spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:05:12.842Z","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:05:12.848Z","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}4{"parentUuid":null,"isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":"Begin"},"uuid":"52eae7e2-cf97-4252-b0a1-9d7763014971","timestamp":"2026-10-30T05:05:12.852Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","shapeHash":"18213ec5b74313d65fd7f93ca4b3fc6bada57b2e8fe551d8bcc086f2da8416dd","timestamp":"2026-10-30T05:05:12.866Z","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/RC0226\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/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}12{"parentUuid":"1a6299c6-b07f-45e7-8371-184a69c12da3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqcd7AheWyReywq7JdM","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":391,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqccqJRryzvfxnMSG8G","requestRef":"a35d4947-153b-40e1-8ee5-731a691294ae","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"71e40dae-029c-4281-8799-484089a1c24d","timestamp":"2026-10-30T05:05:14.363Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"71e40dae-029c-4281-8799-484089a1c24d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqcd7AheWyReywq7JdM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011SH74g9RthMB1AbybiXc83","name":"Bash","input":{"command":"cat TASK.md; 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The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\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 | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\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` 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- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib 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 23:39 .\ndrwxr-xr-x  131 simon-pierreboucher  staff  4192 oct.  30 01:05 ..\n-rw-r--r--    1 simon-pierreboucher  staff   600 oct.  29 23:39 bundle.json\ndrwxr-xr-x    5 simon-pierreboucher  staff   160 oct.  29 23:37 data\n-rw-r--r--    1 simon-pierreboucher  staff  1736 oct.  29 23:39 gate_spec.json\n-rw-r--r--    1 simon-pierreboucher  staff  4631 oct.  29 23:39 TASK.md\ndata:\ntotal 56\ndrwxr-xr-x  5 simon-pierreboucher  staff    160 oct.  29 23:37 .\ndrwxr-xr-x  6 simon-pierreboucher  staff    192 oct.  29 23:39 ..\n-rw-r--r--  1 simon-pierreboucher  staff   6548 oct.  29 23:37 GDPC1.csv\n-rw-r--r--  1 simon-pierreboucher  staff   1573 oct.  29 23:37 README.md\n-rw-r--r--  1 simon-pierreboucher  staff  14211 oct.  29 23:37 UNRATE.csv","is_error":true,"tool_use_id":"toolu_011SH74g9RthMB1AbybiXc83"}]},"uuid":"2ac91e52-dd9b-4da4-b4e4-a6df7d0d3245","timestamp":"2026-10-30T05:05:15.022Z","toolUseResult":"Error: Exit code 1\n# F2-conf-04 — Okun's law in U.S. quarterly data with autocorrelation-robust uncertainty\n\n## Scientific question\nEstimate the Okun's-law relation between changes in the U.S. unemployment rate and real GDP growth, with uncertainty that is valid under serially correlated disturbances. The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\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 | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\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` 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- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). 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They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n  \"task_id\": \"F2-conf-04\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n    \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n    \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n    \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n    \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n    \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n    \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n    \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n    \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"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    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n  \"positive_keys\": [\"se_iid\", \"se_hac\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F2-conf-04\",\n  \"family\": \"F2\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:39:33.081711+00:00\",\n  \"files\": {\n    \"TASK.md\": \"b80f25d137f0d04f5223537e07525015a8d6257e5084a83ce87866da7a71a0eb\",\n    \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n    \"data/README.md\": \"cb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c\",\n    \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n    \"gate_spec.json\": \"939144536131765675ba640605191be350be3312c9098770179c2db3c9096991\"\n  }\n}==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\ndata/GDPC1.csv:0\ndata/UNRATE.csv:0","is_error":false}]},"uuid":"84ced0c3-19c3-48cd-ad3a-e30828dc6f1c","timestamp":"2026-10-30T05:05:17.240Z","toolUseResult":{"stdout":"# Data provenance — F2-conf-04\n\nBoth files are unmodified downloads from FRED (Federal Reserve Bank of St. Louis), retrieved on 2026-10-30 03:32:42 UTC with\n`curl -L \"https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>\"`. They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n  \"task_id\": \"F2-conf-04\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n    \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n    \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n    \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n    \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n    \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n    \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n    \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n    \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"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    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n  \"positive_keys\": [\"se_iid\", \"se_hac\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n{\n  \"task_id\": \"F2-conf-04\",\n  \"family\": \"F2\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:39:33.081711+00:00\",\n  \"files\": {\n    \"TASK.md\": \"b80f25d137f0d04f5223537e07525015a8d6257e5084a83ce87866da7a71a0eb\",\n    \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n    \"data/README.md\": \"cb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c\",\n    \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n    \"gate_spec.json\": \"939144536131765675ba640605191be350be3312c9098770179c2db3c9096991\"\n  }\n}==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\ndata/GDPC1.csv:0\ndata/UNRATE.csv:0","stderr":"","interrupted":false,"isImage":false,"returnCodeInterpretation":"No matches found","noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7d8b26e2-5fd3-4396-9856-91c53c83895e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"db039da04f7a516727e1ae63775ce682eefb16d096f26f08997bd6b51c7219d5","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_014aNUUaXuSjFi282GKJj6EH","name":"Bash","input":{"command":"cat data/README.md gate_spec.json bundle.json; 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Δu_t = u_t − u_{t−1}; g_t = 100·ln(GDPC1_t/GDPC1_{t−1}). β is a linear-projection coefficient (meaningful under covariance stationarity of (Δu, g)); HAC SE valid under weak dependence + finite fourth moments.\n**Sample.** 240 quarters 1960Q1–2019Q4; 1959Q4 levels used only for first differences. Do not extend the window.\n**Outputs.** out/results.json (task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png (scatter + fitted line).\n**Information rules.** Only data/ files, current vintage taken as given; no extension of window.\n**Invalidity conditions.** Missing months/quarters in window (incomplete quarterly averages), duplicates, non-numeric values; n ≠ 240; using annualised growth; using data outside window beyond 1959Q4 levels.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2398,"cache_read_input_tokens":6616,"output_tokens":1469,"output_tokens_details":{"thinking_tokens":41},"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":2398},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqcvo3qvEDZeRgtFg9M","requestRef":"af3e3557-82b6-4ae0-8d2f-84fc13808fef","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"491251dc-7f67-457e-9456-28884bf88c80","timestamp":"2026-10-30T05:05:25.723Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226/out/state","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"491251dc-7f67-457e-9456-28884bf88c80","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012mD7F1DHPR9h6gdCtDu6PU","type":"tool_result","content":"(Bash 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[(\"data/UNRATE.csv\",\"UNRATE\"),(\"data/GDPC1.csv\",\"GDPC1\")]:\n    d=pd.read_csv(f,dtype=str)\n    print(f,d.shape, d.columns.tolist())\n    v=pd.to_numeric(d[c],errors='coerce'); dt=pd.to_datetime(d.observation_date,errors='coerce')\n    print(\" nonnum\",v.isna().sum(),\" baddate\",dt.isna().sum(),\" dupdates\",dt.duplicated().sum(),\" sorted\",dt.is_monotonic_increasing)\n    print(\" range\",dt.min(),dt.max(), \" min/max\",v.min(),v.max())\n    freq='MS' if c=='UNRATE' else 'QS'\n    full=pd.date_range(dt.min(),dt.max(),freq=freq); print(\" gaps\",len(full.difference(dt)))\n    if c=='GDPC1': print(\" non-quarter-start\", (~dt.dt.month.isin([1,4,7,10])|(dt.dt.day!=1)).sum())\n    print(\" nonpositive\",(v<=0).sum())\nEOF","description":"Audit data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2398,"cache_read_input_tokens":6616,"output_tokens":1469,"output_tokens_details":{"thinking_tokens":41},"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":2398},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqcvo3qvEDZeRgtFg9M","requestRef":"af3e3557-82b6-4ae0-8d2f-84fc13808fef","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2406994d-da7e-4714-8ac4-f57b795509f6","timestamp":"2026-10-30T05:05:29.260Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226/out/state","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}27{"parentUuid":"2406994d-da7e-4714-8ac4-f57b795509f6","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"<stdin>\", line 3, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","is_error":true,"tool_use_id":"toolu_01Qm6yRNWGmTeor4VkHrhzvF"}]},"uuid":"bd5d1bc4-1202-4609-bbdb-110ff03284fd","timestamp":"2026-10-30T05:05:29.528Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"<stdin>\", line 3, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","sandboxed":false,"sourceToolAssistantUUID":"2406994d-da7e-4714-8ac4-f57b795509f6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226/out/state","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}28{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"13af151877bf951d3192cf8ae0af447bdd8156d43a0be74b030ed8976ee53bf9","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012mD7F1DHPR9h6gdCtDu6PU","name":"Bash","input":{"command":"mkdir -p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in U.S. quarterly data: slope β of the linear projection Δu_t = α + β g_t + e_t, with uncertainty robust to serial correlation.\n**Estimands.** β (pp change in quarterly-average unemployment rate per 1% q/q real GDP growth, non-annualised), α, R². u_t = mean of 3 monthly UNRATE values in quarter t; Δu_t = u_t − u_{t−1}; g_t = 100·ln(GDPC1_t/GDPC1_{t−1}). β is a linear-projection coefficient (meaningful under covariance stationarity of (Δu, g)); HAC SE valid under weak dependence + finite fourth moments.\n**Sample.** 240 quarters 1960Q1–2019Q4; 1959Q4 levels used only for first differences. Do not extend the window.\n**Outputs.** out/results.json (task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png (scatter + fitted line).\n**Information rules.** Only data/ files, current vintage taken as given; no extension of window.\n**Invalidity conditions.** Missing months/quarters in window (incomplete quarterly averages), duplicates, non-numeric values; n ≠ 240; using annualised growth; using data outside window beyond 1959Q4 levels.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}},{"type":"tool_use","id":"toolu_01Qm6yRNWGmTeor4VkHrhzvF","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\nfor f,c in [(\"data/UNRATE.csv\",\"UNRATE\"),(\"data/GDPC1.csv\",\"GDPC1\")]:\n    d=pd.read_csv(f,dtype=str)\n    print(f,d.shape, d.columns.tolist())\n    v=pd.to_numeric(d[c],errors='coerce'); dt=pd.to_datetime(d.observation_date,errors='coerce')\n    print(\" nonnum\",v.isna().sum(),\" baddate\",dt.isna().sum(),\" dupdates\",dt.duplicated().sum(),\" sorted\",dt.is_monotonic_increasing)\n    print(\" range\",dt.min(),dt.max(), \" min/max\",v.min(),v.max())\n    freq='MS' if c=='UNRATE' else 'QS'\n    full=pd.date_range(dt.min(),dt.max(),freq=freq); print(\" gaps\",len(full.difference(dt)))\n    if c=='GDPC1': print(\" non-quarter-start\", (~dt.dt.month.isin([1,4,7,10])|(dt.dt.day!=1)).sum())\n    print(\" nonpositive\",(v<=0).sum())\nEOF","description":"Audit data files"}}]}}29{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"ffe80e6be045f2576dd684767d784b81be544f23554071c5b58875201d3e67e2","message":{"role":"user","content":[{"tool_use_id":"toolu_012mD7F1DHPR9h6gdCtDu6PU","type":"tool_result","content":"(Bash completed with no output)","is_error":false},{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"<stdin>\", line 3, in <module>\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n    return _read(filepath_or_buffer, kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n    parser = TextFileReader(filepath_or_buffer, **kwds)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n    self._engine = self._make_engine(f, self.engine)\n                   ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n    self.handles = get_handle(\n                   ~~~~~~~~~~^\n        f,\n        ^^\n    ...<6 lines>...\n        storage_options=self.options.get(\"storage_options\", None),\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n    handle = open(\n        handle,\n    ...<3 lines>...\n        newline=\"\",\n    )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","is_error":true,"tool_use_id":"toolu_01Qm6yRNWGmTeor4VkHrhzvF"}]}}30{"type":"api-request","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","id":"5cf6bdfb-e58f-4734-81f1-0b57f4996162","timestamp":"2026-10-30T05:05:29.532Z","version":"2.1.314","querySource":"sdk","shapeHash":"18213ec5b74313d65fd7f93ca4b3fc6bada57b2e8fe551d8bcc086f2da8416dd","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":"af3e3557-82b6-4ae0-8d2f-84fc13808fef","keep":6,"tail":["13af151877bf951d3192cf8ae0af447bdd8156d43a0be74b030ed8976ee53bf9","ffe80e6be045f2576dd684767d784b81be544f23554071c5b58875201d3e67e2"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"3d43c68bce908bb1a49b9e352afb71c59e7e730eaf9929a1d018f77a19a1b48b"}31{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"bd5d1bc4-1202-4609-bbdb-110ff03284fd","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}32{"type":"atis-latch","atis":"","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}33{"parentUuid":"bd5d1bc4-1202-4609-bbdb-110ff03284fd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqdqe21CCaoffEr1Gy7","type":"message","role":"assistant","content":[{"type":"text","text":"The parallel call ran before the cd; re-running from the root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2186,"cache_read_input_tokens":9014,"output_tokens":555,"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":2186},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqdqMAAcAh5W7HtTg98","requestRef":"5cf6bdfb-e58f-4734-81f1-0b57f4996162","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9c9b833c-7266-459c-a7bd-8d70abcdfb3d","timestamp":"2026-10-30T05:05:30.729Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226/out/state","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"9c9b833c-7266-459c-a7bd-8d70abcdfb3d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqdqe21CCaoffEr1Gy7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014gr6vccTXWdxBstQW1578W","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0226 && ls out/state && ~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd\nfor f,c in [(\"data/UNRATE.csv\",\"UNRATE\"),(\"data/GDPC1.csv\",\"GDPC1\")]:\n    d=pd.read_csv(f,dtype=str)\n    print(f,d.shape, d.columns.tolist())\n    v=pd.to_numeric(d[c],errors='coerce'); dt=pd.to_datetime(d.observation_date,errors='coerce')\n    print(\" nonnum\",v.isna().sum(),\" baddate\",dt.isna().sum(),\" dupdates\",dt.duplicated().sum(),\" sorted\",dt.is_monotonic_increasing)\n    print(\" range\",dt.min(),dt.max(), \" min/max\",v.min(),v.max())\n    freq='MS' if c=='UNRATE' else 'QS'\n    full=pd.date_range(dt.min(),dt.max(),freq=freq); print(\" gaps\",len(full.difference(dt)))\n    if c=='GDPC1': print(\" non-quarter-start\", (~dt.dt.month.isin([1,4,7,10])|(dt.dt.day!=1)).sum())\n    print(\" nonpositive\",(v<=0).sum())\nEOF","description":"Audit data files from workspace root"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2186,"cache_read_input_tokens":9014,"output_tokens":555,"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":2186},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqdqMAAcAh5W7HtTg98","requestRef":"5cf6bdfb-e58f-4734-81f1-0b57f4996162","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cfbdd13c-705d-43e7-b328-9220e4ef8274","timestamp":"2026-10-30T05:05:33.970Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226/out/state","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"cfbdd13c-705d-43e7-b328-9220e4ef8274","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014gr6vccTXWdxBstQW1578W","type":"tool_result","content":"contract.md\nlog.md\nphases.json\ndata/UNRATE.csv (945, 2) ['observation_date', 'UNRATE']\n nonnum 1  baddate 0  dupdates 0  sorted True\n range 1948-01-01 00:00:00 2026-09-01 00:00:00  min/max 2.5 14.8\n gaps 0\n nonpositive 0\ndata/GDPC1.csv (319, 2) ['observation_date', 'GDPC1']\n nonnum 0  baddate 0  dupdates 0  sorted True\n range 1947-01-01 00:00:00 2026-07-01 00:00:00  min/max 2172.432 24611.077\n gaps 0\n non-quarter-start 0\n nonpositive 0","is_error":false}]},"uuid":"d7cfb063-d107-417e-93c0-6ea5194c5dec","timestamp":"2026-10-30T05:05:34.245Z","toolUseResult":{"stdout":"contract.md\nlog.md\nphases.json\ndata/UNRATE.csv (945, 2) ['observation_date', 'UNRATE']\n nonnum 1  baddate 0  dupdates 0  sorted True\n range 1948-01-01 00:00:00 2026-09-01 00:00:00  min/max 2.5 14.8\n gaps 0\n nonpositive 0\ndata/GDPC1.csv (319, 2) ['observation_date', 'GDPC1']\n nonnum 0  baddate 0  dupdates 0  sorted True\n range 1947-01-01 00:00:00 2026-07-01 00:00:00  min/max 2172.432 24611.077\n gaps 0\n non-quarter-start 0\n nonpositive 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[(\"data/UNRATE.csv\",\"UNRATE\"),(\"data/GDPC1.csv\",\"GDPC1\")]:\n    d=pd.read_csv(f,dtype=str)\n    print(f,d.shape, d.columns.tolist())\n    v=pd.to_numeric(d[c],errors='coerce'); dt=pd.to_datetime(d.observation_date,errors='coerce')\n    print(\" nonnum\",v.isna().sum(),\" baddate\",dt.isna().sum(),\" dupdates\",dt.duplicated().sum(),\" sorted\",dt.is_monotonic_increasing)\n    print(\" range\",dt.min(),dt.max(), \" min/max\",v.min(),v.max())\n    freq='MS' if c=='UNRATE' else 'QS'\n    full=pd.date_range(dt.min(),dt.max(),freq=freq); print(\" gaps\",len(full.difference(dt)))\n    if c=='GDPC1': print(\" non-quarter-start\", (~dt.dt.month.isin([1,4,7,10])|(dt.dt.day!=1)).sum())\n    print(\" nonpositive\",(v<=0).sum())\nEOF","description":"Audit data files from workspace 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It lies outside 1959Q4–2019Q4, so it does not affect the analysis. analysis.py asserts that all 3 months are present for every quarter used.\n- Annual-rate scaling of GDPC1 cancels in the log ratio, so g_t is unaffected.\n\n## Information availability\nNo input violates the rules; data outside 1959Q4–2019Q4 is read but discarded (1959Q4 used only as the difference base). 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> out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | range | notes |\n|---|---|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | 945 | 2 (observation_date, UNRATE) | 1948-01-01 .. 2026-09-01 monthly | percent; min 2.5 max 14.8; no duplicate dates; sorted; no gaps in date sequence; **1 empty value: 2025-10-01** |\n| data/GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | 319 | 2 (observation_date, GDPC1) | 1947-01-01 .. 2026-07-01 quarterly (all quarter-start dates) | bn chained 2017 $, SAAR; min 2172.4 max 24611.1; no missing; no dups; no gaps; all positive |\nChecksums match data/README.md and bundle.json.\n\n## Anomalies and handling\n- UNRATE 2025-10 missing (empty field; presumably the 2025 federal shutdown gap). It lies outside 1959Q4–2019Q4, so it does not affect the analysis. analysis.py asserts that all 3 months are present for every quarter used.\n- Annual-rate scaling of GDPC1 cancels in the log ratio, so g_t is unaffected.\n\n## Information availability\nNo input violates the rules; data outside 1959Q4–2019Q4 is read but discarded (1959Q4 used only as the difference base). Current-vintage data taken as given.\nEOF\ncat > out/exclusions.csv <<'EOF'\nstep,rule,n_before,n_after,n_removed,rationale\n1,keep quarters 1959Q4-2019Q4 (GDPC1 quarters with complete 3-month UNRATE average),319,241,78,window fixed by TASK.md; 1959Q4 kept only as difference base\n2,drop 1959Q4 (no lagged level for first difference),241,240,1,first differences require t-1 levels; regression sample 1960Q1-2019Q4\nEOF\necho \"- 2026-10-30 P1 passed: audit done; one missing UNRATE value (2025-10) outside window.\" >> out/state/log.md","description":"Write data audit and exclusion ledger"}}]}}46{"type":"api-request","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","id":"e829b200-e7d5-4b2f-bc40-ae85775637e1","timestamp":"2026-10-30T05:05:53.912Z","version":"2.1.314","querySource":"sdk","shapeHash":"18213ec5b74313d65fd7f93ca4b3fc6bada57b2e8fe551d8bcc086f2da8416dd","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":"687531bc-05d3-4856-bc71-bfe5f97a29c8","keep":12,"tail":["c60f6264aa13cad95bd4725b6da7a2ef4dd79363265670ed2e7633e68e2ed710","e6975062706379c9b7155722f51cbc6df78184e7dc76c5bdccb98a56637ce9d7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"da323d7944328ab52bbdda82247b095d2339fbdc9ba217c9852a16bc08c3562c"}47{"parentUuid":"2cb468b4-86f5-41dc-82bf-70958accaf62","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfdreJ4v5TPPJ9WAzz","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":1726,"cache_read_input_tokens":12150,"output_tokens":2904,"output_tokens_details":{"thinking_tokens":70},"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":1726},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":392,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqfdamiZZpn9wM1mjpT","requestRef":"e829b200-e7d5-4b2f-bc40-ae85775637e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5367449f-fc7e-4689-b42e-98306354d115","timestamp":"2026-10-30T05:05:55.549Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"5367449f-fc7e-4689-b42e-98306354d115","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfdreJ4v5TPPJ9WAzz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DqPML6fdq3EUMVoAeZrPW6","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, U.S. 1960Q1-2019Q4, OLS with Newey-West HAC SE.  Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nFIRST, LAST, BASE = pd.Period(\"1960Q1\"), pd.Period(\"2019Q4\"), pd.Period(\"1959Q4\")\nissues = []\n\nun = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\ngd = pd.read_csv(\"data/GDPC1.csv\", dtype={\"observation_date\": str})\nn_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\ngd[\"GDPC1\"] = pd.to_numeric(gd[\"GDPC1\"], errors=\"coerce\")\nfor d in (un, gd):\n    d[\"date\"] = pd.to_datetime(d[\"observation_date\"])\n    assert not d[\"date\"].duplicated().any()\nmiss = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif miss:\n    issues.append({\"issue\": f\"UNRATE missing value(s) at {', '.join(miss)}\",\n                   \"handling\": \"outside the 1959Q4-2019Q4 window; not used. Quarterly averages require all 3 months (asserted for every quarter used).\"})\n\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngd[\"q\"] = gd[\"date\"].dt.to_period(\"Q\")\ngq = gd.set_index(\"q\")[\"GDPC1\"]\n\nn0 = len(gq)\npanel = pd.DataFrame({\"gdp\": gq}).join(uq, how=\"left\")\npanel = panel.loc[(panel.index >= BASE) & (panel.index <= LAST)]\nassert (panel[\"count\"] == 3).all() and panel[\"gdp\"].notna().all() and (panel[\"gdp\"] > 0).all()\nn1 = len(panel)\npanel[\"du\"] = panel[\"mean\"].diff()\npanel[\"g\"] = 100 * np.log(panel[\"gdp\"] / panel[\"gdp\"].shift(1))\ndf = panel.loc[panel.index >= FIRST].copy()\nn2 = len(df)\nassert df[[\"du\", \"g\"]].notna().all().all()\nassert n2 == 240 and df.index[0] == FIRST and df.index[-1] == LAST\n\npd.DataFrame([\n    dict(step=1, rule=\"keep quarters 1959Q4-2019Q4 (GDPC1 quarters with complete 3-month UNRATE average)\",\n         n_before=n0, n_after=n1, n_removed=n0 - n1, rationale=\"window fixed by TASK.md; 1959Q4 kept only as difference base\"),\n    dict(step=2, rule=\"drop 1959Q4 (no lagged level for first difference)\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"first differences need t-1 levels; regression sample 1960Q1-2019Q4\"),\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\ny, X = df[\"du\"].values, sm.add_constant(df[\"g\"].values)\nT = len(y)\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) plug-in-free rule floor(4*(T/100)^(2/9)) -> 4 for T=240\nL = int(np.floor(4 * (T / 100) ** (2 / 9)))\ndef hac(lags):\n    # classic Newey-West: Bartlett weights 1 - j/(L+1), no small-sample df correction\n    return sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lags, \"use_correction\": False})\nh = hac(L)\n# manual cross-check of the HAC SE\ne = ols.resid; Xe = X * e[:, None]; S = Xe.T @ Xe\nfor j in range(1, L + 1):\n    G = Xe[j:].T @ Xe[:-j]; S += (1 - j / (L + 1)) * (G + G.T)\nXXi = np.linalg.inv(X.T @ X); se_manual = np.sqrt((XXi @ S @ XXi)[1, 1])\nbeta, se_h = float(ols.params[1]), float(h.bse[1])\nassert abs(se_manual - se_h) < 1e-10\nz = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_h, beta + z * se_h\np_hac = float(2 * stats.norm.sf(abs(beta / se_h)))\nresid_ac = [float(pd.Series(e).autocorr(k)) for k in (1, 2, 3, 4)]\n\nres = {\n    \"task_id\": \"F2-conf-04\",\n    \"beta_hat\": beta, \"alpha_hat\": float(ols.params[0]), \"r_squared\": float(ols.rsquared),\n    \"se_iid\": float(ols.bse[1]), \"se_hac\": se_h, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n    \"mean_growth\": float(df[\"g\"].mean()),\n    \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n    \"n_analysed\": int(T), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary\n    \"t_hac\": float(beta / se_h), \"p_value_hac\": p_hac,\n    \"baseline_ci_iid_low\": float(beta - z * ols.bse[1]), \"baseline_ci_iid_high\": float(beta + z * ols.bse[1]),\n    \"resid_autocorr_lag1_4\": resid_ac,\n    \"sensitivity_se_hac_by_lag\": {str(k): float(hac(k).bse[1]) for k in (0, 2, 4, 5, 8, 12)},\n    \"mean_du\": float(df[\"du\"].mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\ngg = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 50)\nax.plot(gg, res[\"alpha_hat\"] + beta * gg, color=\"C3\",\n        label=f\"OLS: Δu = {res['alpha_hat']:.3f} + ({beta:.3f}) g\")\nax.axhline(0, lw=0.5, color=\"k\"); ax.axvline(0, lw=0.5, color=\"k\")\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: res[k] for k in (\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.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":1726,"cache_read_input_tokens":12150,"output_tokens":2904,"output_tokens_details":{"thinking_tokens":70},"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":1726},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqfdamiZZpn9wM1mjpT","requestRef":"e829b200-e7d5-4b2f-bc40-ae85775637e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f8b75c18-e612-4c74-84d8-0e5049480520","timestamp":"2026-10-30T05:06:15.901Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}49{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f8b75c18-e612-4c74-84d8-0e5049480520","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}50{"type":"atis-latch","atis":"","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}51{"parentUuid":"f8b75c18-e612-4c74-84d8-0e5049480520","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01DqPML6fdq3EUMVoAeZrPW6","type":"tool_result","content":"{\n \"beta_hat\": -0.25576772774872947,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true\n}\n{\n  \"task_id\": \"F2-conf-04\",\n  \"beta_hat\": -0.25576772774872947,\n  \"alpha_hat\": 0.18438835418210964,\n  \"r_squared\": 0.40825082845620597,\n  \"se_iid\": 0.019960092914910015,\n  \"se_hac\": 0.03583463330296578,\n  \"hac_lags\": 4,\n  \"beta_ci_low\": -0.326002318421742,\n  \"beta_ci_high\": -0.18553313707571695,\n  \"okun_negative_5pct\": true,\n  \"mean_growth\": 0.7535027550652363,\n  \"first_quarter\": \"1960Q1\",\n  \"last_quarter\": \"2019Q4\",\n  \"n_analysed\": 240,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing value(s) at 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; not used. Quarterly averages require all 3 months (asserted for every quarter used).\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272,\n  \"resid_autocorr_lag1_4\": [\n    0.38935129370212984,\n    0.2910132943069726,\n    0.13988210991084532,\n    0.07090953047434041\n  ],\n  \"sensitivity_se_hac_by_lag\": {\n    \"0\": 0.028836223207270188,\n    \"2\": 0.034227320236154,\n    \"4\": 0.03583463330296578,\n    \"5\": 0.03651959798083889,\n    \"8\": 0.03713550417540173,\n    \"12\": 0.03640885110228042\n  },\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n1,keep quarters 1959Q4-2019Q4 (GDPC1 quarters with complete 3-month UNRATE average),319,241,78,window fixed by TASK.md; 1959Q4 kept only as difference base\n2,drop 1959Q4 (no lagged level for first difference),241,240,1,first differences need t-1 levels; regression sample 1960Q1-2019Q4","is_error":false}]},"uuid":"456f6ab9-aba3-4b6b-8315-0db660644f37","timestamp":"2026-10-30T05:06:17.108Z","toolUseResult":{"stdout":"{\n \"beta_hat\": -0.25576772774872947,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true\n}\n{\n  \"task_id\": \"F2-conf-04\",\n  \"beta_hat\": -0.25576772774872947,\n  \"alpha_hat\": 0.18438835418210964,\n  \"r_squared\": 0.40825082845620597,\n  \"se_iid\": 0.019960092914910015,\n  \"se_hac\": 0.03583463330296578,\n  \"hac_lags\": 4,\n  \"beta_ci_low\": -0.326002318421742,\n  \"beta_ci_high\": -0.18553313707571695,\n  \"okun_negative_5pct\": true,\n  \"mean_growth\": 0.7535027550652363,\n  \"first_quarter\": \"1960Q1\",\n  \"last_quarter\": \"2019Q4\",\n  \"n_analysed\": 240,\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing value(s) at 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; not used. Quarterly averages require all 3 months (asserted for every quarter used).\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272,\n  \"resid_autocorr_lag1_4\": [\n    0.38935129370212984,\n    0.2910132943069726,\n    0.13988210991084532,\n    0.07090953047434041\n  ],\n  \"sensitivity_se_hac_by_lag\": {\n    \"0\": 0.028836223207270188,\n    \"2\": 0.034227320236154,\n    \"4\": 0.03583463330296578,\n    \"5\": 0.03651959798083889,\n    \"8\": 0.03713550417540173,\n    \"12\": 0.03640885110228042\n  },\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n1,keep quarters 1959Q4-2019Q4 (GDPC1 quarters with complete 3-month UNRATE average),319,241,78,window fixed by TASK.md; 1959Q4 kept only as difference base\n2,drop 1959Q4 (no lagged level for first difference),241,240,1,first differences need t-1 levels; regression sample 1960Q1-2019Q4","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f8b75c18-e612-4c74-84d8-0e5049480520","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"de4b984ea97319241198381f5a91b37ed939ca05c5a64395a13c4278c132f1d5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01DqPML6fdq3EUMVoAeZrPW6","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, U.S. 1960Q1-2019Q4, OLS with Newey-West HAC SE.  Run from workspace root.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nFIRST, LAST, BASE = pd.Period(\"1960Q1\"), pd.Period(\"2019Q4\"), pd.Period(\"1959Q4\")\nissues = []\n\nun = pd.read_csv(\"data/UNRATE.csv\", dtype={\"observation_date\": str})\ngd = pd.read_csv(\"data/GDPC1.csv\", dtype={\"observation_date\": str})\nn_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\ngd[\"GDPC1\"] = pd.to_numeric(gd[\"GDPC1\"], errors=\"coerce\")\nfor d in (un, gd):\n    d[\"date\"] = pd.to_datetime(d[\"observation_date\"])\n    assert not d[\"date\"].duplicated().any()\nmiss = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif miss:\n    issues.append({\"issue\": f\"UNRATE missing value(s) at {', '.join(miss)}\",\n                   \"handling\": \"outside the 1959Q4-2019Q4 window; not used. Quarterly averages require all 3 months (asserted for every quarter used).\"})\n\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngd[\"q\"] = gd[\"date\"].dt.to_period(\"Q\")\ngq = gd.set_index(\"q\")[\"GDPC1\"]\n\nn0 = len(gq)\npanel = pd.DataFrame({\"gdp\": gq}).join(uq, how=\"left\")\npanel = panel.loc[(panel.index >= BASE) & (panel.index <= LAST)]\nassert (panel[\"count\"] == 3).all() and panel[\"gdp\"].notna().all() and (panel[\"gdp\"] > 0).all()\nn1 = len(panel)\npanel[\"du\"] = panel[\"mean\"].diff()\npanel[\"g\"] = 100 * np.log(panel[\"gdp\"] / panel[\"gdp\"].shift(1))\ndf = panel.loc[panel.index >= FIRST].copy()\nn2 = len(df)\nassert df[[\"du\", \"g\"]].notna().all().all()\nassert n2 == 240 and df.index[0] == FIRST and df.index[-1] == LAST\n\npd.DataFrame([\n    dict(step=1, rule=\"keep quarters 1959Q4-2019Q4 (GDPC1 quarters with complete 3-month UNRATE average)\",\n         n_before=n0, n_after=n1, n_removed=n0 - n1, rationale=\"window fixed by TASK.md; 1959Q4 kept only as difference base\"),\n    dict(step=2, rule=\"drop 1959Q4 (no lagged level for first difference)\", n_before=n1, n_after=n2,\n         n_removed=n1 - n2, rationale=\"first differences need t-1 levels; regression sample 1960Q1-2019Q4\"),\n]).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\ny, X = df[\"du\"].values, sm.add_constant(df[\"g\"].values)\nT = len(y)\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) plug-in-free rule floor(4*(T/100)^(2/9)) -> 4 for T=240\nL = int(np.floor(4 * (T / 100) ** (2 / 9)))\ndef hac(lags):\n    # classic Newey-West: Bartlett weights 1 - j/(L+1), no small-sample df correction\n    return sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lags, \"use_correction\": False})\nh = hac(L)\n# manual cross-check of the HAC SE\ne = ols.resid; Xe = X * e[:, None]; S = Xe.T @ Xe\nfor j in range(1, L + 1):\n    G = Xe[j:].T @ Xe[:-j]; S += (1 - j / (L + 1)) * (G + G.T)\nXXi = np.linalg.inv(X.T @ X); se_manual = np.sqrt((XXi @ S @ XXi)[1, 1])\nbeta, se_h = float(ols.params[1]), float(h.bse[1])\nassert abs(se_manual - se_h) < 1e-10\nz = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_h, beta + z * se_h\np_hac = float(2 * stats.norm.sf(abs(beta / se_h)))\nresid_ac = [float(pd.Series(e).autocorr(k)) for k in (1, 2, 3, 4)]\n\nres = {\n    \"task_id\": \"F2-conf-04\",\n    \"beta_hat\": beta, \"alpha_hat\": float(ols.params[0]), \"r_squared\": float(ols.rsquared),\n    \"se_iid\": float(ols.bse[1]), \"se_hac\": se_h, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n    \"mean_growth\": float(df[\"g\"].mean()),\n    \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n    \"n_analysed\": int(T), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary\n    \"t_hac\": float(beta / se_h), \"p_value_hac\": p_hac,\n    \"baseline_ci_iid_low\": float(beta - z * ols.bse[1]), \"baseline_ci_iid_high\": float(beta + z * ols.bse[1]),\n    \"resid_autocorr_lag1_4\": resid_ac,\n    \"sensitivity_se_hac_by_lag\": {str(k): float(hac(k).bse[1]) for k in (0, 2, 4, 5, 8, 12)},\n    \"mean_du\": float(df[\"du\"].mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\ngg = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 50)\nax.plot(gg, res[\"alpha_hat\"] + beta * gg, color=\"C3\",\n        label=f\"OLS: Δu = {res['alpha_hat']:.3f} + ({beta:.3f}) g\")\nax.axhline(0, lw=0.5, color=\"k\"); ax.axvline(0, lw=0.5, color=\"k\")\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: res[k] for k in (\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\")}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json && cat out/exclusions.csv","description":"Write and run analysis script"}}]}}53{"type":"api-request","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","id":"2a5c677d-e9a6-4183-b393-91055660e467","timestamp":"2026-10-30T05:06:17.112Z","version":"2.1.314","querySource":"sdk","shapeHash":"18213ec5b74313d65fd7f93ca4b3fc6bada57b2e8fe551d8bcc086f2da8416dd","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":"e829b200-e7d5-4b2f-bc40-ae85775637e1","keep":14,"tail":["de4b984ea97319241198381f5a91b37ed939ca05c5a64395a13c4278c132f1d5","c0d43ae8352def662eb9967cb1619357b0454b3c435a4217823102a3f236de1b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b1753aa8efb8463453179c3b8101a9085fb43e6f97d89946e394ba30b584968c"}54{"parentUuid":"456f6ab9-aba3-4b6b-8315-0db660644f37","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqhM1wRL1rWzt4uqcpf","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":3890,"cache_read_input_tokens":13876,"output_tokens":516,"output_tokens_details":{"thinking_tokens":38},"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":3890},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":701,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqhLkZDNnsPTgTWMKRG","requestRef":"2a5c677d-e9a6-4183-b393-91055660e467","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"33bb6acd-233c-43d2-9259-6a48103c4863","timestamp":"2026-10-30T05:06:19.115Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"33bb6acd-233c-43d2-9259-6a48103c4863","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqhM1wRL1rWzt4uqcpf","type":"message","role":"assistant","content":[{"type":"text","text":"Results look plausible (β ≈ −0.26; HAC SE ≈ 1.8× the iid SE, consistent with residual AR(1) ≈ 0.39). Now verification."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3890,"cache_read_input_tokens":13876,"output_tokens":516,"output_tokens_details":{"thinking_tokens":38},"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":3890},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqhLkZDNnsPTgTWMKRG","requestRef":"2a5c677d-e9a6-4183-b393-91055660e467","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"717088a2-95da-4531-a4b5-3ef32c345a94","timestamp":"2026-10-30T05:06:19.115Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"717088a2-95da-4531-a4b5-3ef32c345a94","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqhM1wRL1rWzt4uqcpf","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RdFLcdzHSxYAGgxrET84XE","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json && rm out/results.json out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL && ~/wp29/venv/bin/python - <<'EOF'\nimport json;r=json.load(open('out/results.json'))\nprint(\"ci contains\", r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'])\nprint(\"se>0\", r['se_iid']>0, r['se_hac']>0)\nprint(\"ci width/2 / se\", (r['beta_ci_high']-r['beta_ci_low'])/2/r['se_hac'])\nprint(\"alpha identity mean_du = a + b*mean_g\", abs(r['mean_du']-(r['alpha_hat']+r['beta_hat']*r['mean_growth'])))\nprint(\"ann growth\", 4*r['mean_growth'])\nEOF","description":"Clean rerun and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3890,"cache_read_input_tokens":13876,"output_tokens":516,"output_tokens_details":{"thinking_tokens":38},"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":3890},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqhLkZDNnsPTgTWMKRG","requestRef":"2a5c677d-e9a6-4183-b393-91055660e467","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a002bbff-5e19-475e-a9af-bd1891ae9b4b","timestamp":"2026-10-30T05:06:22.314Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"a002bbff-5e19-475e-a9af-bd1891ae9b4b","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RdFLcdzHSxYAGgxrET84XE","type":"tool_result","content":"IDENTICAL\nci contains True\nse>0 True True\nci width/2 / se 1.9599639845400538\nalpha identity mean_du = a + b*mean_g 3.122502256758253e-17\nann growth 3.0140110202609454","is_error":false}]},"uuid":"21fbe6b0-09b8-4e94-bb25-9dee4ed1cd32","timestamp":"2026-10-30T05:06:23.495Z","toolUseResult":{"stdout":"IDENTICAL\nci contains True\nse>0 True True\nci width/2 / se 1.9599639845400538\nalpha identity mean_du = a + b*mean_g 3.122502256758253e-17\nann growth 3.0140110202609454","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a002bbff-5e19-475e-a9af-bd1891ae9b4b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"c86db817e7a8861902aa3795c805cfc43edfc72ae7ff3c94c026d1281ae20b8a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Results look plausible (β ≈ −0.26; HAC SE ≈ 1.8× the iid SE, consistent with residual AR(1) ≈ 0.39). 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json, figure, exclusions.csv) regenerates byte-identical results.json | PASS |\n| Input checksums match README/bundle | PASS |\n| n_analysed = 240, first 1960Q1, last 2019Q4 (asserted in script) | PASS |\n| Exclusion ledger: 319 − 78 − 1 = 240 = n_analysed | PASS |\n| Every quarter in window has 3 monthly UNRATE values (asserted) | PASS |\n| se_iid = 0.01996 > 0, se_hac = 0.03583 > 0 | PASS |\n| CI [−0.3260, −0.1855] contains beta_hat −0.2558; half-width / se_hac = 1.95996 (z) | PASS |\n| OLS identity mean_du = alpha + beta·mean_growth (error 3e-17) | PASS |\n| Manual Bartlett HAC computation equals statsmodels to 1e-10 (asserted) | PASS |\n| Magnitude: mean_growth 0.754 %/q ≈ 3.0 % annualised — plausible for 1960–2019; beta ≈ −0.26 per % q/q in typical Okun range | PASS |\n| HAC vs baseline: se_hac/se_iid ≈ 1.80, consistent with positive residual autocorrelation (lag-1 0.39) | PASS (expected direction) |\n| okun_negative_5pct consistent: beta<0 and |t_hac| = 7.14 > 1.96 | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and 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":603,"cache_read_input_tokens":17766,"output_tokens":686,"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":603},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqhp95nWeRnhn1tZ1QV","requestRef":"49612497-1874-40e0-94b1-65f3d3a3be9a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5f6dbe2d-d86f-4a73-a71f-44dff0ad7791","timestamp":"2026-10-30T05:06:30.001Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"5f6dbe2d-d86f-4a73-a71f-44dff0ad7791","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01SvMQwVsmr1uwjyU53vF1tj","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/figure1.png: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:beta_hat: ok\n  [ok] key:alpha_hat: ok\n  [ok] key:r_squared: ok\n  [ok] key:se_iid: ok\n  [ok] key:se_hac: ok\n  [ok] key:hac_lags: ok\n  [ok] key:beta_ci_low: ok\n  [ok] key:beta_ci_high: ok\n  [ok] key:okun_negative_5pct: ok\n  [ok] key:mean_growth: ok\n  [ok] key:first_quarter: ok\n  [ok] key:last_quarter: ok\n  [ok] key:n_analysed: 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:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n  [ok] positive:se_iid: 0.019960092914910015\n  [ok] positive:se_hac: 0.03583463330296578\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0226/out/state/gate_receipts/20261030T050631.168808Z.json\n20261030T050631.168808Z.json","is_error":false}]},"uuid":"b7275095-ced4-472c-bf8a-675386c029e6","timestamp":"2026-10-30T05:06:31.179Z","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/figure1.png: present\n  [ok] results_json: parsed\n  [ok] key:task_id: ok\n  [ok] key:beta_hat: ok\n  [ok] key:alpha_hat: ok\n  [ok] key:r_squared: ok\n  [ok] key:se_iid: ok\n  [ok] key:se_hac: ok\n  [ok] key:hac_lags: ok\n  [ok] key:beta_ci_low: ok\n  [ok] key:beta_ci_high: ok\n  [ok] key:okun_negative_5pct: ok\n  [ok] key:mean_growth: ok\n  [ok] key:first_quarter: ok\n  [ok] key:last_quarter: ok\n  [ok] key:n_analysed: 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:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n  [ok] positive:se_iid: 0.019960092914910015\n  [ok] positive:se_hac: 0.03583463330296578\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: 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figure, exclusions.csv) regenerates byte-identical results.json | PASS |\n| Input checksums match README/bundle | PASS |\n| n_analysed = 240, first 1960Q1, last 2019Q4 (asserted in script) | PASS |\n| Exclusion ledger: 319 − 78 − 1 = 240 = n_analysed | PASS |\n| Every quarter in window has 3 monthly UNRATE values (asserted) | PASS |\n| se_iid = 0.01996 > 0, se_hac = 0.03583 > 0 | PASS |\n| CI [−0.3260, −0.1855] contains beta_hat −0.2558; half-width / se_hac = 1.95996 (z) | PASS |\n| OLS identity mean_du = alpha + beta·mean_growth (error 3e-17) | PASS |\n| Manual Bartlett HAC computation equals statsmodels to 1e-10 (asserted) | PASS |\n| Magnitude: mean_growth 0.754 %/q ≈ 3.0 % annualised — plausible for 1960–2019; beta ≈ −0.26 per % q/q in typical Okun range | PASS |\n| HAC vs baseline: se_hac/se_iid ≈ 1.80, consistent with positive residual autocorrelation (lag-1 0.39) | PASS (expected direction) |\n| okun_negative_5pct consistent: beta<0 and |t_hac| = 7.14 > 1.96 | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate"}}]}}63{"type":"api-request","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","id":"a50c9971-2a30-4d84-90af-497af284dacb","timestamp":"2026-10-30T05:06:31.182Z","version":"2.1.314","querySource":"sdk","shapeHash":"18213ec5b74313d65fd7f93ca4b3fc6bada57b2e8fe551d8bcc086f2da8416dd","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":"49612497-1874-40e0-94b1-65f3d3a3be9a","keep":18,"tail":["0299e7c4d5277cabb274315e145b1910caf1eb345fa4465c4c641b9f491cff52","385f6db1432f8ad61bc3981c6fcf86a42b449c5218abeb5fd4784bae5910fc8b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"290d524a1b41dbeacd1866206275075203ded78d7e3c275ac0e015dcbece201e"}64{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b7275095-ced4-472c-bf8a-675386c029e6","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}65{"type":"atis-latch","atis":"","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8"}66{"parentUuid":"b7275095-ced4-472c-bf8a-675386c029e6","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqiPBwDT8Q7A3sQ4EKj","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":1539,"cache_read_input_tokens":18369,"output_tokens":3210,"output_tokens_details":{"thinking_tokens":31},"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":1539},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":501,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqiNupKDtD9ZMCSXqqp","requestRef":"a50c9971-2a30-4d84-90af-497af284dacb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ed880b92-b111-46fe-bbd2-067b507627d9","timestamp":"2026-10-30T05:06:32.800Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"ed880b92-b111-46fe-bbd2-067b507627d9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqiPBwDT8Q7A3sQ4EKj","type":"message","role":"assistant","content":[{"type":"text","text":"P4-gate failures are all P5 deliverables (report, claims, ledger finalization), as expected. Writing the report and claims now."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1539,"cache_read_input_tokens":18369,"output_tokens":3210,"output_tokens_details":{"thinking_tokens":31},"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":1539},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqiNupKDtD9ZMCSXqqp","requestRef":"a50c9971-2a30-4d84-90af-497af284dacb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"956d5571-7b9d-4ac5-af9d-367e54e3180b","timestamp":"2026-10-30T05:06:32.800Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}68{"parentUuid":"956d5571-7b9d-4ac5-af9d-367e54e3180b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqiPBwDT8Q7A3sQ4EKj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01V3LZCTeAQmDmHE3BNNXiaz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4 (F2-conf-04)\n\n## Question\nEstimate the slope β of the linear projection Δu_t = α + β g_t + e_t, where u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (% q/q, not annualised). Sample: 1960Q1–2019Q4. Test whether β is negative and significantly different from zero at 5%, using a Newey–West (Bartlett) HAC standard error that remains valid when the errors are serially correlated.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows; `data/GDPC1.csv`: 319 quarterly rows (`n_input_rows`). SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data issue (`data_issues`): UNRATE is empty for 2025-10. That month is outside the analysis window, so it is not used. The script checks that every quarter in the window has all three monthly values.\n- Exclusions (`out/exclusions.csv`), counted in GDPC1 quarters: 319 → 241 after keeping 1959Q4–2019Q4 (78 removed); → 240 after dropping 1959Q4, which serves only as the base for the first differences (1 removed). `n_analysed` = 240, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4.\n\n## Method\n- OLS of Δu_t on a constant and g_t.\n- Naïve baseline SE: the conventional iid/homoskedastic standard error (`se_iid`).\n- Main SE: the Newey–West HAC estimator with Bartlett weights 1 − j/(L+1), without a small-sample degrees-of-freedom correction (`se_hac`). The script checks it against a manual computation.\n- Bandwidth: L = floor(4·(T/100)^(2/9)) = 4 for T = 240 (`hac_lags`). This is the standard Newey–West (1994) rule of thumb for the Bartlett kernel. It covers the observed residual autocorrelation, which falls from 0.39 at lag 1 to 0.07 at lag 4 (`resid_autocorr_lag1_4`).\n- The 95% CI is β̂ ± 1.96·se_hac, using the normal approximation that the asymptotic HAC theory justifies.\n- The script uses no randomness (`random_seed` = null). `out/analysis.py` regenerates all outputs, including `out/figure1.png` (scatter of Δu_t against g_t with the fitted line).\n\n## Results\n- Okun coefficient: `beta_hat` = −0.256. Each additional 1% of quarterly real GDP growth is associated with a 0.256 pp fall in the unemployment rate in that quarter.\n- Intercept: `alpha_hat` = 0.184 pp. Δu is zero at g ≈ 0.72% q/q (≈ 2.9% annualised), which is computed from `alpha_hat`/`beta_hat`.\n- Fit: `r_squared` = 0.408.\n- Mean growth: `mean_growth` = 0.754% per quarter.\n- The coefficient is negative and significant at 5% using the HAC SE: `okun_negative_5pct` = true, with HAC t-stat `t_hac` = −7.14 and `p_value_hac` ≈ 1e−12.\n\n## Uncertainty\n- `se_iid` = 0.0200 (baseline) and `se_hac` = 0.0358. The HAC SE is about 1.8 times larger, reflecting positive serial correlation in the residuals.\n- HAC 95% CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve iid CI is narrower: [`baseline_ci_iid_low`, `baseline_ci_iid_high`] = [−0.295, −0.217].\n- Bandwidth sensitivity (`sensitivity_se_hac_by_lag`): the HAC SE is 0.0288 at L = 0 (White SE), 0.0342 at L = 2, 0.0358 at L = 4, 0.0365 at L = 5, 0.0371 at L = 8 and 0.0364 at L = 12. The conclusion holds at every bandwidth tried: |t| > 6.\n\n## Limitations and open issues\n- β is a contemporaneous linear-projection coefficient, not a causal effect. The specification omits lags of growth, so dynamic Okun effects are not captured.\n- The data are the current vintage, not real-time data. GDP revisions could change the estimates for other vintages.\n- The coefficient may not have been stable over 1960–2019; no test for structural breaks was done because the task does not ask for one.\n- The bandwidth comes from a rule of thumb, not data-driven selection. The sensitivity results above show this choice does not affect the conclusion.\n- The normal critical value ignores the size distortions HAC tests can have in finite samples. With |t| ≈ 7 these distortions would not change the conclusion.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (n=240) is about -0.256 pp of unemployment per 1% quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Contemporaneous linear projection; not causal; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) HAC standard error is 0.0358, about 1.8 times the naive iid SE of 0.0200, reflecting positively autocorrelated residuals.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_autocorr_lag1_4\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb.\"},\n {\"id\":\"C3\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using the HAC SE; 95% CI [-0.326, -0.186].\",\"results_keys\":[\"okun_negative_5pct\",\"beta_ci_low\",\"beta_ci_high\",\"t_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal critical values; asymptotic HAC inference.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths from 0 to 12 lags.\",\"results_keys\":[\"sensitivity_se_hac_by_lag\",\"beta_hat\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel examined.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754%.\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"Log growth, not annualised.\"}\n]\nEOF\nR=$(ls out/state/gate_receipts | head -1)\ncat > out/state/phases.json <<EOF\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 match README for both files\",\"row/col counts 945x2, 319x2\",\"no duplicates/gaps; 1 missing UNRATE (2025-10) outside window\",\"exclusions.csv sums 319-78-1=240\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline se_iid computed (0.0200) and baseline_ci_iid_* recorded in results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\",\"manual HAC equals statsmodels (asserted)\",\"figure1.png generated\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical results.json\",\"consistency checks in checks.md all PASS\",\"gate run 1 receipt out/state/gate_receipts/$R: reproducibility/keys/CI/SE/exclusions ok; failures only for P5 deliverables not yet written\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 5 claims referencing existing keys\",\"final gate run (see log.md)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed: baseline and HAC estimates computed. P4 passed: clean rerun identical, gate run 1 ($R) failures only P5 items. P5: report and claims written.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v '\\[ok\\]'","description":"Write report, claims, phases; run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1539,"cache_read_input_tokens":18369,"output_tokens":3210,"output_tokens_details":{"thinking_tokens":31},"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":1539},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqiNupKDtD9ZMCSXqqp","requestRef":"a50c9971-2a30-4d84-90af-497af284dacb","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0ac35f1b-c65e-4341-89e0-bb536962d390","timestamp":"2026-10-30T05:06:57.587Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}69{"parentUuid":"0ac35f1b-c65e-4341-89e0-bb536962d390","isSidechain":false,"promptId":"ec6c6a4e-0b2b-46a8-a7bf-f193d6c82764","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01V3LZCTeAQmDmHE3BNNXiaz","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 38 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0226/out/state/gate_receipts/20261030T050658.863407Z.json","is_error":false}]},"uuid":"b307057c-bf85-4117-9bb9-2a1c97c44860","timestamp":"2026-10-30T05:06:58.872Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 38 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0226/out/state/gate_receipts/20261030T050658.863407Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0ac35f1b-c65e-4341-89e0-bb536962d390","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0226","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","version":"2.1.314","gitBranch":"HEAD"}70{"type":"api-request-blob","sessionId":"b0cb25a2-df02-470f-b77c-3ee8aade8ec8","hash":"fceca6659a5486b90b4962c22217324ebcdabaefbc4eb0aea8d935bf1d7a00cd","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4-gate failures are all P5 deliverables (report, claims, ledger finalization), as expected. Writing the report and claims now."},{"type":"tool_use","id":"toolu_01V3LZCTeAQmDmHE3BNNXiaz","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4 (F2-conf-04)\n\n## Question\nEstimate the slope β of the linear projection Δu_t = α + β g_t + e_t, where u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (% q/q, not annualised). Sample: 1960Q1–2019Q4. Test whether β is negative and significantly different from zero at 5%, using a Newey–West (Bartlett) HAC standard error that remains valid when the errors are serially correlated.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows; `data/GDPC1.csv`: 319 quarterly rows (`n_input_rows`). SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data issue (`data_issues`): UNRATE is empty for 2025-10. That month is outside the analysis window, so it is not used. The script checks that every quarter in the window has all three monthly values.\n- Exclusions (`out/exclusions.csv`), counted in GDPC1 quarters: 319 → 241 after keeping 1959Q4–2019Q4 (78 removed); → 240 after dropping 1959Q4, which serves only as the base for the first differences (1 removed). `n_analysed` = 240, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4.\n\n## Method\n- OLS of Δu_t on a constant and g_t.\n- Naïve baseline SE: the conventional iid/homoskedastic standard error (`se_iid`).\n- Main SE: the Newey–West HAC estimator with Bartlett weights 1 − j/(L+1), without a small-sample degrees-of-freedom correction (`se_hac`). The script checks it against a manual computation.\n- Bandwidth: L = floor(4·(T/100)^(2/9)) = 4 for T = 240 (`hac_lags`). This is the standard Newey–West (1994) rule of thumb for the Bartlett kernel. It covers the observed residual autocorrelation, which falls from 0.39 at lag 1 to 0.07 at lag 4 (`resid_autocorr_lag1_4`).\n- The 95% CI is β̂ ± 1.96·se_hac, using the normal approximation that the asymptotic HAC theory justifies.\n- The script uses no randomness (`random_seed` = null). `out/analysis.py` regenerates all outputs, including `out/figure1.png` (scatter of Δu_t against g_t with the fitted line).\n\n## Results\n- Okun coefficient: `beta_hat` = −0.256. Each additional 1% of quarterly real GDP growth is associated with a 0.256 pp fall in the unemployment rate in that quarter.\n- Intercept: `alpha_hat` = 0.184 pp. Δu is zero at g ≈ 0.72% q/q (≈ 2.9% annualised), which is computed from `alpha_hat`/`beta_hat`.\n- Fit: `r_squared` = 0.408.\n- Mean growth: `mean_growth` = 0.754% per quarter.\n- The coefficient is negative and significant at 5% using the HAC SE: `okun_negative_5pct` = true, with HAC t-stat `t_hac` = −7.14 and `p_value_hac` ≈ 1e−12.\n\n## Uncertainty\n- `se_iid` = 0.0200 (baseline) and `se_hac` = 0.0358. The HAC SE is about 1.8 times larger, reflecting positive serial correlation in the residuals.\n- HAC 95% CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve iid CI is narrower: [`baseline_ci_iid_low`, `baseline_ci_iid_high`] = [−0.295, −0.217].\n- Bandwidth sensitivity (`sensitivity_se_hac_by_lag`): the HAC SE is 0.0288 at L = 0 (White SE), 0.0342 at L = 2, 0.0358 at L = 4, 0.0365 at L = 5, 0.0371 at L = 8 and 0.0364 at L = 12. The conclusion holds at every bandwidth tried: |t| > 6.\n\n## Limitations and open issues\n- β is a contemporaneous linear-projection coefficient, not a causal effect. The specification omits lags of growth, so dynamic Okun effects are not captured.\n- The data are the current vintage, not real-time data. GDP revisions could change the estimates for other vintages.\n- The coefficient may not have been stable over 1960–2019; no test for structural breaks was done because the task does not ask for one.\n- The bandwidth comes from a rule of thumb, not data-driven selection. The sensitivity results above show this choice does not affect the conclusion.\n- The normal critical value ignores the size distortions HAC tests can have in finite samples. With |t| ≈ 7 these distortions would not change the conclusion.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (n=240) is about -0.256 pp of unemployment per 1% quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Contemporaneous linear projection; not causal; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) HAC standard error is 0.0358, about 1.8 times the naive iid SE of 0.0200, reflecting positively autocorrelated residuals.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_autocorr_lag1_4\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb.\"},\n {\"id\":\"C3\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using the HAC SE; 95% CI [-0.326, -0.186].\",\"results_keys\":[\"okun_negative_5pct\",\"beta_ci_low\",\"beta_ci_high\",\"t_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal critical values; asymptotic HAC inference.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths from 0 to 12 lags.\",\"results_keys\":[\"sensitivity_se_hac_by_lag\",\"beta_hat\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel examined.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754%.\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"Log growth, not annualised.\"}\n]\nEOF\nR=$(ls out/state/gate_receipts | head -1)\ncat > out/state/phases.json <<EOF\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 match README for both files\",\"row/col counts 945x2, 319x2\",\"no duplicates/gaps; 1 missing UNRATE (2025-10) outside window\",\"exclusions.csv sums 319-78-1=240\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline se_iid computed (0.0200) and baseline_ci_iid_* recorded in results.json\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG\",\"manual HAC equals statsmodels (asserted)\",\"figure1.png generated\"],\"notes\":\"\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical results.json\",\"consistency checks in checks.md all PASS\",\"gate run 1 receipt out/state/gate_receipts/$R: reproducibility/keys/CI/SE/exclusions ok; failures only for P5 deliverables not yet written\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, keys cited\",\"claims.json 5 claims referencing existing keys\",\"final gate run (see log.md)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2/P3 passed: baseline and HAC estimates computed. P4 passed: clean rerun identical, gate run 1 ($R) failures only P5 items. 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All required outputs are written, the analysis script reproduces `out/results.json` exactly from a clean re-run, and the final gate run passes all 38 checks.\n\n| Quantity | Value |\n|---|---|\n| Okun coefficient (β̂) | −0.256 pp of unemployment per 1% quarterly GDP growth |\n| Intercept (α̂) | 0.184 pp |\n| R² | 0.408 |\n| Conventional standard error (the naïve baseline) | 0.0200 |\n| Newey–West standard error, 4 lags | 0.0358 |\n| 95% confidence interval for β | [−0.326, −0.186] |\n| t-statistic (Newey–West) | −7.14 |\n| Mean quarterly GDP growth | 0.754% |\n| Quarters used | 240 (1960Q1–2019Q4) |\n\n- **Lag choice:** I used 4 lags, from the standard Newey–West (1994) rule of thumb, floor(4·(T/100)^(2/9)) for T = 240. The regression residuals are correlated over time (0.39 at one quarter, 0.07 at four), which is why the robust standard error is about 1.8 times the conventional one.\n- **Robustness:** significance holds for every lag choice I tried, from 0 to 12 lags.\n- **Data issue:** the unemployment file has one empty month, 2025-10. It falls outside the sample window, so it has no effect. The script checks that every quarter in the window has all three monthly values.\n\nLimitations:\n- β is a same-quarter association, not a causal effect, and lagged growth is not included.\n- The data are the current vintage, so later GDP revisions could change the numbers.\n- I did not test whether the coefficient changed over 1960–2019, since the task did not ask for it.\n- The confidence interval uses normal critical values, which can be too lenient in samples this size. 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