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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"}],"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). 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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. 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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\n855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 data/cpi_monthly.csv\nc8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea data/cpi_quarterly.csv","is_error":false}]},"uuid":"b29f2dca-3b39-4a55-80a7-007c9fefbc3c","timestamp":"2026-10-30T04:22:12.863Z","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\n855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 data/cpi_monthly.csv\nc8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea 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-p out/state && ~/wp29/venv/bin/python -c \"\nimport pandas as pd, numpy as np\nm=pd.read_csv('data/cpi_monthly.csv',parse_dates=['month']);q=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter'])\nprint(m.shape,q.shape,m.isna().sum().to_dict(),q.isna().sum().to_dict(),m.month.duplicated().sum(),q.quarter.duplicated().sum())\nprint('monthly gaps',(m.month.diff().dt.days.dropna()>31).sum(), 'q gaps', set(q.quarter.dt.to_period('Q').diff().dropna().apply(lambda x:x.n)))\nmq=m.set_index('month').cpi.resample('QS').agg(['mean','count'])\nprint('avg maxdiff',(mq['mean'].values-q.cpi_avg.values).__abs__().max(), mq['count'].min())\ninf=400*np.log(q.cpi_avg).diff();print('infl maxdiff',(inf-q.infl_annualised).abs().max())\nprint(q.infl_annualised.describe())\nprint(q.groupby(q.quarter.dt.quarter).infl_annualised.mean())\n\"","description":"Audit data 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out/state && cat > contract.md <<'EOF'\n# Contract — F3-conf-04\n**Question.** Over 40 rolling quarterly origins (2015-10-01 … 2025-07-01) does a time-series model beat the seasonal-naive forecast of Canadian quarterly CPI inflation (400·Δln of the quarterly-average NSA index) at h = 1, 2, 4, and is the h = 4 squared-error loss differential distinguishable from zero using a HAC standard error (lag ≥ 3)?\n\n**Estimands.** RMSE and MAE per model per horizon over the 40 origins; 1 − RMSE_model/RMSE_snaive per horizon; at h = 4, d̄ = mean(e²_snaive − e²_model), its HAC SE, DM stat = d̄/SE and two-sided p-value. Meaningful under covariance stationarity of d_t (DM assumption) — questionable with COVID / 2021–23 inflation surge in the sample; the 40-origin sample is small.\n\n**Outputs.** out/forecasts.csv (240 rows: origin_date, target_date, horizon, forecast, actual, model); out/results.json (keys listed in TASK.md: task_id, model_name, model_description, n_origins, first_origin, last_origin, rmse/mae snaive & model h1/h2/h4, rel_improvement_rmse_h1/h2/h4, loss_diff_mean_h4, loss_diff_se_h4, hac_lags_h4, hac_kernel_h4, dm_stat_h4, dm_pvalue_h4, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections); out/analysis.py; out/claims.json.\n\n**Information rules.** Origin t uses only quarters ≤ t (estimation, lag selection all within the expanding window). Target = infl of t+h. snaive = infl of t+h−4. HAC lag ≥ 3 reported. No real-time claim.\n\n**Invalidity / impossibility.** Missing target quarters (last target 2026-07-01 exists: OK), any use of data after origin, SE ≤ 0 (e.g. rectangular kernel can give negative variance — avoid by Bartlett).\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows | cols | range |\n|---|---|---|---|---|\n| data/cpi_quarterly.csv | c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea (matches README/bundle) | 127 | 3 (quarter, cpi_avg, infl_annualised) | 1995-01-01 … 2026-07-01 |\n| data/cpi_monthly.csv | 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 (matches) | 381 | 2 (month, cpi) | 1995-01 … 2026-09 |\n\n- Units: cpi index 2002=100 NSA; infl_annualised in percent at annual rate.\n- Missing: only infl_annualised for 1995Q1 (by construction, no prior quarter). No duplicates; no gaps (monthly all consecutive; quarterly consecutive).\n- Consistency: quarterly mean of monthly reproduces cpi_avg to 3.3e-7 (rounding); 400·Δln(cpi_avg) reproduces infl_annualised to 3.3e-6 (rounding of cpi_avg to 6 dp). Every quarter has 3 months.\n- infl_annualised: n=126, mean 2.13, sd 2.39, min −6.04, max 12.15. Strong seasonality (mean by quarter Q1 2.32, Q2 4.18, Q3 1.66, Q4 0.30).\n- Anomalies: none in format; economic outliers in 2020 (COVID) and 2021–2023 (inflation surge) kept as they are genuine data.\n- Availability: data extend beyond the last origin (to 2026Q3) — needed for targets; analysis.py truncates the estimation sample at each origin to quarters ≤ origin. No input violates rules otherwise.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,drop 1995Q1 (infl_annualised undefined),127,126,1,first difference unavailable\\n2,drop first 4 inflation obs used only as initial lags (common AIC sample with p_max=4) - last-origin estimation sample ends 2025-07-01; quarters after last origin used only as targets,126,118,8,initial lags (4) and post-origin quarters 2025Q4-2026Q3 (4) not in final estimation sample\\n\" > ../exclusions.csv\necho \"$(date -u +%FT%TZ) P0 passed; P1 audit done (hashes match, data clean).\" > log.md","description":"Write contract, phase ledger, audit, 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out/state && cat > contract.md <<'EOF'\n# Contract — F3-conf-04\n**Question.** Over 40 rolling quarterly origins (2015-10-01 … 2025-07-01) does a time-series model beat the seasonal-naive forecast of Canadian quarterly CPI inflation (400·Δln of the quarterly-average NSA index) at h = 1, 2, 4, and is the h = 4 squared-error loss differential distinguishable from zero using a HAC standard error (lag ≥ 3)?\n\n**Estimands.** RMSE and MAE per model per horizon over the 40 origins; 1 − RMSE_model/RMSE_snaive per horizon; at h = 4, d̄ = mean(e²_snaive − e²_model), its HAC SE, DM stat = d̄/SE and two-sided p-value. Meaningful under covariance stationarity of d_t (DM assumption) — questionable with COVID / 2021–23 inflation surge in the sample; the 40-origin sample is small.\n\n**Outputs.** out/forecasts.csv (240 rows: origin_date, target_date, horizon, forecast, actual, model); out/results.json (keys listed in TASK.md: task_id, model_name, model_description, n_origins, first_origin, last_origin, rmse/mae snaive & model h1/h2/h4, rel_improvement_rmse_h1/h2/h4, loss_diff_mean_h4, loss_diff_se_h4, hac_lags_h4, hac_kernel_h4, dm_stat_h4, dm_pvalue_h4, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections); out/analysis.py; out/claims.json.\n\n**Information rules.** Origin t uses only quarters ≤ t (estimation, lag selection all within the expanding window). Target = infl of t+h. snaive = infl of t+h−4. HAC lag ≥ 3 reported. No real-time claim.\n\n**Invalidity / impossibility.** Missing target quarters (last target 2026-07-01 exists: OK), any use of data after origin, SE ≤ 0 (e.g. rectangular kernel can give negative variance — avoid by Bartlett).\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows | cols | range |\n|---|---|---|---|---|\n| data/cpi_quarterly.csv | c8652dec1567ec35e3b84ee8fa55545e9bf0ae4990a4d6d05b89a6d22f6801ea (matches README/bundle) | 127 | 3 (quarter, cpi_avg, infl_annualised) | 1995-01-01 … 2026-07-01 |\n| data/cpi_monthly.csv | 855d069176369d30d02cbbe0cc6456b76d2f3967cdaabc1b362c2461ef9015a2 (matches) | 381 | 2 (month, cpi) | 1995-01 … 2026-09 |\n\n- Units: cpi index 2002=100 NSA; infl_annualised in percent at annual rate.\n- Missing: only infl_annualised for 1995Q1 (by construction, no prior quarter). No duplicates; no gaps (monthly all consecutive; quarterly consecutive).\n- Consistency: quarterly mean of monthly reproduces cpi_avg to 3.3e-7 (rounding); 400·Δln(cpi_avg) reproduces infl_annualised to 3.3e-6 (rounding of cpi_avg to 6 dp). Every quarter has 3 months.\n- infl_annualised: n=126, mean 2.13, sd 2.39, min −6.04, max 12.15. Strong seasonality (mean by quarter Q1 2.32, Q2 4.18, Q3 1.66, Q4 0.30).\n- Anomalies: none in format; economic outliers in 2020 (COVID) and 2021–2023 (inflation surge) kept as they are genuine data.\n- Availability: data extend beyond the last origin (to 2026Q3) — needed for targets; analysis.py truncates the estimation sample at each origin to quarters ≤ origin. No input violates rules otherwise.\nEOF\nprintf \"step,rule,n_before,n_after,n_removed,rationale\\n1,drop 1995Q1 (infl_annualised undefined),127,126,1,first difference unavailable\\n2,drop first 4 inflation obs used only as initial lags (common AIC sample with p_max=4) - last-origin estimation sample ends 2025-07-01; quarters after last origin used only as targets,126,118,8,initial lags (4) and post-origin quarters 2025Q4-2026Q3 (4) not in final estimation sample\\n\" > ../exclusions.csv\necho \"$(date -u +%FT%TZ) P0 passed; P1 audit done (hashes match, data clean).\" > log.md","description":"Write contract, phase ledger, audit, 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: seasonal-naive vs AR(p)+seasonal dummies, expanding window, iterated forecasts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nH = [1, 2, 4]; PMAX = 4; MODEL = \"ar_seasdum\"\nq = pd.read_csv(\"data/cpi_quarterly.csv\", parse_dates=[\"quarter\"])\nm = pd.read_csv(\"data/cpi_monthly.csv\")\nn_rows = {\"cpi_quarterly.csv\": int(len(q)), \"cpi_monthly.csv\": int(len(m))}\ns = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True)\ny = s[\"infl_annualised\"].to_numpy(); dates = s[\"quarter\"]; qtr = dates.dt.quarter.to_numpy()\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS\")\nassert len(origins) == 40\npos = {d: i for i, d in enumerate(dates)}\n\ndef design(yv, qv, p, start):\n T = len(yv); rows = range(start, T)\n X = [[1.0 * (qv[t] == k) for k in (1, 2, 3, 4)] + [yv[t - j] for j in range(1, p + 1)] for t in rows]\n return np.array(X), yv[start:]\n\ndef fit_forecast(i_end):\n yv, qv = y[: i_end + 1], qtr[: i_end + 1] # only quarters <= origin\n best = None\n for p in range(PMAX + 1): # AIC on common sample (start = PMAX)\n X, Y = design(yv, qv, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n rss = float(((Y - X @ b) ** 2).sum()); n = len(Y)\n aic = n * np.log(rss / n) + 2 * X.shape[1]\n if best is None or aic < best[0]: best = (aic, p, b, n)\n _, p, b, n = best\n hist = list(yv); qs = list(qv); fc = {}\n for h in range(1, max(H) + 1): # iterated multi-step\n qn = (qs[-1] % 4) + 1\n x = [1.0 * (qn == k) for k in (1, 2, 3, 4)] + [hist[-j] for j in range(1, p + 1)]\n f = float(np.dot(x, b)); hist.append(f); qs.append(qn); fc[h] = f\n return fc, p, n\n\nrows = []; plist = []; n_last = None\nfor o in origins:\n i = pos[o]; fc, p, n = fit_forecast(i); plist.append(p); n_last = n\n for h in H:\n tgt = i + h; act = float(y[tgt])\n assert dates[tgt] == o + pd.DateOffset(months=3 * h)\n assert tgt - 4 <= i\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=float(y[tgt - 4]), actual=act, model=\"snaive\"))\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=fc[h], actual=act, model=MODEL))\nF = pd.DataFrame(rows); F[\"err\"] = F[\"actual\"] - F[\"forecast\"]\nos.makedirs(\"out\", exist_ok=True)\nF.drop(columns=\"err\").to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nR = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"OLS AR(p) on annualised quarterly inflation with four quarterly seasonal intercepts, \"\n \"p in {0..4} chosen by AIC at each origin on a common sample, expanding window from 1995Q2 \"\n \"to the origin, iterated multi-step forecasts.\"),\n \"n_origins\": 40, \"first_origin\": origins[0].date().isoformat(), \"last_origin\": origins[-1].date().isoformat()}\nfor h in H:\n for mod, key in ((\"snaive\", \"snaive\"), (MODEL, \"model\")):\n e = F[(F.horizon == h) & (F.model == mod)][\"err\"].to_numpy()\n R[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2))); R[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in H:\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_snaive_h{h}\"]\n\ndef hac_var_mean(d, L, kernel=\"bartlett\"):\n d = d - d.mean(); T = len(d); lrv = d @ d / T\n for j in range(1, L + 1):\n w = 1 - j / (L + 1) if kernel == \"bartlett\" else 1.0\n lrv += 2 * w * (d[j:] @ d[:-j]) / T\n return lrv / T\n\ne_s = F[(F.horizon == 4) & (F.model == \"snaive\")].sort_values(\"origin_date\")[\"err\"].to_numpy()\ne_m = F[(F.horizon == 4) & (F.model == MODEL)].sort_values(\"origin_date\")[\"err\"].to_numpy()\nd = e_s ** 2 - e_m ** 2; T = len(d); L = 3\ndbar = float(d.mean()); se = float(np.sqrt(hac_var_mean(d, L)))\ndm = dbar / se\nR.update({\"loss_diff_mean_h4\": dbar, \"loss_diff_se_h4\": se, \"hac_lags_h4\": L, \"hac_kernel_h4\": \"bartlett\",\n \"dm_stat_h4\": dm, \"dm_pvalue_h4\": float(2 * stats.norm.sf(abs(dm)))})\n# sensitivity (not primary)\nsens = {}\nfor LL in (3, 4, 6, 8):\n v = hac_var_mean(d, LL); sens[f\"bartlett_L{LL}_se\"] = float(np.sqrt(v))\nvr = hac_var_mean(d, 3, \"rectangular\"); sens[\"rectangular_L3_se\"] = float(np.sqrt(vr)) if vr > 0 else None\nhln = np.sqrt((T + 1 - 2 * 4 + 4 * 3 / T) / T) # Harvey-Leybourne-Newbold, h=4\nsens[\"hln_stat_bartlett_L3\"] = float(dm * hln)\nsens[\"hln_pvalue_t39\"] = float(2 * stats.t.sf(abs(dm * hln), T - 1))\nsens[\"n_origins_model_better_sqerr_h4\"] = int((d > 0).sum())\nR[\"sensitivity_h4\"] = sens\nR[\"selected_ar_order_counts\"] = {str(k): int(v) for k, v in pd.Series(plist).value_counts().sort_index().items()}\nR.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": \"infl_annualised missing for 1995Q1 (no prior quarter)\", \"handling\": \"dropped; series starts 1995Q2\"},\n {\"issue\": \"infl_annualised reproduced from cpi_avg only to ~3e-6 due to 6-dp rounding of cpi_avg\", \"handling\": \"used infl_annualised as given\"},\n {\"issue\": \"COVID-2020 and 2021-2023 inflation-surge outliers within evaluation window\", \"handling\": \"kept (genuine data); noted as threat to DM stationarity assumption\"}],\n \"n_input_rows\": n_rows, \"n_analysed\": int(n_last), \"random_seed\": None})\nwith open(\"out/results.json\", \"w\") as f: json.dump(R, f, indent=2)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"rel\", \"dm\", \"loss\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json | tail -40 && wc -l out/forecasts.csv","description":"Write and run analysis 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code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0075/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01CPJAarNmRfPK9Xc2pmxmet"}]},"uuid":"0930b6b5-1820-41f0-8d7f-2cf0df1c9595","timestamp":"2026-10-30T04:23:04.773Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0075/out/state/out/analysis.py': [Errno 2] No such file or 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: seasonal-naive vs AR(p)+seasonal dummies, expanding window, iterated forecasts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nH = [1, 2, 4]; PMAX = 4; MODEL = \"ar_seasdum\"\nq = pd.read_csv(\"data/cpi_quarterly.csv\", parse_dates=[\"quarter\"])\nm = pd.read_csv(\"data/cpi_monthly.csv\")\nn_rows = {\"cpi_quarterly.csv\": int(len(q)), \"cpi_monthly.csv\": int(len(m))}\ns = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True)\ny = s[\"infl_annualised\"].to_numpy(); dates = s[\"quarter\"]; qtr = dates.dt.quarter.to_numpy()\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS\")\nassert len(origins) == 40\npos = {d: i for i, d in enumerate(dates)}\n\ndef design(yv, qv, p, start):\n T = len(yv); rows = range(start, T)\n X = [[1.0 * (qv[t] == k) for k in (1, 2, 3, 4)] + [yv[t - j] for j in range(1, p + 1)] for t in rows]\n return np.array(X), yv[start:]\n\ndef fit_forecast(i_end):\n yv, qv = y[: i_end + 1], qtr[: i_end + 1] # only quarters <= origin\n best = None\n for p in range(PMAX + 1): # AIC on common sample (start = PMAX)\n X, Y = design(yv, qv, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n rss = float(((Y - X @ b) ** 2).sum()); n = len(Y)\n aic = n * np.log(rss / n) + 2 * X.shape[1]\n if best is None or aic < best[0]: best = (aic, p, b, n)\n _, p, b, n = best\n hist = list(yv); qs = list(qv); fc = {}\n for h in range(1, max(H) + 1): # iterated multi-step\n qn = (qs[-1] % 4) + 1\n x = [1.0 * (qn == k) for k in (1, 2, 3, 4)] + [hist[-j] for j in range(1, p + 1)]\n f = float(np.dot(x, b)); hist.append(f); qs.append(qn); fc[h] = f\n return fc, p, n\n\nrows = []; plist = []; n_last = None\nfor o in origins:\n i = pos[o]; fc, p, n = fit_forecast(i); plist.append(p); n_last = n\n for h in H:\n tgt = i + h; act = float(y[tgt])\n assert dates[tgt] == o + pd.DateOffset(months=3 * h)\n assert tgt - 4 <= i\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=float(y[tgt - 4]), actual=act, model=\"snaive\"))\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=fc[h], actual=act, model=MODEL))\nF = pd.DataFrame(rows); F[\"err\"] = F[\"actual\"] - F[\"forecast\"]\nos.makedirs(\"out\", exist_ok=True)\nF.drop(columns=\"err\").to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nR = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"OLS AR(p) on annualised quarterly inflation with four quarterly seasonal intercepts, \"\n \"p in {0..4} chosen by AIC at each origin on a common sample, expanding window from 1995Q2 \"\n \"to the origin, iterated multi-step forecasts.\"),\n \"n_origins\": 40, \"first_origin\": origins[0].date().isoformat(), \"last_origin\": origins[-1].date().isoformat()}\nfor h in H:\n for mod, key in ((\"snaive\", \"snaive\"), (MODEL, \"model\")):\n e = F[(F.horizon == h) & (F.model == mod)][\"err\"].to_numpy()\n R[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2))); R[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in H:\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_snaive_h{h}\"]\n\ndef hac_var_mean(d, L, kernel=\"bartlett\"):\n d = d - d.mean(); T = len(d); lrv = d @ d / T\n for j in range(1, L + 1):\n w = 1 - j / (L + 1) if kernel == \"bartlett\" else 1.0\n lrv += 2 * w * (d[j:] @ d[:-j]) / T\n return lrv / T\n\ne_s = F[(F.horizon == 4) & (F.model == \"snaive\")].sort_values(\"origin_date\")[\"err\"].to_numpy()\ne_m = F[(F.horizon == 4) & (F.model == MODEL)].sort_values(\"origin_date\")[\"err\"].to_numpy()\nd = e_s ** 2 - e_m ** 2; T = len(d); L = 3\ndbar = float(d.mean()); se = float(np.sqrt(hac_var_mean(d, L)))\ndm = dbar / se\nR.update({\"loss_diff_mean_h4\": dbar, \"loss_diff_se_h4\": se, \"hac_lags_h4\": L, \"hac_kernel_h4\": \"bartlett\",\n \"dm_stat_h4\": dm, \"dm_pvalue_h4\": float(2 * stats.norm.sf(abs(dm)))})\n# sensitivity (not primary)\nsens = {}\nfor LL in (3, 4, 6, 8):\n v = hac_var_mean(d, LL); sens[f\"bartlett_L{LL}_se\"] = float(np.sqrt(v))\nvr = hac_var_mean(d, 3, \"rectangular\"); sens[\"rectangular_L3_se\"] = float(np.sqrt(vr)) if vr > 0 else None\nhln = np.sqrt((T + 1 - 2 * 4 + 4 * 3 / T) / T) # Harvey-Leybourne-Newbold, h=4\nsens[\"hln_stat_bartlett_L3\"] = float(dm * hln)\nsens[\"hln_pvalue_t39\"] = float(2 * stats.t.sf(abs(dm * hln), T - 1))\nsens[\"n_origins_model_better_sqerr_h4\"] = int((d > 0).sum())\nR[\"sensitivity_h4\"] = sens\nR[\"selected_ar_order_counts\"] = {str(k): int(v) for k, v in pd.Series(plist).value_counts().sort_index().items()}\nR.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": \"infl_annualised missing for 1995Q1 (no prior quarter)\", \"handling\": \"dropped; series starts 1995Q2\"},\n {\"issue\": \"infl_annualised reproduced from cpi_avg only to ~3e-6 due to 6-dp rounding of cpi_avg\", \"handling\": \"used infl_annualised as given\"},\n {\"issue\": \"COVID-2020 and 2021-2023 inflation-surge outliers within evaluation window\", \"handling\": \"kept (genuine data); noted as threat to DM stationarity assumption\"}],\n \"n_input_rows\": n_rows, \"n_analysed\": int(n_last), \"random_seed\": None})\nwith open(\"out/results.json\", \"w\") as f: json.dump(R, f, indent=2)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"rel\", \"dm\", \"loss\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json | tail -40 && wc -l out/forecasts.csv","description":"Write and run analysis 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: seasonal-naive vs AR(p)+seasonal dummies, expanding window, iterated forecasts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nH = [1, 2, 4]; PMAX = 4; MODEL = \"ar_seasdum\"\nq = pd.read_csv(\"data/cpi_quarterly.csv\", parse_dates=[\"quarter\"])\nm = pd.read_csv(\"data/cpi_monthly.csv\")\nn_rows = {\"cpi_quarterly.csv\": int(len(q)), \"cpi_monthly.csv\": int(len(m))}\ns = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True)\ny = s[\"infl_annualised\"].to_numpy(); dates = s[\"quarter\"]; qtr = dates.dt.quarter.to_numpy()\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS\")\nassert len(origins) == 40\npos = {d: i for i, d in enumerate(dates)}\n\ndef design(yv, qv, p, start):\n T = len(yv); rows = range(start, T)\n X = [[1.0 * (qv[t] == k) for k in (1, 2, 3, 4)] + [yv[t - j] for j in range(1, p + 1)] for t in rows]\n return np.array(X), yv[start:]\n\ndef fit_forecast(i_end):\n yv, qv = y[: i_end + 1], qtr[: i_end + 1] # only quarters <= origin\n best = None\n for p in range(PMAX + 1): # AIC on common sample (start = PMAX)\n X, Y = design(yv, qv, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n rss = float(((Y - X @ b) ** 2).sum()); n = len(Y)\n aic = n * np.log(rss / n) + 2 * X.shape[1]\n if best is None or aic < best[0]: best = (aic, p, b, n)\n _, p, b, n = best\n hist = list(yv); qs = list(qv); fc = {}\n for h in range(1, max(H) + 1): # iterated multi-step\n qn = (qs[-1] % 4) + 1\n x = [1.0 * (qn == k) for k in (1, 2, 3, 4)] + [hist[-j] for j in range(1, p + 1)]\n f = float(np.dot(x, b)); hist.append(f); qs.append(qn); fc[h] = f\n return fc, p, n\n\nrows = []; plist = []; n_last = None\nfor o in origins:\n i = pos[o]; fc, p, n = fit_forecast(i); plist.append(p); n_last = n\n for h in H:\n tgt = i + h; act = float(y[tgt])\n assert dates[tgt] == o + pd.DateOffset(months=3 * h)\n assert tgt - 4 <= i\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=float(y[tgt - 4]), actual=act, model=\"snaive\"))\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=fc[h], actual=act, model=MODEL))\nF = pd.DataFrame(rows); F[\"err\"] = F[\"actual\"] - F[\"forecast\"]\nos.makedirs(\"out\", exist_ok=True)\nF.drop(columns=\"err\").to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nR = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"OLS AR(p) on annualised quarterly inflation with four quarterly seasonal intercepts, \"\n \"p in {0..4} chosen by AIC at each origin on a common sample, expanding window from 1995Q2 \"\n \"to the origin, iterated multi-step forecasts.\"),\n \"n_origins\": 40, \"first_origin\": origins[0].date().isoformat(), \"last_origin\": origins[-1].date().isoformat()}\nfor h in H:\n for mod, key in ((\"snaive\", \"snaive\"), (MODEL, \"model\")):\n e = F[(F.horizon == h) & (F.model == mod)][\"err\"].to_numpy()\n R[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2))); R[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in H:\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_snaive_h{h}\"]\n\ndef hac_var_mean(d, L, kernel=\"bartlett\"):\n d = d - d.mean(); T = len(d); lrv = d @ d / T\n for j in range(1, L + 1):\n w = 1 - j / (L + 1) if kernel == \"bartlett\" else 1.0\n lrv += 2 * w * (d[j:] @ d[:-j]) / T\n return lrv / T\n\ne_s = F[(F.horizon == 4) & (F.model == \"snaive\")].sort_values(\"origin_date\")[\"err\"].to_numpy()\ne_m = F[(F.horizon == 4) & (F.model == MODEL)].sort_values(\"origin_date\")[\"err\"].to_numpy()\nd = e_s ** 2 - e_m ** 2; T = len(d); L = 3\ndbar = float(d.mean()); se = float(np.sqrt(hac_var_mean(d, L)))\ndm = dbar / se\nR.update({\"loss_diff_mean_h4\": dbar, \"loss_diff_se_h4\": se, \"hac_lags_h4\": L, \"hac_kernel_h4\": \"bartlett\",\n \"dm_stat_h4\": dm, \"dm_pvalue_h4\": float(2 * stats.norm.sf(abs(dm)))})\n# sensitivity (not primary)\nsens = {}\nfor LL in (4, 6, 8):\n sens[f\"bartlett_L{LL}_se\"] = float(np.sqrt(hac_var_mean(d, LL)))\nvr = hac_var_mean(d, 3, \"rectangular\"); sens[\"rectangular_L3_se\"] = float(np.sqrt(vr)) if vr > 0 else None\nhln = np.sqrt((T + 1 - 2 * 4 + 4 * 3 / T) / T) # Harvey-Leybourne-Newbold, h=4\nsens[\"hln_stat_bartlett_L3\"] = float(dm * hln)\nsens[\"hln_pvalue_t39\"] = float(2 * stats.t.sf(abs(dm * hln), T - 1))\nsens[\"n_origins_model_better_sqerr_h4\"] = int((d > 0).sum())\nR[\"sensitivity_h4\"] = sens\nR[\"selected_ar_order_counts\"] = {str(k): int(v) for k, v in pd.Series(plist).value_counts().sort_index().items()}\nR.update({\"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": [{\"issue\": \"infl_annualised missing for 1995Q1 (no prior quarter)\", \"handling\": \"dropped; series starts 1995Q2\"},\n {\"issue\": \"infl_annualised reproduced from cpi_avg only to ~3e-6 due to 6-dp rounding of cpi_avg\", \"handling\": \"used infl_annualised as given\"},\n {\"issue\": \"COVID-2020 and 2021-2023 inflation-surge outliers within evaluation window\", \"handling\": \"kept (genuine data); noted as threat to DM stationarity assumption\"}],\n \"n_input_rows\": n_rows, \"n_analysed\": int(n_last), \"random_seed\": None})\nwith open(\"out/results.json\", \"w\") as f: json.dump(R, f, indent=2)\nprint(json.dumps({k: R[k] for k in R if k.startswith((\"rmse\", \"mae\", \"rel\", \"dm\", \"loss\"))}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && grep -A30 sensitivity out/results.json | head -22 && wc -l out/forecasts.csv","description":"Write and run analysis script from 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\"rmse_snaive_h1\": 2.849125860156572,\n \"mae_snaive_h1\": 2.1374736750000003,\n \"rmse_model_h1\": 2.1918133412897713,\n \"mae_model_h1\": 1.5187711988356731,\n \"rmse_snaive_h2\": 2.846674080377472,\n \"mae_snaive_h2\": 2.1259446750000004,\n \"rmse_model_h2\": 2.46022256417967,\n \"mae_model_h2\": 1.550333358372702,\n \"rmse_snaive_h4\": 2.8823159392575315,\n \"mae_snaive_h4\": 2.181266125,\n \"rmse_model_h4\": 2.3396848872653107,\n \"mae_model_h4\": 1.5936096377164786,\n \"rel_improvement_rmse_h1\": 0.23070673291725996,\n \"rel_improvement_rmse_h2\": 0.13575544838858333,\n \"rel_improvement_rmse_h4\": 0.18826216952885444,\n \"loss_diff_mean_h4\": 2.8336198020003365,\n \"loss_diff_se_h4\": 2.0822850037684537,\n \"dm_stat_h4\": 1.360822268264018,\n \"dm_pvalue_h4\": 0.17356986291430632\n}\n \"sensitivity_h4\": {\n \"bartlett_L4_se\": 1.84792653245693,\n \"bartlett_L6_se\": 1.3749867136255691,\n \"bartlett_L8_se\": 1.1966007926602846,\n \"rectangular_L3_se\": 1.6959903400311052,\n \"hln_stat_bartlett_L3\": 1.2416338055687701,\n \"hln_pvalue_t39\": 0.22178745465698943,\n \"n_origins_model_better_sqerr_h4\": 29\n },\n \"selected_ar_order_counts\": {\n \"1\": 15,\n \"2\": 25\n },\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"infl_annualised missing for 1995Q1 (no prior quarter)\",\n \"handling\": \"dropped; series starts 1995Q2\"\n },\n {\n \"issue\": \"infl_annualised reproduced from cpi_avg only to ~3e-6 due to 6-dp rounding of cpi_avg\",\n 241 out/forecasts.csv","is_error":false}]},"uuid":"88b3eb64-fb63-4e31-bab7-7405aa4c3cab","timestamp":"2026-10-30T04:23:28.322Z","toolUseResult":{"stdout":"{\n \"rmse_snaive_h1\": 2.849125860156572,\n \"mae_snaive_h1\": 2.1374736750000003,\n \"rmse_model_h1\": 2.1918133412897713,\n \"mae_model_h1\": 1.5187711988356731,\n \"rmse_snaive_h2\": 2.846674080377472,\n \"mae_snaive_h2\": 2.1259446750000004,\n \"rmse_model_h2\": 2.46022256417967,\n \"mae_model_h2\": 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-04: seasonal-naive vs AR(p)+seasonal dummies, expanding window, iterated forecasts.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nfrom scipy import stats\n\nH = [1, 2, 4]; PMAX = 4; MODEL = \"ar_seasdum\"\nq = pd.read_csv(\"data/cpi_quarterly.csv\", parse_dates=[\"quarter\"])\nm = pd.read_csv(\"data/cpi_monthly.csv\")\nn_rows = {\"cpi_quarterly.csv\": int(len(q)), \"cpi_monthly.csv\": int(len(m))}\ns = q.dropna(subset=[\"infl_annualised\"]).reset_index(drop=True)\ny = s[\"infl_annualised\"].to_numpy(); dates = s[\"quarter\"]; qtr = dates.dt.quarter.to_numpy()\norigins = pd.date_range(\"2015-10-01\", \"2025-07-01\", freq=\"QS\")\nassert len(origins) == 40\npos = {d: i for i, d in enumerate(dates)}\n\ndef design(yv, qv, p, start):\n T = len(yv); rows = range(start, T)\n X = [[1.0 * (qv[t] == k) for k in (1, 2, 3, 4)] + [yv[t - j] for j in range(1, p + 1)] for t in rows]\n return np.array(X), yv[start:]\n\ndef fit_forecast(i_end):\n yv, qv = y[: i_end + 1], qtr[: i_end + 1] # only quarters <= origin\n best = None\n for p in range(PMAX + 1): # AIC on common sample (start = PMAX)\n X, Y = design(yv, qv, p, PMAX)\n b, *_ = np.linalg.lstsq(X, Y, rcond=None)\n rss = float(((Y - X @ b) ** 2).sum()); n = len(Y)\n aic = n * np.log(rss / n) + 2 * X.shape[1]\n if best is None or aic < best[0]: best = (aic, p, b, n)\n _, p, b, n = best\n hist = list(yv); qs = list(qv); fc = {}\n for h in range(1, max(H) + 1): # iterated multi-step\n qn = (qs[-1] % 4) + 1\n x = [1.0 * (qn == k) for k in (1, 2, 3, 4)] + [hist[-j] for j in range(1, p + 1)]\n f = float(np.dot(x, b)); hist.append(f); qs.append(qn); fc[h] = f\n return fc, p, n\n\nrows = []; plist = []; n_last = None\nfor o in origins:\n i = pos[o]; fc, p, n = fit_forecast(i); plist.append(p); n_last = n\n for h in H:\n tgt = i + h; act = float(y[tgt])\n assert dates[tgt] == o + pd.DateOffset(months=3 * h)\n assert tgt - 4 <= i\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=float(y[tgt - 4]), actual=act, model=\"snaive\"))\n rows.append(dict(origin_date=o.date().isoformat(), target_date=dates[tgt].date().isoformat(), horizon=h,\n forecast=fc[h], actual=act, model=MODEL))\nF = pd.DataFrame(rows); F[\"err\"] = F[\"actual\"] - F[\"forecast\"]\nos.makedirs(\"out\", exist_ok=True)\nF.drop(columns=\"err\").to_csv(\"out/forecasts.csv\", index=False, float_format=\"%.6f\")\n\nR = {\"task_id\": \"F3-conf-04\", \"model_name\": MODEL,\n \"model_description\": (\"OLS AR(p) on annualised quarterly inflation with four quarterly seasonal intercepts, \"\n \"p in {0..4} chosen by AIC at each origin on a common sample, expanding window from 1995Q2 \"\n \"to the origin, iterated multi-step forecasts.\"),\n \"n_origins\": 40, \"first_origin\": origins[0].date().isoformat(), \"last_origin\": origins[-1].date().isoformat()}\nfor h in H:\n for mod, key in ((\"snaive\", \"snaive\"), (MODEL, \"model\")):\n e = F[(F.horizon == h) & (F.model == mod)][\"err\"].to_numpy()\n R[f\"rmse_{key}_h{h}\"] = float(np.sqrt(np.mean(e ** 2))); R[f\"mae_{key}_h{h}\"] = float(np.mean(np.abs(e)))\nfor h in H:\n R[f\"rel_improvement_rmse_h{h}\"] = 1 - R[f\"rmse_model_h{h}\"] / R[f\"rmse_snaive_h{h}\"]\n\ndef hac_var_mean(d, L, kernel=\"bartlett\"):\n d = d - d.mean(); T = len(d); lrv = d @ d / T\n for j in range(1, L + 1):\n w = 1 - j / (L + 1) if kernel == \"bartlett\" else 1.0\n lrv += 2 * w * (d[j:] @ d[:-j]) / T\n return lrv / T\n\ne_s = F[(F.horizon == 4) & (F.model == \"snaive\")].sort_values(\"origin_date\")[\"err\"].to_numpy()\ne_m = F[(F.horizon == 4) & (F.model == MODEL)].sort_values(\"origin_date\")[\"err\"].to_numpy()\nd = e_s ** 2 - e_m ** 2; T = len(d); L = 3\ndbar = float(d.mean()); se = float(np.sqrt(hac_var_mean(d, L)))\ndm = dbar / se\nR.update({\"loss_diff_mean_h4\": dbar, \"loss_diff_se_h4\": se, \"hac_lags_h4\": L, \"hac_kernel_h4\": 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\"sensitivity_h4\": {\n \"bartlett_L4_se\": 1.84792653245693,\n \"bartlett_L4_pvalue\": 0.12517609522502945,\n \"bartlett_L6_se\": 1.3749867136255691,\n \"bartlett_L6_pvalue\": 0.03931884953277284,\n \"bartlett_L8_se\": 1.1966007926602846,\n \"bartlett_L8_pvalue\": 0.017881743617665084,\n \"rectangular_L3_se\": 1.6959903400311052,\n \"hln_stat_bartlett_L3\": 1.2416338055687701,\n \"hln_pvalue_t39\": 0.22178745465698943,\n \"iid_se_L0\": 1.9716700152128979,\n \"d_autocorr_lag1_to_4\": [\n 0.20771087168628657,\n -0.054263869940997765,\n -0.28400474251358954,\n -0.4063168650705624\n ],\n \"n_origins_model_better_sqerr_h4\": 29\n },\n \"selected_ar_order_counts\": {\n \"1\": 15,\n \"2\": 25\n },\n \"n_analysed\": 118,","is_error":false}]},"uuid":"574e3f34-af57-48ed-9fa4-96aca8fcbe67","timestamp":"2026-10-30T04:23:37.599Z","toolUseResult":{"stdout":"IDENTICAL\n \"sensitivity_h4\": {\n \"bartlett_L4_se\": 1.84792653245693,\n \"bartlett_L4_pvalue\": 0.12517609522502945,\n \"bartlett_L6_se\": 1.3749867136255691,\n \"bartlett_L6_pvalue\": 0.03931884953277284,\n \"bartlett_L8_se\": 1.1966007926602846,\n \"bartlett_L8_pvalue\": 0.017881743617665084,\n \"rectangular_L3_se\": 1.6959903400311052,\n \"hln_stat_bartlett_L3\": 1.2416338055687701,\n \"hln_pvalue_t39\": 0.22178745465698943,\n \"iid_se_L0\": 1.9716700152128979,\n \"d_autocorr_lag1_to_4\": [\n 0.20771087168628657,\n -0.054263869940997765,\n -0.28400474251358954,\n -0.4063168650705624\n ],\n \"n_origins_model_better_sqerr_h4\": 29\n },\n \"selected_ar_order_counts\": {\n \"1\": 15,\n \"2\": 25\n },\n \"n_analysed\": 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reproducibility"}}]}}56{"type":"api-request","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","id":"8cf0154b-8540-4052-a644-d273570cbeb8","timestamp":"2026-10-30T04:23:37.603Z","version":"2.1.314","querySource":"sdk","shapeHash":"c715b1bd7f48eb68e478c4f2a66efbebba4fdaa9e45872a607b951f5efcfd0f9","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":"c862aa31-0527-4e63-9c2b-8705dcb73280","keep":16,"tail":["244e3db60ef235f53bea31b80c0b891247ed46ac649eb3fc40e3e2e42db9d0aa","2aeb61b263120c8c0768c26b435291a0074544b874a0c4eeb0bd38613732b3f1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1d025d5e1c1f0f963b8b62cd9680c99009ed2a0bc7761899131d1b4ed8cff6b4"}57{"parentUuid":"574e3f34-af57-48ed-9fa4-96aca8fcbe67","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnShbfBAFa5GonZV5Nh","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":1161,"cache_read_input_tokens":20578,"output_tokens":1448,"output_tokens_details":{"thinking_tokens":36},"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":1161},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":310,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnShLHmgK1aKkLUsFNx","requestRef":"8cf0154b-8540-4052-a644-d273570cbeb8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a4c0437e-1a86-4c0b-bf31-5c4c466d5e3e","timestamp":"2026-10-30T04:23:39.094Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"a4c0437e-1a86-4c0b-bf31-5c4c466d5e3e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnShbfBAFa5GonZV5Nh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019rAMA3siTLwsN2DcQvW9Z5","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nR=json.load(open('out/results.json'));F=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date'])\nout=[]\nout.append(f\"forecasts rows = {len(F)} (expect 240): {len(F)==240}\")\nout.append(f\"40 origins x 3 h x 2 models unique: {F.groupby(['origin_date','horizon','model']).size().max()==1 and F.origin_date.nunique()==40}\")\nout.append(f\"target = origin + 3h months: {all(F.target_date==F.apply(lambda r:r.origin_date+pd.DateOffset(months=3*r.horizon),axis=1))}\")\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']).set_index('quarter').infl_annualised\nsn=F[F.model=='snaive']\nout.append(f\"snaive forecast equals infl at target-4q and that quarter <= origin: {np.allclose(sn.forecast.values,[q[t-pd.DateOffset(months=12)] for t in sn.target_date],atol=1e-6) and all(sn.target_date-pd.DateOffset(months=12)<=sn.origin_date)}\")\nout.append(f\"actuals match data: {np.allclose(F.actual.values,q[F.target_date].values,atol=1e-6)}\")\nfor h in (1,2,4):\n for m,k in (('snaive','snaive'),('ar_seasdum','model')):\n e=F[(F.horizon==h)&(F.model==m)].eval('actual-forecast')\n out.append(f\"rmse_{k}_h{h} recomputed from csv {np.sqrt((e**2).mean()):.6f} vs {R[f'rmse_{k}_h{h}']:.6f}; MAE<=RMSE: {R[f'mae_{k}_h{h}']<=R[f'rmse_{k}_h{h}']}\")\nout.append(f\"rel improvements consistent: {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))}\")\nout.append(f\"loss_diff_mean_h4 = rmse_snaive_h4^2 - rmse_model_h4^2: {abs(R['loss_diff_mean_h4']-(R['rmse_snaive_h4']**2-R['rmse_model_h4']**2))<1e-9}\")\nout.append(f\"SE>0: {R['loss_diff_se_h4']>0}; hac lag {R['hac_lags_h4']}>=3: {R['hac_lags_h4']>=3}; dm=mean/se: {abs(R['dm_stat_h4']-R['loss_diff_mean_h4']/R['loss_diff_se_h4'])<1e-12}; p in (0,1): {0<R['dm_pvalue_h4']<1}\")\nout.append(f\"first_origin<last_origin: {R['first_origin']<R['last_origin']}\")\nout.append(\"exclusions: 127-1=126; 126-8=118 = n_analysed %s\"%(R['n_analysed']==118))\nout.append(\"magnitudes: model RMSEs 2.19-2.46 vs snaive 2.85-2.88, sd(infl)=2.39 — plausible\")\nout.append(\"information rule: analysis.py slices y[:i_end+1] (quarters <= origin) for estimation and AIC; asserts target-4 <= origin\")\nout.append(\"rerun of analysis.py twice -> byte-identical results.json (cmp)\")\nopen('out/state/checks.md','w').write(\"# Verification checks\\n\\n\"+\"\\n\".join(\"- \"+o for o in out)+\"\\n\")\nprint(\"\\n\".join(out))\nEOF","description":"Run internal 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":1161,"cache_read_input_tokens":20578,"output_tokens":1448,"output_tokens_details":{"thinking_tokens":36},"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":1161},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnShLHmgK1aKkLUsFNx","requestRef":"8cf0154b-8540-4052-a644-d273570cbeb8","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"848d02b4-df41-4e5c-b9a7-d72cc6197cd7","timestamp":"2026-10-30T04:23:49.033Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"848d02b4-df41-4e5c-b9a7-d72cc6197cd7","isSidechain":false,"promptId":"6dcaa53e-1c59-4f89-85f6-a1842e13dfc3","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019rAMA3siTLwsN2DcQvW9Z5","type":"tool_result","content":"forecasts rows = 240 (expect 240): True\n40 origins x 3 h x 2 models unique: True\ntarget = origin + 3h months: True\nsnaive forecast equals infl at target-4q and that quarter <= origin: True\nactuals match data: True\nrmse_snaive_h1 recomputed from csv 2.849126 vs 2.849126; MAE<=RMSE: True\nrmse_model_h1 recomputed from csv 2.191813 vs 2.191813; MAE<=RMSE: True\nrmse_snaive_h2 recomputed from csv 2.846674 vs 2.846674; MAE<=RMSE: True\nrmse_model_h2 recomputed from csv 2.460223 vs 2.460223; MAE<=RMSE: True\nrmse_snaive_h4 recomputed from csv 2.882316 vs 2.882316; MAE<=RMSE: True\nrmse_model_h4 recomputed from csv 2.339685 vs 2.339685; MAE<=RMSE: True\nrel improvements consistent: True\nloss_diff_mean_h4 = rmse_snaive_h4^2 - rmse_model_h4^2: True\nSE>0: True; hac lag 3>=3: True; dm=mean/se: True; p in (0,1): True\nfirst_origin<last_origin: True\nexclusions: 127-1=126; 126-8=118 = n_analysed True\nmagnitudes: model RMSEs 2.19-2.46 vs snaive 2.85-2.88, sd(infl)=2.39 — plausible\ninformation rule: analysis.py slices y[:i_end+1] (quarters <= origin) for estimation and AIC; asserts target-4 <= origin\nrerun of analysis.py twice -> byte-identical results.json (cmp)","is_error":false}]},"uuid":"85fdefbf-1ed4-43b6-9164-6add4c7d3a9d","timestamp":"2026-10-30T04:23:49.461Z","toolUseResult":{"stdout":"forecasts rows = 240 (expect 240): True\n40 origins x 3 h x 2 models unique: True\ntarget = origin + 3h months: True\nsnaive forecast equals infl at target-4q and that quarter <= origin: True\nactuals match data: True\nrmse_snaive_h1 recomputed from csv 2.849126 vs 2.849126; MAE<=RMSE: True\nrmse_model_h1 recomputed from csv 2.191813 vs 2.191813; MAE<=RMSE: True\nrmse_snaive_h2 recomputed from csv 2.846674 vs 2.846674; MAE<=RMSE: True\nrmse_model_h2 recomputed from csv 2.460223 vs 2.460223; MAE<=RMSE: True\nrmse_snaive_h4 recomputed from csv 2.882316 vs 2.882316; MAE<=RMSE: True\nrmse_model_h4 recomputed from csv 2.339685 vs 2.339685; MAE<=RMSE: True\nrel improvements consistent: True\nloss_diff_mean_h4 = rmse_snaive_h4^2 - rmse_model_h4^2: True\nSE>0: True; hac lag 3>=3: True; dm=mean/se: True; p in (0,1): True\nfirst_origin<last_origin: True\nexclusions: 127-1=126; 126-8=118 = n_analysed True\nmagnitudes: model RMSEs 2.19-2.46 vs snaive 2.85-2.88, sd(infl)=2.39 — plausible\ninformation rule: analysis.py slices y[:i_end+1] (quarters <= origin) for estimation and AIC; asserts target-4 <= origin\nrerun of analysis.py twice -> byte-identical results.json 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- <<'EOF'\nimport json,pandas as pd,numpy as np\nR=json.load(open('out/results.json'));F=pd.read_csv('out/forecasts.csv',parse_dates=['origin_date','target_date'])\nout=[]\nout.append(f\"forecasts rows = {len(F)} (expect 240): {len(F)==240}\")\nout.append(f\"40 origins x 3 h x 2 models unique: {F.groupby(['origin_date','horizon','model']).size().max()==1 and F.origin_date.nunique()==40}\")\nout.append(f\"target = origin + 3h months: {all(F.target_date==F.apply(lambda r:r.origin_date+pd.DateOffset(months=3*r.horizon),axis=1))}\")\nq=pd.read_csv('data/cpi_quarterly.csv',parse_dates=['quarter']).set_index('quarter').infl_annualised\nsn=F[F.model=='snaive']\nout.append(f\"snaive forecast equals infl at target-4q and that quarter <= origin: {np.allclose(sn.forecast.values,[q[t-pd.DateOffset(months=12)] for t in sn.target_date],atol=1e-6) and all(sn.target_date-pd.DateOffset(months=12)<=sn.origin_date)}\")\nout.append(f\"actuals match data: {np.allclose(F.actual.values,q[F.target_date].values,atol=1e-6)}\")\nfor h in (1,2,4):\n for m,k in (('snaive','snaive'),('ar_seasdum','model')):\n e=F[(F.horizon==h)&(F.model==m)].eval('actual-forecast')\n out.append(f\"rmse_{k}_h{h} recomputed from csv {np.sqrt((e**2).mean()):.6f} vs {R[f'rmse_{k}_h{h}']:.6f}; MAE<=RMSE: {R[f'mae_{k}_h{h}']<=R[f'rmse_{k}_h{h}']}\")\nout.append(f\"rel improvements consistent: {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))}\")\nout.append(f\"loss_diff_mean_h4 = rmse_snaive_h4^2 - rmse_model_h4^2: {abs(R['loss_diff_mean_h4']-(R['rmse_snaive_h4']**2-R['rmse_model_h4']**2))<1e-9}\")\nout.append(f\"SE>0: {R['loss_diff_se_h4']>0}; hac lag {R['hac_lags_h4']}>=3: {R['hac_lags_h4']>=3}; dm=mean/se: {abs(R['dm_stat_h4']-R['loss_diff_mean_h4']/R['loss_diff_se_h4'])<1e-12}; p in (0,1): {0<R['dm_pvalue_h4']<1}\")\nout.append(f\"first_origin<last_origin: {R['first_origin']<R['last_origin']}\")\nout.append(\"exclusions: 127-1=126; 126-8=118 = n_analysed %s\"%(R['n_analysed']==118))\nout.append(\"magnitudes: model RMSEs 2.19-2.46 vs snaive 2.85-2.88, sd(infl)=2.39 — plausible\")\nout.append(\"information rule: analysis.py slices y[:i_end+1] (quarters <= origin) for estimation and AIC; asserts target-4 <= origin\")\nout.append(\"rerun of analysis.py twice -> byte-identical results.json (cmp)\")\nopen('out/state/checks.md','w').write(\"# Verification checks\\n\\n\"+\"\\n\".join(\"- \"+o for o in out)+\"\\n\")\nprint(\"\\n\".join(out))\nEOF","description":"Run internal consistency checks"}}]}}61{"type":"api-request","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","id":"6edd032d-192f-4d59-9969-89998e8ff2fb","timestamp":"2026-10-30T04:23:49.465Z","version":"2.1.314","querySource":"sdk","shapeHash":"c715b1bd7f48eb68e478c4f2a66efbebba4fdaa9e45872a607b951f5efcfd0f9","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":"8cf0154b-8540-4052-a644-d273570cbeb8","keep":18,"tail":["ba67e9d48bfa1a5737c62cfc971344520341942ea9896d1059cfdb590af17a97","d9fa29475cb6990e5aa18ac121a0308f816c5c569c17ddfa18a66f471419c8c2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1bf3384aa00189f1336844e5a76c4c7daa5a3b4f3756ec9dd829d2fec7c09fc3"}62{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"85fdefbf-1ed4-43b6-9164-6add4c7d3a9d","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c"}63{"type":"atis-latch","atis":"","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c"}64{"parentUuid":"85fdefbf-1ed4-43b6-9164-6add4c7d3a9d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnTaHQvHNeKDZjU7kG1","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSpAcKEAgTGAI4AUIIdGhpbmtpbmcSDPY7Q+rYBipYWzxnYhoMIntKpuMi5bW67ulkIjBASWvsDiYUTaeBf8p8aRa9RjPu8yCoQamthsKVHyQ+oQrRuE80BWGucCpAEw3fv8AqwQZ7ZiAg7q0PnqG99pUwULcKYIUh8fbDtl5lIOWk2UYdLGG2QgYAAyyvuhS/cUkrlo68N6W2oy9HmcRjMKw13+0pi1zVtSkyTr42AcGgmMJuetskq0e4m7wQ7k6+xvNqs5nG3rz+8Tz0glaNhpZZ1gM/+1qqp2Bhm7Prf/BvNYUvXaZWUvwGekRrZz0y9YFfwAy4YGAiEpdBWtZnAE4RzfwVAXg8DgW4QVJ6O0P6lz0w4c8TSofy9QOjHjHvIfnFxbg2W7eRHOo6q6xKgUiev1m1Ek9i7Lv8VEJgh7T5R5h5LplbaVGHwKQ6BOhth8creXD1PQD0Jqj1KSmqlwfN5k2RmfLzXXEUtcrSpM8d/1omlZyKjQFyC/T3aAzheKJlhwQXdppYHxhXTUfhQNF72jhvUv9mLb3e7IWgTHv4uUgchsR7paO9GAVucPzvQ2cafOn1qwsIZjnft4Ds3Ns6nG0DkBuYBDzpcAyC/Hgj77XsKSbZTDMLLLJA3wkmxa19+iyAdQyrM4df3IUX1Rwar12wtuG81w1+NxjstAVV9DDJbr64yK3YcNBe9XVYpvWgfnNf/oZD45v3ZiyKynAKFaKJjE8BfhKRB/NjOvcfIvZhiHNvAveVfP4K1UhI2DfzrFmBlUbHhjJBzHlFFz0fZVhqM1z56FbvLxQJFrkZcCxGBKtewEtB/DCr4MZFdPXfQTlJ1X5xcZV+kZZXga+PoFy4m4ui1HBVnyH9gfzzHYNyDSRoDKm/I6ANwVyjSasw14fLVGIB/jOAXALJXHTR8byUKt12BM2lUtIDz+OD0Tgqy28EzyBhT1dlQdB7oPS62yi6dqRGFG+BbwSE4D3gE36rshnIC3/Wf7M75++NmCLQjeVIsTQpUXV39DMlHUtyxaVJr2V9Rn+dnPija5a6fJmbf1FjC4ZaQWbEUljMSSWTI3z5rie2N1KOf2qcotW/IxFAc9aYUIrwqrr+dZYvpp03QhXcJ+/ocY+Q2otlyHU1cDRNAMg4C7zP0yG3oKnm35rQ6jFi0XCFMCH0+GgMk33PcSkMZNN6BCLXhr4f1zT6+FSVceIS/qE1FiLLt3p5RG1J2c+c7+ARd3koCpD5QPi8xxgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2050,"cache_read_input_tokens":21739,"output_tokens":3531,"output_tokens_details":{"thinking_tokens":53},"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":2050},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":404,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXnTa2H3gNQirv7qkU8v","requestRef":"6edd032d-192f-4d59-9969-89998e8ff2fb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ff62991d-b8e2-4dd9-a6d4-c2fd4eaa2c50","timestamp":"2026-10-30T04:23:51.188Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}65{"parentUuid":"ff62991d-b8e2-4dd9-a6d4-c2fd4eaa2c50","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXnTaHQvHNeKDZjU7kG1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G32pwJvVN5pnCAS1aeRPEC","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-04 — Multi-horizon forecasts of Canadian quarterly CPI inflation\n\n## Question\nOver 40 expanding-window origins (`first_origin` = 2015-10-01 to `last_origin` = 2025-07-01, `n_origins` = 40), does a seasonal AR model forecast annualised, non-seasonally-adjusted quarterly CPI inflation better than the seasonal-naive forecast at h = 1, 2, 4, and is the h = 4 squared-error loss differential significantly different from zero when its serial correlation (from overlapping 4-step errors) is accounted for?\n\n## Data and exclusions\nInput `data/cpi_quarterly.csv` (127 rows; `n_input_rows`) — SHA-256 matches the README; `data/cpi_monthly.csv` (381 rows) was used only for an audit (quarterly averages and inflation reproduced to rounding precision, see `out/state/data_audit.md`). Issues (`data_issues`): 1995Q1 inflation is undefined and dropped; `infl_annualised` used as given; the 2020 COVID and 2021–23 inflation-surge observations are kept. Exclusion ledger (`out/exclusions.csv`): 127 → 126 (1995Q1) → 118 observations in the last estimation sample (`n_analysed` = 118; 4 initial lags and the 4 post-origin quarters 2025Q4–2026Q3, which serve only as targets).\n\n## Method\n- Baseline `snaive`: forecast of quarter t+h = inflation of quarter t+h−4.\n- Model `ar_seasdum` (`model_name`, `model_description`): OLS regression of inflation on four quarterly seasonal intercepts plus p own lags, p ∈ {0,…,4} chosen by AIC at each origin on a common sample; estimated on all quarters ≤ origin (expanding window from 1995Q2); multi-step forecasts are iterated. AIC picked p = 1 at 15 origins and p = 2 at 25 (`selected_ar_order_counts`). No randomness (`random_seed` = null).\n- Evaluation: RMSE and MAE over the 40 origins per horizon; relative improvement 1 − RMSE_model/RMSE_snaive.\n- h = 4 test: d_t = e²_snaive,t − e²_model,t; standard error of d̄ from a Newey–West (Bartlett kernel, `hac_kernel_h4`) long-run variance with truncation lag `hac_lags_h4` = 3 (= h − 1, fixed in advance); DM statistic compared with N(0,1), two-sided.\n\n## Results\n| h | RMSE snaive | RMSE model | MAE snaive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 | 2.849 (`rmse_snaive_h1`) | 2.192 (`rmse_model_h1`) | 2.137 (`mae_snaive_h1`) | 1.519 (`mae_model_h1`) | 0.231 (`rel_improvement_rmse_h1`) |\n| 2 | 2.847 (`rmse_snaive_h2`) | 2.460 (`rmse_model_h2`) | 2.126 (`mae_snaive_h2`) | 1.550 (`mae_model_h2`) | 0.136 (`rel_improvement_rmse_h2`) |\n| 4 | 2.882 (`rmse_snaive_h4`) | 2.340 (`rmse_model_h4`) | 2.181 (`mae_snaive_h4`) | 1.594 (`mae_model_h4`) | 0.188 (`rel_improvement_rmse_h4`) |\n\nUnits: percentage points of annualised inflation. The seasonal AR has lower RMSE and MAE than the seasonal-naive forecast at all three horizons (RMSE gains of 14–23 %).\n\nAt h = 4: d̄ = 2.834 squared pp (`loss_diff_mean_h4`), HAC SE = 2.082 (`loss_diff_se_h4`), DM = 1.36 (`dm_stat_h4`), two-sided p = 0.174 (`dm_pvalue_h4`). With the pre-specified test, the h = 4 gain is **not** statistically distinguishable from zero at conventional levels. The model had the smaller squared error at 29 of 40 origins (`sensitivity_h4.n_origins_model_better_sqerr_h4`).\n\n## Uncertainty\n- Sensitivity of the h = 4 test (reported in `sensitivity_h4`, not the primary result): Harvey–Leybourne–Newbold small-sample correction with t(39) gives p = 0.222 (`hln_pvalue_t39`). The sample autocorrelations of d_t at lags 3 and 4 are negative (−0.28, −0.41; `d_autocorr_lag1_to_4`), so longer Bartlett truncations *shrink* the SE: p = 0.125 at lag 4, 0.039 at lag 6, 0.018 at lag 8 (`bartlett_L4_pvalue`, `bartlett_L6_pvalue`, `bartlett_L8_pvalue`). The verdict is therefore sensitive to the bandwidth; the primary lag 3 was fixed from the h − 1 rule before results were seen, and we do not switch to a lag that delivers significance. With 40 overlapping observations the long-run variance is poorly estimated whichever lag is used.\n- The relative-improvement figures at h = 1 and h = 2 have no formal test here (not required); they are point estimates from 40 forecasts each.\n\n## Limitations and open issues\n- Errors are computed on the current vintage; no real-time validity is claimed.\n- The evaluation window contains the 2020 pandemic shock and the 2021–23 inflation surge; d_t is unlikely to be covariance-stationary, which the DM asymptotics assume, and a few large errors dominate squared-error loss.\n- Only one model class was tried (no search across models on the evaluation sample), which avoids data snooping but means the result is specific to this specification.\n- The DM test treats forecasts as given (Diebold–Mariano), not as a test of population model superiority with estimation uncertainty (West/Giacomini–White).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The seasonal AR model has lower out-of-sample RMSE and MAE than the seasonal-naive forecast at h = 1, 2 and 4 over the 40 origins.\",\"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\"],\"status\":\"supported\",\"caveat\":\"Point estimates from 40 forecasts per horizon; no formal test at h=1,2.\"},\n {\"id\":\"C2\",\"text\":\"Relative RMSE improvements over seasonal-naive are about 0.23, 0.14 and 0.19 at h = 1, 2, 4.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"rel_improvement_rmse_h4\"],\"status\":\"supported\",\"caveat\":\"Sample includes the 2020-2023 shocks that dominate squared errors.\"},\n {\"id\":\"C3\",\"text\":\"At h = 4 the mean squared-error loss differential is positive (model better) but not statistically distinguishable from zero using a Bartlett HAC SE with lag 3 (p about 0.17).\",\"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\":\"Result is bandwidth-sensitive: longer Bartlett lags (6, 8) give p below 0.05 because d_t has negative autocorrelation at lags 3-4; HLN correction gives p about 0.22.\"},\n {\"id\":\"C4\",\"text\":\"The h = 4 significance verdict depends on the HAC truncation lag.\",\"results_keys\":[\"sensitivity_h4\"],\"status\":\"supported\",\"caveat\":\"Sensitivity analysis only; primary lag pre-specified as h-1=3.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of both inputs match README/bundle\",\"row/col counts, missing, duplicates, gaps checked\",\"monthly->quarterly aggregation reproduced (max diff 3.3e-7)\",\"exclusion ledger written: 127-1-8=118\"],\"notes\":\"data clean\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"seasonal-naive baseline computed with same 40-origin evaluation (rmse/mae_snaive_h*) in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json and forecasts.csv; no randomness (random_seed null)\"],\"notes\":\"AR(p)+seasonal dummies, AIC p<=4, iterated\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun byte-identical (cmp)\",\"consistency checks in out/state/checks.md all True\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"$(date -u +%FT%TZ) P1-P3 passed; primary HAC lag 3 Bartlett fixed a priori; sensitivity shows bandwidth dependence, reported not switched.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Write report, claims, update phases, run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2050,"cache_read_input_tokens":21739,"output_tokens":3531,"output_tokens_details":{"thinking_tokens":53},"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":2050},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnTa2H3gNQirv7qkU8v","requestRef":"6edd032d-192f-4d59-9969-89998e8ff2fb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2c24beac-2821-40ca-a71b-e45093458d22","timestamp":"2026-10-30T04:24:18.555Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"2c24beac-2821-40ca-a71b-e45093458d22","isSidechain":false,"promptId":"6dcaa53e-1c59-4f89-85f6-a1842e13dfc3","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 57 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\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 [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 4 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 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: 2.1918133412897713\n [ok] positive:rmse_model_h2: 2.46022256417967\n [ok] positive:rmse_model_h4: 2.3396848872653107\n [ok] positive:loss_diff_se_h4: 2.0822850037684537\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/RC0075/out/state/gate_receipts/20261030T042419.384302Z.json","is_error":true,"tool_use_id":"toolu_01G32pwJvVN5pnCAS1aeRPEC"}]},"uuid":"e7cf898a-6edf-4675-b5ed-0527b47851ec","timestamp":"2026-10-30T04:24:19.390Z","toolUseResult":"Error: Exit code 1\nPGAR GATE pgar_gate/1.0 — FAILED (1 of 57 checks failed)\n [ok] file:out/results.json: present\n [ok] file:out/report.md: present\n [ok] file:out/analysis.py: present\n [ok] file:out/claims.json: present\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 [ok] section:Question: present\n [ok] section:Data and exclusions: present\n [ok] section:Method: present\n [ok] section:Results: present\n [ok] section:Uncertainty: present\n [ok] section:Limitations and open issues: present\n [ok] claims_json: 4 claims, all keys resolve\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [ok] exclusion_ledger: 2 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: 2.1918133412897713\n [ok] positive:rmse_model_h2: 2.46022256417967\n [ok] positive:rmse_model_h4: 2.3396848872653107\n [ok] positive:loss_diff_se_h4: 2.0822850037684537\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/RC0075/out/state/gate_receipts/20261030T042419.384302Z.json","sandboxed":false,"sourceToolAssistantUUID":"2c24beac-2821-40ca-a71b-e45093458d22","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","hash":"11d484c0d2315ca0b6334a714ce7b785cafcc2688f1ae7c206544128772a29eb","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01G32pwJvVN5pnCAS1aeRPEC","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-04 — Multi-horizon forecasts of Canadian quarterly CPI inflation\n\n## Question\nOver 40 expanding-window origins (`first_origin` = 2015-10-01 to `last_origin` = 2025-07-01, `n_origins` = 40), does a seasonal AR model forecast annualised, non-seasonally-adjusted quarterly CPI inflation better than the seasonal-naive forecast at h = 1, 2, 4, and is the h = 4 squared-error loss differential significantly different from zero when its serial correlation (from overlapping 4-step errors) is accounted for?\n\n## Data and exclusions\nInput `data/cpi_quarterly.csv` (127 rows; `n_input_rows`) — SHA-256 matches the README; `data/cpi_monthly.csv` (381 rows) was used only for an audit (quarterly averages and inflation reproduced to rounding precision, see `out/state/data_audit.md`). Issues (`data_issues`): 1995Q1 inflation is undefined and dropped; `infl_annualised` used as given; the 2020 COVID and 2021–23 inflation-surge observations are kept. Exclusion ledger (`out/exclusions.csv`): 127 → 126 (1995Q1) → 118 observations in the last estimation sample (`n_analysed` = 118; 4 initial lags and the 4 post-origin quarters 2025Q4–2026Q3, which serve only as targets).\n\n## Method\n- Baseline `snaive`: forecast of quarter t+h = inflation of quarter t+h−4.\n- Model `ar_seasdum` (`model_name`, `model_description`): OLS regression of inflation on four quarterly seasonal intercepts plus p own lags, p ∈ {0,…,4} chosen by AIC at each origin on a common sample; estimated on all quarters ≤ origin (expanding window from 1995Q2); multi-step forecasts are iterated. AIC picked p = 1 at 15 origins and p = 2 at 25 (`selected_ar_order_counts`). No randomness (`random_seed` = null).\n- Evaluation: RMSE and MAE over the 40 origins per horizon; relative improvement 1 − RMSE_model/RMSE_snaive.\n- h = 4 test: d_t = e²_snaive,t − e²_model,t; standard error of d̄ from a Newey–West (Bartlett kernel, `hac_kernel_h4`) long-run variance with truncation lag `hac_lags_h4` = 3 (= h − 1, fixed in advance); DM statistic compared with N(0,1), two-sided.\n\n## Results\n| h | RMSE snaive | RMSE model | MAE snaive | MAE model | 1 − RMSE ratio |\n|---|---|---|---|---|---|\n| 1 | 2.849 (`rmse_snaive_h1`) | 2.192 (`rmse_model_h1`) | 2.137 (`mae_snaive_h1`) | 1.519 (`mae_model_h1`) | 0.231 (`rel_improvement_rmse_h1`) |\n| 2 | 2.847 (`rmse_snaive_h2`) | 2.460 (`rmse_model_h2`) | 2.126 (`mae_snaive_h2`) | 1.550 (`mae_model_h2`) | 0.136 (`rel_improvement_rmse_h2`) |\n| 4 | 2.882 (`rmse_snaive_h4`) | 2.340 (`rmse_model_h4`) | 2.181 (`mae_snaive_h4`) | 1.594 (`mae_model_h4`) | 0.188 (`rel_improvement_rmse_h4`) |\n\nUnits: percentage points of annualised inflation. The seasonal AR has lower RMSE and MAE than the seasonal-naive forecast at all three horizons (RMSE gains of 14–23 %).\n\nAt h = 4: d̄ = 2.834 squared pp (`loss_diff_mean_h4`), HAC SE = 2.082 (`loss_diff_se_h4`), DM = 1.36 (`dm_stat_h4`), two-sided p = 0.174 (`dm_pvalue_h4`). With the pre-specified test, the h = 4 gain is **not** statistically distinguishable from zero at conventional levels. The model had the smaller squared error at 29 of 40 origins (`sensitivity_h4.n_origins_model_better_sqerr_h4`).\n\n## Uncertainty\n- Sensitivity of the h = 4 test (reported in `sensitivity_h4`, not the primary result): Harvey–Leybourne–Newbold small-sample correction with t(39) gives p = 0.222 (`hln_pvalue_t39`). The sample autocorrelations of d_t at lags 3 and 4 are negative (−0.28, −0.41; `d_autocorr_lag1_to_4`), so longer Bartlett truncations *shrink* the SE: p = 0.125 at lag 4, 0.039 at lag 6, 0.018 at lag 8 (`bartlett_L4_pvalue`, `bartlett_L6_pvalue`, `bartlett_L8_pvalue`). The verdict is therefore sensitive to the bandwidth; the primary lag 3 was fixed from the h − 1 rule before results were seen, and we do not switch to a lag that delivers significance. With 40 overlapping observations the long-run variance is poorly estimated whichever lag is used.\n- The relative-improvement figures at h = 1 and h = 2 have no formal test here (not required); they are point estimates from 40 forecasts each.\n\n## Limitations and open issues\n- Errors are computed on the current vintage; no real-time validity is claimed.\n- The evaluation window contains the 2020 pandemic shock and the 2021–23 inflation surge; d_t is unlikely to be covariance-stationary, which the DM asymptotics assume, and a few large errors dominate squared-error loss.\n- Only one model class was tried (no search across models on the evaluation sample), which avoids data snooping but means the result is specific to this specification.\n- The DM test treats forecasts as given (Diebold–Mariano), not as a test of population model superiority with estimation uncertainty (West/Giacomini–White).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The seasonal AR model has lower out-of-sample RMSE and MAE than the seasonal-naive forecast at h = 1, 2 and 4 over the 40 origins.\",\"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\"],\"status\":\"supported\",\"caveat\":\"Point estimates from 40 forecasts per horizon; no formal test at h=1,2.\"},\n {\"id\":\"C2\",\"text\":\"Relative RMSE improvements over seasonal-naive are about 0.23, 0.14 and 0.19 at h = 1, 2, 4.\",\"results_keys\":[\"rel_improvement_rmse_h1\",\"rel_improvement_rmse_h2\",\"rel_improvement_rmse_h4\"],\"status\":\"supported\",\"caveat\":\"Sample includes the 2020-2023 shocks that dominate squared errors.\"},\n {\"id\":\"C3\",\"text\":\"At h = 4 the mean squared-error loss differential is positive (model better) but not statistically distinguishable from zero using a Bartlett HAC SE with lag 3 (p about 0.17).\",\"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\":\"Result is bandwidth-sensitive: longer Bartlett lags (6, 8) give p below 0.05 because d_t has negative autocorrelation at lags 3-4; HLN correction gives p about 0.22.\"},\n {\"id\":\"C4\",\"text\":\"The h = 4 significance verdict depends on the HAC truncation lag.\",\"results_keys\":[\"sensitivity_h4\"],\"status\":\"supported\",\"caveat\":\"Sensitivity analysis only; primary lag pre-specified as h-1=3.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of both inputs match README/bundle\",\"row/col counts, missing, duplicates, gaps checked\",\"monthly->quarterly aggregation reproduced (max diff 3.3e-7)\",\"exclusion ledger written: 127-1-8=118\"],\"notes\":\"data clean\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"seasonal-naive baseline computed with same 40-origin evaluation (rmse/mae_snaive_h*) in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json and forecasts.csv; no randomness (random_seed null)\"],\"notes\":\"AR(p)+seasonal dummies, AIC p<=4, iterated\"}\np['P4']={\"status\":\"running\",\"checks\":[\"clean rerun byte-identical (cmp)\",\"consistency checks in out/state/checks.md all True\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"$(date -u +%FT%TZ) P1-P3 passed; 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The final gate check passes 57/57, and rerunning `out/analysis.py` reproduces `out/results.json` exactly.\n\n**Model (`ar_seasdum`):** a regression of inflation on its own recent values plus a separate constant for each calendar quarter. At each origin it uses only quarters up to that origin, picks 0–4 lagged values by AIC (it chose 1 lag at 15 origins and 2 at the other 25), and builds longer-horizon forecasts step by step. It uses no randomness.\n\n**Results over the 40 origins** (percentage points of annualised inflation):\n\n| Horizon | RMSE seasonal-naive | RMSE model | RMSE improvement |\n|---|---|---|---|\n| 1 quarter | 2.85 | 2.19 | 23% |\n| 2 quarters | 2.85 | 2.46 | 14% |\n| 4 quarters | 2.88 | 2.34 | 19% |\n\nThe model's average absolute error (MAE) is also lower at every horizon.\n\n**4-quarter test:** the model's squared error is lower by an average of 2.83 (in squared percentage points). Allowing for the overlap between consecutive 4-quarter forecasts (Newey–West, Bartlett kernel, lag 3 = h − 1, fixed in advance), the standard error is 2.08. That gives a Diebold–Mariano statistic of 1.36 and a two-sided p-value of 0.174. The model had the smaller squared error at 29 of the 40 origins.\n\n**Main caveat:** the verdict depends on the lag length chosen for the standard error. The loss differences are negatively correlated at lags 3–4, so longer lags make the standard error smaller: p is 0.125 at lag 4, 0.039 at lag 6 and 0.018 at lag 8. A common small-sample correction (Harvey–Leybourne–Newbold) gives p = 0.22. The report gives all of these but keeps the lag-3 result as the answer rather than switching to a lag that delivers significance.\n\n**Other limitations:**\n- The errors use today's version of the data, so they say nothing about how the model would have done in real time.\n- The test period includes the 2020 pandemic shock and the 2021–23 inflation surge. This makes the test's statistical assumptions questionable, and a few large errors dominate the squared-error comparison.\n- I tried only this one model, which avoids fitting to the test period but means the result applies to this specification only.\n\nThe data passed every audit check: both file hashes match the README, and the quarterly values reproduce from the monthly file to rounding precision. The only exclusion is 1995Q1, which has no inflation value, so 118 observations are used in the last estimation window.\n\nEverything is in `out/`; open `report.md` first:\n- `analysis.py`\n- `results.json`\n- `forecasts.csv` (240 rows)\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":320,"cache_read_input_tokens":29117,"output_tokens":1085,"output_tokens_details":{"thinking_tokens":19},"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":320},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXnWdNXreoEqfBuAa7AP","requestRef":"82543b88-05aa-4335-a2ba-07cb34ade52c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5332be98-6449-43fa-844d-9030e8e261e1","timestamp":"2026-10-30T04:24:41.529Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0075","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","version":"2.1.314","gitBranch":"HEAD"}82{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5332be98-6449-43fa-844d-9030e8e261e1","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c"}83{"type":"cost-state","sessionId":"03a74f63-9951-4969-b20c-aa61ff73568c","totalCostUSD":0.5236852,"totalAPIDuration":147108,"totalAPIDurationWithoutRetries":147100,"totalToolDuration":6079,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":153375,"startTime":1793334128167,"modelUsage":{"claude-opus-5-5":{"inputTokens":28,"outputTokens":17156,"thinkingTokens":1060,"cacheReadInputTokens":203941,"cacheCreationInputTokens":27933,"cacheCreationEphemeral5mInputTokens":27933,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.5236852}},"hasUnknownModelCost":false}84