spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:10:00.128Z","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:10:00.133Z","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}4{"parentUuid":null,"isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":"Begin"},"uuid":"e81744fe-3d82-463f-9d86-437f25f069b7","timestamp":"2026-10-30T04:10:00.136Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","shapeHash":"50ad0a330267252a180741055d971d812c096ce1b9d19ff49e21a4ee80165a04","timestamp":"2026-10-30T04:10:00.148Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0032\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}12{"parentUuid":"1afc95de-c835-4d3a-9db3-b9c17e689f1a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmQgBuz8DJn7Ce1ivk1","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":4,"cache_creation_input_tokens":2603,"cache_read_input_tokens":1504,"output_tokens":117,"output_tokens_details":{"thinking_tokens":15},"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":2603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":372,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmQfvYWL8P2QtqapH8k","requestRef":"95cfb060-5c2a-459a-a881-7d957974de17","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b4c8f1bd-1b07-461f-9f77-9102d610fb5d","timestamp":"2026-10-30T04:10:04.885Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"b4c8f1bd-1b07-461f-9f77-9102d610fb5d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmQgBuz8DJn7Ce1ivk1","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019G3nubGTfKRkdx9DbeLExU","name":"Bash","input":{"command":"cat TASK.md; 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The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n  - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n  - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n  - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n  - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\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 | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` and `out/forecasts.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x   6 simon-pierreboucher  staff   192 oct.  29 23:52 .\ndrwxr-xr-x  32 simon-pierreboucher  staff  1024 oct.  30 00:09 ..\n-rw-r--r--   1 simon-pierreboucher  staff   517 oct.  29 23:52 bundle.json\ndrwxr-xr-x   4 simon-pierreboucher  staff   128 oct.  29 23:46 data\n-rw-r--r--   1 simon-pierreboucher  staff  1862 oct.  29 23:47 gate_spec.json\n-rw-r--r--   1 simon-pierreboucher  staff  5520 oct.  29 23:47 TASK.md\ndata:\ntotal 64\ndrwxr-xr-x  4 simon-pierreboucher  staff    128 oct.  29 23:46 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:52 ..\n-rw-r--r--  1 simon-pierreboucher  staff  28378 oct.  29 23:50 ip_predictors.csv\n-rw-r--r--  1 simon-pierreboucher  staff   2224 oct.  29 23:50 README.md","is_error":true,"tool_use_id":"toolu_019G3nubGTfKRkdx9DbeLExU"}]},"uuid":"3b1b9595-781f-435e-8db3-900d03d5c3f3","timestamp":"2026-10-30T04:10:05.549Z","toolUseResult":"Error: Exit code 1\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth with standardised predictors\n\n## Scientific question\nDoes a fixed-penalty ridge regression on eight standardised monthly predictors forecast next month's US industrial production growth better than two simple baselines — the naive (last value) forecast and the expanding historical mean — over 60 rolling origins from 2021-09 to 2026-08? The estimands are the out-of-sample RMSE and MAE of the three forecasts (naive, expanding mean, ridge) at horizon h = 1 month, and the relative improvements of the ridge forecast over each baseline, 1 − RMSE_ridge / RMSE_baseline.\n\n## Inputs\n- `data/ip_predictors.csv` — observed data derived from six FRED series (INDPRO, MCOILWTICO, FEDFUNDS, PERMIT, NEWORDER, TCU; retrieved 2026-10-30 UTC; current vintage; see `data/README.md` for URLs, retrieval times, transformations, SHA-256 and licence). Columns: `month` (YYYY-MM-01), `ip_growth` (target: 100 × Δln of the industrial production index, percent per month), `oil_chg` (100 × Δln WTI monthly price), `fedfunds` (effective federal funds rate, percent, level), `permit_chg` (100 × Δln building permits), `neworder_chg` (100 × Δln core capital goods orders), `tcu` (capacity utilisation, percent of capacity, level). 417 monthly rows, 1992-01 to 2026-09; the first one or two rows of the change columns are empty.\n\n## Information-availability rules\n- Forecast origins: the 60 months t = 2021-09-01, …, 2026-08-01 (the `month` value). Horizon h = 1: the target of origin t is `ip_growth` of month t + 1.\n- At origin t, every value dated ≤ t is treated as available (publication lags are ignored in this exercise, as stated in `data/README.md`); no value dated > t may be used in any way. In particular, **every statistic used to transform or scale the data (means, standard deviations, target mean) must be computed from the training window of that origin only**, and recomputed at every origin.\n- Baselines (both required): naive = `ip_growth` of month t; expanding mean = the arithmetic mean of `ip_growth` over the months 1992-04-01 to t inclusive.\n- The ridge model (fully specified; implement exactly this):\n  - training rows at origin t: all months s with 1992-03-01 ≤ s and s + 1 ≤ t, with target y_{s+1} = `ip_growth` of month s + 1 and feature vector z_s = (ip_growth_s, ip_growth_{s−1}, ip_growth_{s−2}, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s, tcu_s) (8 features);\n  - standardise each feature as (z − μ_j) / σ_j, with μ_j the mean and σ_j the sample standard deviation (denominator n − 1) over the training rows of that origin; centre the target with its training mean ȳ;\n  - coefficients b = (Z′Z + λ I)⁻¹ Z′ (y − ȳ) with λ = 50, Z the standardised training feature matrix (no intercept column; the intercept is ȳ and is not penalised);\n  - forecast = ȳ + ((z_t − μ) / σ) · b, where z_t is the feature vector at the origin month t, standardised with the same μ, σ.\n- Errors are computed on the current-vintage data as given; do not claim real-time validity.\n\n## Required outputs\n- `out/forecasts.csv` — one row per (origin, model), columns `origin_date`, `target_date`, `horizon` (always 1), `forecast`, `actual`, `model` ∈ {`naive`, `mean`, `ridge`}. Expected: 60 × 3 = 180 rows.\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F3-conf-03\" |\n| `n_origins` | integer | — | 60 |\n| `first_origin`, `last_origin` | string | date | first and last origin dates |\n| `lambda` | number | — | ridge penalty used (50) |\n| `normalisation_window` | string | — | how the standardisation statistics were computed: \"expanding\" (training window of each origin), \"full_sample\", or \"none\" |\n| `n_features` | integer | — | number of ridge features (8) |\n| `rmse_naive`, `mae_naive` | number | percent per month | naive-forecast errors |\n| `rmse_mean`, `mae_mean` | number | percent per month | expanding-mean errors |\n| `rmse_ridge`, `mae_ridge` | number | percent per month | ridge-forecast errors |\n| `rel_improvement_vs_naive` | number | fraction | 1 − rmse_ridge / rmse_naive |\n| `rel_improvement_vs_mean` | number | fraction | 1 − rmse_ridge / rmse_mean |\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 | — | {\"ip_predictors.csv\": number of data rows read, excluding the header} |\n| `n_analysed` | integer | — | training rows at the last origin |\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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SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\n{\n  \"task_id\": \"F3-conf-03\",\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    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"lambda\": {\"type\": \"number\", \"nullable\": false},\n    \"normalisation_window\": {\"type\": \"string\", \"nullable\": false},\n    \"n_features\": {\"type\": \"integer\", \"nullable\": false},\n    \"rmse_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\"],\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-03\",\n  \"family\": \"F3\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n  \"files\": {\n    \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n    \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n    \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n    \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n  }\n}month,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\n7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b  data/ip_predictors.csv\n225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e  data/README.md","is_error":false}]},"uuid":"48d65537-2d91-4a30-8f15-2c779b4fdb6e","timestamp":"2026-10-30T04:10:07.367Z","toolUseResult":{"stdout":"# data/README.md — F3-conf-03\n\n## ip_predictors.csv\nOne row per month (`month`, YYYY-MM-01), 1992-01 to 2026-09 (417 data rows), built from six FRED series (Federal Reserve Bank of St. Louis), each retrieved with `https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>` on 2026-10-30 (UTC times below). SHA-256 of this file: 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b. License: FRED terms of use (https://fred.stlouisfed.org/legal/); the underlying series are US government data (public domain).\n\n| column | derived from | transformation | units | retrieved (UTC) |\n|---|---|---|---|---|\n| `ip_growth` | INDPRO — Industrial Production: Total Index (2017=100, SA, monthly; Federal Reserve Board) | 100 × (ln INDPRO_m − ln INDPRO_{m−1}), 6 decimals | percent per month | 2026-10-30T03:20:36Z |\n| `oil_chg` | MCOILWTICO — Crude Oil Prices: WTI, monthly average (USD/barrel, NSA; EIA) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `fedfunds` | FEDFUNDS — Effective Federal Funds Rate, monthly average (percent; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n| `permit_chg` | PERMIT — New Privately-Owned Housing Units Authorized (thousands, SAAR; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `neworder_chg` | NEWORDER — Manufacturers' New Orders: Nondefense Capital Goods Excluding Aircraft (millions USD, SA; Census) | 100 × Δ ln, 6 decimals | percent per month | 2026-10-30T03:24:48Z |\n| `tcu` | TCU — Capacity Utilization: Total Index (percent of capacity, SA; Federal Reserve Board) | level | percent | 2026-10-30T03:24:48Z |\n\nEmpty cells: the first month of each change series (1992-01) and `neworder_chg` for 1992-01/02 (NEWORDER starts 1992-02). No other missing values.\n\n**Vintage caveat**: all series are the current (final) vintage as of retrieval; INDPRO, TCU and NEWORDER are revised substantially after first publication, and publication lags are ignored in this exercise (a value dated month m is treated as known at the end of month m). Results therefore measure the predictability of the revised series under a stylised timing assumption — not historical real-time performance.\n{\n  \"task_id\": \"F3-conf-03\",\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    \"n_origins\": {\"type\": \"integer\", \"nullable\": false},\n    \"first_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"last_origin\": {\"type\": \"string\", \"nullable\": false},\n    \"lambda\": {\"type\": \"number\", \"nullable\": false},\n    \"normalisation_window\": {\"type\": \"string\", \"nullable\": false},\n    \"n_features\": {\"type\": \"integer\", \"nullable\": false},\n    \"rmse_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"rmse_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"mae_ridge\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_naive\": {\"type\": \"number\", \"nullable\": false},\n    \"rel_improvement_vs_mean\": {\"type\": \"number\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": true},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"chronology\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [],\n  \"positive_keys\": [\"rmse_naive\", \"rmse_mean\", \"rmse_ridge\"],\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-03\",\n  \"family\": \"F3\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:52:35.811207+00:00\",\n  \"files\": {\n    \"TASK.md\": \"05343e6f63af28c29a93e789bd621a175efcbd63bafd46f524d3bde9ca21dd1e\",\n    \"data/README.md\": \"225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e\",\n    \"data/ip_predictors.csv\": \"7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b\",\n    \"gate_spec.json\": \"7f7e7d36571218fec139ba1dbf9a3f01ee6c0bac7bc8238debd58d44a69c7071\"\n  }\n}month,ip_growth,oil_chg,fedfunds,permit_chg,neworder_chg,tcu\n1992-01-01,-0.525126,-3.708965,4.030000,1.496754,,79.327800\n1992-02-01,0.703489,1.164034,4.060000,6.209822,,79.732900\n1992-03-01,0.848952,-0.474559,3.980000,-5.746644,4.054668,80.246400\n1992-04-01,0.756147,6.694709,3.730000,-2.621873,2.060424,80.680200\n2026-07-01,0.232439,-5.265329,3.630000,4.204396,0.625394,76.440900\n2026-08-01,0.063739,4.186545,3.630000,-2.115735,1.538598,76.418400\n2026-09-01,0.660373,14.827615,3.750000,-0.357016,0.462226,76.850400\n7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b  data/ip_predictors.csv\n225f52077afb6f435f67e2b398cbe59963ab1ce6d0d0d4e8257fd27c777ede6e  data/README.md","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"938b4f1d-593d-41e9-b081-be2b824f52dd","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"b2f9af3bc45781654ae58690f10b4feb33c4a7bccd333e6a9af68682cd179bfc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GLqDa1uZ58yYfU2xupr4kt","name":"Bash","input":{"command":"cat data/README.md; 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rel. improvement 1 − RMSE_ridge/RMSE_baseline for each baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), fixed model spec; not real-time performance.\n**Ridge spec.** Training rows s with 1992-03 ≤ s, s+1 ≤ t; target y_{s+1}; features (ip_s, ip_{s-1}, ip_{s-2}, oil_s, ff_s, permit_s, neworder_s, tcu_s); standardise with training mean / sd (ddof=1) per origin; centre y by training mean; b=(Z'Z+50I)^{-1}Z'(y-ȳ); forecast ȳ + z̃_t·b.\n**Outputs.** out/forecasts.csv (180 rows: origin_date,target_date,horizon,forecast,actual,model∈{naive,mean,ridge}); out/results.json keys: task_id, n_origins, first_origin, last_origin, lambda, normalisation_window, n_features, rmse/mae_{naive,mean,ridge}, rel_improvement_vs_naive, rel_improvement_vs_mean, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed (training rows at last origin), random_seed; out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol files.\n**Information rules.** At origin t only values dated ≤ t; all scaling statistics from origin's training window, recomputed each origin. Targets (t+1) used only for scoring.\n**Invalidity.** Leakage of t+1 data or full-sample scaling; missing values in required features within training window; fewer than 60 origins with available targets (2026-09 target exists, so fine).\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n- File: data/ip_predictors.csv, SHA-256 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json).\n- 417 data rows × 7 columns (month, ip_growth, oil_chg, fedfunds, permit_chg, neworder_chg, tcu); all numeric float except month (YYYY-MM-01). Range 1992-01-01 … 2026-09-01, consecutive months, no duplicates.\n- Units: ip_growth, oil_chg, permit_chg, neworder_chg = 100×Δln (percent/month); fedfunds percent level; tcu percent of capacity.\n- Missing: only neworder_chg for 1992-01 and 1992-02 (2 cells). 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Code slices data to ≤ t for every fitting step.\n- Vintage: current vintage; publication lags ignored per README — not real-time.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data audited; no rows excluded (README/data discrepancy on 1992-01 noted).\nEOF","description":"Write contract, audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":370,"cache_read_input_tokens":10466,"output_tokens":1643,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":370},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmRYxciJe3yPobXaShx","requestRef":"981a7bca-b3a1-4cb9-bdcf-09aed163cc4f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"57e02d9b-3719-4e1e-853d-1415ace1866b","timestamp":"2026-10-30T04:10:30.884Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"57e02d9b-3719-4e1e-853d-1415ace1866b","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QfR3e6C4ciyykLP9gtbuqx","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"90265185-5348-49c1-94a8-5d578ab85a3e","timestamp":"2026-10-30T04:10:30.945Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"57e02d9b-3719-4e1e-853d-1415ace1866b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"0832de549b1d3e71905f4a09ea0aeca287e25505bff033ac05e5ca173b0dc7dc","message":{"role":"assistant","content":[{"type":"text","text":"Now the contract, audit, and ledger files."},{"type":"tool_use","id":"toolu_01QfR3e6C4ciyykLP9gtbuqx","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F3-conf-03\n**Question.** Over 60 monthly rolling origins t = 2021-09 … 2026-08, does a fixed-penalty (λ=50) ridge regression on 8 standardised predictors forecast ip_growth at t+1 better than (a) naive (ip_growth_t) and (b) expanding mean of ip_growth over 1992-04..t?\n**Estimands.** Out-of-sample RMSE and MAE (percent per month) of naive, mean and ridge at h=1 over the 60 origins; rel. improvement 1 − RMSE_ridge/RMSE_baseline for each baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), fixed model spec; not real-time performance.\n**Ridge spec.** Training rows s with 1992-03 ≤ s, s+1 ≤ t; target y_{s+1}; features (ip_s, ip_{s-1}, ip_{s-2}, oil_s, ff_s, permit_s, neworder_s, tcu_s); standardise with training mean / sd (ddof=1) per origin; centre y by training mean; b=(Z'Z+50I)^{-1}Z'(y-ȳ); forecast ȳ + z̃_t·b.\n**Outputs.** out/forecasts.csv (180 rows: origin_date,target_date,horizon,forecast,actual,model∈{naive,mean,ridge}); out/results.json keys: task_id, n_origins, first_origin, last_origin, lambda, normalisation_window, n_features, rmse/mae_{naive,mean,ridge}, rel_improvement_vs_naive, rel_improvement_vs_mean, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed (training rows at last origin), random_seed; out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol files.\n**Information rules.** At origin t only values dated ≤ t; all scaling statistics from origin's training window, recomputed each origin. Targets (t+1) used only for scoring.\n**Invalidity.** Leakage of t+1 data or full-sample scaling; missing values in required features within training window; fewer than 60 origins with available targets (2026-09 target exists, so fine).\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n- File: data/ip_predictors.csv, SHA-256 7e388665caf3e4e7305ae6ed8f5625ab12a6c8df5c5696c1b5dd5b86da62a39b (matches README and bundle.json).\n- 417 data rows × 7 columns (month, ip_growth, oil_chg, fedfunds, permit_chg, neworder_chg, tcu); all numeric float except month (YYYY-MM-01). Range 1992-01-01 … 2026-09-01, consecutive months, no duplicates.\n- Units: ip_growth, oil_chg, permit_chg, neworder_chg = 100×Δln (percent/month); fedfunds percent level; tcu percent of capacity.\n- Missing: only neworder_chg for 1992-01 and 1992-02 (2 cells). These months are never used as ridge feature rows (training starts s=1992-03) so no exclusion needed.\n- Anomaly/discrepancy: README says the first month (1992-01) of every change series is empty, but ip_growth, oil_chg and permit_chg have values in 1992-01. Used as given (ip_growth 1992-01 is needed as lag-2 for s=1992-03). Recorded in data_issues.\n- Outliers (|z|>5, full sample, for audit only): ip_growth 2020-04 (−14.14), 2020-06 (+6.35); oil_chg 2020-03/04/05 (±55); permit_chg 2008-07 (−24.8). Genuine COVID/GFC-era movements; kept (task specifies no outlier treatment). They lie in the training windows, not in the evaluation period.\n- Availability: file contains 2026-09 (dated > last origin 2026-08); it is used only as the target of the last origin. Code slices data to ≤ t for every fitting step.\n- Vintage: current vintage; publication lags ignored per README — not real-time.\nEOF\ncat > out/state/log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1 passed: data audited; no rows excluded (README/data discrepancy on 1992-01 noted).\nEOF","description":"Write contract, audit, 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> out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAMBDA = 50.0\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFIRST_ORIGIN = pd.Timestamp(\"2021-09-01\")\nLAST_ORIGIN = pd.Timestamp(\"2026-08-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique\n# lags use only past values (shift forward in time)\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1)  # target of row s = ip_growth at s+1\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows = []\nlast_ntrain = None\nfor t in origins:\n    tgt = t + pd.DateOffset(months=1)\n    actual = float(d.loc[tgt, \"ip_growth\"])\n    avail = d.loc[:t]  # information set: dated <= t\n    # baselines\n    f_naive = float(avail.loc[t, \"ip_growth\"])\n    f_mean = float(avail.loc[MEAN_START:t, \"ip_growth\"].mean())\n    # ridge training rows: TRAIN_START <= s, s+1 <= t  -> s <= t - 1 month\n    s_end = t - pd.DateOffset(months=1)\n    tr = avail.loc[TRAIN_START:s_end]\n    X = tr[FEATS].to_numpy(float)\n    # target y_{s+1}: all s+1 <= t, so values are inside the information set\n    y = avail[\"ip_growth\"].shift(-1).loc[TRAIN_START:s_end].to_numpy(float)\n    assert np.isfinite(X).all() and np.isfinite(y).all()\n    mu = X.mean(axis=0)\n    sd = X.std(axis=0, ddof=1)\n    Z = (X - mu) / sd\n    ybar = y.mean()\n    b = np.linalg.solve(Z.T @ Z + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n    zt = (avail.loc[t, FEATS].to_numpy(float) - mu) / sd\n    f_ridge = float(ybar + zt @ b)\n    last_ntrain = len(tr)\n    for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\ndef metrics(m):\n    e = fc.loc[fc.model == m, \"actual\"].to_numpy() - fc.loc[fc.model == m, \"forecast\"].to_numpy()\n    return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrmse_n, mae_n = metrics(\"naive\")\nrmse_m, mae_m = metrics(\"mean\")\nrmse_r, mae_r = metrics(\"ridge\")\n\n# exclusion ledger: raw rows -> training rows at the last origin\ns_end_last = LAST_ORIGIN - pd.DateOffset(months=1)\nn1 = int((d.index >= TRAIN_START).sum())\nn2 = int(((d.index >= TRAIN_START) & (d.index <= s_end_last)).sum())\nexcl = pd.DataFrame([\n    dict(step=1, rule=\"month >= 1992-03-01 (lag-2 of ip_growth and neworder_chg required)\", n_before=n_raw, n_after=n1,\n         n_removed=n_raw - n1, rationale=\"1992-01/02 lack neworder_chg and/or two ip_growth lags; task fixes training start at 1992-03\"),\n    dict(step=2, rule=\"s + 1 <= last origin 2026-08-01 (s <= 2026-07-01)\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"information rule: targets dated after the origin may not be used in training (2026-08 row's target 2026-09 is the evaluation target; 2026-09 has no target)\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n2 == last_ntrain\n\nres = {\n    \"task_id\": \"F3-conf-03\",\n    \"n_origins\": int(len(origins)),\n    \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n    \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n    \"lambda\": LAMBDA,\n    \"normalisation_window\": \"expanding\",\n    \"n_features\": len(FEATS),\n    \"rmse_naive\": rmse_n, \"mae_naive\": mae_n,\n    \"rmse_mean\": rmse_m, \"mae_mean\": mae_m,\n    \"rmse_ridge\": rmse_r, \"mae_ridge\": mae_r,\n    \"rel_improvement_vs_naive\": 1 - rmse_r / rmse_n,\n    \"rel_improvement_vs_mean\": 1 - rmse_r / rmse_m,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n         \"handling\": \"these months are never ridge feature rows (training starts 1992-03); no imputation needed\"},\n        {\"issue\": \"README states 1992-01 is empty for every change series, but ip_growth, oil_chg and permit_chg have values in 1992-01\",\n         \"handling\": \"values used as given; ip_growth 1992-01 serves as lag-2 feature of the 1992-03 training row\"},\n        {\"issue\": \"extreme COVID-2020 and 2008 observations (e.g. ip_growth 2020-04 = -14.1, oil_chg 2020-03..05 about +/-55)\",\n         \"handling\": \"kept unchanged as genuine data; they enter training windows only (not the evaluation period)\"},\n        {\"issue\": \"current-vintage data with publication lags ignored\",\n         \"handling\": \"results describe revised-data predictability under stylised timing, not real-time performance\"},\n    ],\n    \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n    \"n_analysed\": int(last_ntrain),\n    \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"mae_naive\", \"rmse_mean\", \"mae_mean\", \"rmse_ridge\", \"mae_ridge\",\n                                      \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && wc -l out/forecasts.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1658,"cache_read_input_tokens":10836,"output_tokens":3081,"output_tokens_details":{"thinking_tokens":184},"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":1658},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmSgbmYL8ykQTkbhFsf","requestRef":"39c623fd-da73-41fc-9b86-11abd7ef80c5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"45213384-c9b9-49a1-9a63-fb7e4de27296","timestamp":"2026-10-30T04:10:56.512Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"45213384-c9b9-49a1-9a63-fb7e4de27296","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01YRqDFjTCXZaYvrFsZ4mRPw","type":"tool_result","content":"{\n  \"rmse_naive\": 0.8830699166753823,\n  \"mae_naive\": 0.6989594000000001,\n  \"rmse_mean\": 0.5439355781141625,\n  \"mae_mean\": 0.42929777256194207,\n  \"rmse_ridge\": 0.5607989303521244,\n  \"mae_ridge\": 0.45141289074453916,\n  \"rel_improvement_vs_naive\": 0.3649439078805412,\n  \"rel_improvement_vs_mean\": -0.031002480654837017,\n  \"n_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,month >= 1992-03-01 (lag-2 of ip_growth and neworder_chg required),417,415,2,1992-01/02 lack neworder_chg and/or two ip_growth lags; task fixes training start at 1992-03\n2,s + 1 <= last origin 2026-08-01 (s <= 2026-07-01),415,413,2,information rule: targets dated after the origin may not be used in training (2026-08 row's target 2026-09 is the evaluation target; 2026-09 has no target)\n     181 out/forecasts.csv","is_error":false}]},"uuid":"779b7342-6aea-4139-aa02-19bc8797cc0f","timestamp":"2026-10-30T04:10:56.765Z","toolUseResult":{"stdout":"{\n  \"rmse_naive\": 0.8830699166753823,\n  \"mae_naive\": 0.6989594000000001,\n  \"rmse_mean\": 0.5439355781141625,\n  \"mae_mean\": 0.42929777256194207,\n  \"rmse_ridge\": 0.5607989303521244,\n  \"mae_ridge\": 0.45141289074453916,\n  \"rel_improvement_vs_naive\": 0.3649439078805412,\n  \"rel_improvement_vs_mean\": -0.031002480654837017,\n  \"n_analysed\": 413\n}\nstep,rule,n_before,n_after,n_removed,rationale\n1,month >= 1992-03-01 (lag-2 of ip_growth and neworder_chg required),417,415,2,1992-01/02 lack neworder_chg and/or two ip_growth lags; task fixes training start at 1992-03\n2,s + 1 <= last origin 2026-08-01 (s <= 2026-07-01),415,413,2,information rule: targets dated after the origin may not be used in training (2026-08 row's target 2026-09 is the evaluation target; 2026-09 has no target)\n     181 out/forecasts.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"45213384-c9b9-49a1-9a63-fb7e4de27296","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"edd7058c1d72d5d87b5de518c910b3062e843e1764822b7d83edebb8bd12af63","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01YRqDFjTCXZaYvrFsZ4mRPw","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F3-conf-03: rolling-origin ridge vs naive / expanding-mean forecasts of US IP growth (h=1).\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/forecasts.csv, out/exclusions.csv. Deterministic (no randomness).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nDATA = \"data/ip_predictors.csv\"\nOUT = \"out\"\nLAMBDA = 50.0\nTRAIN_START = pd.Timestamp(\"1992-03-01\")\nMEAN_START = pd.Timestamp(\"1992-04-01\")\nFIRST_ORIGIN = pd.Timestamp(\"2021-09-01\")\nLAST_ORIGIN = pd.Timestamp(\"2026-08-01\")\nFEATS = [\"ip_growth\", \"ip_lag1\", \"ip_lag2\", \"oil_chg\", \"fedfunds\", \"permit_chg\", \"neworder_chg\", \"tcu\"]\n\nraw = pd.read_csv(DATA, parse_dates=[\"month\"])\nn_raw = len(raw)\nd = raw.sort_values(\"month\").set_index(\"month\")\nassert d.index.is_unique\n# lags use only past values (shift forward in time)\nd[\"ip_lag1\"] = d[\"ip_growth\"].shift(1)\nd[\"ip_lag2\"] = d[\"ip_growth\"].shift(2)\nd[\"y_next\"] = d[\"ip_growth\"].shift(-1)  # target of row s = ip_growth at s+1\n\norigins = pd.date_range(FIRST_ORIGIN, LAST_ORIGIN, freq=\"MS\")\nrows = []\nlast_ntrain = None\nfor t in origins:\n    tgt = t + pd.DateOffset(months=1)\n    actual = float(d.loc[tgt, \"ip_growth\"])\n    avail = d.loc[:t]  # information set: dated <= t\n    # baselines\n    f_naive = float(avail.loc[t, \"ip_growth\"])\n    f_mean = float(avail.loc[MEAN_START:t, \"ip_growth\"].mean())\n    # ridge training rows: TRAIN_START <= s, s+1 <= t  -> s <= t - 1 month\n    s_end = t - pd.DateOffset(months=1)\n    tr = avail.loc[TRAIN_START:s_end]\n    X = tr[FEATS].to_numpy(float)\n    # target y_{s+1}: all s+1 <= t, so values are inside the information set\n    y = avail[\"ip_growth\"].shift(-1).loc[TRAIN_START:s_end].to_numpy(float)\n    assert np.isfinite(X).all() and np.isfinite(y).all()\n    mu = X.mean(axis=0)\n    sd = X.std(axis=0, ddof=1)\n    Z = (X - mu) / sd\n    ybar = y.mean()\n    b = np.linalg.solve(Z.T @ Z + LAMBDA * np.eye(Z.shape[1]), Z.T @ (y - ybar))\n    zt = (avail.loc[t, FEATS].to_numpy(float) - mu) / sd\n    f_ridge = float(ybar + zt @ b)\n    last_ntrain = len(tr)\n    for m, f in ((\"naive\", f_naive), (\"mean\", f_mean), (\"ridge\", f_ridge)):\n        rows.append(dict(origin_date=t.strftime(\"%Y-%m-%d\"), target_date=tgt.strftime(\"%Y-%m-%d\"),\n                         horizon=1, forecast=f, actual=actual, model=m))\n\nfc = pd.DataFrame(rows)\nos.makedirs(OUT, exist_ok=True)\nfc.to_csv(os.path.join(OUT, \"forecasts.csv\"), index=False, float_format=\"%.10f\")\n\ndef metrics(m):\n    e = fc.loc[fc.model == m, \"actual\"].to_numpy() - fc.loc[fc.model == m, \"forecast\"].to_numpy()\n    return float(np.sqrt(np.mean(e ** 2))), float(np.mean(np.abs(e)))\n\nrmse_n, mae_n = metrics(\"naive\")\nrmse_m, mae_m = metrics(\"mean\")\nrmse_r, mae_r = metrics(\"ridge\")\n\n# exclusion ledger: raw rows -> training rows at the last origin\ns_end_last = LAST_ORIGIN - pd.DateOffset(months=1)\nn1 = int((d.index >= TRAIN_START).sum())\nn2 = int(((d.index >= TRAIN_START) & (d.index <= s_end_last)).sum())\nexcl = pd.DataFrame([\n    dict(step=1, rule=\"month >= 1992-03-01 (lag-2 of ip_growth and neworder_chg required)\", n_before=n_raw, n_after=n1,\n         n_removed=n_raw - n1, rationale=\"1992-01/02 lack neworder_chg and/or two ip_growth lags; task fixes training start at 1992-03\"),\n    dict(step=2, rule=\"s + 1 <= last origin 2026-08-01 (s <= 2026-07-01)\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"information rule: targets dated after the origin may not be used in training (2026-08 row's target 2026-09 is the evaluation target; 2026-09 has no target)\"),\n])\nexcl.to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\nassert n2 == last_ntrain\n\nres = {\n    \"task_id\": \"F3-conf-03\",\n    \"n_origins\": int(len(origins)),\n    \"first_origin\": origins[0].strftime(\"%Y-%m-%d\"),\n    \"last_origin\": origins[-1].strftime(\"%Y-%m-%d\"),\n    \"lambda\": LAMBDA,\n    \"normalisation_window\": \"expanding\",\n    \"n_features\": len(FEATS),\n    \"rmse_naive\": rmse_n, \"mae_naive\": mae_n,\n    \"rmse_mean\": rmse_m, \"mae_mean\": mae_m,\n    \"rmse_ridge\": rmse_r, \"mae_ridge\": mae_r,\n    \"rel_improvement_vs_naive\": 1 - rmse_r / rmse_n,\n    \"rel_improvement_vs_mean\": 1 - rmse_r / rmse_m,\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": [\n        {\"issue\": \"neworder_chg missing for 1992-01 and 1992-02\",\n         \"handling\": \"these months are never ridge feature rows (training starts 1992-03); no imputation needed\"},\n        {\"issue\": \"README states 1992-01 is empty for every change series, but ip_growth, oil_chg and permit_chg have values in 1992-01\",\n         \"handling\": \"values used as given; ip_growth 1992-01 serves as lag-2 feature of the 1992-03 training row\"},\n        {\"issue\": \"extreme COVID-2020 and 2008 observations (e.g. ip_growth 2020-04 = -14.1, oil_chg 2020-03..05 about +/-55)\",\n         \"handling\": \"kept unchanged as genuine data; they enter training windows only (not the evaluation period)\"},\n        {\"issue\": \"current-vintage data with publication lags ignored\",\n         \"handling\": \"results describe revised-data predictability under stylised timing, not real-time performance\"},\n    ],\n    \"n_input_rows\": {\"ip_predictors.csv\": int(n_raw)},\n    \"n_analysed\": int(last_ntrain),\n    \"random_seed\": None,\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\nprint(json.dumps({k: res[k] for k in [\"rmse_naive\", \"mae_naive\", \"rmse_mean\", \"mae_mean\", \"rmse_ridge\", \"mae_ridge\",\n                                      \"rel_improvement_vs_naive\", \"rel_improvement_vs_mean\", \"n_analysed\"]}, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && wc -l out/forecasts.csv","description":"Write and run analysis 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P4 verification: reproducibility, independent recomputation, and a leakage 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out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo REPRO_IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nd=pd.read_csv('data/ip_predictors.csv')\nfc=pd.read_csv('out/forecasts.csv'); r=json.load(open('out/results.json'))\n# independent ridge recompute via augmented least squares at 3 origins, integer indexing\nip=d.ip_growth.values\ndef feat(i): return np.r_[ip[i],ip[i-1],ip[i-2],d.loc[i,['oil_chg','fedfunds','permit_chg','neworder_chg','tcu']].values.astype(float)]\nmi={m:i for i,m in enumerate(d.month)}\nmx=0\nfor o in fc.origin_date.unique():\n    it=mi[o]; idx=range(2,it)  # s index from 1992-03 (idx2) to it-1\n    X=np.array([feat(i) for i in idx]); y=ip[[i+1 for i in idx]]\n    mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd\n    A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.r_[y-y.mean(),np.zeros(8)]\n    b=np.linalg.lstsq(A,bb,rcond=None)[0]\n    f=y.mean()+((feat(it)-mu)/sd)@b\n    mx=max(mx,abs(f-fc[(fc.origin_date==o)&(fc.model=='ridge')].forecast.iloc[0]))\n    fm=ip[3:it+1].mean(); assert abs(fm-fc[(fc.origin_date==o)&(fc.model=='mean')].forecast.iloc[0])<1e-8\n    assert abs(ip[it]-fc[(fc.origin_date==o)&(fc.model=='naive')].forecast.iloc[0])<1e-8\n    assert abs(ip[it+1]-fc[(fc.origin_date==o)].actual.iloc[0])<1e-8\nprint('max ridge diff indep', mx, 'last ntrain', len(idx))\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon', fc.horizon.unique())\nprint('target=origin+1', (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\nprint('rel identities', abs(r['rel_improvement_vs_naive']-(1-r['rmse_ridge']/r['rmse_naive']))<1e-12, abs(r['rel_improvement_vs_mean']-(1-r['rmse_ridge']/r['rmse_mean']))<1e-12)\nprint('mae<=rmse', all(r['mae_'+m]<=r['rmse_'+m] for m in ['naive','mean','ridge']))\ne=pd.read_csv('out/exclusions.csv'); print('excl sums', 417-e.n_removed.sum()==r['n_analysed'])\nEOF","description":"Reproducibility and independent 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":1387,"cache_read_input_tokens":15970,"output_tokens":1283,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1387},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmVKDUfx68W766io1fE","requestRef":"f3372cc4-f0b4-479f-ac72-3ff44edca01b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c3b3b20c-4f69-4099-b0da-ba6a9f725637","timestamp":"2026-10-30T04:11:17.046Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"c3b3b20c-4f69-4099-b0da-ba6a9f725637","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DUWQNBgHATJ4qeDn6MdU8R","type":"tool_result","content":"REPRO_IDENTICAL\nmax ridge diff indep 4.7272019632060847e-11 last ntrain 413\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon [1]\ntarget=origin+1 True\nrel identities True True\nmae<=rmse True\nexcl sums True","is_error":false}]},"uuid":"cfb72c74-3a22-471e-8151-aab0f1fd0ce2","timestamp":"2026-10-30T04:11:19.352Z","toolUseResult":{"stdout":"REPRO_IDENTICAL\nmax ridge diff indep 4.7272019632060847e-11 last ntrain 413\nrows 180 {'mean': 60, 'naive': 60, 'ridge': 60} horizon [1]\ntarget=origin+1 True\nrel identities True True\nmae<=rmse True\nexcl sums True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c3b3b20c-4f69-4099-b0da-ba6a9f725637","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"99465e11bb64c96fa878147e380d6c2debf1d3a512f094eadb33b42c4a6d18d2","message":{"role":"assistant","content":[{"type":"text","text":"Now P4 verification: reproducibility, independent recomputation, and a leakage test."},{"type":"tool_use","id":"toolu_01DUWQNBgHATJ4qeDn6MdU8R","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; cp out/forecasts.csv /tmp/f1.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && cmp out/forecasts.csv /tmp/f1.csv && echo REPRO_IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nd=pd.read_csv('data/ip_predictors.csv')\nfc=pd.read_csv('out/forecasts.csv'); r=json.load(open('out/results.json'))\n# independent ridge recompute via augmented least squares at 3 origins, integer indexing\nip=d.ip_growth.values\ndef feat(i): return np.r_[ip[i],ip[i-1],ip[i-2],d.loc[i,['oil_chg','fedfunds','permit_chg','neworder_chg','tcu']].values.astype(float)]\nmi={m:i for i,m in enumerate(d.month)}\nmx=0\nfor o in fc.origin_date.unique():\n    it=mi[o]; idx=range(2,it)  # s index from 1992-03 (idx2) to it-1\n    X=np.array([feat(i) for i in idx]); y=ip[[i+1 for i in idx]]\n    mu=X.mean(0); sd=X.std(0,ddof=1); Z=(X-mu)/sd\n    A=np.vstack([Z,np.sqrt(50)*np.eye(8)]); bb=np.r_[y-y.mean(),np.zeros(8)]\n    b=np.linalg.lstsq(A,bb,rcond=None)[0]\n    f=y.mean()+((feat(it)-mu)/sd)@b\n    mx=max(mx,abs(f-fc[(fc.origin_date==o)&(fc.model=='ridge')].forecast.iloc[0]))\n    fm=ip[3:it+1].mean(); assert abs(fm-fc[(fc.origin_date==o)&(fc.model=='mean')].forecast.iloc[0])<1e-8\n    assert abs(ip[it]-fc[(fc.origin_date==o)&(fc.model=='naive')].forecast.iloc[0])<1e-8\n    assert abs(ip[it+1]-fc[(fc.origin_date==o)].actual.iloc[0])<1e-8\nprint('max ridge diff indep', mx, 'last ntrain', len(idx))\nprint('rows',len(fc), fc.groupby('model').size().to_dict(), 'horizon', fc.horizon.unique())\nprint('target=origin+1', (pd.to_datetime(fc.target_date)==pd.to_datetime(fc.origin_date)+pd.DateOffset(months=1)).all())\nprint('rel identities', abs(r['rel_improvement_vs_naive']-(1-r['rmse_ridge']/r['rmse_naive']))<1e-12, abs(r['rel_improvement_vs_mean']-(1-r['rmse_ridge']/r['rmse_mean']))<1e-12)\nprint('mae<=rmse', all(r['mae_'+m]<=r['rmse_'+m] for m in ['naive','mean','ridge']))\ne=pd.read_csv('out/exclusions.csv'); print('excl sums', 417-e.n_removed.sum()==r['n_analysed'])\nEOF","description":"Reproducibility and independent consistency checks"}}]}}56{"type":"api-request","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","id":"5fd6db56-ccfa-45d6-afb5-a7431956e91d","timestamp":"2026-10-30T04:11:19.354Z","version":"2.1.314","querySource":"sdk","shapeHash":"50ad0a330267252a180741055d971d812c096ce1b9d19ff49e21a4ee80165a04","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":"f3372cc4-f0b4-479f-ac72-3ff44edca01b","keep":16,"tail":["99465e11bb64c96fa878147e380d6c2debf1d3a512f094eadb33b42c4a6d18d2","683c70d5ab2bec6920586e16216de9243252da43a43a0ed861123c575c950b4f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"249711f43a4db599d231996c283bd09c9ac319fe4279191f042f836027e8e8c9"}57{"parentUuid":"cfb72c74-3a22-471e-8151-aab0f1fd0ce2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmWFpd1eVpu5nan6V93","type":"message","role":"assistant","content":[{"type":"text","text":"Leakage test: perturb data after each origin and confirm forecasts don't change."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1397,"cache_read_input_tokens":17357,"output_tokens":587,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1397},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXmWFZFTzgNeWyUmV79y","requestRef":"5fd6db56-ccfa-45d6-afb5-a7431956e91d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ab2f737a-1155-4b42-8eef-57b74e951365","timestamp":"2026-10-30T04:11:20.581Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"ab2f737a-1155-4b42-8eef-57b74e951365","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmWFpd1eVpu5nan6V93","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_012YTVNMYTutnFj89kVd69nC","name":"Bash","input":{"command":"rm -rf /tmp/leak && mkdir -p /tmp/leak/data && cp -r out /tmp/leak/ && cd /tmp/leak && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, subprocess, json\n# for origin 2024-01: corrupt all rows > origin; forecast at that origin must be unchanged\nsrc='/Users/simon-pierreboucher/wp29/runs/RC0032/'\nd=pd.read_csv(src+'data/ip_predictors.csv'); orig=pd.read_csv(src+'out/forecasts.csv')\no='2024-01-01'\nm=d.month>o; rng=np.random.default_rng(0)\nfor c in d.columns[1:]: d.loc[m,c]=d.loc[m,c]+rng.normal(0,100,m.sum())\nd.to_csv('data/ip_predictors.csv',index=False)\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],check=True,capture_output=True)\nnew=pd.read_csv('out/forecasts.csv')\na=orig[orig.origin_date<=o].forecast.values; b=new[new.origin_date<=o].forecast.values\nprint('max forecast change at origins<=2024-01 after corrupting future:', np.abs(a-b).max())\nEOF","description":"Leakage test with corrupted future data in temp copy"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1397,"cache_read_input_tokens":17357,"output_tokens":587,"output_tokens_details":{"thinking_tokens":0},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1397},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmWFZFTzgNeWyUmV79y","requestRef":"5fd6db56-ccfa-45d6-afb5-a7431956e91d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cb1e7901-eb49-442f-84f2-c32d9d161c86","timestamp":"2026-10-30T04:11:24.805Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"cb1e7901-eb49-442f-84f2-c32d9d161c86","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012YTVNMYTutnFj89kVd69nC","type":"tool_result","content":"max forecast change at origins<=2024-01 after corrupting future: 0.0\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0032","is_error":false}]},"uuid":"4686eecd-5340-48c4-b7f5-cefb009bcba2","timestamp":"2026-10-30T04:11:25.534Z","toolUseResult":{"stdout":"max forecast change at origins<=2024-01 after corrupting future: 0.0","stderr":"\nShell cwd was reset to /Users/simon-pierreboucher/wp29/runs/RC0032","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"cb1e7901-eb49-442f-84f2-c32d9d161c86","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}60{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"f9558600afa5df10dc9b84611b1d09f0eb508c6bf25dae8cdd72a154ff8afec2","message":{"role":"assistant","content":[{"type":"text","text":"Leakage test: perturb data after each origin and confirm forecasts don't change."},{"type":"tool_use","id":"toolu_012YTVNMYTutnFj89kVd69nC","name":"Bash","input":{"command":"rm -rf /tmp/leak && mkdir -p /tmp/leak/data && cp -r out /tmp/leak/ && cd /tmp/leak && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, subprocess, json\n# for origin 2024-01: corrupt all rows > origin; forecast at that origin must be unchanged\nsrc='/Users/simon-pierreboucher/wp29/runs/RC0032/'\nd=pd.read_csv(src+'data/ip_predictors.csv'); orig=pd.read_csv(src+'out/forecasts.csv')\no='2024-01-01'\nm=d.month>o; rng=np.random.default_rng(0)\nfor c in d.columns[1:]: d.loc[m,c]=d.loc[m,c]+rng.normal(0,100,m.sum())\nd.to_csv('data/ip_predictors.csv',index=False)\nsubprocess.run(['/Users/simon-pierreboucher/wp29/venv/bin/python','out/analysis.py'],check=True,capture_output=True)\nnew=pd.read_csv('out/forecasts.csv')\na=orig[orig.origin_date<=o].forecast.values; b=new[new.origin_date<=o].forecast.values\nprint('max forecast change at origins<=2024-01 after corrupting future:', np.abs(a-b).max())\nEOF","description":"Leakage test with corrupted future data in temp copy"}}]}}61{"type":"api-request","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","id":"817a25ef-a763-4894-8c64-ca73dc01ac03","timestamp":"2026-10-30T04:11:25.536Z","version":"2.1.314","querySource":"sdk","shapeHash":"50ad0a330267252a180741055d971d812c096ce1b9d19ff49e21a4ee80165a04","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":"5fd6db56-ccfa-45d6-afb5-a7431956e91d","keep":18,"tail":["f9558600afa5df10dc9b84611b1d09f0eb508c6bf25dae8cdd72a154ff8afec2","bcd43ae8e891ab004ae8562cd4980b6a47df9cc0aa5c5de5432185b8fbdad3d0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c65cab17c3db5116dde43d6954490f3383b69ff886b77fafa14d5449fa6a7d13"}62{"parentUuid":"4686eecd-5340-48c4-b7f5-cefb009bcba2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmWiF7MFeKv6xnYtsbe","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":654,"cache_read_input_tokens":18754,"output_tokens":1164,"output_tokens_details":{"thinking_tokens":51},"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":654},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":346,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmWhyVJu5vZ2SnyP48y","requestRef":"817a25ef-a763-4894-8c64-ca73dc01ac03","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d65ed39e-2ca9-44fc-803b-0cbf4151b974","timestamp":"2026-10-30T04:11:27.069Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}63{"parentUuid":"d65ed39e-2ca9-44fc-803b-0cbf4151b974","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmWiF7MFeKv6xnYtsbe","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KLYa1Q6ACRUf7iP11JMiEN","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n1. Reproducibility: re-ran out/analysis.py; results.json and forecasts.csv byte-identical (cmp). PASS\n2. Independent recomputation (integer indexing, ridge via augmented least squares) at all 60 origins: max |Δ ridge forecast| = 4.7e-11; naive, mean, actual match to 1e-8. PASS\n3. Leakage test: in a temp copy, all rows dated > 2024-01 corrupted with N(0,100) noise; forecasts at origins ≤ 2024-01 unchanged (max change 0.0). Confirms standardisation stats and fits use only data ≤ origin. PASS\n4. forecasts.csv: 180 rows, 60 per model, horizon all 1, target_date = origin_date + 1 month. PASS\n5. Identities: rel_improvement_vs_naive = 1 − rmse_ridge/rmse_naive, same for mean (|diff|<1e-12). PASS\n6. MAE ≤ RMSE for each model; all RMSE > 0. PASS\n7. Exclusion ledger: 417 − (2+2) = 413 = n_analysed = training rows at last origin. PASS\n8. Chronology: first_origin 2021-09-01 < last_origin 2026-08-01; n_origins 60. PASS\n9. Magnitudes: ridge RMSE (0.561) between mean (0.544) and naive (0.883) baselines; plausible for monthly IP growth (sd ≈1.0 full sample incl. COVID). PASS\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"contract.md written covering question, estimands, outputs, info rules, invalidity\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"sha256 matches README/bundle\", \"417x7, no duplicate months, consecutive\", \"missing: neworder_chg 1992-01/02 only\", \"README discrepancy on 1992-01 noted\", \"outliers listed\"], \"notes\": \"data_audit.md\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive and expanding-mean baselines computed with same 60-origin RMSE/MAE rule (rmse_naive, rmse_mean)\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py implements ridge spec exactly, expanding standardisation, no randomness\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"rerun byte-identical\", \"independent recomputation max diff 4.7e-11\", \"leakage test 0.0 change\", \"count/identity/chronology checks pass (checks.md)\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write checks and 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":654,"cache_read_input_tokens":18754,"output_tokens":1164,"output_tokens_details":{"thinking_tokens":51},"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":654},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmWhyVJu5vZ2SnyP48y","requestRef":"817a25ef-a763-4894-8c64-ca73dc01ac03","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9ac46049-b309-4799-ad28-a19c69943a59","timestamp":"2026-10-30T04:11:35.668Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}64{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"9ac46049-b309-4799-ad28-a19c69943a59","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}65{"type":"atis-latch","atis":"","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}66{"parentUuid":"9ac46049-b309-4799-ad28-a19c69943a59","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KLYa1Q6ACRUf7iP11JMiEN","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 38 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:lambda: ok\n  [ok] key:normalisation_window: ok\n  [ok] key:n_features: ok\n  [ok] key:rmse_naive: ok\n  [ok] key:mae_naive: ok\n  [ok] key:rmse_mean: ok\n  [ok] key:mae_mean: ok\n  [ok] key:rmse_ridge: ok\n  [ok] key:mae_ridge: ok\n  [ok] key:rel_improvement_vs_naive: ok\n  [ok] key:rel_improvement_vs_mean: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2021-09-01 vs 2026-08-01\n  [ok] reproducibility: regenerated in 0.5s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0032/out/state/gate_receipts/20261030T041136.221986Z.json\n20261030T041136.221986Z.json","is_error":false}]},"uuid":"8a5fa643-6757-4815-b1e1-bc0ac4d2d957","timestamp":"2026-10-30T04:11:36.229Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 38 checks failed)\n  [ok] file:out/results.json: present\n  [FAIL] file:out/report.md: missing or empty\n  [ok] file:out/analysis.py: present\n  [FAIL] file:out/claims.json: missing or empty\n  [ok] file:out/forecasts.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:n_origins: ok\n  [ok] key:first_origin: ok\n  [ok] key:last_origin: ok\n  [ok] key:lambda: ok\n  [ok] key:normalisation_window: ok\n  [ok] key:n_features: ok\n  [ok] key:rmse_naive: ok\n  [ok] key:mae_naive: ok\n  [ok] key:rmse_mean: ok\n  [ok] key:mae_mean: ok\n  [ok] key:rmse_ridge: ok\n  [ok] key:mae_ridge: ok\n  [ok] key:rel_improvement_vs_naive: ok\n  [ok] key:rel_improvement_vs_mean: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:n_analysed: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] chronology:first_origin<=last_origin: 2021-09-01 vs 2026-08-01\n  [ok] reproducibility: regenerated in 0.5s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0032/out/state/gate_receipts/20261030T041136.221986Z.json\n20261030T041136.221986Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9ac46049-b309-4799-ad28-a19c69943a59","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}67{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"f3fc8ab7306e8a22dc2872b127863a9172ec52ed01182ae83db2de717f20534f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01KLYa1Q6ACRUf7iP11JMiEN","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n1. Reproducibility: re-ran out/analysis.py; results.json and forecasts.csv byte-identical (cmp). PASS\n2. Independent recomputation (integer indexing, ridge via augmented least squares) at all 60 origins: max |Δ ridge forecast| = 4.7e-11; naive, mean, actual match to 1e-8. PASS\n3. Leakage test: in a temp copy, all rows dated > 2024-01 corrupted with N(0,100) noise; forecasts at origins ≤ 2024-01 unchanged (max change 0.0). Confirms standardisation stats and fits use only data ≤ origin. PASS\n4. forecasts.csv: 180 rows, 60 per model, horizon all 1, target_date = origin_date + 1 month. PASS\n5. Identities: rel_improvement_vs_naive = 1 − rmse_ridge/rmse_naive, same for mean (|diff|<1e-12). PASS\n6. MAE ≤ RMSE for each model; all RMSE > 0. PASS\n7. Exclusion ledger: 417 − (2+2) = 413 = n_analysed = training rows at last origin. PASS\n8. Chronology: first_origin 2021-09-01 < last_origin 2026-08-01; n_origins 60. PASS\n9. Magnitudes: ridge RMSE (0.561) between mean (0.544) and naive (0.883) baselines; plausible for monthly IP growth (sd ≈1.0 full sample incl. COVID). PASS\nEOF\ncat > out/state/phases.json <<'EOF'\n{\n \"P0\": {\"status\": \"passed\", \"checks\": [\"contract.md written covering question, estimands, outputs, info rules, invalidity\"], \"notes\": \"\"},\n \"P1\": {\"status\": \"passed\", \"checks\": [\"sha256 matches README/bundle\", \"417x7, no duplicate months, consecutive\", \"missing: neworder_chg 1992-01/02 only\", \"README discrepancy on 1992-01 noted\", \"outliers listed\"], \"notes\": \"data_audit.md\"},\n \"P2\": {\"status\": \"passed\", \"checks\": [\"naive and expanding-mean baselines computed with same 60-origin RMSE/MAE rule (rmse_naive, rmse_mean)\"], \"notes\": \"\"},\n \"P3\": {\"status\": \"passed\", \"checks\": [\"analysis.py implements ridge spec exactly, expanding standardisation, no randomness\"], \"notes\": \"\"},\n \"P4\": {\"status\": \"running\", \"checks\": [\"rerun byte-identical\", \"independent recomputation max diff 4.7e-11\", \"leakage test 0.0 change\", \"count/identity/chronology checks pass (checks.md)\"], \"notes\": \"\"},\n \"P5\": {\"status\": \"pending\", \"checks\": [], \"notes\": \"\"}\n}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .; ls out/state/gate_receipts","description":"Write checks and phases, run gate"}}]}}68{"type":"api-request","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","id":"278cbded-61c2-496c-b077-fd9ad892f170","timestamp":"2026-10-30T04:11:36.231Z","version":"2.1.314","querySource":"sdk","shapeHash":"50ad0a330267252a180741055d971d812c096ce1b9d19ff49e21a4ee80165a04","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":"817a25ef-a763-4894-8c64-ca73dc01ac03","keep":20,"tail":["f3fc8ab7306e8a22dc2872b127863a9172ec52ed01182ae83db2de717f20534f","ef03b13254b86d13c6ea22158f2f17064ffabb40994da02e485d15a31b1a973e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d6330b0f50187348b430af44b01daaad9b375756d7e54e2511649fa1e59ec201"}69{"parentUuid":"8a5fa643-6757-4815-b1e1-bc0ac4d2d957","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXVvE3n7G7BhiYCcsZ","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":1988,"cache_read_input_tokens":19408,"output_tokens":3140,"output_tokens_details":{"thinking_tokens":56},"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":1988},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":865,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXmXVhLD6C79zxHiKLDJ","requestRef":"278cbded-61c2-496c-b077-fd9ad892f170","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"dde11e61-8e42-4120-a81b-7e0fbe0c99d2","timestamp":"2026-10-30T04:11:38.376Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}70{"parentUuid":"dde11e61-8e42-4120-a81b-7e0fbe0c99d2","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXVvE3n7G7BhiYCcsZ","type":"message","role":"assistant","content":[{"type":"text","text":"The P4 contract checks all pass (results keys, exclusion ledger, chronology, reproducibility). The checks that still fail are for P5 deliverables I haven't written yet. Next: the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1988,"cache_read_input_tokens":19408,"output_tokens":3140,"output_tokens_details":{"thinking_tokens":56},"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":1988},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXmXVhLD6C79zxHiKLDJ","requestRef":"278cbded-61c2-496c-b077-fd9ad892f170","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d375bafc-fb7a-40d6-be37-1fc6090bb86d","timestamp":"2026-10-30T04:11:38.379Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}71{"parentUuid":"d375bafc-fb7a-40d6-be37-1fc6090bb86d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmXVvE3n7G7BhiYCcsZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Sxc1qnTS9HnpHA7KaNZgPM","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (key `n_features`) forecast next month's US industrial production growth (100×Δln INDPRO) better than the naive last-value forecast and the expanding historical mean? Evaluation uses `n_origins` = 60 rolling origins from `first_origin` (2021-09-01) to `last_origin` (2026-08-01), horizon h = 1.\n\n## Data and exclusions\n- `data/ip_predictors.csv` (`n_input_rows`: 417 rows, 1992-01 to 2026-09; SHA-256 matches `data/README.md`). Full audit in `out/state/data_audit.md`.\n- Missing values: only `neworder_chg` for 1992-01/02; those months are never ridge feature rows. The README says every change series is empty in 1992-01, but `ip_growth`, `oil_chg` and `permit_chg` do have 1992-01 values. I used them as given. Extreme 2020 (COVID) and 2008 values were kept. All of this is listed under `data_issues`.\n- Exclusion ledger (`out/exclusions.csv`): 417 raw rows, minus 2 (before the 1992-03 training start), minus 2 (rows whose target falls after the last origin) = 413 training rows at the last origin (`n_analysed`).\n\n## Method\n- Naive forecast: `ip_growth` of month t. Expanding mean: mean of `ip_growth` from 1992-04 to t.\n- Ridge: at each origin t, the training rows are s = 1992-03 … t−1, with target `ip_growth`_{s+1}. The features are ip_growth_s, its lags 1 and 2, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s. Each feature is standardised with the mean and sample SD (ddof = 1) of that origin's training rows, recomputed at every origin (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ. Then b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), and the forecast is ȳ + z̃_t·b. No values dated after t are used. A test in which all data after a given origin were corrupted left the forecasts at that origin unchanged (`out/state/checks.md`).\n- Metrics: RMSE and MAE of actual − forecast over the 60 origins. Relative improvement = 1 − RMSE_ridge/RMSE_baseline.\n- Uncertainty: two-sided Diebold–Mariano test on squared-error loss differentials (h = 1, Harvey–Leybourne–Newbold small-sample correction, t₅₉ reference). There is no randomness (`random_seed` = null).\n- Replication: `~/wp29/venv/bin/python out/analysis.py`. It writes `out/results.json`, `out/forecasts.csv` (180 rows) and `out/exclusions.csv`.\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.883 (`rmse_naive`) | 0.699 (`mae_naive`) |\n| expanding mean | 0.544 (`rmse_mean`) | 0.429 (`mae_mean`) |\n| ridge λ=50 | 0.561 (`rmse_ridge`) | 0.451 (`mae_ridge`) |\n\n- Ridge vs naive: relative RMSE improvement +0.365 (`rel_improvement_vs_naive`). Ridge's RMSE is about 36% lower.\n- Ridge vs expanding mean: relative RMSE improvement −0.031 (`rel_improvement_vs_mean`). Ridge's RMSE is about 3% higher than the mean's, and its MAE is also higher (`mae_ridge` > `mae_mean`).\n- Answer: ridge beats the naive forecast but not the expanding historical mean over 2021-09 to 2026-08.\n\n## Uncertainty\n- Ridge vs naive: DM statistic −3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). The ridge advantage over naive is statistically significant at conventional levels.\n- Ridge vs mean: DM statistic +0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). The two forecasts are statistically indistinguishable. The small ridge disadvantage is well within sampling noise.\n- With 60 evaluation points, RMSE differences of a few percent are not reliably detectable.\n\n## Limitations and open issues\n- The data are the current (revised) vintage and publication lags are ignored. These results describe revised-data predictability under a stylised timing assumption, not real-time forecasting performance.\n- λ = 50 is fixed by the task and was not tuned. Other penalties or feature sets could give different results; none were explored, by design.\n- The training windows include the extreme 2020 observations. Under ridge's squared loss these may distort the coefficients. Following the task, no outlier treatment was applied.\n- The DM test assumes a covariance-stationary loss differential. The evaluation window is a single, partly post-COVID regime.\n- The README/data discrepancy about 1992-01 values is noted but unresolved. It affects only the earliest training row's lag-2 feature.\n- `feasibility` = \"complete\".\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has lower RMSE than the naive forecast, a relative improvement of about 36%.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data, publication lags ignored.\"},\n {\"id\": \"C2\", \"text\": \"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3% higher and its MAE is also higher.\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"mae_ridge\", \"mae_mean\", \"rel_improvement_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Fixed lambda=50, not tuned.\"},\n {\"id\": \"C3\", \"text\": \"The ridge advantage over naive is statistically significant (DM p about 0.001); the difference between ridge and the expanding mean is not (DM p about 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\", \"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Only 60 evaluation points; DM assumes stationary loss differential.\"},\n {\"id\": \"C4\", \"text\": \"All standardisation statistics were computed only from each origin's training window (expanding), with 413 training rows at the last origin.\", \"results_keys\": [\"normalisation_window\", \"n_analysed\"], \"status\": \"supported\", \"caveat\": \"Verified by future-data corruption test in out/state/checks.md.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']['status']='passed'; p['P4']['checks'].append('gate run 20261030T041136.221986Z: all P4 contract checks ok (results keys, exclusions, chronology, reproducibility); remaining failures were P5 deliverables not yet written')\np['P5']['status']='passed'; p['P5']['checks']=['report.md with 6 sections citing results keys','claims.json 4 claims referencing existing keys','final gate run (receipt recorded below)']\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed: baselines and ridge computed; DM test added.\n- 2026-10-30 P4 passed: repro identical, indep recompute, leakage test; gate receipt 20261030T041136.221986Z.\n- 2026-10-30 P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Write report, claims, update ledger, rerun gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1988,"cache_read_input_tokens":19408,"output_tokens":3140,"output_tokens_details":{"thinking_tokens":56},"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":1988},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXmXVhLD6C79zxHiKLDJ","requestRef":"278cbded-61c2-496c-b077-fd9ad892f170","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b3c98b3d-911e-4627-9f00-d639a6d85047","timestamp":"2026-10-30T04:12:01.931Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}72{"parentUuid":"b3c98b3d-911e-4627-9f00-d639a6d85047","isSidechain":false,"promptId":"fe8cdab8-3930-4a86-bf04-f2877f08232d","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Sxc1qnTS9HnpHA7KaNZgPM","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 38 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0032/out/state/gate_receipts/20261030T041202.510719Z.json","is_error":false}]},"uuid":"d81ef049-bd6e-44a4-a108-8e67efbc82b4","timestamp":"2026-10-30T04:12:02.519Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 38 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0032/out/state/gate_receipts/20261030T041202.510719Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b3c98b3d-911e-4627-9f00-d639a6d85047","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0032","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","version":"2.1.314","gitBranch":"HEAD"}73{"type":"api-request-blob","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","hash":"b7edc3b14d664389d1f7f5e09a37fe9ed03e126f21699bfcff7d92bf82e4759e","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"The P4 contract checks all pass (results keys, exclusion ledger, chronology, reproducibility). The checks that still fail are for P5 deliverables I haven't written yet. Next: the report and claims."},{"type":"tool_use","id":"toolu_01Sxc1qnTS9HnpHA7KaNZgPM","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F3-conf-03 — Rolling-origin ridge forecasts of US industrial production growth\n\n## Question\nDoes a fixed-penalty ridge regression (λ = 50, key `lambda`) on 8 standardised monthly predictors (key `n_features`) forecast next month's US industrial production growth (100×Δln INDPRO) better than the naive last-value forecast and the expanding historical mean? Evaluation uses `n_origins` = 60 rolling origins from `first_origin` (2021-09-01) to `last_origin` (2026-08-01), horizon h = 1.\n\n## Data and exclusions\n- `data/ip_predictors.csv` (`n_input_rows`: 417 rows, 1992-01 to 2026-09; SHA-256 matches `data/README.md`). Full audit in `out/state/data_audit.md`.\n- Missing values: only `neworder_chg` for 1992-01/02; those months are never ridge feature rows. The README says every change series is empty in 1992-01, but `ip_growth`, `oil_chg` and `permit_chg` do have 1992-01 values. I used them as given. Extreme 2020 (COVID) and 2008 values were kept. All of this is listed under `data_issues`.\n- Exclusion ledger (`out/exclusions.csv`): 417 raw rows, minus 2 (before the 1992-03 training start), minus 2 (rows whose target falls after the last origin) = 413 training rows at the last origin (`n_analysed`).\n\n## Method\n- Naive forecast: `ip_growth` of month t. Expanding mean: mean of `ip_growth` from 1992-04 to t.\n- Ridge: at each origin t, the training rows are s = 1992-03 … t−1, with target `ip_growth`_{s+1}. The features are ip_growth_s, its lags 1 and 2, oil_chg_s, fedfunds_s, permit_chg_s, neworder_chg_s and tcu_s. Each feature is standardised with the mean and sample SD (ddof = 1) of that origin's training rows, recomputed at every origin (`normalisation_window` = \"expanding\"). The target is centred on its training mean ȳ. Then b = (Z′Z + 50 I)⁻¹ Z′(y − ȳ), and the forecast is ȳ + z̃_t·b. No values dated after t are used. A test in which all data after a given origin were corrupted left the forecasts at that origin unchanged (`out/state/checks.md`).\n- Metrics: RMSE and MAE of actual − forecast over the 60 origins. Relative improvement = 1 − RMSE_ridge/RMSE_baseline.\n- Uncertainty: two-sided Diebold–Mariano test on squared-error loss differentials (h = 1, Harvey–Leybourne–Newbold small-sample correction, t₅₉ reference). There is no randomness (`random_seed` = null).\n- Replication: `~/wp29/venv/bin/python out/analysis.py`. It writes `out/results.json`, `out/forecasts.csv` (180 rows) and `out/exclusions.csv`.\n\n## Results\n| model | RMSE (%/month) | MAE (%/month) |\n|---|---|---|\n| naive | 0.883 (`rmse_naive`) | 0.699 (`mae_naive`) |\n| expanding mean | 0.544 (`rmse_mean`) | 0.429 (`mae_mean`) |\n| ridge λ=50 | 0.561 (`rmse_ridge`) | 0.451 (`mae_ridge`) |\n\n- Ridge vs naive: relative RMSE improvement +0.365 (`rel_improvement_vs_naive`). Ridge's RMSE is about 36% lower.\n- Ridge vs expanding mean: relative RMSE improvement −0.031 (`rel_improvement_vs_mean`). Ridge's RMSE is about 3% higher than the mean's, and its MAE is also higher (`mae_ridge` > `mae_mean`).\n- Answer: ridge beats the naive forecast but not the expanding historical mean over 2021-09 to 2026-08.\n\n## Uncertainty\n- Ridge vs naive: DM statistic −3.36 (`dm_stat_ridge_vs_naive`), p = 0.0014 (`dm_pvalue_ridge_vs_naive`). The ridge advantage over naive is statistically significant at conventional levels.\n- Ridge vs mean: DM statistic +0.54 (`dm_stat_ridge_vs_mean`), p = 0.59 (`dm_pvalue_ridge_vs_mean`). The two forecasts are statistically indistinguishable. The small ridge disadvantage is well within sampling noise.\n- With 60 evaluation points, RMSE differences of a few percent are not reliably detectable.\n\n## Limitations and open issues\n- The data are the current (revised) vintage and publication lags are ignored. These results describe revised-data predictability under a stylised timing assumption, not real-time forecasting performance.\n- λ = 50 is fixed by the task and was not tuned. Other penalties or feature sets could give different results; none were explored, by design.\n- The training windows include the extreme 2020 observations. Under ridge's squared loss these may distort the coefficients. Following the task, no outlier treatment was applied.\n- The DM test assumes a covariance-stationary loss differential. The evaluation window is a single, partly post-COVID regime.\n- The README/data discrepancy about 1992-01 values is noted but unresolved. It affects only the earliest training row's lag-2 feature.\n- `feasibility` = \"complete\".\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\": \"C1\", \"text\": \"Over 60 origins (2021-09 to 2026-08, h=1), the ridge forecast has lower RMSE than the naive forecast, a relative improvement of about 36%.\", \"results_keys\": [\"rmse_ridge\", \"rmse_naive\", \"rel_improvement_vs_naive\", \"n_origins\"], \"status\": \"supported\", \"caveat\": \"Current-vintage data, publication lags ignored.\"},\n {\"id\": \"C2\", \"text\": \"The ridge forecast does not beat the expanding historical mean: its RMSE is about 3% higher and its MAE is also higher.\", \"results_keys\": [\"rmse_ridge\", \"rmse_mean\", \"mae_ridge\", \"mae_mean\", \"rel_improvement_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Fixed lambda=50, not tuned.\"},\n {\"id\": \"C3\", \"text\": \"The ridge advantage over naive is statistically significant (DM p about 0.001); the difference between ridge and the expanding mean is not (DM p about 0.59).\", \"results_keys\": [\"dm_stat_ridge_vs_naive\", \"dm_pvalue_ridge_vs_naive\", \"dm_stat_ridge_vs_mean\", \"dm_pvalue_ridge_vs_mean\"], \"status\": \"supported\", \"caveat\": \"Only 60 evaluation points; DM assumes stationary loss differential.\"},\n {\"id\": \"C4\", \"text\": \"All standardisation statistics were computed only from each origin's training window (expanding), with 413 training rows at the last origin.\", \"results_keys\": [\"normalisation_window\", \"n_analysed\"], \"status\": \"supported\", \"caveat\": \"Verified by future-data corruption test in out/state/checks.md.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']['status']='passed'; p['P4']['checks'].append('gate run 20261030T041136.221986Z: all P4 contract checks ok (results keys, exclusions, chronology, reproducibility); remaining failures were P5 deliverables not yet written')\np['P5']['status']='passed'; p['P5']['checks']=['report.md with 6 sections citing results keys','claims.json 4 claims referencing existing keys','final gate run (receipt recorded below)']\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed: baselines and ridge computed; DM test added.\n- 2026-10-30 P4 passed: repro identical, indep recompute, leakage test; gate receipt 20261030T041136.221986Z.\n- 2026-10-30 P5: report and claims written; final gate run.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Write report, claims, update ledger, rerun gate"}}]}}74{"type":"api-request","sessionId":"727ad226-08bf-4945-bbc6-b74780271679","id":"d449c06b-9660-494a-a1d1-f0ae451598ab","timestamp":"2026-10-30T04:12:02.522Z","version":"2.1.314","querySource":"sdk","shapeHash":"50ad0a330267252a180741055d971d812c096ce1b9d19ff49e21a4ee80165a04","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":"278cbded-61c2-496c-b077-fd9ad892f170","keep":22,"tail":["b7edc3b14d664389d1f7f5e09a37fe9ed03e126f21699bfcff7d92bf82e4759e","d1f1520faf71b3a09f64c349bf87b01d6ce298750496be7b09b7a3631a15db26"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"ef59631573c4ed69d4850f53da90cf16e49faf0df0065370ea1dd9c27ea10fb8"}75{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"d81ef049-bd6e-44a4-a108-8e67efbc82b4","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}76{"type":"atis-latch","atis":"","sessionId":"727ad226-08bf-4945-bbc6-b74780271679"}77{"parentUuid":"d81ef049-bd6e-44a4-a108-8e67efbc82b4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXmZSPCfbiFwARyyPfXp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H4XwXKXe6ukdUPpJpsHN1k","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['checks'][-1]='final gate run 20261030T041202.510719Z: PASSED 0/38 failed'\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 Final gate PASSED (receipt 20261030T041202.510719Z). 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All phases P0–P5 are marked passed, and the final gate run passed with 0 of 38 checks failing.\n\nResults over the 60 origins (2021-09 to 2026-08, forecasting one month ahead, errors in percent per month):\n\n| Forecast | RMSE | MAE |\n|---|---|---|\n| Naive (last value) | 0.883 | 0.699 |\n| Expanding mean | 0.544 | 0.429 |\n| Ridge (λ = 50) | 0.561 | 0.451 |\n\n- **Ridge vs naive:** RMSE is 36.5% lower. A Diebold–Mariano test, which I added to measure uncertainty, puts this at p ≈ 0.001, so the gain is statistically significant.\n- **Ridge vs expanding mean:** RMSE is 3.1% higher, and MAE is also higher. 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