-01 00:00:00\ncpi_avg 151.533333\ninfl_annualised 12.149214\nmo 4\nName: 109, dtype: object","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c1eaf40f-fa5e-4290-82a2-58a4fead9851","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}28{"type":"api-request-blob","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","hash":"a375baaf06c559bfe17d669d738a8428cabd051f7baff7d3e64925a7cf29e763","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01QwqgPKjGpYS1D1jom1ELS1","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F3-conf-04\n\n**Question.** Over 40 rolling quarterly origins (2015-10-01 … 2025-07-01), does a chosen time-series model forecast Canadian quarterly CPI inflation (400·Δln of quarterly-average NSA CPI) better than the seasonal-naive forecast at h = 1, 2, 4; and at h = 4 is the mean squared-error loss differential distinguishable from zero using a HAC (long-run variance) SE with truncation lag ≥ 3?\n\n**Estimands.** Out-of-sample RMSE and MAE per horizon for snaive and model; 1 − RMSE_model/RMSE_snaive per h; at h=4, d̄ = mean(e²_snaive − e²_model) over 40 origins, its HAC SE, DM statistic d̄/SE and two-sided p-value. Meaningful under covariance stationarity of the loss differential (DM assumptions); with only 40 overlapping observations, asymptotic normal inference is approximate.\n\n**Outputs.** out/forecasts.csv (240 rows: origin_date, target_date, horizon, forecast, actual, model); out/results.json with all keys in TASK.md table; out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py (regenerates results.json + forecasts.csv deterministically from data/); out/claims.json.\n\n**Information rules.** A forecast at origin t uses only quarters ≤ t (estimation, lag selection, all transformations inside the expanding window). Target = infl_annualised at t+h. Snaive forecast = value at t+h−4. Data as given; no real-time claim.\n\n**Invalidating conditions.** Missing target quarters (last target 2026-07-01 must exist), use of data > t, HAC lag < 3, non-positive SE, row counts ≠ 240.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}},{"type":"tool_use","id":"toolu_01Em9QdWQpdGjjGoeHCn5yZq","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0081 && ~/wp29/venv/bin/python -c \"\nimport pandas as pd, numpy as np\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']); m=pd.read_csv('data/cpi_monthly.csv',parse_dates=['month'])\nprint(q.shape,m.shape); print(q.isna().sum().to_dict(), m.isna().sum().to_dict())\nprint(q.quarter.duplicated().sum(), m.month.duplicated().sum())\nprint((q.quarter.diff().dt.days.dropna().between(89,92)).all(), (m.month.diff().dt.days.dropna().between(28,31)).all())\nprint(q.quarter.dt.month.isin([1,4,7,10]).all())\nm['q']=m.month.dt.to_period('Q'); a=m.groupby('q').cpi.agg(['mean','size'])\nprint('max |cpi_avg diff|', np.abs(a['mean'].values-q.cpi_avg.values).max(), a['size'].min())\ninf=400*np.log(q.cpi_avg).diff(); print('max infl diff', np.abs(inf-q.infl_annualised).max())\nprint(q.infl_annualised.describe())\nq['mo']=q.quarter.dt.month; print(q.groupby('mo').infl_annualised.mean())\nprint(q.infl_annualised.abs().sort_values().tail(5)); print(q.loc[q.infl_annualised.abs().idxmax()])\n\"","description":"Audit data schema, duplicates, 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151.533333\ninfl_annualised 12.149214\nmo 4\nName: 109, dtype: 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out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/cpi_quarterly.csv | c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea | 127 | 3 (quarter, cpi_avg, infl_annualised) |\n| data/cpi_monthly.csv | 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 | 381 | 2 (month, cpi) |\n| data/README.md | 8ddc03b23fc4efce8ce697879fbb2ddca04c40ae54eb772a04e9777444098d0a | — | — |\n\nAll hashes match README.md and bundle.json.\n\n- Units: cpi index (2002=100, NSA); infl_annualised in percent at annual rate.\n- Date ranges: quarterly 1995-01-01 … 2026-07-01 (127 consecutive quarters, all start months in {1,4,7,10}); monthly 1995-01 … 2026-09 (381 consecutive months).\n- Missing: only infl_annualised for 1995Q1 (by construction, no lag). No duplicates in either file.\n- Derivation check: quarterly mean of monthly values reproduces cpi_avg (max abs diff 3.3e-7, every quarter has 3 months); 400·Δln cpi_avg reproduces infl_annualised (max abs diff 3.3e-6, rounding).\n- Distribution: mean 2.13, sd 2.39, min −6.04, max 12.15 (2022Q2 — genuine inflation surge, not an error; kept). Strong seasonality: mean by quarter Q1 2.32, Q2 4.18, Q3 1.66, Q4 0.30.\n- Availability: last target needed is 2026-07-01 (origin 2025-07-01, h=4) — present. No input violates the availability rules; the analysis code truncates the series at each origin before any estimation.\n- Exclusions: 1995Q1 dropped (no inflation value). See out/exclusions.csv.\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,drop quarters with missing infl_annualised,127,126,1,1995Q1 has no previous quarter so inflation undefined\\n' > exclusions.csv\ncd state && ~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('phases.json'));p['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed and matched README\",\"row/col counts 127x3, 381x2\",\"missing/duplicate/date-continuity checks\",\"quarterly derivation reproduced from monthly\",\"exclusion ledger 127-1=126\"],\"notes\":\"\"};json.dump(p,open('phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: data clean, 1 exclusion (1995Q1).\" >> log.md","description":"Write data audit, exclusions, update ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2576,"cache_read_input_tokens":10082,"output_tokens":1244,"output_tokens_details":{"thinking_tokens":106},"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":2576},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnSxbosq1wjfLhazRuH","requestRef":"f6e52af6-d3ba-4e53-b55d-4b0bb7aa5890","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"eaf65ad6-d233-4a3a-b0a8-13314c1f8bca","timestamp":"2026-10-30T04:23:54.420Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"eaf65ad6-d233-4a3a-b0a8-13314c1f8bca","isSidechain":false,"promptId":"c28e28dd-9014-4a65-afa2-bd4ce774e93a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013uQXBJAkUDNdWJC737uyhL","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"f48987d2-964d-404b-aa87-dcaf1b596adc","timestamp":"2026-10-30T04:23:54.486Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"eaf65ad6-d233-4a3a-b0a8-13314c1f8bca","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081/out/state","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}36{"type":"api-request-blob","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","hash":"4b2d4145fb05905af38669ed156025d4201f02660771a550efbf626f8a9592fb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_013uQXBJAkUDNdWJC737uyhL","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols |\n|---|---|---|---|\n| data/cpi_quarterly.csv | c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea | 127 | 3 (quarter, cpi_avg, infl_annualised) |\n| data/cpi_monthly.csv | 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 | 381 | 2 (month, cpi) |\n| data/README.md | 8ddc03b23fc4efce8ce697879fbb2ddca04c40ae54eb772a04e9777444098d0a | — | — |\n\nAll hashes match README.md and bundle.json.\n\n- Units: cpi index (2002=100, NSA); infl_annualised in percent at annual rate.\n- Date ranges: quarterly 1995-01-01 … 2026-07-01 (127 consecutive quarters, all start months in {1,4,7,10}); monthly 1995-01 … 2026-09 (381 consecutive months).\n- Missing: only infl_annualised for 1995Q1 (by construction, no lag). No duplicates in either file.\n- Derivation check: quarterly mean of monthly values reproduces cpi_avg (max abs diff 3.3e-7, every quarter has 3 months); 400·Δln cpi_avg reproduces infl_annualised (max abs diff 3.3e-6, rounding).\n- Distribution: mean 2.13, sd 2.39, min −6.04, max 12.15 (2022Q2 — genuine inflation surge, not an error; kept). Strong seasonality: mean by quarter Q1 2.32, Q2 4.18, Q3 1.66, Q4 0.30.\n- Availability: last target needed is 2026-07-01 (origin 2025-07-01, h=4) — present. No input violates the availability rules; the analysis code truncates the series at each origin before any estimation.\n- Exclusions: 1995Q1 dropped (no inflation value). See out/exclusions.csv.\nEOF\ncd .. && printf 'step,rule,n_before,n_after,n_removed,rationale\\n1,drop quarters with missing infl_annualised,127,126,1,1995Q1 has no previous quarter so inflation undefined\\n' > exclusions.csv\ncd state && ~/wp29/venv/bin/python - <<'EOF'\nimport json;p=json.load(open('phases.json'));p['P1']={\"status\":\"passed\",\"checks\":[\"sha256 computed and matched README\",\"row/col counts 127x3, 381x2\",\"missing/duplicate/date-continuity checks\",\"quarterly derivation reproduced from monthly\",\"exclusion ledger 127-1=126\"],\"notes\":\"\"};json.dump(p,open('phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed: data clean, 1 exclusion (1995Q1).\" >> log.md","description":"Write data audit, exclusions, update 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: multi-horizon forecasts of Canadian quarterly CPI inflation vs seasonal naive.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json and out/forecasts.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nHORIZONS = [1, 2, 4]; PMAX = 8; HAC_LAGS = 3; MODEL = \"ar_seas_aic\"\n\nq = pd.read_csv(os.path.join(DATA, \"cpi_quarterly.csv\"), parse_dates=[\"quarter\"])\nm = pd.read_csv(os.path.join(DATA, \"cpi_monthly.csv\"))\nn_q_raw, n_m_raw = len(q), len(m)\nq = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True) # drops 1995Q1 only\ny_all = q.set_index(\"quarter\")[\"infl_annualised\"]\ndates = list(y_all.index)\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS-JAN\")\nassert len(origins) == 40\n\ndef design(y, qtr, p, start):\n \"\"\"Rows t=start..T-1: [Q1..Q4 dummies, y_{t-1..t-p}]; target y_t.\"\"\"\n T = len(y)\n X = [np.eye(4)[qtr[start:T]]]\n for j in range(1, p + 1):\n X.append(y[start - j:T - j, None])\n return np.hstack(X), y[start:T]\n\ndef fit_select(y, qtr):\n \"\"\"AIC lag selection p in 0..PMAX on common sample (first PMAX obs held out), then OLS refit on max sample.\"\"\"\n best = None\n for p in range(PMAX + 1):\n X, Y = design(y, qtr, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n ssr = np.sum((Y - X @ b) ** 2); n = len(Y)\n aic = n * np.log(ssr / n) + 2 * X.shape[1]\n if best is None or aic < best[0]:\n best = (aic, p)\n p = best[1]\n X, Y = design(y, qtr, p, p)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n return p, b, len(Y)\n\ndef iterate(y, qtr_last, p, b, H):\n hist = list(y); out = []\n for h in range(1, H + 1):\n qq = (qtr_last + h) % 4\n f = b[qq] + sum(b[4 + j - 1] * hist[-j] for j in range(1, p + 1))\n hist.append(f); out.append(f)\n return out\n\nrows = []; lag_choice = {}; n_last = None\nfor t in origins:\n idx = dates.index(t)\n hist = y_all.iloc[: idx + 1] # quarters <= t only\n y = hist.values; qtr = ((hist.index.month - 1) // 3).values\n p, b, n_est = fit_select(y, qtr)\n lag_choice[str(t.date())] = p; n_last = n_est\n fc = iterate(y, qtr[-1], p, b, max(HORIZONS))\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=3 * h)\n actual = float(y_all.loc[tgt])\n sn_date = tgt - pd.DateOffset(months=12)\n assert sn_date <= t\n sn = float(y_all.loc[sn_date])\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=sn, actual=actual, model=\"snaive\"))\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=float(fc[h - 1]), actual=actual, model=MODEL))\nfc_df = pd.DataFrame(rows).sort_values([\"model\", \"horizon\", \"origin_date\"]).reset_index(drop=True)\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ndef hac_var_mean(d, L, kernel):\n d = np.asarray(d) - np.mean(d); n = len(d)\n g = [np.sum(d[j:] * d[:n - j]) / n for j in range(L + 1)]\n w = [1 - j / (L + 1) if kernel == \"bartlett\" else 1.0 for j in range(L + 1)]\n lrv = g[0] + 2 * sum(w[j] * g[j] for j in range(1, L + 1))\n return lrv / n\n\nres = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, \"\n \"p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date())}\nerr = {}\nfor mod, key in [(\"snaive\", \"snaive\"), (MODEL, \"model\")]:\n for h in HORIZONS:\n s = fc_df[(fc_df.model == mod) & (fc_df.horizon == h)].sort_values(\"origin_date\")\n e = (s.actual - s.forecast).values; err[(key, h)] = e\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_snaive_h{h}\"]\nd = err[(\"snaive\", 4)] ** 2 - err[(\"model\", 4)] ** 2\nn = len(d); dbar = float(np.mean(d))\nse = float(np.sqrt(hac_var_mean(d, HAC_LAGS, \"bartlett\")))\ndm = dbar / se\nres.update(loss_diff_mean_h4=dbar, loss_diff_se_h4=se, hac_lags_h4=HAC_LAGS, hac_kernel_h4=\"bartlett\",\n dm_stat_h4=dm, dm_pvalue_h4=float(2 * stats.norm.sf(abs(dm))))\n# sensitivity (not required keys)\nsens = {}\nfor L, k in [(4, \"bartlett\"), (6, \"bartlett\"), (3, \"rectangular\")]:\n v = hac_var_mean(d, L, k)\n if v > 0:\n s_ = np.sqrt(v); sens[f\"{k}_L{L}\"] = {\"se\": float(s_), \"dm\": float(dbar / s_), \"p_normal\": float(2 * stats.norm.sf(abs(dbar / s_)))}\n else:\n sens[f\"{k}_L{L}\"] = {\"se\": None, \"dm\": None, \"p_normal\": None, \"note\": \"non-positive long-run variance\"}\nhln = np.sqrt((n + 1 - 2 * 4 + 4 * 3 / n) / n) # Harvey-Leybourne-Newbold small-sample factor, h=4\nsens[\"hln_bartlett_L3_t39\"] = {\"dm\": float(dm * hln), \"p_t\": float(2 * stats.t.sf(abs(dm * hln), n - 1))}\nres[\"sensitivity_h4\"] = sens\nres[\"n_loss_diff_positive_h4\"] = int(np.sum(d > 0))\nres[\"selected_lags_by_origin\"] = lag_choice\nres.update(feasibility=\"complete\", feasibility_reason=None,\n data_issues=[{\"issue\": \"infl_annualised missing for 1995Q1 (no preceding quarter)\", \"handling\": \"row dropped; estimation starts 1995Q2\"},\n {\"issue\": \"extreme value 12.15 in 2022Q2 (inflation surge)\", \"handling\": \"genuine observation, kept\"}],\n n_input_rows={\"cpi_quarterly.csv\": n_q_raw, \"cpi_monthly.csv\": n_m_raw},\n n_analysed=int(n_last), random_seed=None)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k not in (\"selected_lags_by_origin\",)}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis 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Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nHORIZONS = [1, 2, 4]; PMAX = 8; HAC_LAGS = 3; MODEL = \"ar_seas_aic\"\n\nq = pd.read_csv(os.path.join(DATA, \"cpi_quarterly.csv\"), parse_dates=[\"quarter\"])\nm = pd.read_csv(os.path.join(DATA, \"cpi_monthly.csv\"))\nn_q_raw, n_m_raw = len(q), len(m)\nq = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True) # drops 1995Q1 only\ny_all = q.set_index(\"quarter\")[\"infl_annualised\"]\ndates = list(y_all.index)\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS-JAN\")\nassert len(origins) == 40\n\ndef design(y, qtr, p, start):\n \"\"\"Rows t=start..T-1: [Q1..Q4 dummies, y_{t-1..t-p}]; target y_t.\"\"\"\n T = len(y)\n X = [np.eye(4)[qtr[start:T]]]\n for j in range(1, p + 1):\n X.append(y[start - j:T - j, None])\n return np.hstack(X), y[start:T]\n\ndef fit_select(y, qtr):\n \"\"\"AIC lag selection p in 0..PMAX on common sample (first PMAX obs held out), then OLS refit on max sample.\"\"\"\n best = None\n for p in range(PMAX + 1):\n X, Y = design(y, qtr, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n ssr = np.sum((Y - X @ b) ** 2); n = len(Y)\n aic = n * np.log(ssr / n) + 2 * X.shape[1]\n if best is None or aic < best[0]:\n best = (aic, p)\n p = best[1]\n X, Y = design(y, qtr, p, p)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n return p, b, len(Y)\n\ndef iterate(y, qtr_last, p, b, H):\n hist = list(y); out = []\n for h in range(1, H + 1):\n qq = (qtr_last + h) % 4\n f = b[qq] + sum(b[4 + j - 1] * hist[-j] for j in range(1, p + 1))\n hist.append(f); out.append(f)\n return out\n\nrows = []; lag_choice = {}; n_last = None\nfor t in origins:\n idx = dates.index(t)\n hist = y_all.iloc[: idx + 1] # quarters <= t only\n y = hist.values; qtr = ((hist.index.month - 1) // 3).values\n p, b, n_est = fit_select(y, qtr)\n lag_choice[str(t.date())] = p; n_last = n_est\n fc = iterate(y, qtr[-1], p, b, max(HORIZONS))\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=3 * h)\n actual = float(y_all.loc[tgt])\n sn_date = tgt - pd.DateOffset(months=12)\n assert sn_date <= t\n sn = float(y_all.loc[sn_date])\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=sn, actual=actual, model=\"snaive\"))\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=float(fc[h - 1]), actual=actual, model=MODEL))\nfc_df = pd.DataFrame(rows).sort_values([\"model\", \"horizon\", \"origin_date\"]).reset_index(drop=True)\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\n\ndef hac_var_mean(d, L, kernel):\n d = np.asarray(d) - np.mean(d); n = len(d)\n g = [np.sum(d[j:] * d[:n - j]) / n for j in range(L + 1)]\n w = [1 - j / (L + 1) if kernel == \"bartlett\" else 1.0 for j in range(L + 1)]\n lrv = g[0] + 2 * sum(w[j] * g[j] for j in range(1, L + 1))\n return lrv / n\n\nres = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, \"\n \"p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date())}\nerr = {}\nfor mod, key in [(\"snaive\", \"snaive\"), (MODEL, \"model\")]:\n for h in HORIZONS:\n s = fc_df[(fc_df.model == mod) & (fc_df.horizon == h)].sort_values(\"origin_date\")\n e = (s.actual - s.forecast).values; err[(key, h)] = e\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_snaive_h{h}\"]\nd = err[(\"snaive\", 4)] ** 2 - err[(\"model\", 4)] ** 2\nn = len(d); 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writes out/results.json and out/forecasts.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nHORIZONS = [1, 2, 4]; PMAX = 8; HAC_LAGS = 3; MODEL = \"ar_seas_aic\"\n\nq = pd.read_csv(os.path.join(DATA, \"cpi_quarterly.csv\"), parse_dates=[\"quarter\"])\nm = pd.read_csv(os.path.join(DATA, \"cpi_monthly.csv\"))\nn_q_raw, n_m_raw = len(q), len(m)\nq = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True) # drops 1995Q1 only\ny_all = q.set_index(\"quarter\")[\"infl_annualised\"]\ndates = list(y_all.index)\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS-JAN\")\nassert len(origins) == 40\n\ndef design(y, qtr, p, start):\n \"\"\"Rows t=start..T-1: [Q1..Q4 dummies, y_{t-1..t-p}]; target y_t.\"\"\"\n T = len(y)\n X = [np.eye(4)[qtr[start:T]]]\n for j in range(1, p + 1):\n X.append(y[start - j:T - j, None])\n return np.hstack(X), y[start:T]\n\ndef fit_select(y, qtr):\n \"\"\"AIC lag selection p in 0..PMAX on common sample (first PMAX obs held out), then OLS refit on max sample.\"\"\"\n best = None\n for p in range(PMAX + 1):\n X, Y = design(y, qtr, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n ssr = np.sum((Y - X @ b) ** 2); n = len(Y)\n aic = n * np.log(ssr / n) + 2 * X.shape[1]\n if best is None or aic < best[0]:\n best = (aic, p)\n p = best[1]\n X, Y = design(y, qtr, p, p)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n return p, b, len(Y)\n\ndef iterate(y, qtr_last, p, b, H):\n hist = list(y); out = []\n for h in range(1, H + 1):\n qq = (qtr_last + h) % 4\n f = b[qq] + sum(b[4 + j - 1] * hist[-j] for j in range(1, p + 1))\n hist.append(f); out.append(f)\n return out\n\nrows = []; lag_choice = {}; n_last = None\nfor t in origins:\n idx = dates.index(t)\n hist = y_all.iloc[: idx + 1] # quarters <= t only\n assert hist.index.max() == t\n y = hist.values; qtr = ((hist.index.month - 1) // 3).values\n p, b, n_est = fit_select(y, qtr)\n lag_choice[str(t.date())] = p; n_last = n_est\n fc = iterate(y, qtr[-1], p, b, max(HORIZONS))\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=3 * h)\n actual = float(y_all.loc[tgt])\n sn_date = tgt - pd.DateOffset(months=12)\n assert sn_date <= t\n sn = float(y_all.loc[sn_date])\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=sn, actual=actual, model=\"snaive\"))\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=float(fc[h - 1]), actual=actual, model=MODEL))\nfc_df = pd.DataFrame(rows).sort_values([\"model\", \"horizon\", \"origin_date\"]).reset_index(drop=True)\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\nfc_df = pd.read_csv(os.path.join(OUT, \"forecasts.csv\")) # metrics computed from the written file\n\ndef hac_var_mean(d, L, kernel):\n d = np.asarray(d) - np.mean(d); n = len(d)\n g = [np.sum(d[j:] * d[:n - j]) / n for j in range(L + 1)]\n w = [1 - j / (L + 1) if kernel == \"bartlett\" else 1.0 for j in range(L + 1)]\n lrv = g[0] + 2 * sum(w[j] * g[j] for j in range(1, L + 1))\n return lrv / n\n\nres = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, \"\n \"p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date())}\nerr = {}\nfor mod, key in [(\"snaive\", \"snaive\"), (MODEL, \"model\")]:\n for h in HORIZONS:\n s = fc_df[(fc_df.model == mod) & (fc_df.horizon == h)].sort_values(\"origin_date\")\n e = (s.actual - s.forecast).values; err[(key, h)] = e\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_snaive_h{h}\"]\nd = err[(\"snaive\", 4)] ** 2 - err[(\"model\", 4)] ** 2\nn = len(d); dbar = float(np.mean(d))\nse = float(np.sqrt(hac_var_mean(d, HAC_LAGS, \"bartlett\")))\ndm = dbar / se\nres.update(loss_diff_mean_h4=dbar, loss_diff_se_h4=se, hac_lags_h4=HAC_LAGS, hac_kernel_h4=\"bartlett\",\n dm_stat_h4=dm, dm_pvalue_h4=float(2 * stats.norm.sf(abs(dm))))\n# sensitivity (supplementary keys)\nsens = {}\nfor L, k in [(4, \"bartlett\"), (6, \"bartlett\"), (3, \"rectangular\")]:\n v = hac_var_mean(d, L, k)\n if v > 0:\n s_ = np.sqrt(v); sens[f\"{k}_L{L}\"] = {\"se\": float(s_), \"dm\": float(dbar / s_), \"p_normal\": float(2 * stats.norm.sf(abs(dbar / s_)))}\n else:\n sens[f\"{k}_L{L}\"] = {\"se\": None, \"dm\": None, \"p_normal\": None, \"note\": \"non-positive long-run variance\"}\nhln = np.sqrt((n + 1 - 2 * 4 + 4 * 3 / n) / n) # Harvey-Leybourne-Newbold small-sample factor, h=4\nsens[\"hln_bartlett_L3_t39\"] = {\"dm\": float(dm * hln), \"p_t\": float(2 * stats.t.sf(abs(dm * hln), n - 1))}\nres[\"sensitivity_h4\"] = sens\nres[\"n_loss_diff_positive_h4\"] = int(np.sum(d > 0))\nres[\"selected_lags_by_origin\"] = lag_choice\nres.update(feasibility=\"complete\", feasibility_reason=None,\n data_issues=[{\"issue\": \"infl_annualised missing for 1995Q1 (no preceding quarter)\", \"handling\": \"row dropped; estimation starts 1995Q2\"},\n {\"issue\": \"extreme value 12.15 in 2022Q2 (inflation surge)\", \"handling\": \"genuine observation, kept\"}],\n n_input_rows={\"cpi_quarterly.csv\": n_q_raw, \"cpi_monthly.csv\": n_m_raw},\n n_analysed=int(n_last), random_seed=None)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"selected_lags_by_origin\"}, indent=2))\nprint(\"selected lags:\", sorted(set(lag_choice.values())))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script from root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":153,"cache_read_input_tokens":17628,"output_tokens":3363,"output_tokens_details":{"thinking_tokens":24},"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":153},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnW9oLDZuFpiJC7wkFv","requestRef":"c36a2f7f-d836-46a2-a808-ca23d24c884e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"500e7f3d-a687-461e-bf9f-6655b700b27c","timestamp":"2026-10-30T04:24:47.542Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"500e7f3d-a687-461e-bf9f-6655b700b27c","isSidechain":false,"promptId":"c28e28dd-9014-4a65-afa2-bd4ce774e93a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01EatVm2bjhmfxroi4ubQw2W","type":"tool_result","content":"{\n \"task_id\": \"F3-conf-04\",\n \"model_name\": \"ar_seas_aic\",\n \"model_description\": \"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\",\n \"n_origins\": 40,\n \"first_origin\": \"2015-10-01\",\n \"last_origin\": \"2025-07-01\",\n \"rmse_snaive_h1\": 2.849125860156572,\n \"mae_snaive_h1\": 2.1374736750000003,\n \"rmse_snaive_h2\": 2.846674080377472,\n \"mae_snaive_h2\": 2.1259446750000004,\n \"rmse_snaive_h4\": 2.8823159392575315,\n \"mae_snaive_h4\": 2.181266125,\n \"rmse_model_h1\": 2.19298172952465,\n \"mae_model_h1\": 1.5194119750000001,\n \"rmse_model_h2\": 2.462562954183725,\n \"mae_model_h2\": 1.552562775,\n \"rmse_model_h4\": 2.3430306045734604,\n \"mae_model_h4\": 1.597327275,\n \"rel_improvement_rmse_h1\": 0.23029664635309022,\n \"rel_improvement_rmse_h2\": 0.13493329947445665,\n \"rel_improvement_rmse_h4\": 0.18710139556144145,\n \"loss_diff_mean_h4\": 2.8179527597301504,\n \"loss_diff_se_h4\": 2.0862370781374437,\n \"hac_lags_h4\": 3,\n \"hac_kernel_h4\": \"bartlett\",\n \"dm_stat_h4\": 1.3507346740505493,\n \"dm_pvalue_h4\": 0.17678044078432542,\n \"sensitivity_h4\": {\n \"bartlett_L4\": {\n \"se\": 1.8512808782370995,\n \"dm\": 1.5221638125564034,\n \"p_normal\": 0.12796803996974365\n },\n \"bartlett_L6\": {\n \"se\": 1.378408090519042,\n \"dm\": 2.0443530323948154,\n \"p_normal\": 0.0409186836754166\n },\n \"rectangular_L3\": {\n \"se\": 1.6984335302793625,\n \"dm\": 1.6591480970507257,\n \"p_normal\": 0.09708595339670391\n },\n \"hln_bartlett_L3_t39\": {\n \"dm\": 1.232429739553389,\n \"p_t\": 0.22516688921114056\n }\n },\n \"n_loss_diff_positive_h4\": 29,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"infl_annualised missing for 1995Q1 (no preceding quarter)\",\n \"handling\": \"row dropped; estimation starts 1995Q2\"\n },\n {\n \"issue\": \"extreme value 12.15 in 2022Q2 (inflation surge)\",\n \"handling\": \"genuine observation, kept\"\n }\n ],\n \"n_input_rows\": {\n \"cpi_quarterly.csv\": 127,\n \"cpi_monthly.csv\": 381\n },\n \"n_analysed\": 121,\n \"random_seed\": null\n}\nselected lags: [1, 2]","is_error":false}]},"uuid":"55220a72-de16-4512-b782-5cf91e342005","timestamp":"2026-10-30T04:24:48.281Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F3-conf-04\",\n \"model_name\": \"ar_seas_aic\",\n \"model_description\": \"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\",\n \"n_origins\": 40,\n \"first_origin\": \"2015-10-01\",\n \"last_origin\": \"2025-07-01\",\n \"rmse_snaive_h1\": 2.849125860156572,\n \"mae_snaive_h1\": 2.1374736750000003,\n \"rmse_snaive_h2\": 2.846674080377472,\n \"mae_snaive_h2\": 2.1259446750000004,\n \"rmse_snaive_h4\": 2.8823159392575315,\n \"mae_snaive_h4\": 2.181266125,\n \"rmse_model_h1\": 2.19298172952465,\n \"mae_model_h1\": 1.5194119750000001,\n \"rmse_model_h2\": 2.462562954183725,\n \"mae_model_h2\": 1.552562775,\n \"rmse_model_h4\": 2.3430306045734604,\n \"mae_model_h4\": 1.597327275,\n \"rel_improvement_rmse_h1\": 0.23029664635309022,\n \"rel_improvement_rmse_h2\": 0.13493329947445665,\n \"rel_improvement_rmse_h4\": 0.18710139556144145,\n \"loss_diff_mean_h4\": 2.8179527597301504,\n \"loss_diff_se_h4\": 2.0862370781374437,\n \"hac_lags_h4\": 3,\n \"hac_kernel_h4\": \"bartlett\",\n \"dm_stat_h4\": 1.3507346740505493,\n \"dm_pvalue_h4\": 0.17678044078432542,\n \"sensitivity_h4\": {\n \"bartlett_L4\": {\n \"se\": 1.8512808782370995,\n \"dm\": 1.5221638125564034,\n \"p_normal\": 0.12796803996974365\n },\n \"bartlett_L6\": {\n \"se\": 1.378408090519042,\n \"dm\": 2.0443530323948154,\n \"p_normal\": 0.0409186836754166\n },\n \"rectangular_L3\": {\n \"se\": 1.6984335302793625,\n \"dm\": 1.6591480970507257,\n \"p_normal\": 0.09708595339670391\n },\n \"hln_bartlett_L3_t39\": {\n \"dm\": 1.232429739553389,\n \"p_t\": 0.22516688921114056\n }\n },\n \"n_loss_diff_positive_h4\": 29,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"infl_annualised missing for 1995Q1 (no preceding quarter)\",\n \"handling\": \"row dropped; estimation starts 1995Q2\"\n },\n {\n \"issue\": \"extreme value 12.15 in 2022Q2 (inflation surge)\",\n \"handling\": \"genuine observation, kept\"\n }\n ],\n \"n_input_rows\": {\n \"cpi_quarterly.csv\": 127,\n \"cpi_monthly.csv\": 381\n },\n \"n_analysed\": 121,\n \"random_seed\": null\n}\nselected lags: [1, 2]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"500e7f3d-a687-461e-bf9f-6655b700b27c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","hash":"667b0cbb429767240dac41f7f8c30dbd709874b175853531bf2e71a0636616bd","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EatVm2bjhmfxroi4ubQw2W","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: multi-horizon forecasts of Canadian quarterly CPI inflation vs seasonal naive.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json and out/forecasts.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nfrom scipy import stats\n\nROOT = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nDATA = os.path.join(ROOT, \"data\"); OUT = os.path.join(ROOT, \"out\")\nHORIZONS = [1, 2, 4]; PMAX = 8; HAC_LAGS = 3; MODEL = \"ar_seas_aic\"\n\nq = pd.read_csv(os.path.join(DATA, \"cpi_quarterly.csv\"), parse_dates=[\"quarter\"])\nm = pd.read_csv(os.path.join(DATA, \"cpi_monthly.csv\"))\nn_q_raw, n_m_raw = len(q), len(m)\nq = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True) # drops 1995Q1 only\ny_all = q.set_index(\"quarter\")[\"infl_annualised\"]\ndates = list(y_all.index)\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS-JAN\")\nassert len(origins) == 40\n\ndef design(y, qtr, p, start):\n \"\"\"Rows t=start..T-1: [Q1..Q4 dummies, y_{t-1..t-p}]; target y_t.\"\"\"\n T = len(y)\n X = [np.eye(4)[qtr[start:T]]]\n for j in range(1, p + 1):\n X.append(y[start - j:T - j, None])\n return np.hstack(X), y[start:T]\n\ndef fit_select(y, qtr):\n \"\"\"AIC lag selection p in 0..PMAX on common sample (first PMAX obs held out), then OLS refit on max sample.\"\"\"\n best = None\n for p in range(PMAX + 1):\n X, Y = design(y, qtr, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n ssr = np.sum((Y - X @ b) ** 2); n = len(Y)\n aic = n * np.log(ssr / n) + 2 * X.shape[1]\n if best is None or aic < best[0]:\n best = (aic, p)\n p = best[1]\n X, Y = design(y, qtr, p, p)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n return p, b, len(Y)\n\ndef iterate(y, qtr_last, p, b, H):\n hist = list(y); out = []\n for h in range(1, H + 1):\n qq = (qtr_last + h) % 4\n f = b[qq] + sum(b[4 + j - 1] * hist[-j] for j in range(1, p + 1))\n hist.append(f); out.append(f)\n return out\n\nrows = []; lag_choice = {}; n_last = None\nfor t in origins:\n idx = dates.index(t)\n hist = y_all.iloc[: idx + 1] # quarters <= t only\n assert hist.index.max() == t\n y = hist.values; qtr = ((hist.index.month - 1) // 3).values\n p, b, n_est = fit_select(y, qtr)\n lag_choice[str(t.date())] = p; n_last = n_est\n fc = iterate(y, qtr[-1], p, b, max(HORIZONS))\n for h in HORIZONS:\n tgt = t + pd.DateOffset(months=3 * h)\n actual = float(y_all.loc[tgt])\n sn_date = tgt - pd.DateOffset(months=12)\n assert sn_date <= t\n sn = float(y_all.loc[sn_date])\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=sn, actual=actual, model=\"snaive\"))\n rows.append(dict(origin_date=str(t.date()), target_date=str(tgt.date()), horizon=h, forecast=float(fc[h - 1]), actual=actual, model=MODEL))\nfc_df = pd.DataFrame(rows).sort_values([\"model\", \"horizon\", \"origin_date\"]).reset_index(drop=True)\nfc_df.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.6f\")\nfc_df = pd.read_csv(os.path.join(OUT, \"forecasts.csv\")) # metrics computed from the written file\n\ndef hac_var_mean(d, L, kernel):\n d = np.asarray(d) - np.mean(d); n = len(d)\n g = [np.sum(d[j:] * d[:n - j]) / n for j in range(L + 1)]\n w = [1 - j / (L + 1) if kernel == \"bartlett\" else 1.0 for j in range(L + 1)]\n lrv = g[0] + 2 * sum(w[j] * g[j] for j in range(1, L + 1))\n return lrv / n\n\nres = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"Iterated (recursive) AR(p) for annualised quarterly inflation with four quarter-of-year intercepts, \"\n \"p in 0..8 selected by AIC on a common sample at each origin, estimated by OLS on an expanding window of all quarters <= origin (from 1995Q2).\"),\n \"n_origins\": len(origins), \"first_origin\": str(origins[0].date()), \"last_origin\": str(origins[-1].date())}\nerr = {}\nfor mod, key in [(\"snaive\", \"snaive\"), (MODEL, \"model\")]:\n for h in HORIZONS:\n s = fc_df[(fc_df.model == mod) & (fc_df.horizon == h)].sort_values(\"origin_date\")\n e = (s.actual - s.forecast).values; err[(key, h)] = e\n res[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2)))\n res[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in HORIZONS:\n res[f\"rel_improvement_rmse_h{h}\"] = 1 - res[f\"rmse_model_h{h}\"] / res[f\"rmse_snaive_h{h}\"]\nd = err[(\"snaive\", 4)] ** 2 - err[(\"model\", 4)] ** 2\nn = len(d); dbar = float(np.mean(d))\nse = float(np.sqrt(hac_var_mean(d, HAC_LAGS, \"bartlett\")))\ndm = dbar / se\nres.update(loss_diff_mean_h4=dbar, loss_diff_se_h4=se, hac_lags_h4=HAC_LAGS, hac_kernel_h4=\"bartlett\",\n dm_stat_h4=dm, dm_pvalue_h4=float(2 * stats.norm.sf(abs(dm))))\n# sensitivity (supplementary keys)\nsens = {}\nfor L, k in [(4, \"bartlett\"), (6, \"bartlett\"), (3, \"rectangular\")]:\n v = hac_var_mean(d, L, k)\n if v > 0:\n s_ = np.sqrt(v); sens[f\"{k}_L{L}\"] = {\"se\": float(s_), \"dm\": float(dbar / s_), \"p_normal\": float(2 * stats.norm.sf(abs(dbar / s_)))}\n else:\n sens[f\"{k}_L{L}\"] = {\"se\": None, \"dm\": None, \"p_normal\": None, \"note\": \"non-positive long-run variance\"}\nhln = np.sqrt((n + 1 - 2 * 4 + 4 * 3 / n) / n) # Harvey-Leybourne-Newbold small-sample factor, h=4\nsens[\"hln_bartlett_L3_t39\"] = {\"dm\": float(dm * hln), \"p_t\": float(2 * stats.t.sf(abs(dm * hln), n - 1))}\nres[\"sensitivity_h4\"] = sens\nres[\"n_loss_diff_positive_h4\"] = int(np.sum(d > 0))\nres[\"selected_lags_by_origin\"] = lag_choice\nres.update(feasibility=\"complete\", feasibility_reason=None,\n data_issues=[{\"issue\": \"infl_annualised missing for 1995Q1 (no preceding quarter)\", \"handling\": \"row dropped; estimation starts 1995Q2\"},\n {\"issue\": \"extreme value 12.15 in 2022Q2 (inflation surge)\", \"handling\": \"genuine observation, kept\"}],\n n_input_rows={\"cpi_quarterly.csv\": n_q_raw, \"cpi_monthly.csv\": n_m_raw},\n n_analysed=int(n_last), random_seed=None)\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"selected_lags_by_origin\"}, indent=2))\nprint(\"selected lags:\", sorted(set(lag_choice.values())))\nEOF\n~/wp29/venv/bin/python out/analysis.py","description":"Write and run analysis script from root"}}]}}54{"type":"api-request","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","id":"2c52ac2e-b978-4654-b95d-81abbdcb910e","timestamp":"2026-10-30T04:24:48.284Z","version":"2.1.314","querySource":"sdk","shapeHash":"dce16387455aafe5642cd53441c6748f84dc29774375640c7d3453dadb461f6d","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":"c36a2f7f-d836-46a2-a808-ca23d24c884e","keep":14,"tail":["667b0cbb429767240dac41f7f8c30dbd709874b175853531bf2e71a0636616bd","2141072e2b5b1e1b305c3552f12a15e69e93a62b300f04c21f31647367b17305"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"8e893bd762a2bb229b6ef6e456b3d8a74180a553e943360113ce6ed3310ff6b7"}55{"parentUuid":"55220a72-de16-4512-b782-5cf91e342005","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnXtLi6ms6LLrwQSjtH","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4499,"cache_read_input_tokens":17781,"output_tokens":1295,"output_tokens_details":{"thinking_tokens":170},"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":4499},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1575,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnXt46mVVVwzMKoAga2","requestRef":"2c52ac2e-b978-4654-b95d-81abbdcb910e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"aa6e1040-0121-431f-9b53-bc017765e081","timestamp":"2026-10-30T04:24:51.769Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"aa6e1040-0121-431f-9b53-bc017765e081","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnXtLi6ms6LLrwQSjtH","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0199JsbD4ys9D3vavomL2fTV","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nr=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date'])\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']).set_index('quarter').infl_annualised\nprint('rows',len(f), f.groupby(['model','horizon']).size().unique())\nprint('target=origin+h', ((f.target_date.dt.to_period('Q')-f.origin_date.dt.to_period('Q')).apply(lambda x:x.n)==f.horizon).all())\nprint('actual matches', np.allclose(f.actual, q.loc[f.target_date].values))\ns=f[f.model=='snaive']; print('snaive matches t+h-4', np.allclose(s.forecast, q.loc[s.target_date-pd.DateOffset(months=12)].values), (s.target_date-pd.DateOffset(months=12)<=s.origin_date).all())\nprint('rmse>=mae', all(r[f'rmse_{k}_h{h}']>=r[f'mae_{k}_h{h}'] for k in ['snaive','model'] for h in [1,2,4]))\nprint('rel check', all(abs(r[f'rel_improvement_rmse_h{h}']-(1-r[f'rmse_model_h{h}']/r[f'rmse_snaive_h{h}']))<1e-12 for h in [1,2,4]))\nprint('dbar identity', r['loss_diff_mean_h4'], r['rmse_snaive_h4']**2-r['rmse_model_h4']**2)\nprint('dm identity', abs(r['dm_stat_h4']-r['loss_diff_mean_h4']/r['loss_diff_se_h4'])<1e-12, r['loss_diff_se_h4']>0, r['hac_lags_h4']>=3)\n# statsmodels cross-check of HAC SE\nimport statsmodels.api as sm\ns4=f[f.horizon==4].pivot(index='origin_date',columns='model',values=['forecast','actual'])\nd=(s4['actual']['snaive']-s4['forecast']['snaive'])**2-(s4['actual']['ar_seas_aic']-s4['forecast']['ar_seas_aic'])**2\nm=sm.OLS(d.values,np.ones(len(d))).fit(cov_type='HAC',cov_kwds={'maxlags':3,'use_correction':False})\nprint('statsmodels NW se', m.bse[0], 'ours', r['loss_diff_se_h4'])\nEOF","description":"Reproducibility and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4499,"cache_read_input_tokens":17781,"output_tokens":1295,"output_tokens_details":{"thinking_tokens":170},"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":4499},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnXt46mVVVwzMKoAga2","requestRef":"2c52ac2e-b978-4654-b95d-81abbdcb910e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"920d5c59-7697-4a67-9e70-209306a5ad9b","timestamp":"2026-10-30T04:24:59.617Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}57{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"920d5c59-7697-4a67-9e70-209306a5ad9b","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105"}58{"type":"atis-latch","atis":"","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105"}59{"parentUuid":"920d5c59-7697-4a67-9e70-209306a5ad9b","isSidechain":false,"promptId":"c28e28dd-9014-4a65-afa2-bd4ce774e93a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0199JsbD4ys9D3vavomL2fTV","type":"tool_result","content":"IDENTICAL\nrows 240 [40]\ntarget=origin+h True\nactual matches True\nsnaive matches t+h-4 True True\nrmse>=mae True\nrel check True\ndbar identity 2.8179527597301504 2.81795275973015\ndm identity True True True\nstatsmodels NW se 2.086237078137444 ours 2.0862370781374437","is_error":false}]},"uuid":"fd584a99-f1c4-4917-961e-2f55651427d7","timestamp":"2026-10-30T04:25:01.259Z","toolUseResult":{"stdout":"IDENTICAL\nrows 240 [40]\ntarget=origin+h True\nactual matches True\nsnaive matches t+h-4 True True\nrmse>=mae True\nrel check True\ndbar identity 2.8179527597301504 2.81795275973015\ndm identity True True True\nstatsmodels NW se 2.086237078137444 ours 2.0862370781374437","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"920d5c59-7697-4a67-9e70-209306a5ad9b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","hash":"75b44aeddca02039bd4786b377de5756b914c0c84c8deb712639e001b7f610e5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_0199JsbD4ys9D3vavomL2fTV","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; rm out/results.json out/forecasts.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && cmp /tmp/f1.csv out/forecasts.csv && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nr=json.load(open('out/results.json')); f=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date'])\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']).set_index('quarter').infl_annualised\nprint('rows',len(f), f.groupby(['model','horizon']).size().unique())\nprint('target=origin+h', ((f.target_date.dt.to_period('Q')-f.origin_date.dt.to_period('Q')).apply(lambda x:x.n)==f.horizon).all())\nprint('actual matches', np.allclose(f.actual, q.loc[f.target_date].values))\ns=f[f.model=='snaive']; print('snaive matches t+h-4', np.allclose(s.forecast, q.loc[s.target_date-pd.DateOffset(months=12)].values), (s.target_date-pd.DateOffset(months=12)<=s.origin_date).all())\nprint('rmse>=mae', all(r[f'rmse_{k}_h{h}']>=r[f'mae_{k}_h{h}'] for k in ['snaive','model'] for h in [1,2,4]))\nprint('rel check', all(abs(r[f'rel_improvement_rmse_h{h}']-(1-r[f'rmse_model_h{h}']/r[f'rmse_snaive_h{h}']))<1e-12 for h in [1,2,4]))\nprint('dbar identity', r['loss_diff_mean_h4'], r['rmse_snaive_h4']**2-r['rmse_model_h4']**2)\nprint('dm identity', abs(r['dm_stat_h4']-r['loss_diff_mean_h4']/r['loss_diff_se_h4'])<1e-12, r['loss_diff_se_h4']>0, r['hac_lags_h4']>=3)\n# statsmodels cross-check of HAC SE\nimport statsmodels.api as sm\ns4=f[f.horizon==4].pivot(index='origin_date',columns='model',values=['forecast','actual'])\nd=(s4['actual']['snaive']-s4['forecast']['snaive'])**2-(s4['actual']['ar_seas_aic']-s4['forecast']['ar_seas_aic'])**2\nm=sm.OLS(d.values,np.ones(len(d))).fit(cov_type='HAC',cov_kwds={'maxlags':3,'use_correction':False})\nprint('statsmodels NW se', m.bse[0], 'ours', r['loss_diff_se_h4'])\nEOF","description":"Reproducibility and consistency checks"}}]}}61{"type":"api-request","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","id":"7480b59a-949a-47ab-bba7-9a98c2f01382","timestamp":"2026-10-30T04:25:01.262Z","version":"2.1.314","querySource":"sdk","shapeHash":"dce16387455aafe5642cd53441c6748f84dc29774375640c7d3453dadb461f6d","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":"2c52ac2e-b978-4654-b95d-81abbdcb910e","keep":16,"tail":["75b44aeddca02039bd4786b377de5756b914c0c84c8deb712639e001b7f610e5","b76d55abf4a7a434b5e8a37bbe7efa5204b59ac5984621f32cad1769750d79d2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"44e20368d9ae398d4b041d1b6229c4c853c35ea98b4deb3c0594b1cf967e9a23"}62{"parentUuid":"fd584a99-f1c4-4917-961e-2f55651427d7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnYqpwRJThGH1hKLfqp","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1418,"cache_read_input_tokens":22280,"output_tokens":990,"output_tokens_details":{"thinking_tokens":119},"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":1418},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1156,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnYqYKVsMJEdpXUWfF3","requestRef":"7480b59a-949a-47ab-bba7-9a98c2f01382","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"195960f4-75c3-4a6f-9a07-3de32649d069","timestamp":"2026-10-30T04:25:10.821Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"195960f4-75c3-4a6f-9a07-3de32649d069","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnYqpwRJThGH1hKLfqp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01WYESZyQbQoQVemKjDjo1ow","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json, forecasts.csv; re-ran analysis.py) gives byte-identical files | PASS |\n| forecasts.csv has 240 rows, 40 per (model, horizon) | PASS |\n| target_date = origin_date + h quarters for every row | PASS |\n| `actual` equals infl_annualised at target_date | PASS |\n| snaive forecast equals infl_annualised at target − 4 quarters, and that date ≤ origin | PASS |\n| Estimation sample at each origin ends exactly at the origin (assert in code: hist.index.max() == t) | PASS |\n| RMSE ≥ MAE for all model/horizon pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_snaive (|diff| < 1e-12) | PASS |\n| loss_diff_mean_h4 = rmse_snaive_h4² − rmse_model_h4² (2.81795 both) | PASS |\n| dm_stat_h4 = mean/se; se > 0; hac_lags_h4 = 3 ≥ h−1 | PASS |\n| HAC SE cross-checked against statsmodels OLS-on-constant HAC (Bartlett, maxlags=3, no small-sample correction): 2.086237 vs 2.086237 | PASS |\n| first_origin < last_origin; n_origins = 40 | PASS |\n| Exclusion ledger: 127 − 1 = 126 analysable quarters; last estimation sample 1995Q2–2025Q3 = 122 quarters, minus p=1 lag = 121 = n_analysed | PASS |\n| Magnitudes: model RMSE (2.19–2.46) below snaive RMSE (≈2.85–2.88) and of the same order as the series sd (2.39) | plausible |\n| Sensitivity note: HAC SE decreases with longer Bartlett lags (L=4: 1.85, L=6: 1.38) — negative higher-order autocorrelations in d_t; primary lag L=3 was fixed before seeing results | reported, not a failure |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run 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GATE pgar_gate/1.0 — FAILED (10 of 57 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/forecasts.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:model_name: ok\n [ok] key:model_description: ok\n [ok] key:n_origins: ok\n [ok] key:first_origin: ok\n [ok] key:last_origin: ok\n [ok] key:rmse_snaive_h1: ok\n [ok] key:mae_snaive_h1: ok\n [ok] key:rmse_snaive_h2: ok\n [ok] key:mae_snaive_h2: ok\n [ok] key:rmse_snaive_h4: ok\n [ok] key:mae_snaive_h4: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h2: ok\n [ok] key:mae_model_h2: ok\n [ok] key:rmse_model_h4: ok\n [ok] key:mae_model_h4: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h2: ok\n [ok] key:rel_improvement_rmse_h4: ok\n [ok] key:loss_diff_mean_h4: ok\n [ok] key:loss_diff_se_h4: ok\n [ok] key:hac_lags_h4: ok\n [ok] key:hac_kernel_h4: ok\n [ok] key:dm_stat_h4: ok\n [ok] key:dm_pvalue_h4: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 1 steps consistent\n [ok] positive:rmse_snaive_h1: 2.849125860156572\n [ok] positive:rmse_snaive_h2: 2.846674080377472\n [ok] positive:rmse_snaive_h4: 2.8823159392575315\n [ok] positive:rmse_model_h1: 2.19298172952465\n [ok] positive:rmse_model_h2: 2.462562954183725\n [ok] positive:rmse_model_h4: 2.3430306045734604\n [ok] positive:loss_diff_se_h4: 2.0862370781374437\n [ok] chronology:first_origin<=last_origin: 2015-10-01 vs 2025-07-01\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0081/out/state/gate_receipts/20261030T042518.566884Z.json\n20261030T042518.566884Z.json","is_error":false}]},"uuid":"6fa42ada-7a83-4599-b614-300b55f795ce","timestamp":"2026-10-30T04:25:18.578Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 57 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/forecasts.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:model_name: ok\n [ok] key:model_description: ok\n [ok] key:n_origins: ok\n [ok] key:first_origin: ok\n [ok] key:last_origin: ok\n [ok] key:rmse_snaive_h1: ok\n [ok] key:mae_snaive_h1: ok\n [ok] key:rmse_snaive_h2: ok\n [ok] key:mae_snaive_h2: ok\n [ok] key:rmse_snaive_h4: ok\n [ok] key:mae_snaive_h4: ok\n [ok] key:rmse_model_h1: ok\n [ok] key:mae_model_h1: ok\n [ok] key:rmse_model_h2: ok\n [ok] key:mae_model_h2: ok\n [ok] key:rmse_model_h4: ok\n [ok] key:mae_model_h4: ok\n [ok] key:rel_improvement_rmse_h1: ok\n [ok] key:rel_improvement_rmse_h2: ok\n [ok] key:rel_improvement_rmse_h4: ok\n [ok] key:loss_diff_mean_h4: ok\n [ok] key:loss_diff_se_h4: ok\n [ok] key:hac_lags_h4: ok\n [ok] key:hac_kernel_h4: ok\n [ok] key:dm_stat_h4: ok\n [ok] key:dm_pvalue_h4: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:n_analysed: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 1 steps consistent\n [ok] positive:rmse_snaive_h1: 2.849125860156572\n [ok] positive:rmse_snaive_h2: 2.846674080377472\n [ok] positive:rmse_snaive_h4: 2.8823159392575315\n [ok] positive:rmse_model_h1: 2.19298172952465\n [ok] positive:rmse_model_h2: 2.462562954183725\n [ok] positive:rmse_model_h4: 2.3430306045734604\n [ok] positive:loss_diff_se_h4: 2.0862370781374437\n [ok] chronology:first_origin<=last_origin: 2015-10-01 vs 2025-07-01\n [ok] reproducibility: regenerated in 0.7s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0081/out/state/gate_receipts/20261030T042518.566884Z.json\n20261030T042518.566884Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"333836f0-90ab-49f5-9487-f2af38750b65","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}65{"type":"api-request-blob","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","hash":"bbde02b9a8022fe3c80a6acc846750fabd6be10b7bd0c5c36e3055609ad6db6f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01WYESZyQbQoQVemKjDjo1ow","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json, forecasts.csv; re-ran analysis.py) gives byte-identical files | PASS |\n| forecasts.csv has 240 rows, 40 per (model, horizon) | PASS |\n| target_date = origin_date + h quarters for every row | PASS |\n| `actual` equals infl_annualised at target_date | PASS |\n| snaive forecast equals infl_annualised at target − 4 quarters, and that date ≤ origin | PASS |\n| Estimation sample at each origin ends exactly at the origin (assert in code: hist.index.max() == t) | PASS |\n| RMSE ≥ MAE for all model/horizon pairs | PASS |\n| rel_improvement = 1 − rmse_model/rmse_snaive (|diff| < 1e-12) | PASS |\n| loss_diff_mean_h4 = rmse_snaive_h4² − rmse_model_h4² (2.81795 both) | PASS |\n| dm_stat_h4 = mean/se; se > 0; hac_lags_h4 = 3 ≥ h−1 | PASS |\n| HAC SE cross-checked against statsmodels OLS-on-constant HAC (Bartlett, maxlags=3, no small-sample correction): 2.086237 vs 2.086237 | PASS |\n| first_origin < last_origin; n_origins = 40 | PASS |\n| Exclusion ledger: 127 − 1 = 126 analysable quarters; last estimation sample 1995Q2–2025Q3 = 122 quarters, minus p=1 lag = 121 = n_analysed | PASS |\n| Magnitudes: model RMSE (2.19–2.46) below snaive RMSE (≈2.85–2.88) and of the same order as the series sd (2.39) | plausible |\n| Sensitivity note: HAC SE decreases with longer Bartlett lags (L=4: 1.85, L=6: 1.38) — negative higher-order autocorrelations in d_t; primary lag L=3 was fixed before seeing results | reported, not a failure |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run 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> out/report.md <<'EOF'\n# F3-conf-04 — Multi-horizon forecasts of Canadian quarterly CPI inflation\n\n## Question\nOver 40 rolling quarterly origins, 2015-10-01 to 2025-07-01 (`n_origins`, `first_origin`, `last_origin`), does a time-series model forecast Canadian quarterly CPI inflation (400 × Δln of the quarterly-average NSA CPI) better than the seasonal-naive forecast at horizons 1, 2 and 4? And at h = 4, is the mean squared-error loss differential different from zero once we allow for the serial correlation that overlapping 4-step errors create?\n\n## Data and exclusions\n- `data/cpi_quarterly.csv`: 127 rows, 1995Q1–2026Q3 (`n_input_rows`). `data/cpi_monthly.csv`: 381 rows (`n_input_rows`), used only for the provenance audit. Both SHA-256 hashes match `data/README.md`. Averaging the monthly index by quarter reproduces `cpi_avg` (max abs diff 3e-7), and 400·Δln reproduces `infl_annualised` (max abs diff 3e-6). Neither file has duplicates or gaps (details in `out/state/data_audit.md`).\n- Exclusions (`out/exclusions.csv`): one row removed, 1995Q1, because its inflation value is undefined. That leaves 126 analysable quarters (`data_issues`).\n- The 2022Q2 value (12.15 %) is a genuine part of the inflation surge. I kept it (`data_issues`).\n- The last estimation sample (origin 2025Q3) has 121 regression observations (`n_analysed`).\n\n## Method\n- **Baseline (seasonal naive):** the forecast for target t+h is the value at t+h−4.\n- **Model (`model_name` = `ar_seas_aic`):** `model_description`. The regression has four quarter-of-year intercepts plus p lags of inflation. At each origin, AIC picks p from 0 to 8, comparing all candidates on a common sample. The chosen model is then re-estimated by OLS on every available quarter up to and including the origin. Forecasts for h = 2 and 4 are iterated from the one-step equation. At each origin the code cuts the series at the origin before doing any estimation or selection, so no later quarter is used (this is asserted in `out/analysis.py`). AIC chose p = 1 or 2 across origins (`selected_lags_by_origin`).\n- **Evaluation:** errors are e = actual − forecast. RMSE and MAE are taken over the 40 origins at each horizon.\n- **h = 4 test (Diebold–Mariano):** d_t = e²_snaive,t − e²_model,t. Its mean is `loss_diff_mean_h4`, and its standard error comes from a Bartlett-kernel (Newey–West) long-run variance (`hac_kernel_h4`) with truncation lag 3 = h−1 (`hac_lags_h4`). The test statistic is `dm_stat_h4` and its p-value `dm_pvalue_h4` is two-sided against N(0,1). I fixed the lag and kernel before seeing any results. Nothing in the analysis is random (`random_seed` = null).\n\n## Results\n| h | RMSE snaive | RMSE model | MAE snaive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 | 2.849 (`rmse_snaive_h1`) | 2.193 (`rmse_model_h1`) | 2.137 (`mae_snaive_h1`) | 1.519 (`mae_model_h1`) | 0.230 (`rel_improvement_rmse_h1`) |\n| 2 | 2.847 (`rmse_snaive_h2`) | 2.463 (`rmse_model_h2`) | 2.126 (`mae_snaive_h2`) | 1.553 (`mae_model_h2`) | 0.135 (`rel_improvement_rmse_h2`) |\n| 4 | 2.882 (`rmse_snaive_h4`) | 2.343 (`rmse_model_h4`) | 2.181 (`mae_snaive_h4`) | 1.597 (`mae_model_h4`) | 0.187 (`rel_improvement_rmse_h4`) |\n\nRMSE and MAE are in percentage points of annualised inflation. Over this window the model has lower RMSE and MAE than the seasonal-naive forecast at all three horizons. The RMSE reductions are 23 %, 13 % and 19 %.\n\nAt h = 4, the mean loss differential is d̄ = 2.818 squared pp (`loss_diff_mean_h4`), with HAC SE 2.086 (`loss_diff_se_h4`). That gives DM = 1.351 (`dm_stat_h4`) and a two-sided p = 0.177 (`dm_pvalue_h4`). The model's squared error was smaller at 29 of the 40 origins (`n_loss_diff_positive_h4`). **At conventional levels, the h = 4 improvement cannot be distinguished statistically from zero.**\n\n## Uncertainty\n- The conclusion depends on the HAC choices (`sensitivity_h4`). With Bartlett lag 4, p = 0.128. With a rectangular kernel at lag 3, p = 0.097. With the Harvey–Leybourne–Newbold small-sample correction and t(39) reference, p = 0.225. With Bartlett lag 6, p = 0.041. The SE shrinks as the lag grows, which means d_t has negative autocorrelations at higher lags. The lag-6 result was not pre-specified and is the only one below 0.05; I treat it as a sensitivity check, not as evidence. The pre-specified result (lag 3, p = 0.18) stands.\n- 40 overlapping observations is a small sample for long-run-variance estimation, so the normal approximation is rough. Squared-error losses are also strongly affected by the 2021–2023 inflation surge, so a handful of quarters drive much of d̄.\n- No uncertainty is reported for the RMSE and MAE at h = 1 and 2. The task only asks for a formal test at h = 4.\n\n## Limitations and open issues\n- I chose one model ahead of time and did not compare alternatives (SARIMA, direct forecasts) on the test window, so there is no search-induced bias. However, the results describe only this model.\n- The errors are computed on the current data vintage, and a quarter is treated as known at the end of that quarter. No real-time validity is claimed. Actual CPI publication comes about three weeks later, and basket updates may have small effects.\n- The DM test compares forecasts, not models. Both forecasts come from expanding-window estimates, and the model is not nested in the seasonal-naive forecast in any parameter sense, so applying standard DM asymptotics is reasonable but approximate.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over the 40 origins the seasonal-AR model has lower RMSE and MAE than the seasonal-naive forecast at h = 1, 2 and 4, with relative RMSE improvements of about 23%, 13% and 19%.\",\"results_keys\":[\"rmse_snaive_h1\",\"rmse_model_h1\",\"rmse_snaive_h2\",\"rmse_model_h2\",\"rmse_snaive_h4\",\"rmse_model_h4\",\"mae_snaive_h1\",\"mae_model_h1\",\"mae_snaive_h2\",\"mae_model_h2\",\"mae_snaive_h4\",\"mae_model_h4\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"rel_improvement_rmse_h4\"],\"status\":\"supported\",\"caveat\":\"Point estimates on one 40-quarter window; no formal test at h = 1, 2.\"},\n {\"id\":\"C2\",\"text\":\"At h = 4 the mean squared-error loss differential (2.82 squared pp in favour of the model) is not statistically distinguishable from zero: Bartlett HAC SE with lag 3 gives DM = 1.35, two-sided p = 0.18.\",\"results_keys\":[\"loss_diff_mean_h4\",\"loss_diff_se_h4\",\"hac_lags_h4\",\"hac_kernel_h4\",\"dm_stat_h4\",\"dm_pvalue_h4\"],\"status\":\"supported\",\"caveat\":\"Small sample with overlapping errors; p ranges 0.04-0.23 across HAC choices in sensitivity_h4; pre-specified choice reported.\"},\n {\"id\":\"C3\",\"text\":\"The model's squared 4-step error was smaller than the seasonal-naive one at 29 of the 40 origins.\",\"results_keys\":[\"n_loss_diff_positive_h4\"],\"status\":\"supported\",\"caveat\":\"Descriptive count; not a test.\"},\n {\"id\":\"C4\",\"text\":\"Forecasts use only quarters up to and including each origin; the last estimation sample has 121 observations.\",\"results_keys\":[\"n_analysed\",\"first_origin\",\"last_origin\",\"n_origins\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; no real-time claim.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json,glob\np=json.load(open('out/state/phases.json'))\nr1='out/state/gate_receipts/20261030T042518.566884Z.json'\np['P2']={\"status\":\"passed\",\"checks\":[\"snaive forecasts computed with same evaluation rule (rmse/mae_snaive_h*)\",\"snaive = value at t+h-4 verified <= origin\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json+forecasts.csv\",\"no randomness (random_seed null)\",\"origin truncation asserted in code\"],\"notes\":\"\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical\",\"all consistency checks in checks.md PASS\",\"HAC SE cross-checked with statsmodels\",f\"gate run 1 receipt {r1}: all contract checks for results/forecasts/reproducibility/exclusions pass; failures only for P5 deliverables (report, claims) and non-terminal phases, expected at this stage\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md written with 6 sections citing keys\",\"claims.json written (4 claims)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30 P2/P3 passed: snaive baseline + ar_seas_aic in analysis.py (HAC Bartlett L=3 pre-specified).\n- 2026-10-30 P4 passed: reproducible, checks.md all pass; gate run 1 (P5 files missing, expected).\n- 2026-10-30 P5: report and claims written; running final gate.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Write report, claims, update ledger, rerun 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> out/report.md <<'EOF'\n# F3-conf-04 — Multi-horizon forecasts of Canadian quarterly CPI inflation\n\n## Question\nOver 40 rolling quarterly origins, 2015-10-01 to 2025-07-01 (`n_origins`, `first_origin`, `last_origin`), does a time-series model forecast Canadian quarterly CPI inflation (400 × Δln of the quarterly-average NSA CPI) better than the seasonal-naive forecast at horizons 1, 2 and 4? And at h = 4, is the mean squared-error loss differential different from zero once we allow for the serial correlation that overlapping 4-step errors create?\n\n## Data and exclusions\n- `data/cpi_quarterly.csv`: 127 rows, 1995Q1–2026Q3 (`n_input_rows`). `data/cpi_monthly.csv`: 381 rows (`n_input_rows`), used only for the provenance audit. Both SHA-256 hashes match `data/README.md`. Averaging the monthly index by quarter reproduces `cpi_avg` (max abs diff 3e-7), and 400·Δln reproduces `infl_annualised` (max abs diff 3e-6). Neither file has duplicates or gaps (details in `out/state/data_audit.md`).\n- Exclusions (`out/exclusions.csv`): one row removed, 1995Q1, because its inflation value is undefined. That leaves 126 analysable quarters (`data_issues`).\n- The 2022Q2 value (12.15 %) is a genuine part of the inflation surge. I kept it (`data_issues`).\n- The last estimation sample (origin 2025Q3) has 121 regression observations (`n_analysed`).\n\n## Method\n- **Baseline (seasonal naive):** the forecast for target t+h is the value at t+h−4.\n- **Model (`model_name` = `ar_seas_aic`):** `model_description`. The regression has four quarter-of-year intercepts plus p lags of inflation. At each origin, AIC picks p from 0 to 8, comparing all candidates on a common sample. The chosen model is then re-estimated by OLS on every available quarter up to and including the origin. Forecasts for h = 2 and 4 are iterated from the one-step equation. At each origin the code cuts the series at the origin before doing any estimation or selection, so no later quarter is used (this is asserted in `out/analysis.py`). AIC chose p = 1 or 2 across origins (`selected_lags_by_origin`).\n- **Evaluation:** errors are e = actual − forecast. RMSE and MAE are taken over the 40 origins at each horizon.\n- **h = 4 test (Diebold–Mariano):** d_t = e²_snaive,t − e²_model,t. Its mean is `loss_diff_mean_h4`, and its standard error comes from a Bartlett-kernel (Newey–West) long-run variance (`hac_kernel_h4`) with truncation lag 3 = h−1 (`hac_lags_h4`). The test statistic is `dm_stat_h4` and its p-value `dm_pvalue_h4` is two-sided against N(0,1). I fixed the lag and kernel before seeing any results. Nothing in the analysis is random (`random_seed` = null).\n\n## Results\n| h | RMSE snaive | RMSE model | MAE snaive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 | 2.849 (`rmse_snaive_h1`) | 2.193 (`rmse_model_h1`) | 2.137 (`mae_snaive_h1`) | 1.519 (`mae_model_h1`) | 0.230 (`rel_improvement_rmse_h1`) |\n| 2 | 2.847 (`rmse_snaive_h2`) | 2.463 (`rmse_model_h2`) | 2.126 (`mae_snaive_h2`) | 1.553 (`mae_model_h2`) | 0.135 (`rel_improvement_rmse_h2`) |\n| 4 | 2.882 (`rmse_snaive_h4`) | 2.343 (`rmse_model_h4`) | 2.181 (`mae_snaive_h4`) | 1.597 (`mae_model_h4`) | 0.187 (`rel_improvement_rmse_h4`) |\n\nRMSE and MAE are in percentage points of annualised inflation. Over this window the model has lower RMSE and MAE than the seasonal-naive forecast at all three horizons. The RMSE reductions are 23 %, 13 % and 19 %.\n\nAt h = 4, the mean loss differential is d̄ = 2.818 squared pp (`loss_diff_mean_h4`), with HAC SE 2.086 (`loss_diff_se_h4`). That gives DM = 1.351 (`dm_stat_h4`) and a two-sided p = 0.177 (`dm_pvalue_h4`). The model's squared error was smaller at 29 of the 40 origins (`n_loss_diff_positive_h4`). **At conventional levels, the h = 4 improvement cannot be distinguished statistically from zero.**\n\n## Uncertainty\n- The conclusion depends on the HAC choices (`sensitivity_h4`). With Bartlett lag 4, p = 0.128. With a rectangular kernel at lag 3, p = 0.097. With the Harvey–Leybourne–Newbold small-sample correction and t(39) reference, p = 0.225. With Bartlett lag 6, p = 0.041. The SE shrinks as the lag grows, which means d_t has negative autocorrelations at higher lags. The lag-6 result was not pre-specified and is the only one below 0.05; I treat it as a sensitivity check, not as evidence. The pre-specified result (lag 3, p = 0.18) stands.\n- 40 overlapping observations is a small sample for long-run-variance estimation, so the normal approximation is rough. Squared-error losses are also strongly affected by the 2021–2023 inflation surge, so a handful of quarters drive much of d̄.\n- No uncertainty is reported for the RMSE and MAE at h = 1 and 2. The task only asks for a formal test at h = 4.\n\n## Limitations and open issues\n- I chose one model ahead of time and did not compare alternatives (SARIMA, direct forecasts) on the test window, so there is no search-induced bias. However, the results describe only this model.\n- The errors are computed on the current data vintage, and a quarter is treated as known at the end of that quarter. No real-time validity is claimed. Actual CPI publication comes about three weeks later, and basket updates may have small effects.\n- The DM test compares forecasts, not models. Both forecasts come from expanding-window estimates, and the model is not nested in the seasonal-naive forecast in any parameter sense, so applying standard DM asymptotics is reasonable but approximate.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over the 40 origins the seasonal-AR model has lower RMSE and MAE than the seasonal-naive forecast at h = 1, 2 and 4, with relative RMSE improvements of about 23%, 13% and 19%.\",\"results_keys\":[\"rmse_snaive_h1\",\"rmse_model_h1\",\"rmse_snaive_h2\",\"rmse_model_h2\",\"rmse_snaive_h4\",\"rmse_model_h4\",\"mae_snaive_h1\",\"mae_model_h1\",\"mae_snaive_h2\",\"mae_model_h2\",\"mae_snaive_h4\",\"mae_model_h4\",\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"rel_improvement_rmse_h4\"],\"status\":\"supported\",\"caveat\":\"Point estimates on one 40-quarter window; no formal test at h = 1, 2.\"},\n {\"id\":\"C2\",\"text\":\"At h = 4 the mean squared-error loss differential (2.82 squared pp in favour of the model) is not statistically distinguishable from zero: Bartlett HAC SE with lag 3 gives DM = 1.35, two-sided p = 0.18.\",\"results_keys\":[\"loss_diff_mean_h4\",\"loss_diff_se_h4\",\"hac_lags_h4\",\"hac_kernel_h4\",\"dm_stat_h4\",\"dm_pvalue_h4\"],\"status\":\"supported\",\"caveat\":\"Small sample with overlapping errors; p ranges 0.04-0.23 across HAC choices in sensitivity_h4; pre-specified choice reported.\"},\n {\"id\":\"C3\",\"text\":\"The model's squared 4-step error was smaller than the seasonal-naive one at 29 of the 40 origins.\",\"results_keys\":[\"n_loss_diff_positive_h4\"],\"status\":\"supported\",\"caveat\":\"Descriptive count; not a test.\"},\n {\"id\":\"C4\",\"text\":\"Forecasts use only quarters up to and including each origin; the last estimation sample has 121 observations.\",\"results_keys\":[\"n_analysed\",\"first_origin\",\"last_origin\",\"n_origins\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; no real-time claim.\"}\n]\nEOF\n~/wp29/venv/bin/python - 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The final gate run passed all 57 checks, and a clean re-run of `out/analysis.py` reproduces the outputs byte for byte.\n\n**Model (`ar_seas_aic`):** an autoregression with a separate intercept for each quarter of the year. At each origin, AIC picks the number of lags (0 to 8; it chose 1 or 2). It is fitted by OLS on all quarters up to and including the origin, and the h = 2 and h = 4 forecasts are built step by step from the one-step equation. The code checks at every origin that no later quarter is used.\n\n**Results over the 40 origins (2015Q4–2025Q3), in percentage points of annualised inflation:**\n\n| h | RMSE seasonal naive | RMSE model | RMSE reduction |\n|---|---|---|---|\n| 1 | 2.85 | 2.19 | 23% |\n| 2 | 2.85 | 2.46 | 13% |\n| 4 | 2.88 | 2.34 | 19% |\n\n- **MAE:** also lower for the model at every horizon.\n- **Test at h = 4:** the mean difference in squared errors is 2.82 in the model's favour. Its standard error is 2.09, allowing for the overlap between consecutive 4-step errors (Newey–West, lag 3, set before seeing results). That gives a test statistic of 1.35 and p = 0.18.\n- **Per origin:** the model's 4-step squared error was smaller at 29 of the 40 origins.\n\n**Limitations:**\n- **Sensitivity of the h = 4 test:** with other reasonable standard-error choices, p ranges from 0.04 to 0.23. Only the lag-6 version, which I did not plan in advance, gives p below 0.05. I report it as a sensitivity check, not as evidence.\n- **Small sample:** 40 overlapping errors is few for this kind of test.\n- **Inflation surge:** the 2021–2023 quarters have a large influence on the squared-error comparison.\n- **Data vintage:** errors are computed on today's data, so they don't show how the forecasts would have done in real time.\n- **No test at h = 1 and 2:** the task only asks for one at h = 4.\n\nThe data needed no fixes: one row was excluded (1995Q1, which has no inflation value). I kept the 12.15% reading for 2022Q2 because it is a real observation.\n\nFiles are in `out/`:\n- `results.json`\n- `forecasts.csv`\n- `analysis.py`\n- `report.md`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":338,"cache_read_input_tokens":30003,"output_tokens":951,"output_tokens_details":{"thinking_tokens":16},"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":338},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXncyTw9FPoqsKBfeMVh","requestRef":"3358cafa-8f9c-41e6-a907-fc32ea82b2a1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5bb8382d-1aa5-4779-b496-7053998f50e7","timestamp":"2026-10-30T04:26:07.721Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0081","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","version":"2.1.314","gitBranch":"HEAD"}80{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5bb8382d-1aa5-4779-b496-7053998f50e7","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105"}81{"type":"cost-state","sessionId":"38f0a3ac-67da-49ba-838e-07bf5c278105","totalCostUSD":0.5454846,"totalAPIDuration":166270,"totalAPIDurationWithoutRetries":166262,"totalToolDuration":5399,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":171795,"startTime":1793334195933,"modelUsage":{"claude-opus-5-5":{"inputTokens":26,"outputTokens":18190,"thinkingTokens":1441,"cacheReadInputTokens":186978,"cacheCreationInputTokens":28837,"cacheCreationEphemeral5mInputTokens":28837,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5454846}},"hasUnknownModelCost":false}82