spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:05:02.446Z","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:05:02.452Z","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}4{"parentUuid":null,"isSidechain":false,"promptId":"82aa89be-a4a9-4493-8616-cefb4c788a4c","type":"user","message":{"role":"user","content":"Begin"},"uuid":"22c40cab-f907-4888-92bd-b504b204410a","timestamp":"2026-10-30T05:05:02.457Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","shapeHash":"4f7e450178ef010fbf90281fc2ff1a316b2ccee21e8df755c5dd76b4e0cf378f","timestamp":"2026-10-30T05:05:02.470Z","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/RC0225\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/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}12{"parentUuid":"28b777aa-5bc2-46c0-9c71-f51581da27e3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqbx5f3eiPZp8WKczJV","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSgwUKEAgTGAI4AUIIdGhpbmtpbmcSDL5qRx7HVNPTwXdYvhoMdT+1g5iOxT1wpIMJIjARyVvgY9L7A97A41Xc1iTdRQhboHiWQUqVtQnJarbmKYDMFTsVkPprHY4JV503npUqoATFY6LD9GbUwyV1uGiBROICMg4bDcmQRytSbFRVZfGVICxAyRf2XyGGjSZtMML7NDqcx35PjjzV4EBqi6cjfaJZhS30ZTWpujCf6yxZq+RSFtNoZ7cO0jHtcAN8QjB08aCldutS7WEuvjv34KmPwjHYxeCuUUpiDfdKfXFASGm2/y9ySNf22o1+xK7ARx/vLzdfcSsoH0vZID2PJ3t9dk8DW4wshZPFktwulyHdHSIuJgHwdZTobL8HrFeqkroUawHZF1rOspl1Bon+t5Kb4a+oozoXPyvhs+gMe181bGb3x1TiG38CZ7+Bm1EX8zd0ZNuwloaK9zOIy9f8kDqWoAGKusxfF/WIXLmzy+23qXRHSVFU2nC0OXJhSOmpYB7dauSD4rkWZw86jIPcUcdeC+N2pi6EbPNqm7Ugc6nFiEpqkzF0ry2PPnPw5OLwGNKMQHv6sEBOVIkwhHAvEE0kxc5Nw5s2ztdsdBN9d0CMlzNg5DuriIOIGGh5fkmmruwSJsvFrPnHwpspQHD+O4rCyWimzXm9cqUPJCRr5j99m1x9xn/CXwFeGeDauJKa/p1y6tSEkmzCUHwMZJvEzAKJNibU+o2f1/mTkj4OpHaIk4p1fP7uGoN35Ad5FMtU0hBHFDMPEiSnk54AWnCN9SlSHCT04ZXiC++6VqKF+5qpxMirN8j7JkVOT0Kecg0ilbFyabFjqse6pVyjKajKSvCCecsWGAE="}],"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":450,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqbwnJEUodt2EgtHfev","requestRef":"19441c1d-edd7-490d-b5a5-8267b67611d2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"31ce4188-9a56-4ccf-8512-2337e057f1ad","timestamp":"2026-10-30T05:05:05.466Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"31ce4188-9a56-4ccf-8512-2337e057f1ad","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqbx5f3eiPZp8WKczJV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_018c9b92Vh8ngBXMAqe4WSeY","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_018c9b92Vh8ngBXMAqe4WSeY"}]},"uuid":"10c431ad-a144-416b-a732-87272065613c","timestamp":"2026-10-30T05:05:06.017Z","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}1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\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","is_error":false}]},"uuid":"3014ccd7-25d7-4fab-9da0-5c4a7ee80720","timestamp":"2026-10-30T05:05:07.889Z","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\": 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\"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}1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\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 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{"type":"tool_use","id":"toolu_01NtsCDgHnb8HW9ZDXpFDoAJ","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in US quarterly data: slope β of Δu_t = α + β g_t + e_t, 1960Q1–2019Q4 (240 quarters).\n- u_t = mean of 3 monthly UNRATE values in quarter t (pp); Δu_t = u_t − u_{t−1}.\n- g_t = 100·ln(GDPC1_t/GDPC1_{t−1}), quarter-on-quarter, NOT annualised.\n- 1960Q1 differences use 1959Q4 levels.\n**Estimands.** β (linear-projection slope, OLS), α, R². Interpretation as a projection coefficient requires only finite second moments and stationarity of (Δu, g); HAC inference requires weak dependence (mixing) of g_t·e_t.\n**Uncertainty.** conventional iid SE (baseline) and Newey–West Bartlett HAC SE with justified bandwidth; 95% CI from HAC SE; okun_negative_5pct = β̂<0 and |β̂/se_hac|>1.96.\n**Outputs.** out/results.json keys: 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. Also out/report.md (6 sections), out/analysis.py, out/claims.json, out/figure1.png, out/exclusions.csv.\n**Availability rules.** Only data/ files; current vintage; no observations outside 1960Q1–2019Q4 except 1959Q4 levels for differencing; do not extend window.\n**Invalidating conditions.** Missing monthly UNRATE or GDPC1 values within 1959Q4–2019Q4; incomplete quarters; checksum mismatch; using annualised growth.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\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\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | data rows | cols | range | missing | duplicates | gaps |\n|---|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fa…2a06c (matches README & bundle) | 945 | 2 (observation_date, UNRATE) | 1948-01 to 2026-09 monthly | 1 (2025-10-01 empty) | 0 dup dates | 0 month gaps; sorted |\n| GDPC1.csv | 1c6c6e9f…b3343 (matches README & bundle) | 319 | 2 (observation_date, GDPC1) | 1947Q1 to 2026Q3 quarterly | 0 | 0 | 0 quarter gaps; sorted |\n\nUnits: UNRATE percent (SA); GDPC1 billions chained 2017 USD SAAR (annual-rate scaling cancels in log ratio).\nUNRATE range 2.5–14.8; GDPC1 2172–24611 (all positive, logs valid).\n\nAnomaly: UNRATE 2025-10 missing (no October 2025 CPS release, federal shutdown). It lies outside the 1959Q4–2019Q4 window used, so it has no effect; recorded in data_issues.\nAvailability: files contain data after 2019Q4 and before 1959Q4; these are excluded by the window filter (see out/exclusions.csv). No violation once filtered.\nEOF\necho \"- 2026-10-30 P0 passed (contract written). P1 audit done: checksums match, one missing UNRATE month (2025-10) outside window.\" > out/state/log.md","description":"Write contract, phase ledger, audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":831,"cache_read_input_tokens":8923,"output_tokens":1912,"output_tokens_details":{"thinking_tokens":368},"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":831},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqcbh6QfpWMdpnSNoXD","requestRef":"e26ea334-73cf-4c83-a854-d8114cc84c58","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ccc314d7-93d1-4a39-9a22-d3da7966783a","timestamp":"2026-10-30T05:05:29.121Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"ccc314d7-93d1-4a39-9a22-d3da7966783a","isSidechain":false,"promptId":"82aa89be-a4a9-4493-8616-cefb4c788a4c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01NtsCDgHnb8HW9ZDXpFDoAJ","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"f5e03134-3e96-4c25-9cc5-d76d73570e90","timestamp":"2026-10-30T05:05:29.169Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"ccc314d7-93d1-4a39-9a22-d3da7966783a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","hash":"50acd9dacf4e944ad2dbfa107565331312962f64bc5f5c46718e856faf0471ed","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01NtsCDgHnb8HW9ZDXpFDoAJ","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in US quarterly data: slope β of Δu_t = α + β g_t + e_t, 1960Q1–2019Q4 (240 quarters).\n- u_t = mean of 3 monthly UNRATE values in quarter t (pp); Δu_t = u_t − u_{t−1}.\n- g_t = 100·ln(GDPC1_t/GDPC1_{t−1}), quarter-on-quarter, NOT annualised.\n- 1960Q1 differences use 1959Q4 levels.\n**Estimands.** β (linear-projection slope, OLS), α, R². Interpretation as a projection coefficient requires only finite second moments and stationarity of (Δu, g); HAC inference requires weak dependence (mixing) of g_t·e_t.\n**Uncertainty.** conventional iid SE (baseline) and Newey–West Bartlett HAC SE with justified bandwidth; 95% CI from HAC SE; okun_negative_5pct = β̂<0 and |β̂/se_hac|>1.96.\n**Outputs.** out/results.json keys: 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. Also out/report.md (6 sections), out/analysis.py, out/claims.json, out/figure1.png, out/exclusions.csv.\n**Availability rules.** Only data/ files; current vintage; no observations outside 1960Q1–2019Q4 except 1959Q4 levels for differencing; do not extend window.\n**Invalidating conditions.** Missing monthly UNRATE or GDPC1 values within 1959Q4–2019Q4; incomplete quarters; checksum mismatch; using annualised growth.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\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\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | data rows | cols | range | missing | duplicates | gaps |\n|---|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fa…2a06c (matches README & bundle) | 945 | 2 (observation_date, UNRATE) | 1948-01 to 2026-09 monthly | 1 (2025-10-01 empty) | 0 dup dates | 0 month gaps; sorted |\n| GDPC1.csv | 1c6c6e9f…b3343 (matches README & bundle) | 319 | 2 (observation_date, GDPC1) | 1947Q1 to 2026Q3 quarterly | 0 | 0 | 0 quarter gaps; sorted |\n\nUnits: UNRATE percent (SA); GDPC1 billions chained 2017 USD SAAR (annual-rate scaling cancels in log ratio).\nUNRATE range 2.5–14.8; GDPC1 2172–24611 (all positive, logs valid).\n\nAnomaly: UNRATE 2025-10 missing (no October 2025 CPS release, federal shutdown). It lies outside the 1959Q4–2019Q4 window used, so it has no effect; recorded in data_issues.\nAvailability: files contain data after 2019Q4 and before 1959Q4; these are excluded by the window filter (see out/exclusions.csv). No violation once filtered.\nEOF\necho \"- 2026-10-30 P0 passed (contract written). P1 audit done: checksums match, one missing UNRATE month (2025-10) outside window.\" > out/state/log.md","description":"Write contract, phase ledger, audit, log"}}]}}32{"type":"api-request","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","id":"fa08943d-ae93-4255-989a-ee01682dd426","timestamp":"2026-10-30T05:05:29.172Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f7e450178ef010fbf90281fc2ff1a316b2ccee21e8df755c5dd76b4e0cf378f","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":"e26ea334-73cf-4c83-a854-d8114cc84c58","keep":8,"tail":["50acd9dacf4e944ad2dbfa107565331312962f64bc5f5c46718e856faf0471ed","24e8d652c0866a0b6ef80dba888cc36001b0a0f32731fef1ec88fd1d49558e7c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"91c8462a33a1d2aa9a45bc7a9294c14b17e8a5e8080acf9f84488bbc0521b9db"}33{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f5e03134-3e96-4c25-9cc5-d76d73570e90","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}34{"type":"atis-latch","atis":"","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}35{"parentUuid":"f5e03134-3e96-4c25-9cc5-d76d73570e90","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqdoynzzPQuLmCDaeuK","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1927,"cache_read_input_tokens":9754,"output_tokens":2795,"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":1927},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqdoiB4vWt2pYJiCm6H","requestRef":"fa08943d-ae93-4255-989a-ee01682dd426","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"53891734-7378-4cae-bc8b-22098f6cca83","timestamp":"2026-10-30T05:05:29.847Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"53891734-7378-4cae-bc8b-22098f6cca83","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqdoynzzPQuLmCDaeuK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013U6grFQebjyfLKE81foDPe","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os, math\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1  # 1959Q4 levels used for first differences\n\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nn_input_rows = {\"UNRATE.csv\": int(len(un_raw)), \"GDPC1.csv\": int(len(gdp_raw))}\n\ndata_issues = []\nun = un_raw.copy()\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nun[\"q\"] = pd.to_datetime(un[\"observation_date\"]).dt.to_period(\"Q\")\nmiss = un[un[\"UNRATE\"].isna()]\nfor _, r in miss.iterrows():\n    inwin = LEVEL_START <= r[\"q\"] <= LAST\n    data_issues.append({\"issue\": f\"UNRATE missing for {r['observation_date']}\",\n                        \"handling\": (\"inside window: quarter would be dropped\" if inwin else\n                                     \"outside the 1959Q4-2019Q4 window; no effect on the analysis\")})\ngdp = gdp_raw.copy()\ngdp[\"GDPC1\"] = pd.to_numeric(gdp[\"GDPC1\"], errors=\"coerce\")\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nfor _, r in gdp[gdp[\"GDPC1\"].isna()].iterrows():\n    data_issues.append({\"issue\": f\"GDPC1 missing for {r['observation_date']}\", \"handling\": \"quarter dropped\"})\n\n# quarterly average of the three monthly observations; require all 3 present\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq = uq[uq[\"count\"] == 3][\"mean\"].rename(\"u\")\ndf = pd.concat([uq, gdp.set_index(\"q\")[\"GDPC1\"]], axis=1, join=\"inner\").sort_index()\n\nexcl = []\nn0 = len(df)\ndf = df[(df.index >= LEVEL_START) & (df.index <= LAST)]\nexcl.append((\"1\", \"keep quarters 1959Q4-2019Q4 (levels)\", n0, len(df),\n             \"task window; 1959Q4 kept only for differencing\"))\nassert len(df) == 241 and (df.index[1:].astype(int) - df.index[:-1].astype(int) == 1).all()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"GDPC1\"] / df[\"GDPC1\"].shift(1))\nn1 = len(df)\ndf = df.dropna(subset=[\"du\", \"g\"])\nexcl.append((\"2\", \"drop 1959Q4 (no prior level for differences)\", n1, len(df),\n             \"first-difference loses one observation\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed_tmp\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nT = len(df)\nX = sm.add_constant(df[\"g\"].values)\ny = df[\"du\"].values\nols = sm.OLS(y, X).fit()\n# Newey-West (1994) plug-in style rule of thumb: floor(4*(T/100)^(2/9))\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\",\n                                                   \"use_correction\": False})\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = 1.959963984540054\nlo, hi = beta - z * se_hac, beta + z * se_hac\nt_hac = beta / se_hac\nsens = {}\nfor L in [0, 1, 2, 4, 6, 8, 12]:\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": False})\n    sens[str(L)] = round(float(r.bse[1]), 10)\nresid = ols.resid\nrho = [float(np.corrcoef(resid[k:], resid[:-k])[0, 1]) for k in range(1, 5)]\n\nresults = {\n    \"task_id\": \"F2-conf-04\",\n    \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": hac_lags,\n    \"beta_ci_low\": lo, \"beta_ci_high\": hi,\n    \"okun_negative_5pct\": bool(beta < 0 and abs(t_hac) > z),\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),\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"random_seed\": None,\n    # supplementary keys\n    \"t_stat_hac\": float(t_hac),\n    \"p_value_hac_two_sided\": float(2 * (1 - __import__(\"scipy.stats\").stats.norm.cdf(abs(t_hac)))),\n    \"se_hac_by_lags\": sens,\n    \"resid_autocorr_lag1_4\": rho,\n    \"mean_du\": float(df[\"du\"].mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\nxs = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 50)\nax.plot(xs, alpha + beta * xs, color=\"red\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, lw=0.5, color=\"k\"); ax.axvline(0, lw=0.5, color=\"k\")\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\")\nax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=%d)\" % T); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\",\n                                          \"beta_ci_high\", \"okun_negative_5pct\", \"n_analysed\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json 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":1927,"cache_read_input_tokens":9754,"output_tokens":2795,"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":1927},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqdoiB4vWt2pYJiCm6H","requestRef":"fa08943d-ae93-4255-989a-ee01682dd426","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cf83c209-7f8c-455e-8c84-91cfcfc497a3","timestamp":"2026-10-30T05:05:50.535Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"cf83c209-7f8c-455e-8c84-91cfcfc497a3","isSidechain":false,"promptId":"82aa89be-a4a9-4493-8616-cefb4c788a4c","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 939, in _finalize_columns_and_data\n    columns = _validate_or_indexify_columns(contents, columns)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 986, in _validate_or_indexify_columns\n    raise AssertionError(\n    ...<2 lines>...\n    )\nAssertionError: 6 columns passed, passed data had 5 columns\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0225/out/analysis.py\", line 51, in <module>\n    pd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed_tmp\", \"rationale\"]).assign(\n    ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/frame.py\", line 855, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ~~~~~~~~~~~~~~~~~~~~~^\n        # error: Argument 3 to \"nested_data_to_arrays\" has incompatible\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    ...<4 lines>...\n        dtype,\n        ^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 520, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 845, in to_arrays\n    content, columns = _finalize_columns_and_data(arr, columns, dtype)\n                       ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 942, in _finalize_columns_and_data\n    raise ValueError(err) from err\nValueError: 6 columns passed, passed data had 5 columns","is_error":true,"tool_use_id":"toolu_013U6grFQebjyfLKE81foDPe"}]},"uuid":"36fc24db-b360-47c4-af0e-d025a1e7471d","timestamp":"2026-10-30T05:05:51.636Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 939, in _finalize_columns_and_data\n    columns = _validate_or_indexify_columns(contents, columns)\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 986, in _validate_or_indexify_columns\n    raise AssertionError(\n    ...<2 lines>...\n    )\nAssertionError: 6 columns passed, passed data had 5 columns\n\nThe above exception was the direct cause of the following exception:\n\nTraceback (most recent call last):\n  File \"/Users/simon-pierreboucher/wp29/runs/RC0225/out/analysis.py\", line 51, in <module>\n    pd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed_tmp\", \"rationale\"]).assign(\n    ~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/frame.py\", line 855, in __init__\n    arrays, columns, index = nested_data_to_arrays(\n                             ~~~~~~~~~~~~~~~~~~~~~^\n        # error: Argument 3 to \"nested_data_to_arrays\" has incompatible\n        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n    ...<4 lines>...\n        dtype,\n        ^^^^^^\n    )\n    ^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 520, in nested_data_to_arrays\n    arrays, columns = to_arrays(data, columns, dtype=dtype)\n                      ~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 845, in to_arrays\n    content, columns = _finalize_columns_and_data(arr, columns, dtype)\n                       ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/core/internals/construction.py\", line 942, in _finalize_columns_and_data\n    raise ValueError(err) from err\nValueError: 6 columns passed, passed data had 5 columns","sandboxed":false,"sourceToolAssistantUUID":"cf83c209-7f8c-455e-8c84-91cfcfc497a3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","hash":"bef31be8cb52e5a801bfc636993765e3e52fdcdbded688241ad5a465f6dbb86d","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_013U6grFQebjyfLKE81foDPe","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os, math\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1  # 1959Q4 levels used for first differences\n\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nn_input_rows = {\"UNRATE.csv\": int(len(un_raw)), \"GDPC1.csv\": int(len(gdp_raw))}\n\ndata_issues = []\nun = un_raw.copy()\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nun[\"q\"] = pd.to_datetime(un[\"observation_date\"]).dt.to_period(\"Q\")\nmiss = un[un[\"UNRATE\"].isna()]\nfor _, r in miss.iterrows():\n    inwin = LEVEL_START <= r[\"q\"] <= LAST\n    data_issues.append({\"issue\": f\"UNRATE missing for {r['observation_date']}\",\n                        \"handling\": (\"inside window: quarter would be dropped\" if inwin else\n                                     \"outside the 1959Q4-2019Q4 window; no effect on the analysis\")})\ngdp = gdp_raw.copy()\ngdp[\"GDPC1\"] = pd.to_numeric(gdp[\"GDPC1\"], errors=\"coerce\")\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nfor _, r in gdp[gdp[\"GDPC1\"].isna()].iterrows():\n    data_issues.append({\"issue\": f\"GDPC1 missing for {r['observation_date']}\", \"handling\": \"quarter dropped\"})\n\n# quarterly average of the three monthly observations; require all 3 present\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq = uq[uq[\"count\"] == 3][\"mean\"].rename(\"u\")\ndf = pd.concat([uq, gdp.set_index(\"q\")[\"GDPC1\"]], axis=1, join=\"inner\").sort_index()\n\nexcl = []\nn0 = len(df)\ndf = df[(df.index >= LEVEL_START) & (df.index <= LAST)]\nexcl.append((\"1\", \"keep quarters 1959Q4-2019Q4 (levels)\", n0, len(df),\n             \"task window; 1959Q4 kept only for differencing\"))\nassert len(df) == 241 and (df.index[1:].astype(int) - df.index[:-1].astype(int) == 1).all()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"GDPC1\"] / df[\"GDPC1\"].shift(1))\nn1 = len(df)\ndf = df.dropna(subset=[\"du\", \"g\"])\nexcl.append((\"2\", \"drop 1959Q4 (no prior level for differences)\", n1, len(df),\n             \"first-difference loses one observation\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed_tmp\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nT = len(df)\nX = sm.add_constant(df[\"g\"].values)\ny = df[\"du\"].values\nols = sm.OLS(y, X).fit()\n# Newey-West (1994) plug-in style rule of thumb: floor(4*(T/100)^(2/9))\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\",\n                                                   \"use_correction\": False})\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = 1.959963984540054\nlo, hi = beta - z * se_hac, beta + z * se_hac\nt_hac = beta / se_hac\nsens = {}\nfor L in [0, 1, 2, 4, 6, 8, 12]:\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": False})\n    sens[str(L)] = round(float(r.bse[1]), 10)\nresid = ols.resid\nrho = [float(np.corrcoef(resid[k:], resid[:-k])[0, 1]) for k in range(1, 5)]\n\nresults = {\n    \"task_id\": \"F2-conf-04\",\n    \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": hac_lags,\n    \"beta_ci_low\": lo, \"beta_ci_high\": hi,\n    \"okun_negative_5pct\": bool(beta < 0 and abs(t_hac) > z),\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),\n    \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"random_seed\": None,\n    # supplementary keys\n    \"t_stat_hac\": float(t_hac),\n    \"p_value_hac_two_sided\": float(2 * (1 - __import__(\"scipy.stats\").stats.norm.cdf(abs(t_hac)))),\n    \"se_hac_by_lags\": sens,\n    \"resid_autocorr_lag1_4\": rho,\n    \"mean_du\": float(df[\"du\"].mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(results, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\nxs = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 50)\nax.plot(xs, alpha + beta * xs, color=\"red\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, lw=0.5, color=\"k\"); ax.axvline(0, lw=0.5, color=\"k\")\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\")\nax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=%d)\" % T); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\",\n                                          \"beta_ci_high\", \"okun_negative_5pct\", \"n_analysed\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Write and run analysis script"}}]}}39{"type":"api-request","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","id":"c493c8c5-d47b-4a15-87d2-c00b890d0894","timestamp":"2026-10-30T05:05:51.640Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f7e450178ef010fbf90281fc2ff1a316b2ccee21e8df755c5dd76b4e0cf378f","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":"fa08943d-ae93-4255-989a-ee01682dd426","keep":10,"tail":["bef31be8cb52e5a801bfc636993765e3e52fdcdbded688241ad5a465f6dbb86d","f9202a7c7deb8597a3b63f85aea0019a948d6be5e4aad749b61c3ea691713e95"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f38e5777a2e90e762112105a839e151eacba97523c8f5bc644711df7cd3d98c9"}40{"parentUuid":"36fc24db-b360-47c4-af0e-d025a1e7471d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfU6XVLrkHYUMmBdN9","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":3877,"cache_read_input_tokens":11681,"output_tokens":904,"output_tokens_details":{"thinking_tokens":223},"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":3877},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2034,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqfTpACMFjDeeRxRUhw","requestRef":"c493c8c5-d47b-4a15-87d2-c00b890d0894","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"85f37d12-88af-42df-9a91-9ecdcbf3627f","timestamp":"2026-10-30T05:05:54.771Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"85f37d12-88af-42df-9a91-9ecdcbf3627f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfU6XVLrkHYUMmBdN9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0144mudRAwSERscmmNED1aYB","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\ns=open('analysis.py').read()\ns=s.replace('''df = pd.concat([uq, gdp.set_index(\"q\")[\"GDPC1\"]], axis=1, join=\"inner\").sort_index()\n\nexcl = []\nn0 = len(df)\n''','''df = gdp.set_index(\"q\")[[\"GDPC1\"]].join(uq, how=\"left\").sort_index()\nexcl = []\nn0 = len(df)  # n_raw = GDPC1 quarters\ndf = df.dropna(subset=[\"u\", \"GDPC1\"])\nexcl.append((\"0\", \"drop quarters lacking GDPC1 or a complete 3-month UNRATE average\", n0, len(df),\n             \"1947Q1-Q4 precede UNRATE start; 2025Q4 lacks Oct-2025 UNRATE; all outside window\"))\nn0 = len(df)\n''')\ns=s.replace('''pd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed_tmp\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n].to_csv''','''pd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n].to_csv''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Fix exclusion ledger and 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\"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_analysed\": 240\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 for 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; no effect on the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_stat_hac\": -7.137445096377235,\n  \"p_value_hac_two_sided\": 9.50794998288984e-13,\n  \"se_hac_by_lags\": {\n    \"0\": 0.0288362232,\n    \"1\": 0.0323585965,\n    \"2\": 0.0342273202,\n    \"4\": 0.0358346333,\n    \"6\": 0.0368790286,\n    \"8\": 0.0371355042,\n    \"12\": 0.0364088511\n  },\n  \"resid_autocorr_lag1_4\": [\n    0.38935129370212984,\n    0.2910132943069726,\n    0.13988210991084532,\n    0.07090953047434041\n  ],\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n0,drop quarters lacking GDPC1 or a complete 3-month UNRATE average,319,314,5,1947Q1-Q4 precede UNRATE start; 2025Q4 lacks Oct-2025 UNRATE; all outside window\n1,keep quarters 1959Q4-2019Q4 (levels),314,241,73,task window; 1959Q4 kept only for differencing\n2,drop 1959Q4 (no prior level for differences),241,240,1,first-difference loses one observation","is_error":false}]},"uuid":"d9e7217d-8353-4e38-b765-3280b177f87f","timestamp":"2026-10-30T05:06:00.492Z","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_analysed\": 240\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 for 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; no effect on the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_stat_hac\": -7.137445096377235,\n  \"p_value_hac_two_sided\": 9.50794998288984e-13,\n  \"se_hac_by_lags\": {\n    \"0\": 0.0288362232,\n    \"1\": 0.0323585965,\n    \"2\": 0.0342273202,\n    \"4\": 0.0358346333,\n    \"6\": 0.0368790286,\n    \"8\": 0.0371355042,\n    \"12\": 0.0364088511\n  },\n  \"resid_autocorr_lag1_4\": [\n    0.38935129370212984,\n    0.2910132943069726,\n    0.13988210991084532,\n    0.07090953047434041\n  ],\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n0,drop quarters lacking GDPC1 or a complete 3-month UNRATE average,319,314,5,1947Q1-Q4 precede UNRATE start; 2025Q4 lacks Oct-2025 UNRATE; all outside window\n1,keep quarters 1959Q4-2019Q4 (levels),314,241,73,task window; 1959Q4 kept only for differencing\n2,drop 1959Q4 (no prior level for differences),241,240,1,first-difference loses one 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n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n].to_csv''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Fix exclusion ledger and 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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\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nr=json.load(open('out/results.json'))\nu=pd.read_csv('data/UNRATE.csv'); u['q']=pd.to_datetime(u.observation_date).dt.to_period('Q')\nuq=u.groupby('q').UNRATE.mean()\ng=pd.read_csv('data/GDPC1.csv'); g.index=pd.to_datetime(g.observation_date).dt.to_period('Q')\nidx=pd.period_range('1959Q4','2019Q4',freq='Q')\nU=uq.loc[idx].values; G=g.GDPC1.loc[idx].values\ny=np.diff(U); x=100*np.diff(np.log(G)); T=len(y)\nX=np.c_[np.ones(T),x]; b=np.linalg.lstsq(X,y,rcond=None)[0]; e=y-X@b\nXtXi=np.linalg.inv(X.T@X); L=4\nS=sum((X*e[:,None]).T@(X*e[:,None]) for _ in [0])\nfor l in range(1,L+1):\n    w=1-l/(L+1); G_=(X[l:]*e[l:,None]).T@(X[:-l]*e[:-l,None]); S=S+w*(G_+G_.T)\nV=XtXi@S@XtXi; seiid=np.sqrt(e@e/(T-2)*XtXi[1,1])\nprint('beta',b[1],r['beta_hat']); print('se_hac',np.sqrt(V[1,1]),r['se_hac']); print('se_iid',seiid,r['se_iid'])\nprint('mean g',x.mean(),r['mean_growth'],'T',T)\nprint('ci contains', r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'], 'se>0', r['se_iid']>0 and r['se_hac']>0)\nprint('ci half-width/se', (r['beta_ci_high']-r['beta_ci_low'])/2/r['se_hac'])\nEOF","description":"Rerun determinism check and independent 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-0.2557677277487258 -0.25576772774872947\nse_hac 0.035834633302965685 0.03583463330296578\nse_iid 0.019960092914909834 0.019960092914910015\nmean g 0.753502755065237 0.7535027550652363 T 240\nci contains True se>0 True\nci half-width/se 1.9599639845400538","is_error":false}]},"uuid":"6359c03d-8569-4375-a5eb-474b1cb711a0","timestamp":"2026-10-30T05:06:09.927Z","toolUseResult":{"stdout":"IDENTICAL\nbeta -0.2557677277487258 -0.25576772774872947\nse_hac 0.035834633302965685 0.03583463330296578\nse_iid 0.019960092914909834 0.019960092914910015\nmean g 0.753502755065237 0.7535027550652363 T 240\nci contains True se>0 True\nci half-width/se 1.9599639845400538","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"db0a8028-9f59-4750-82de-74bec5284227","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}51{"type":"api-request-blob","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","hash":"a27240764bd24d02e5c49b88e32524662fdcfa84eeca40e86d17c1200da9e35a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now verification: clean rerun, independent manual Newey–West computation, and identity checks."},{"type":"tool_use","id":"toolu_01Fmk2LhuYysr46GCyuzzXbW","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\n~/wp29/venv/bin/python - <<'EOF'\nimport pandas as pd, numpy as np, json\nr=json.load(open('out/results.json'))\nu=pd.read_csv('data/UNRATE.csv'); u['q']=pd.to_datetime(u.observation_date).dt.to_period('Q')\nuq=u.groupby('q').UNRATE.mean()\ng=pd.read_csv('data/GDPC1.csv'); g.index=pd.to_datetime(g.observation_date).dt.to_period('Q')\nidx=pd.period_range('1959Q4','2019Q4',freq='Q')\nU=uq.loc[idx].values; G=g.GDPC1.loc[idx].values\ny=np.diff(U); x=100*np.diff(np.log(G)); T=len(y)\nX=np.c_[np.ones(T),x]; b=np.linalg.lstsq(X,y,rcond=None)[0]; e=y-X@b\nXtXi=np.linalg.inv(X.T@X); L=4\nS=sum((X*e[:,None]).T@(X*e[:,None]) for _ in [0])\nfor l in range(1,L+1):\n    w=1-l/(L+1); G_=(X[l:]*e[l:,None]).T@(X[:-l]*e[:-l,None]); S=S+w*(G_+G_.T)\nV=XtXi@S@XtXi; seiid=np.sqrt(e@e/(T-2)*XtXi[1,1])\nprint('beta',b[1],r['beta_hat']); print('se_hac',np.sqrt(V[1,1]),r['se_hac']); print('se_iid',seiid,r['se_iid'])\nprint('mean g',x.mean(),r['mean_growth'],'T',T)\nprint('ci contains', r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'], 'se>0', r['se_iid']>0 and r['se_hac']>0)\nprint('ci half-width/se', (r['beta_ci_high']-r['beta_ci_low'])/2/r['se_hac'])\nEOF","description":"Rerun determinism check and independent recomputation"}}]}}52{"type":"api-request","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","id":"9db34720-35db-4236-b221-267fd3069ebc","timestamp":"2026-10-30T05:06:09.931Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f7e450178ef010fbf90281fc2ff1a316b2ccee21e8df755c5dd76b4e0cf378f","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":"54fb1886-ae22-4014-8799-845ee4b036d6","keep":14,"tail":["a27240764bd24d02e5c49b88e32524662fdcfa84eeca40e86d17c1200da9e35a","b182dc90fe9ca114b023f8b501bde5838218ceafd1f8df5153e7990bbd20b384"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0b79764b465a315caa83807cf8eb5469adc427722be0154e78a3a35b96976e33"}53{"parentUuid":"6359c03d-8569-4375-a5eb-474b1cb711a0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqgpHEo7D8zG3dzG8dj","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01K2s1fNwvZcEE1MkfVW9cSK","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Determinism: deleted results.json/figure1.png/exclusions.csv, re-ran analysis.py; `cmp` with previous results.json → byte-identical. PASS\n2. Independent recomputation (numpy, hand-coded Bartlett NW with L=4, no df correction): beta, se_hac, se_iid, mean_growth agree with results.json to ~1e-15. PASS\n3. n_analysed = 240 = 319 − 5 − 73 − 1 (exclusions.csv sums). PASS\n4. first_quarter = 1960Q1, last_quarter = 2019Q4; no data beyond 2019Q4 or before 1959Q4 enters. PASS\n5. se_iid > 0, se_hac > 0; CI contains beta_hat; CI half-width / se_hac = 1.95996. PASS\n6. okun_negative_5pct consistent: beta_hat < 0 and |t_hac| = 7.14 > 1.96. PASS\n7. Magnitude vs baseline: se_hac/se_iid ≈ 1.80, consistent with residual lag-1 autocorrelation 0.39; beta ≈ −0.26 per 1% q/q growth is in the conventional Okun range (≈ −0.3 to −0.5 per 1pp of annual growth ⇔ ≈ −0.07 to −0.13 per annualised point; q/q not annualised scaling ×4 → ≈ −0.26). PASS\n8. Units: g is q/q not annualised (mean 0.75%/quarter ≈ 3.0%/yr). PASS\n9. Bandwidth sensitivity: se_hac ranges 0.0324–0.0371 for L in 1..12; conclusion unchanged. PASS\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Record 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":1128,"cache_read_input_tokens":17427,"output_tokens":738,"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":1128},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqgoz8aM78qQYKj9Efv","requestRef":"9db34720-35db-4236-b221-267fd3069ebc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"82ff51ed-7b6d-4d04-aca8-3ad731cc659a","timestamp":"2026-10-30T05:06:17.003Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"82ff51ed-7b6d-4d04-aca8-3ad731cc659a","isSidechain":false,"promptId":"82aa89be-a4a9-4493-8616-cefb4c788a4c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01K2s1fNwvZcEE1MkfVW9cSK","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: 3 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 1.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0225/out/state/gate_receipts/20261030T050618.264574Z.json\n20261030T050618.264574Z.json","is_error":false}]},"uuid":"0d1f783c-ee3a-401b-8aa4-2481e4fc35d5","timestamp":"2026-10-30T05:06:18.277Z","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: 3 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 1.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0225/out/state/gate_receipts/20261030T050618.264574Z.json\n20261030T050618.264574Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"82ff51ed-7b6d-4d04-aca8-3ad731cc659a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","hash":"50a35e4610f87ce082dbc66f25342adb084a85f34f7aecf77944e19bedf5d399","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01K2s1fNwvZcEE1MkfVW9cSK","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Determinism: deleted results.json/figure1.png/exclusions.csv, re-ran analysis.py; `cmp` with previous results.json → byte-identical. PASS\n2. Independent recomputation (numpy, hand-coded Bartlett NW with L=4, no df correction): beta, se_hac, se_iid, mean_growth agree with results.json to ~1e-15. PASS\n3. n_analysed = 240 = 319 − 5 − 73 − 1 (exclusions.csv sums). PASS\n4. first_quarter = 1960Q1, last_quarter = 2019Q4; no data beyond 2019Q4 or before 1959Q4 enters. PASS\n5. se_iid > 0, se_hac > 0; CI contains beta_hat; CI half-width / se_hac = 1.95996. PASS\n6. okun_negative_5pct consistent: beta_hat < 0 and |t_hac| = 7.14 > 1.96. PASS\n7. Magnitude vs baseline: se_hac/se_iid ≈ 1.80, consistent with residual lag-1 autocorrelation 0.39; beta ≈ −0.26 per 1% q/q growth is in the conventional Okun range (≈ −0.3 to −0.5 per 1pp of annual growth ⇔ ≈ −0.07 to −0.13 per annualised point; q/q not annualised scaling ×4 → ≈ −0.26). PASS\n8. Units: g is q/q not annualised (mean 0.75%/quarter ≈ 3.0%/yr). PASS\n9. Bandwidth sensitivity: se_hac ranges 0.0324–0.0371 for L in 1..12; conclusion unchanged. 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Now the report, claims and ledger."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1591,"cache_read_input_tokens":18555,"output_tokens":2638,"output_tokens_details":{"thinking_tokens":39},"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":1591},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqhRez3p2W4iHkPVWpn","requestRef":"711c73b7-9f99-4534-bf1a-a22f96dc462d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c5048df2-1b61-4537-b7d7-2c5e71cc74a7","timestamp":"2026-10-30T05:06:19.577Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}59{"parentUuid":"c5048df2-1b61-4537-b7d7-2c5e71cc74a7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqhRwboKT6duNHyMZBh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LsEpN56SjiE2bzHeQdphG5","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\nHow strongly does the change in the U.S. unemployment rate move with real GDP growth? We estimate the slope β (the Okun coefficient) in the linear projection Δu_t = α + β g_t + e_t. Here u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is quarter-on-quarter growth in percent, not annualised. The standard errors must stay valid when the disturbances are serially correlated.\n\n## Data and exclusions\n- `data/UNRATE.csv` (945 monthly rows) and `data/GDPC1.csv` (319 quarterly rows) were read as given (`n_input_rows`). Both SHA-256 checksums match `data/README.md`.\n- One data issue was found (`data_issues`): the UNRATE value for October 2025 is missing. That month is outside the window used here, so it has no effect.\n- Exclusion ledger (`out/exclusions.csv`), counted in GDPC1 quarters:\n  - Start: 319 quarters.\n  - Drop 5 quarters without a complete 3-month UNRATE average (1947Q1–Q4 and 2025Q4) → 314.\n  - Keep the levels for 1959Q4–2019Q4 → 241.\n  - Drop 1959Q4, which has no prior level to difference against → 240.\n- Result: `n_analysed` = 240 quarters, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4. Nothing after 2019Q4 is used.\n\n## Method\n- **Estimator:** OLS of Δu_t on a constant and g_t.\n- **Baseline uncertainty:** the conventional (iid, homoskedastic) standard error (`se_iid`).\n- **Main uncertainty:** a Newey–West HAC standard error with a Bartlett kernel (`se_hac`). It uses no small-sample degrees-of-freedom correction.\n- **Bandwidth:** `hac_lags` = 4. This comes from the standard Newey–West rule of thumb L = floor(4·(T/100)^(2/9)) with T = 240.\n- **Inference:** a 95% CI equal to β̂ ± 1.96·se_hac, and a two-sided normal-approximation test of H0: β = 0.\n- **Reproducibility:** no randomness is involved (`random_seed` = null). `out/analysis.py` regenerates all outputs and `out/figure1.png`.\n\n## Results\n- **Okun coefficient:** `beta_hat` = −0.256. Each extra 1% of quarterly real GDP growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter.\n- **Intercept:** `alpha_hat` = 0.184 pp. Together with the slope, this implies that unemployment holds steady at about 0.72% quarterly growth (≈2.9% a year). That is close to the sample mean growth, `mean_growth` = 0.754% per quarter.\n- **Fit:** `r_squared` = 0.408.\n- **Significance:** `okun_negative_5pct` = true. β̂ is negative, and H0: β = 0 is rejected at the 5% level using the HAC standard error (`t_stat_hac` = −7.14, `p_value_hac_two_sided` ≈ 1e−12).\n- Figure 1 (`out/figure1.png`) shows the scatter with the fitted line.\n\n## Uncertainty\n- **Standard errors:** `se_hac` = 0.0358 versus `se_iid` = 0.0200. The HAC standard error is about 1.8 times the naïve one.\n- **Why they differ:** the OLS residuals are serially correlated. Their autocorrelations at lags 1–4 are about 0.39, 0.29, 0.14 and 0.07 (`resid_autocorr_lag1_4`). Assuming iid errors therefore understates the uncertainty substantially.\n- **95% HAC CI:** [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186].\n- **Bandwidth sensitivity:** across bandwidths from 0 to 12 lags, the HAC standard error ranges from 0.029 to 0.037 (`se_hac_by_lags`). The conclusion that β is significantly negative does not depend on the bandwidth.\n\n## Limitations and open issues\n- **Vintage:** the data are the current vintage, not real-time data. GDP revisions could change the estimates slightly.\n- **Interpretation:** β is a contemporaneous linear projection, not a causal or structural parameter. Lagged growth is excluded, as the task specifies.\n- **Stability:** the sample is treated as having one stable relationship. Possible changes in the Okun coefficient over 1960–2019 are not modelled.\n- **Inference method:** the HAC inference relies on normal approximations in large samples. The rule-of-thumb bandwidth is not data-driven. Fixed-b critical values would give a somewhat wider interval, but they were not computed.\n- **Completeness:** the task was completed as specified (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 is about -0.256 pp of unemployment per 1% of quarterly real GDP growth.\",\"results_keys\":[\"beta_hat\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Contemporaneous linear projection; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using a Newey-West HAC SE with 4 lags; 95% CI [-0.326, -0.186].\",\"results_keys\":[\"okun_negative_5pct\",\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"t_stat_hac\",\"p_value_hac_two_sided\"],\"status\":\"supported\",\"caveat\":\"Normal approximation; rule-of-thumb bandwidth; robust across 0-12 lags.\"},\n {\"id\":\"C3\",\"text\":\"Residuals are positively autocorrelated, and the HAC SE is about 1.8 times the conventional iid SE, so iid inference understates uncertainty.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"resid_autocorr_lag1_4\",\"se_hac_by_lags\"],\"status\":\"supported\",\"caveat\":\"Ratio depends somewhat on bandwidth.\"},\n {\"id\":\"C4\",\"text\":\"Growth explains about 41% of the variance of quarterly unemployment changes; the implied growth rate that keeps unemployment stable is about 0.72% per quarter, close to the sample mean growth of 0.754%.\",\"results_keys\":[\"r_squared\",\"alpha_hat\",\"beta_hat\",\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"The stable-unemployment growth rate is -alpha_hat/beta_hat, derived from these reported keys.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));print(-r['alpha_hat']/r['beta_hat'])\"","description":"Write report and claims; check derived number"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1591,"cache_read_input_tokens":18555,"output_tokens":2638,"output_tokens_details":{"thinking_tokens":39},"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":1591},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqhRez3p2W4iHkPVWpn","requestRef":"711c73b7-9f99-4534-bf1a-a22f96dc462d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"26488bac-a598-4068-a913-14b5cdb7a456","timestamp":"2026-10-30T05:06:39.317Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"26488bac-a598-4068-a913-14b5cdb7a456","isSidechain":false,"promptId":"82aa89be-a4a9-4493-8616-cefb4c788a4c","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LsEpN56SjiE2bzHeQdphG5","type":"tool_result","content":"0.7209211099660543","is_error":false}]},"uuid":"a7bb7cbe-d705-405f-8979-ac8d996fd59e","timestamp":"2026-10-30T05:06:39.382Z","toolUseResult":{"stdout":"0.7209211099660543","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"26488bac-a598-4068-a913-14b5cdb7a456","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0225","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","version":"2.1.314","gitBranch":"HEAD"}61{"type":"api-request-blob","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","hash":"f70f5d378e4aa5081f79ccec43ff29e2ccf9d68222093250078d8b8063e67f79","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4 gate fails only on P5 deliverables (expected). Now the report, claims and ledger."},{"type":"tool_use","id":"toolu_01LsEpN56SjiE2bzHeQdphG5","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\nHow strongly does the change in the U.S. unemployment rate move with real GDP growth? We estimate the slope β (the Okun coefficient) in the linear projection Δu_t = α + β g_t + e_t. Here u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is quarter-on-quarter growth in percent, not annualised. The standard errors must stay valid when the disturbances are serially correlated.\n\n## Data and exclusions\n- `data/UNRATE.csv` (945 monthly rows) and `data/GDPC1.csv` (319 quarterly rows) were read as given (`n_input_rows`). Both SHA-256 checksums match `data/README.md`.\n- One data issue was found (`data_issues`): the UNRATE value for October 2025 is missing. That month is outside the window used here, so it has no effect.\n- Exclusion ledger (`out/exclusions.csv`), counted in GDPC1 quarters:\n  - Start: 319 quarters.\n  - Drop 5 quarters without a complete 3-month UNRATE average (1947Q1–Q4 and 2025Q4) → 314.\n  - Keep the levels for 1959Q4–2019Q4 → 241.\n  - Drop 1959Q4, which has no prior level to difference against → 240.\n- Result: `n_analysed` = 240 quarters, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4. Nothing after 2019Q4 is used.\n\n## Method\n- **Estimator:** OLS of Δu_t on a constant and g_t.\n- **Baseline uncertainty:** the conventional (iid, homoskedastic) standard error (`se_iid`).\n- **Main uncertainty:** a Newey–West HAC standard error with a Bartlett kernel (`se_hac`). It uses no small-sample degrees-of-freedom correction.\n- **Bandwidth:** `hac_lags` = 4. This comes from the standard Newey–West rule of thumb L = floor(4·(T/100)^(2/9)) with T = 240.\n- **Inference:** a 95% CI equal to β̂ ± 1.96·se_hac, and a two-sided normal-approximation test of H0: β = 0.\n- **Reproducibility:** no randomness is involved (`random_seed` = null). `out/analysis.py` regenerates all outputs and `out/figure1.png`.\n\n## Results\n- **Okun coefficient:** `beta_hat` = −0.256. Each extra 1% of quarterly real GDP growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter.\n- **Intercept:** `alpha_hat` = 0.184 pp. Together with the slope, this implies that unemployment holds steady at about 0.72% quarterly growth (≈2.9% a year). That is close to the sample mean growth, `mean_growth` = 0.754% per quarter.\n- **Fit:** `r_squared` = 0.408.\n- **Significance:** `okun_negative_5pct` = true. β̂ is negative, and H0: β = 0 is rejected at the 5% level using the HAC standard error (`t_stat_hac` = −7.14, `p_value_hac_two_sided` ≈ 1e−12).\n- Figure 1 (`out/figure1.png`) shows the scatter with the fitted line.\n\n## Uncertainty\n- **Standard errors:** `se_hac` = 0.0358 versus `se_iid` = 0.0200. The HAC standard error is about 1.8 times the naïve one.\n- **Why they differ:** the OLS residuals are serially correlated. Their autocorrelations at lags 1–4 are about 0.39, 0.29, 0.14 and 0.07 (`resid_autocorr_lag1_4`). Assuming iid errors therefore understates the uncertainty substantially.\n- **95% HAC CI:** [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186].\n- **Bandwidth sensitivity:** across bandwidths from 0 to 12 lags, the HAC standard error ranges from 0.029 to 0.037 (`se_hac_by_lags`). The conclusion that β is significantly negative does not depend on the bandwidth.\n\n## Limitations and open issues\n- **Vintage:** the data are the current vintage, not real-time data. GDP revisions could change the estimates slightly.\n- **Interpretation:** β is a contemporaneous linear projection, not a causal or structural parameter. Lagged growth is excluded, as the task specifies.\n- **Stability:** the sample is treated as having one stable relationship. Possible changes in the Okun coefficient over 1960–2019 are not modelled.\n- **Inference method:** the HAC inference relies on normal approximations in large samples. The rule-of-thumb bandwidth is not data-driven. Fixed-b critical values would give a somewhat wider interval, but they were not computed.\n- **Completeness:** the task was completed as specified (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 is about -0.256 pp of unemployment per 1% of quarterly real GDP growth.\",\"results_keys\":[\"beta_hat\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Contemporaneous linear projection; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using a Newey-West HAC SE with 4 lags; 95% CI [-0.326, -0.186].\",\"results_keys\":[\"okun_negative_5pct\",\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"t_stat_hac\",\"p_value_hac_two_sided\"],\"status\":\"supported\",\"caveat\":\"Normal approximation; rule-of-thumb bandwidth; robust across 0-12 lags.\"},\n {\"id\":\"C3\",\"text\":\"Residuals are positively autocorrelated, and the HAC SE is about 1.8 times the conventional iid SE, so iid inference understates uncertainty.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"resid_autocorr_lag1_4\",\"se_hac_by_lags\"],\"status\":\"supported\",\"caveat\":\"Ratio depends somewhat on bandwidth.\"},\n {\"id\":\"C4\",\"text\":\"Growth explains about 41% of the variance of quarterly unemployment changes; the implied growth rate that keeps unemployment stable is about 0.72% per quarter, close to the sample mean growth of 0.754%.\",\"results_keys\":[\"r_squared\",\"alpha_hat\",\"beta_hat\",\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"The stable-unemployment growth rate is -alpha_hat/beta_hat, derived from these reported keys.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));print(-r['alpha_hat']/r['beta_hat'])\"","description":"Write report and claims; check derived number"}}]}}62{"type":"api-request","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56","id":"a459b3f4-f2b2-4136-b20e-2648b76f747c","timestamp":"2026-10-30T05:06:39.385Z","version":"2.1.314","querySource":"sdk","shapeHash":"4f7e450178ef010fbf90281fc2ff1a316b2ccee21e8df755c5dd76b4e0cf378f","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":"711c73b7-9f99-4534-bf1a-a22f96dc462d","keep":18,"tail":["f70f5d378e4aa5081f79ccec43ff29e2ccf9d68222093250078d8b8063e67f79","a0ecbb7d3fce32bb84c1276805e99929d7175e10d057cbffdc94bae76ce44bd1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5b138391c3244ce63bdb401bfd6f2406bfea3259828b4fc0045a49e470d237a4"}63{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a7bb7cbe-d705-405f-8979-ac8d996fd59e","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}64{"type":"atis-latch","atis":"","sessionId":"07c0f836-0e20-417d-a9ac-344f94618d56"}65{"parentUuid":"a7bb7cbe-d705-405f-8979-ac8d996fd59e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqizB7NodcMpj6JCu8q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013ME1PXDs2maa6W5NW5L3pe","name":"Bash","input":{"command":"cat > 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 of both CSVs match README and bundle\",\"row counts 945/319\",\"missing/duplicates/gaps audited: 1 missing UNRATE (2025-10) outside window\",\"exclusion ledger sums 319-5-73-1=240\"],\"notes\":\"see data_audit.md\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed (se_iid=0.0200)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, no RNG; writes results.json, exclusions.csv, figure1.png\"],\"notes\":\"NW lag rule floor(4(T/100)^(2/9))=4\"},\n\"P4\":{\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"independent numpy NW recomputation matches\",\"identity checks in checks.md all PASS\",\"gate run 20261030T050618.264574Z: only P5 artifacts missing (expected at P4)\"],\"notes\":\"gate_receipts/20261030T050618.264574Z.json\"},\n\"P5\":{\"status\":\"passed\",\"checks\":[\"report.md with 6 sections citing results keys\",\"claims.json written\",\"final gate run (see log)\"],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P2-P4 passed (baseline, HAC, verification; 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The final gate check passed all 38 items, and rerunning the analysis script reproduced the results file byte for byte.\n\n| Quantity | Value |\n|---|---|\n| Okun coefficient (β̂) | −0.256 pp of unemployment per 1% of quarterly growth |\n| Intercept (α̂) | 0.184 pp |\n| R² | 0.408 |\n| Quarters used | 240 |\n| Conventional standard error | 0.0200 |\n| Robust (Newey–West) standard error | 0.0358, with 4 lags |\n| 95% confidence interval (robust) | [−0.326, −0.186] |\n| t-statistic (robust) | −7.14 |\n\n- **Why the robust error matters:** the regression residuals are positively correlated from one quarter to the next (about 0.39 at a one-quarter lag), so the robust standard error is about 1.8 times the conventional one.\n- **Choice of 4 lags:** it comes from the standard Newey–West rule of thumb for 240 quarters. Using anywhere from 0 to 12 lags gives a standard error between 0.029 and 0.037, so the conclusion does not depend on this choice.\n- **Breakeven growth:** the fit implies unemployment holds steady at about 0.72% growth per quarter, close to the sample average of 0.75%.\n- **Check:** a separate hand-coded calculation in numpy gave the same coefficient and standard errors.\n- **Data:** both files match their published checksums. One unemployment value is missing (October 2025), but it falls outside the window, so it has no effect.\n\n**Limitations:**\n- The data are today's revised figures, not what was known in real time.\n- β is a same-quarter association, not a causal effect.\n- The relationship is assumed stable over 1960–2019.\n- The confidence interval relies on a large-sample normal approximation. A more conservative method for this kind of robust error (fixed-b critical values) would give a somewhat wider interval; I did not compute it.\n\nEverything is in `out/`. 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