spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:02:46.234Z","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:02:46.241Z","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}4{"parentUuid":null,"isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":"Begin"},"uuid":"29dd78fd-b73e-4174-af27-a22229c540d2","timestamp":"2026-10-30T05:02:46.245Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","timestamp":"2026-10-30T05:02:46.260Z","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/RC0217\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"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"301098cc-ad4c-4f8a-8eed-ca1bb20da012","timestamp":"2026-10-30T05:02:46.260Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0e31c28d5acce97ddb8b8b848b00c3d3c9392c3f3f2b0f0397ecbcce79e25d08"}8{"parentUuid":"29dd78fd-b73e-4174-af27-a22229c540d2","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"58a5992c-42c9-4c03-85c9-0a67b1c68a23","timestamp":"2026-10-30T05:02:46.251Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"58a5992c-42c9-4c03-85c9-0a67b1c68a23","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"65076e04-646d-4c03-a669-ae078f4943b3","timestamp":"2026-10-30T05:02:46.251Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"65076e04-646d-4c03-a669-ae078f4943b3","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"17dbd0de-5a51-4224-85cb-ad34e1e3df13","timestamp":"2026-10-30T05:02:46.252Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; 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/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}12{"parentUuid":"17dbd0de-5a51-4224-85cb-ad34e1e3df13","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqRoaeptEtsadHgU2Wg","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2209,"cache_read_input_tokens":1504,"output_tokens":128,"output_tokens_details":{"thinking_tokens":20},"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":437,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqRoH3yrS89BwSseopG","requestRef":"301098cc-ad4c-4f8a-8eed-ca1bb20da012","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"805836c5-b88c-4c3a-97a3-41b55e6d7c61","timestamp":"2026-10-30T05:02:47.670Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"805836c5-b88c-4c3a-97a3-41b55e6d7c61","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqRoaeptEtsadHgU2Wg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01MzKUx2JmAEtyfs9XHQWjYL","name":"Bash","input":{"command":"cat TASK.md; ls -laR; cat out/state/* 2>/dev/null | head -50","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2209,"cache_read_input_tokens":1504,"output_tokens":128,"output_tokens_details":{"thinking_tokens":20},"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":2209},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqRoH3yrS89BwSseopG","requestRef":"301098cc-ad4c-4f8a-8eed-ca1bb20da012","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0c98684a-48ef-4564-a95e-79613768516e","timestamp":"2026-10-30T05:02:48.351Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"0c98684a-48ef-4564-a95e-79613768516e","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MzKUx2JmAEtyfs9XHQWjYL","type":"tool_result","content":"# 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). 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  127 simon-pierreboucher  staff  4064 oct.  30 01:02 ..\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\n\n./data:\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\n(eval):1: no matches found: out/state/*","is_error":false}]},"uuid":"53b56b8f-a59f-4531-8225-b351e1100f85","timestamp":"2026-10-30T05:02:48.465Z","toolUseResult":{"stdout":"# 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). 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  127 simon-pierreboucher  staff  4064 oct.  30 01:02 ..\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\n\n./data:\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\n(eval):1: no matches found: out/state/*","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0c98684a-48ef-4564-a95e-79613768516e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"f083d8d0b361793129d7972d8a91030c076368f6893922c44ab42d296b2357bd","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MzKUx2JmAEtyfs9XHQWjYL","name":"Bash","input":{"command":"cat TASK.md; ls -laR; cat out/state/* 2>/dev/null | head -50","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"fadaff19-0061-4cc6-9380-e3f8a3b5d62a","timestamp":"2026-10-30T05:02:48.469Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"301098cc-ad4c-4f8a-8eed-ca1bb20da012","keep":2,"tail":["f083d8d0b361793129d7972d8a91030c076368f6893922c44ab42d296b2357bd","74695cc4c4a44db2b5107cdfd1d2f15d31211c5d16a8ba5f36f9faa6f4f03267"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a436f356c0068681c136673a9f21681aa9f07a189779b65b2da9e86dc6532e13"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"53b56b8f-a59f-4531-8225-b351e1100f85","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}18{"type":"atis-latch","atis":"","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}19{"parentUuid":"53b56b8f-a59f-4531-8225-b351e1100f85","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqRxtzhy8UjJ4RURwnk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01PBJJ1WBQutmSWGcWtYmW5f","name":"Bash","input":{"command":"cat data/README.md; cat bundle.json gate_spec.json; head -3 data/*.csv; tail -3 data/*.csv; shasum -a 256 data/*; wc -l data/*.csv","description":"Inspect data files and metadata"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2525,"cache_read_input_tokens":3713,"output_tokens":144,"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":2525},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqRxds8fSBS5JGHuii5","requestRef":"fadaff19-0061-4cc6-9380-e3f8a3b5d62a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7de5c817-045d-4c9c-86a4-232851f5d5db","timestamp":"2026-10-30T05:02:50.535Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"7de5c817-045d-4c9c-86a4-232851f5d5db","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01PBJJ1WBQutmSWGcWtYmW5f","type":"tool_result","content":"# 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  \"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}{\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==> 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\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total","is_error":false}]},"uuid":"5127146a-b98a-4a7f-b0f3-bc7db54ccf7d","timestamp":"2026-10-30T05:02:50.599Z","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  \"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}{\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==> 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\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7de5c817-045d-4c9c-86a4-232851f5d5db","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"9199f3cdfa97decf87efb5776651388dac4861255565b9ced46b38fb2724f0fc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01PBJJ1WBQutmSWGcWtYmW5f","name":"Bash","input":{"command":"cat data/README.md; cat bundle.json gate_spec.json; head -3 data/*.csv; tail -3 data/*.csv; shasum -a 256 data/*; wc -l data/*.csv","description":"Inspect data files and metadata"}}]}}22{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"dbfa9f4a-a87a-4735-abee-5a3644d1e3e6","timestamp":"2026-10-30T05:02:50.603Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"fadaff19-0061-4cc6-9380-e3f8a3b5d62a","keep":4,"tail":["9199f3cdfa97decf87efb5776651388dac4861255565b9ced46b38fb2724f0fc","8334e9803874861e02eac35789bccb3ad64e5541efdde52c5c7b132e3bad4a1c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9d261a15a6ab4933809b93e0e786747ffef274f38abd712dfe1693005fff2699"}23{"parentUuid":"5127146a-b98a-4a7f-b0f3-bc7db54ccf7d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqS89N3fWEvmw9K7XKk","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":2355,"cache_read_input_tokens":6238,"output_tokens":445,"output_tokens_details":{"thinking_tokens":137},"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":2355},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":985,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqS7tEcKD2naNkWpLqn","requestRef":"dbfa9f4a-a87a-4735-abee-5a3644d1e3e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"226b7cae-1dbc-476f-a432-17fec5768bfb","timestamp":"2026-10-30T05:03:01.453Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"226b7cae-1dbc-476f-a432-17fec5768bfb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqS89N3fWEvmw9K7XKk","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014zWKTqEfCdvd66CHiduXXL","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['UNRATE','GDPC1']:\n    d=pd.read_csv(f'data/{f}.csv',keep_default_na=False,dtype=str)\n    print(f,d.shape, d.observation_date.duplicated().sum())\n    v=pd.to_numeric(d[f],errors='coerce'); print('nonnumeric:',d[v.isna()])\n    dt=pd.to_datetime(d.observation_date); print(dt.min(),dt.max(), dt.diff().dt.days.value_counts().to_dict())\n    print(v.describe())\n\"","description":"Audit data for missing/duplicate values"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2355,"cache_read_input_tokens":6238,"output_tokens":445,"output_tokens_details":{"thinking_tokens":137},"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":2355},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqS7tEcKD2naNkWpLqn","requestRef":"dbfa9f4a-a87a-4735-abee-5a3644d1e3e6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9588db72-8049-48e2-8c0a-5d7a59a10a00","timestamp":"2026-10-30T05:03:03.511Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"9588db72-8049-48e2-8c0a-5d7a59a10a00","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014zWKTqEfCdvd66CHiduXXL","type":"tool_result","content":"UNRATE (945, 2) 0\nnonnumeric:     observation_date UNRATE\n933       2025-10-01       \n1948-01-01 00:00:00 2026-09-01 00:00:00 {31.0: 551, 30.0: 314, 28.0: 59, 29.0: 20}\ncount    944.000000\nmean       5.652966\nstd        1.703534\nmin        2.500000\n25%        4.300000\n50%        5.450000\n75%        6.700000\nmax       14.800000\nName: UNRATE, dtype: float64\nGDPC1 (319, 2) 0\nnonnumeric: Empty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: []\n1947-01-01 00:00:00 2026-07-01 00:00:00 {92.0: 158, 91.0: 100, 90.0: 60}\ncount      319.000000\nmean     10458.210204\nstd       6485.042448\nmin       2172.432000\n25%       4848.918000\n50%       8920.193000\n75%      16340.058000\nmax      24611.077000\nName: GDPC1, dtype: float64","is_error":false}]},"uuid":"d95d8edf-c319-4c06-ac99-417462f4f919","timestamp":"2026-10-30T05:03:03.833Z","toolUseResult":{"stdout":"UNRATE (945, 2) 0\nnonnumeric:     observation_date UNRATE\n933       2025-10-01       \n1948-01-01 00:00:00 2026-09-01 00:00:00 {31.0: 551, 30.0: 314, 28.0: 59, 29.0: 20}\ncount    944.000000\nmean       5.652966\nstd        1.703534\nmin        2.500000\n25%        4.300000\n50%        5.450000\n75%        6.700000\nmax       14.800000\nName: UNRATE, dtype: float64\nGDPC1 (319, 2) 0\nnonnumeric: Empty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: []\n1947-01-01 00:00:00 2026-07-01 00:00:00 {92.0: 158, 91.0: 100, 90.0: 60}\ncount      319.000000\nmean     10458.210204\nstd       6485.042448\nmin       2172.432000\n25%       4848.918000\n50%       8920.193000\n75%      16340.058000\nmax      24611.077000\nName: GDPC1, dtype: float64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9588db72-8049-48e2-8c0a-5d7a59a10a00","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"7b14a9168db93ba5064536db3c36be48d81c8eb6390f5fb26797bcfdc67583da","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014zWKTqEfCdvd66CHiduXXL","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['UNRATE','GDPC1']:\n    d=pd.read_csv(f'data/{f}.csv',keep_default_na=False,dtype=str)\n    print(f,d.shape, d.observation_date.duplicated().sum())\n    v=pd.to_numeric(d[f],errors='coerce'); print('nonnumeric:',d[v.isna()])\n    dt=pd.to_datetime(d.observation_date); print(dt.min(),dt.max(), dt.diff().dt.days.value_counts().to_dict())\n    print(v.describe())\n\"","description":"Audit data for missing/duplicate values"}}]}}27{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"31b55534-5f4f-4dd5-8608-3ff2569cfc22","timestamp":"2026-10-30T05:03:03.836Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"dbfa9f4a-a87a-4735-abee-5a3644d1e3e6","keep":6,"tail":["7b14a9168db93ba5064536db3c36be48d81c8eb6390f5fb26797bcfdc67583da","c162de80a4ef13fd4d5ec3099d8106f7dbaf9c65a82f795236a47cc896aaf4f6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"0abf65bd6cf5a38c4687718f320cab63189faf00edf3803d7c5d0f2c2990129d"}28{"parentUuid":"d95d8edf-c319-4c06-ac99-417462f4f919","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqT6fapTehCtjZFthzs","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":813,"cache_read_input_tokens":8593,"output_tokens":1182,"output_tokens_details":{"thinking_tokens":246},"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":813},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2248,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqT6MFEJV5NTWcDY2T7","requestRef":"31b55534-5f4f-4dd5-8608-3ff2569cfc22","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"578f2d1c-d3f5-484b-a6fd-912a875de2e0","timestamp":"2026-10-30T05:03:07.411Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"578f2d1c-d3f5-484b-a6fd-912a875de2e0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqT6fapTehCtjZFthzs","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UJyRgbi9o4sJh54FueRYsP","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question:** Okun's law in U.S. quarterly data 1960Q1–2019Q4: slope β of Δu_t = α + β g_t + e_t.\n**Estimands:** β (linear-projection slope; pp change in unemployment per 1% q/q real GDP growth, non-annualised), α, R². Meaningful as a linear projection (no causal claim); inference requires stationarity / weak dependence of (Δu, g); HAC SE valid under serially correlated, heteroskedastic errors.\n**Construction:** u_t = mean of 3 monthly UNRATE values in quarter t; Δu_t = u_t − u_{t−1}; g_t = 100·ln(GDPC1_t/GDPC1_{t−1}). Sample 1960Q1–2019Q4 (240 quarters); 1959Q4 levels used for first differences only.\n**Outputs:** out/results.json (task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png; plus out/exclusions.csv and out/state/*.\n**Information rules:** only data/ files, current vintage; no observations outside window except 1959Q4 levels; do not extend window.\n**Invalidity/impossibility:** missing monthly data inside 1959Q4–2019Q4, missing GDP quarters, n≠240, annualised growth, using data outside window.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30: P0 passed (contract written). P1 started.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":813,"cache_read_input_tokens":8593,"output_tokens":1182,"output_tokens_details":{"thinking_tokens":246},"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":813},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqT6MFEJV5NTWcDY2T7","requestRef":"31b55534-5f4f-4dd5-8608-3ff2569cfc22","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1dcfe431-db12-4d2a-832a-0764771afc16","timestamp":"2026-10-30T05:03:14.655Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}30{"parentUuid":"1dcfe431-db12-4d2a-832a-0764771afc16","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01UJyRgbi9o4sJh54FueRYsP","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"f6a72c66-2eb9-448e-a607-320ba87c0125","timestamp":"2026-10-30T05:03:14.710Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1dcfe431-db12-4d2a-832a-0764771afc16","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}31{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"3b44a383af3ee3c7dd9484d783d6642a5ed00dff11b650dc7266e89a29f3dbe2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01UJyRgbi9o4sJh54FueRYsP","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question:** Okun's law in U.S. quarterly data 1960Q1–2019Q4: slope β of Δu_t = α + β g_t + e_t.\n**Estimands:** β (linear-projection slope; pp change in unemployment per 1% q/q real GDP growth, non-annualised), α, R². Meaningful as a linear projection (no causal claim); inference requires stationarity / weak dependence of (Δu, g); HAC SE valid under serially correlated, heteroskedastic errors.\n**Construction:** u_t = mean of 3 monthly UNRATE values in quarter t; Δu_t = u_t − u_{t−1}; g_t = 100·ln(GDPC1_t/GDPC1_{t−1}). Sample 1960Q1–2019Q4 (240 quarters); 1959Q4 levels used for first differences only.\n**Outputs:** out/results.json (task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png; plus out/exclusions.csv and out/state/*.\n**Information rules:** only data/ files, current vintage; no observations outside window except 1959Q4 levels; do not extend window.\n**Invalidity/impossibility:** missing monthly data inside 1959Q4–2019Q4, missing GDP quarters, n≠240, annualised growth, using data outside window.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30: P0 passed (contract written). P1 started.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}32{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"5a2874c7-7be9-4824-affd-f67f68bdbecc","timestamp":"2026-10-30T05:03:14.713Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"31b55534-5f4f-4dd5-8608-3ff2569cfc22","keep":8,"tail":["3b44a383af3ee3c7dd9484d783d6642a5ed00dff11b650dc7266e89a29f3dbe2","0da115e6789b5e56177d1a793d0d0b738d7c216be10900d4a5d95c13b8f5fe1b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9b1a6503c607da502e09773819037ccafe7c675f7f4ae6624756a7d7f8f56a12"}33{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f6a72c66-2eb9-448e-a607-320ba87c0125","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}34{"type":"atis-latch","atis":"","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}35{"parentUuid":"f6a72c66-2eb9-448e-a607-320ba87c0125","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqTu8pbM5BwzS6vUur6","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (covers audit counts, baseline, HAC, figure, exclusions)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1197,"cache_read_input_tokens":9406,"output_tokens":3113,"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":1197},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqTtqxa1GQxFLNp33za","requestRef":"5a2874c7-7be9-4824-affd-f67f68bdbecc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8aa03684-e019-4220-8039-129053bde215","timestamp":"2026-10-30T05:03:15.987Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"8aa03684-e019-4220-8039-129053bde215","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqTu8pbM5BwzS6vUur6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_013f4hQmXcpNobvqJQva76S2","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, U.S. quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/exclusions.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1  # 1959Q4 levels needed for first differences\n\nun = pd.read_csv(\"data/UNRATE.csv\", keep_default_na=False, dtype=str)\ngd = pd.read_csv(\"data/GDPC1.csv\", keep_default_na=False, dtype=str)\nn_input_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\ndata_issues = []\nexcl = []\n\ndef add_excl(step, rule, nb, na, why):\n    excl.append(dict(step=step, rule=rule, n_before=nb, n_after=na, n_removed=nb - na, rationale=why))\n\n# --- unemployment: monthly -> quarterly average\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"v\"] = pd.to_numeric(un[\"UNRATE\"].replace(\"\", np.nan), errors=\"coerce\")\nmiss = un[un[\"v\"].isna()]\nif len(miss):\n    inwin = [d for d in miss[\"date\"] if LEVEL_START <= d.to_period(\"Q\") <= LAST]\n    data_issues.append({\n        \"issue\": f\"UNRATE missing (empty) for {', '.join(miss['observation_date'])}\",\n        \"handling\": (\"outside the 1959Q4-2019Q4 window; does not affect the analysis\" if not inwin\n                     else \"INSIDE window: affected quarter averaged over available months\")})\nassert not un[\"date\"].duplicated().any() and not gd[\"observation_date\"].duplicated().any()\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nm = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)]\nassert m[\"v\"].notna().all()\ncnt = m.groupby(\"q\")[\"v\"].count()\nassert (cnt == 3).all(), \"incomplete quarter in window\"\nu = m.groupby(\"q\")[\"v\"].mean()\n\n# --- GDP\ngd[\"q\"] = pd.to_datetime(gd[\"observation_date\"]).dt.to_period(\"Q\")\ngd[\"v\"] = pd.to_numeric(gd[\"GDPC1\"], errors=\"coerce\")\nassert gd[\"v\"].notna().all()\ny = gd.set_index(\"q\")[\"v\"]\ny = y[(y.index >= LEVEL_START) & (y.index <= LAST)]\n\ndf = pd.DataFrame({\"u\": u, \"y\": y})\nassert df.notna().all().all() and len(df) == 241\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"y\"] / df[\"y\"].shift(1))\n\n# exclusion ledger (in units of quarters, starting from the full GDP quarterly range)\nnq_all = len(pd.period_range(gd[\"q\"].min(), gd[\"q\"].max(), freq=\"Q\"))\nadd_excl(1, \"keep quarters 1959Q4-2019Q4 (levels window)\", nq_all, len(df),\n         \"task window 1960Q1-2019Q4 plus 1959Q4 levels for first differences; window not extended\")\nd = df.dropna().loc[FIRST:LAST]\nadd_excl(2, \"drop 1959Q4 (no first difference; levels only)\", len(df), len(d),\n         \"1959Q4 used only as lag level for 1960Q1 differences\")\nn = len(d)\nassert n == 240\n\n# --- OLS: baseline iid SE and Newey-West HAC SE\nX = sm.add_constant(d[\"g\"].values)\nY = d[\"du\"].values\nols = sm.OLS(Y, X).fit()\nT = n\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))  # Newey-West (1994) plug-in rule -> 4\nhac = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\", \"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 = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_hac, beta + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(beta / se_hac)))\n\n# sensitivity of HAC SE to bandwidth (reported, not used for headline)\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)] = float(r.bse[1])\nhac_dfc = float(sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"use_correction\": True}).bse[1])\nresid_ac1 = float(pd.Series(ols.resid).autocorr(1))\n\nres = {\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\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n    \"mean_growth\": float(d[\"g\"].mean()),\n    \"first_quarter\": str(d.index[0]), \"last_quarter\": str(d.index[-1]),\n    \"n_analysed\": int(n),\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_hac\": float(beta / se_hac), \"p_value_hac\": p_hac,\n    \"se_hac_by_lags\": sens, \"se_hac_lag4_dfcorrected\": hac_dfc,\n    \"residual_autocorr_lag1\": resid_ac1,\n    \"mean_du\": float(d[\"du\"].mean()),\n    \"baseline_ci_iid_low\": float(beta - z * se_iid), \"baseline_ci_iid_high\": float(beta + z * se_iid),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\npd.DataFrame(excl).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(d[\"g\"], d[\"du\"], s=12, alpha=0.6)\ngx = np.linspace(d[\"g\"].min(), d[\"g\"].max(), 100)\nax.plot(gx, alpha + beta * gx, color=\"red\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: res[k] for k in [\"beta_hat\", \"alpha_hat\", \"se_iid\", \"se_hac\", \"hac_lags\",\n                                     \"beta_ci_low\", \"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":1197,"cache_read_input_tokens":9406,"output_tokens":3113,"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":1197},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqTtqxa1GQxFLNp33za","requestRef":"5a2874c7-7be9-4824-affd-f67f68bdbecc","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e1f0bdbe-311c-48a3-86d1-6c95b3a18083","timestamp":"2026-10-30T05:03:38.756Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"e1f0bdbe-311c-48a3-86d1-6c95b3a18083","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013f4hQmXcpNobvqJQva76S2","type":"tool_result","content":"{\n \"beta_hat\": -0.25576772774872947,\n \"alpha_hat\": 0.18438835418210964,\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 (empty) for 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; does not affect the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"se_hac_by_lags\": {\n    \"0\": 0.028836223207270188,\n    \"1\": 0.032358596534706116,\n    \"2\": 0.034227320236154,\n    \"4\": 0.03583463330296578,\n    \"6\": 0.03687902855135959,\n    \"8\": 0.03713550417540173,\n    \"12\": 0.03640885110228042\n  },\n  \"se_hac_lag4_dfcorrected\": 0.03598488399675887,\n  \"residual_autocorr_lag1\": 0.38935129370212984,\n  \"mean_du\": -0.008333333333333335,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272\n}step,rule,n_before,n_after,n_removed,rationale\n1,keep quarters 1959Q4-2019Q4 (levels window),319,241,78,task window 1960Q1-2019Q4 plus 1959Q4 levels for first differences; window not extended\n2,drop 1959Q4 (no first difference; levels only),241,240,1,1959Q4 used only as lag level for 1960Q1 differences","is_error":false}]},"uuid":"2285d591-4c20-4af6-b9ee-922a6994e892","timestamp":"2026-10-30T05:03:39.972Z","toolUseResult":{"stdout":"{\n \"beta_hat\": -0.25576772774872947,\n \"alpha_hat\": 0.18438835418210964,\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 (empty) for 2025-10-01\",\n      \"handling\": \"outside the 1959Q4-2019Q4 window; does not affect the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"se_hac_by_lags\": {\n    \"0\": 0.028836223207270188,\n    \"1\": 0.032358596534706116,\n    \"2\": 0.034227320236154,\n    \"4\": 0.03583463330296578,\n    \"6\": 0.03687902855135959,\n    \"8\": 0.03713550417540173,\n    \"12\": 0.03640885110228042\n  },\n  \"se_hac_lag4_dfcorrected\": 0.03598488399675887,\n  \"residual_autocorr_lag1\": 0.38935129370212984,\n  \"mean_du\": -0.008333333333333335,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272\n}step,rule,n_before,n_after,n_removed,rationale\n1,keep quarters 1959Q4-2019Q4 (levels window),319,241,78,task window 1960Q1-2019Q4 plus 1959Q4 levels for first differences; window not extended\n2,drop 1959Q4 (no first difference; levels only),241,240,1,1959Q4 used only as lag level for 1960Q1 differences","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e1f0bdbe-311c-48a3-86d1-6c95b3a18083","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"6df0cb174fe4c16409073fca14989926268ecb33907ece051ce8ff4490b9cebe","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (covers audit counts, baseline, HAC, figure, exclusions)."},{"type":"tool_use","id":"toolu_013f4hQmXcpNobvqJQva76S2","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, U.S. quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/exclusions.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1  # 1959Q4 levels needed for first differences\n\nun = pd.read_csv(\"data/UNRATE.csv\", keep_default_na=False, dtype=str)\ngd = pd.read_csv(\"data/GDPC1.csv\", keep_default_na=False, dtype=str)\nn_input_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\ndata_issues = []\nexcl = []\n\ndef add_excl(step, rule, nb, na, why):\n    excl.append(dict(step=step, rule=rule, n_before=nb, n_after=na, n_removed=nb - na, rationale=why))\n\n# --- unemployment: monthly -> quarterly average\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"v\"] = pd.to_numeric(un[\"UNRATE\"].replace(\"\", np.nan), errors=\"coerce\")\nmiss = un[un[\"v\"].isna()]\nif len(miss):\n    inwin = [d for d in miss[\"date\"] if LEVEL_START <= d.to_period(\"Q\") <= LAST]\n    data_issues.append({\n        \"issue\": f\"UNRATE missing (empty) for {', '.join(miss['observation_date'])}\",\n        \"handling\": (\"outside the 1959Q4-2019Q4 window; does not affect the analysis\" if not inwin\n                     else \"INSIDE window: affected quarter averaged over available months\")})\nassert not un[\"date\"].duplicated().any() and not gd[\"observation_date\"].duplicated().any()\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nm = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)]\nassert m[\"v\"].notna().all()\ncnt = m.groupby(\"q\")[\"v\"].count()\nassert (cnt == 3).all(), \"incomplete quarter in window\"\nu = m.groupby(\"q\")[\"v\"].mean()\n\n# --- GDP\ngd[\"q\"] = pd.to_datetime(gd[\"observation_date\"]).dt.to_period(\"Q\")\ngd[\"v\"] = pd.to_numeric(gd[\"GDPC1\"], errors=\"coerce\")\nassert gd[\"v\"].notna().all()\ny = gd.set_index(\"q\")[\"v\"]\ny = y[(y.index >= LEVEL_START) & (y.index <= LAST)]\n\ndf = pd.DataFrame({\"u\": u, \"y\": y})\nassert df.notna().all().all() and len(df) == 241\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"y\"] / df[\"y\"].shift(1))\n\n# exclusion ledger (in units of quarters, starting from the full GDP quarterly range)\nnq_all = len(pd.period_range(gd[\"q\"].min(), gd[\"q\"].max(), freq=\"Q\"))\nadd_excl(1, \"keep quarters 1959Q4-2019Q4 (levels window)\", nq_all, len(df),\n         \"task window 1960Q1-2019Q4 plus 1959Q4 levels for first differences; window not extended\")\nd = df.dropna().loc[FIRST:LAST]\nadd_excl(2, \"drop 1959Q4 (no first difference; levels only)\", len(df), len(d),\n         \"1959Q4 used only as lag level for 1960Q1 differences\")\nn = len(d)\nassert n == 240\n\n# --- OLS: baseline iid SE and Newey-West HAC SE\nX = sm.add_constant(d[\"g\"].values)\nY = d[\"du\"].values\nols = sm.OLS(Y, X).fit()\nT = n\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))  # Newey-West (1994) plug-in rule -> 4\nhac = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\", \"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 = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_hac, beta + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(beta / se_hac)))\n\n# sensitivity of HAC SE to bandwidth (reported, not used for headline)\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)] = float(r.bse[1])\nhac_dfc = float(sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"use_correction\": True}).bse[1])\nresid_ac1 = float(pd.Series(ols.resid).autocorr(1))\n\nres = {\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\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n    \"mean_growth\": float(d[\"g\"].mean()),\n    \"first_quarter\": str(d.index[0]), \"last_quarter\": str(d.index[-1]),\n    \"n_analysed\": int(n),\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_hac\": float(beta / se_hac), \"p_value_hac\": p_hac,\n    \"se_hac_by_lags\": sens, \"se_hac_lag4_dfcorrected\": hac_dfc,\n    \"residual_autocorr_lag1\": resid_ac1,\n    \"mean_du\": float(d[\"du\"].mean()),\n    \"baseline_ci_iid_low\": float(beta - z * se_iid), \"baseline_ci_iid_high\": float(beta + z * se_iid),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as f:\n    json.dump(res, f, indent=2)\npd.DataFrame(excl).to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(d[\"g\"], d[\"du\"], s=12, alpha=0.6)\ngx = np.linspace(d[\"g\"].min(), d[\"g\"].max(), 100)\nax.plot(gx, alpha + beta * gx, color=\"red\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout()\nfig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: res[k] for k in [\"beta_hat\", \"alpha_hat\", \"se_iid\", \"se_hac\", \"hac_lags\",\n                                     \"beta_ci_low\", \"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":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"6fec5d64-c879-4ae3-9f5d-00fb3b75234e","timestamp":"2026-10-30T05:03:39.975Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"5a2874c7-7be9-4824-affd-f67f68bdbecc","keep":10,"tail":["6df0cb174fe4c16409073fca14989926268ecb33907ece051ce8ff4490b9cebe","552aea4884b8845704ec9263d0287d548f8952d7171526a1bcdbe04a9f6bb84c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"09a31e82957ea94c88d1aefd839829066b70a7f8278fa35ed6e74d64ea5b82e2"}40{"parentUuid":"2285d591-4c20-4af6-b9ee-922a6994e892","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqVm8fkfStQ4REzw6Yx","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":4095,"cache_read_input_tokens":10603,"output_tokens":1011,"output_tokens_details":{"thinking_tokens":58},"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":4095},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":502,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqVksYwjVaptXgXLkZH","requestRef":"6fec5d64-c879-4ae3-9f5d-00fb3b75234e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"44810474-9b54-4916-9dd2-d93ee89fa96b","timestamp":"2026-10-30T05:03:42.981Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"44810474-9b54-4916-9dd2-d93ee89fa96b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqVm8fkfStQ4REzw6Yx","type":"message","role":"assistant","content":[{"type":"text","text":"Now verification: re-run from clean and compare, plus consistency checks."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4095,"cache_read_input_tokens":10603,"output_tokens":1011,"output_tokens_details":{"thinking_tokens":58},"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":4095},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqVksYwjVaptXgXLkZH","requestRef":"6fec5d64-c879-4ae3-9f5d-00fb3b75234e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f9e128df-48da-4215-8fd1-721afde7482a","timestamp":"2026-10-30T05:03:42.981Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"f9e128df-48da-4215-8fd1-721afde7482a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqVm8fkfStQ4REzw6Yx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Pu9SyDqjuN893s32fwWgyR","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/figure1.png out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ~/wp29/venv/bin/python -c \"\nimport json,pandas as pd\nr=json.load(open('out/results.json'));e=pd.read_csv('out/exclusions.csv')\nprint('ci contains', r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'])\nprint('se pos', r['se_iid']>0, r['se_hac']>0)\nprint('excl sum', 319-e.n_removed.sum()==r['n_analysed'], (e.n_before-e.n_after==e.n_removed).all())\nprint('ci symmetric', abs((r['beta_ci_high']-r['beta_hat'])/r['se_hac']-1.959964)<1e-5)\nprint('hac>iid', r['se_hac']>r['se_iid'])\n# independent recomputation by hand\nimport numpy as np\nu=pd.read_csv('data/UNRATE.csv',parse_dates=[0]); g=pd.read_csv('data/GDPC1.csv',parse_dates=[0])\nu=u.set_index('observation_date').UNRATE.resample('QS').mean(); g=g.set_index('observation_date').GDPC1\nd=pd.DataFrame({'du':u.diff(),'g':100*np.log(g).diff()}).loc['1960-01-01':'2019-10-01']\nx=d.g.values-d.g.mean(); y=d.du.values; b=(x*y).sum()/(x*x).sum(); e=y-d.du.mean()-b*x\nX=np.c_[np.ones(len(y)),d.g.values];Q=np.linalg.inv(X.T@X);S=np.zeros((2,2));L=4\nfor j in range(L+1):\n  w=1 if j==0 else 2*(1-j/(L+1)); G=sum(np.outer(X[t]*e[t],X[t-j]*e[t-j]) for t in range(j,len(y)))\n  S+= (G if j==0 else (G+G.T)/2*w/1) if j==0 else (1-j/(L+1))*(G+G.T)\nV=Q@S@Q; print('manual beta',b,'manual se_hac',np.sqrt(V[1,1]), len(d), d.g.mean())\n\"; ls out","description":"Rerun, compare, and run consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4095,"cache_read_input_tokens":10603,"output_tokens":1011,"output_tokens_details":{"thinking_tokens":58},"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":4095},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqVksYwjVaptXgXLkZH","requestRef":"6fec5d64-c879-4ae3-9f5d-00fb3b75234e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b0dd9ded-577e-448a-a2e3-3c9638858e8e","timestamp":"2026-10-30T05:03:50.040Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"b0dd9ded-577e-448a-a2e3-3c9638858e8e","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Pu9SyDqjuN893s32fwWgyR","type":"tool_result","content":"IDENTICAL\nci contains True\nse pos True True\nexcl sum True True\nci symmetric True\nhac>iid True\nmanual beta -0.25576772774872575 manual se_hac 0.03583463330296568 240 0.753502755065237\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate","is_error":false}]},"uuid":"b23ea6da-c0e6-4325-9ac1-bcc9436a7e02","timestamp":"2026-10-30T05:03:51.554Z","toolUseResult":{"stdout":"IDENTICAL\nci contains True\nse pos True True\nexcl sum True True\nci symmetric True\nhac>iid True\nmanual beta -0.25576772774872575 manual se_hac 0.03583463330296568 240 0.753502755065237\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b0dd9ded-577e-448a-a2e3-3c9638858e8e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}44{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"4107025781badf3007fbdfa1a8a4dd64445b3d504ed9af2dc19e385daf0efa2a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now verification: re-run from clean and compare, plus consistency checks."},{"type":"tool_use","id":"toolu_01Pu9SyDqjuN893s32fwWgyR","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm out/results.json out/figure1.png out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL; ~/wp29/venv/bin/python -c \"\nimport json,pandas as pd\nr=json.load(open('out/results.json'));e=pd.read_csv('out/exclusions.csv')\nprint('ci contains', r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'])\nprint('se pos', r['se_iid']>0, r['se_hac']>0)\nprint('excl sum', 319-e.n_removed.sum()==r['n_analysed'], (e.n_before-e.n_after==e.n_removed).all())\nprint('ci symmetric', abs((r['beta_ci_high']-r['beta_hat'])/r['se_hac']-1.959964)<1e-5)\nprint('hac>iid', r['se_hac']>r['se_iid'])\n# independent recomputation by hand\nimport numpy as np\nu=pd.read_csv('data/UNRATE.csv',parse_dates=[0]); g=pd.read_csv('data/GDPC1.csv',parse_dates=[0])\nu=u.set_index('observation_date').UNRATE.resample('QS').mean(); g=g.set_index('observation_date').GDPC1\nd=pd.DataFrame({'du':u.diff(),'g':100*np.log(g).diff()}).loc['1960-01-01':'2019-10-01']\nx=d.g.values-d.g.mean(); y=d.du.values; b=(x*y).sum()/(x*x).sum(); e=y-d.du.mean()-b*x\nX=np.c_[np.ones(len(y)),d.g.values];Q=np.linalg.inv(X.T@X);S=np.zeros((2,2));L=4\nfor j in range(L+1):\n  w=1 if j==0 else 2*(1-j/(L+1)); G=sum(np.outer(X[t]*e[t],X[t-j]*e[t-j]) for t in range(j,len(y)))\n  S+= (G if j==0 else (G+G.T)/2*w/1) if j==0 else (1-j/(L+1))*(G+G.T)\nV=Q@S@Q; print('manual beta',b,'manual se_hac',np.sqrt(V[1,1]), len(d), d.g.mean())\n\"; ls out","description":"Rerun, compare, and run consistency checks"}}]}}45{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"e6a5055e-ebda-4d14-aece-09738351da5f","timestamp":"2026-10-30T05:03:51.557Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"6fec5d64-c879-4ae3-9f5d-00fb3b75234e","keep":12,"tail":["4107025781badf3007fbdfa1a8a4dd64445b3d504ed9af2dc19e385daf0efa2a","3abbc4a7a756d87efbf80834a0e70ee518f70ccdc8b710f81bf9dbec0f677bb1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c6627653c950036872c0ad907671f72cf29948a4bf1d0415757ba913e32b56f9"}46{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"b23ea6da-c0e6-4325-9ac1-bcc9436a7e02","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}47{"type":"atis-latch","atis":"","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}48{"parentUuid":"b23ea6da-c0e6-4325-9ac1-bcc9436a7e02","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWchwspfqCuZivheBY","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":1123,"cache_read_input_tokens":14698,"output_tokens":2073,"output_tokens_details":{"thinking_tokens":62},"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":1123},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":799,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqWcQak8hCWoDjpQpdE","requestRef":"e6a5055e-ebda-4d14-aece-09738351da5f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"be434feb-d2b8-4513-8728-d5e26b4bec80","timestamp":"2026-10-30T05:03:53.901Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"be434feb-d2b8-4513-8728-d5e26b4bec80","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWchwspfqCuZivheBY","type":"message","role":"assistant","content":[{"type":"text","text":"All checks pass, and an independent manual recomputation gives the same β̂ and HAC SE. Now the documentation files."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1123,"cache_read_input_tokens":14698,"output_tokens":2073,"output_tokens_details":{"thinking_tokens":62},"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":1123},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqWcQak8hCWoDjpQpdE","requestRef":"e6a5055e-ebda-4d14-aece-09738351da5f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7179125e-4be7-46a2-889f-ae8b3ae1f975","timestamp":"2026-10-30T05:03:53.901Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}50{"parentUuid":"7179125e-4be7-46a2-889f-ae8b3ae1f975","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqWchwspfqCuZivheBY","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_016N51Q4xupGaum46iikX1MA","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | range | missing | duplicates |\n|---|---|---|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | 945 | 2 (observation_date, UNRATE) | 1948-01-01 to 2026-09-01, monthly | 1 empty value: 2025-10-01 | 0 duplicate dates |\n| data/GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | 319 | 2 (observation_date, GDPC1) | 1947Q1 to 2026Q3, quarterly | 0 | 0 |\n| data/README.md | cb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c | – | – | – | – | – |\n\nSHA-256 of both CSVs match README.md and bundle.json. Row counts match TASK.md (945, 319).\nUnits: UNRATE percent (SA), range 2.5–14.8; GDPC1 billions chained 2017 $ SAAR, 2172–24611. Date spacing regular (no gaps).\nAnomaly: UNRATE for Oct 2025 is empty (no release, likely the 2025 federal shutdown). It lies outside the 1959Q4–2019Q4 window → no effect; recorded in results.json `data_issues`.\nEvery quarter 1959Q4–2019Q4 has exactly 3 monthly UNRATE values (asserted in analysis.py).\nAvailability: the files are a current vintage (not real-time), which the task explicitly allows. Data after 2019Q4 and before 1959Q4 are read but discarded (exclusion ledger step 1).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: deleted results.json/figure1.png/exclusions.csv, re-ran analysis.py; `cmp` with previous results.json → byte-identical. PASS\n2. CI contains estimate: -0.3260 < -0.2558 < -0.1855. PASS\n3. SEs positive: se_iid=0.01996, se_hac=0.03583. PASS\n4. Exclusion ledger: 319 GDP quarters − 78 − 1 = 240 = n_analysed; n_before−n_after=n_removed per row. PASS\n5. CI built as beta ± 1.959964·se_hac (checked numerically). PASS\n6. Window: first_quarter=1960Q1, last_quarter=2019Q4, n=240; 3 months in every quarter (assert). PASS\n7. Independent recomputation (pandas resample + hand-coded Bartlett HAC, L=4, no df correction): beta=-0.2557677, se_hac=0.0358346, mean g=0.75350 — matches to ~1e-14. PASS\n8. Magnitude sanity: beta≈-0.26 for non-annualised growth (≈-0.064 per annualised pp) is in the conventional Okun range; mean growth 0.75%/q (≈3%/yr) plausible; se_hac > se_iid consistent with positive residual autocorrelation (lag-1 ρ=0.39). PASS\n9. Growth not annualised: g = 100·ln ratio of consecutive quarters (code inspected; mean 0.75 not 3.0). PASS\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (n=240) is about -0.256 pp of unemployment per 1% quarterly (non-annualised) real GDP growth, with intercept about 0.184 pp and R² about 0.41.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Linear projection on current-vintage data; not a causal or structural estimate.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) HAC standard error is about 0.036, roughly 1.8 times the conventional iid SE of about 0.020, reflecting positive residual autocorrelation (lag-1 about 0.39).\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"residual_autocorr_lag1\"],\"status\":\"supported\",\"caveat\":\"Bandwidth chosen by the Newey-West (1994) plug-in rule floor(4(T/100)^(2/9)); SE ranges 0.029-0.037 across 0-12 lags (se_hac_by_lags).\"},\n {\"id\":\"C3\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using the HAC SE: 95% CI about [-0.326, -0.186], t about -7.1.\",\"results_keys\":[\"okun_negative_5pct\",\"beta_ci_low\",\"beta_ci_high\",\"t_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal (asymptotic) critical values; conclusion robust to all bandwidths examined.\"},\n {\"id\":\"C4\",\"text\":\"Mean quarterly real GDP growth over the sample is about 0.75% (non-annualised).\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"Current vintage GDP data.\"}\n]\nEOF\necho ok","description":"Write audit, checks, claims"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1123,"cache_read_input_tokens":14698,"output_tokens":2073,"output_tokens_details":{"thinking_tokens":62},"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":1123},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqWcQak8hCWoDjpQpdE","requestRef":"e6a5055e-ebda-4d14-aece-09738351da5f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3164a200-14e1-4331-8b7b-c0733b507835","timestamp":"2026-10-30T05:04:10.932Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}51{"parentUuid":"3164a200-14e1-4331-8b7b-c0733b507835","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_016N51Q4xupGaum46iikX1MA","type":"tool_result","content":"ok","is_error":false}]},"uuid":"e92f945d-f4e2-456a-a054-a87f3a333222","timestamp":"2026-10-30T05:04:10.985Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3164a200-14e1-4331-8b7b-c0733b507835","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}52{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"58fa8ebd89249c5bf5d87c6489d5342e65a5f42a49451c892f541704ad4c66f2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"All checks pass, and an independent manual recomputation gives the same β̂ and HAC SE. Now the documentation files."},{"type":"tool_use","id":"toolu_016N51Q4xupGaum46iikX1MA","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 | data rows | cols | range | missing | duplicates |\n|---|---|---|---|---|---|---|\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | 945 | 2 (observation_date, UNRATE) | 1948-01-01 to 2026-09-01, monthly | 1 empty value: 2025-10-01 | 0 duplicate dates |\n| data/GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | 319 | 2 (observation_date, GDPC1) | 1947Q1 to 2026Q3, quarterly | 0 | 0 |\n| data/README.md | cb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c | – | – | – | – | – |\n\nSHA-256 of both CSVs match README.md and bundle.json. Row counts match TASK.md (945, 319).\nUnits: UNRATE percent (SA), range 2.5–14.8; GDPC1 billions chained 2017 $ SAAR, 2172–24611. Date spacing regular (no gaps).\nAnomaly: UNRATE for Oct 2025 is empty (no release, likely the 2025 federal shutdown). It lies outside the 1959Q4–2019Q4 window → no effect; recorded in results.json `data_issues`.\nEvery quarter 1959Q4–2019Q4 has exactly 3 monthly UNRATE values (asserted in analysis.py).\nAvailability: the files are a current vintage (not real-time), which the task explicitly allows. Data after 2019Q4 and before 1959Q4 are read but discarded (exclusion ledger step 1).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Reproducibility: deleted results.json/figure1.png/exclusions.csv, re-ran analysis.py; `cmp` with previous results.json → byte-identical. PASS\n2. CI contains estimate: -0.3260 < -0.2558 < -0.1855. PASS\n3. SEs positive: se_iid=0.01996, se_hac=0.03583. PASS\n4. Exclusion ledger: 319 GDP quarters − 78 − 1 = 240 = n_analysed; n_before−n_after=n_removed per row. PASS\n5. CI built as beta ± 1.959964·se_hac (checked numerically). PASS\n6. Window: first_quarter=1960Q1, last_quarter=2019Q4, n=240; 3 months in every quarter (assert). PASS\n7. Independent recomputation (pandas resample + hand-coded Bartlett HAC, L=4, no df correction): beta=-0.2557677, se_hac=0.0358346, mean g=0.75350 — matches to ~1e-14. PASS\n8. Magnitude sanity: beta≈-0.26 for non-annualised growth (≈-0.064 per annualised pp) is in the conventional Okun range; mean growth 0.75%/q (≈3%/yr) plausible; se_hac > se_iid consistent with positive residual autocorrelation (lag-1 ρ=0.39). PASS\n9. Growth not annualised: g = 100·ln ratio of consecutive quarters (code inspected; mean 0.75 not 3.0). PASS\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (n=240) is about -0.256 pp of unemployment per 1% quarterly (non-annualised) real GDP growth, with intercept about 0.184 pp and R² about 0.41.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Linear projection on current-vintage data; not a causal or structural estimate.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) HAC standard error is about 0.036, roughly 1.8 times the conventional iid SE of about 0.020, reflecting positive residual autocorrelation (lag-1 about 0.39).\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"residual_autocorr_lag1\"],\"status\":\"supported\",\"caveat\":\"Bandwidth chosen by the Newey-West (1994) plug-in rule floor(4(T/100)^(2/9)); SE ranges 0.029-0.037 across 0-12 lags (se_hac_by_lags).\"},\n {\"id\":\"C3\",\"text\":\"The Okun coefficient is negative and significantly different from zero at the 5% level using the HAC SE: 95% CI about [-0.326, -0.186], t about -7.1.\",\"results_keys\":[\"okun_negative_5pct\",\"beta_ci_low\",\"beta_ci_high\",\"t_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal (asymptotic) critical values; conclusion robust to all bandwidths examined.\"},\n {\"id\":\"C4\",\"text\":\"Mean quarterly real GDP growth over the sample is about 0.75% (non-annualised).\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"Current vintage GDP data.\"}\n]\nEOF\necho ok","description":"Write audit, checks, claims"}}]}}53{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"3735451b-a6b5-4ca1-b960-00f66a09c40d","timestamp":"2026-10-30T05:04:10.988Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"e6a5055e-ebda-4d14-aece-09738351da5f","keep":14,"tail":["58fa8ebd89249c5bf5d87c6489d5342e65a5f42a49451c892f541704ad4c66f2","3bb516b8528b01ae7d867da225f19d2b0d591511ad4df6579a076783684d5fa0"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"085f68641b3e187a5159895cdf1e2020428a20e1c597a6e1768d15f99462ced8"}54{"parentUuid":"e92f945d-f4e2-456a-a054-a87f3a333222","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqY3oqTUpWEcjjFatND","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_011vMwxH2XFx1ABbxNhggZWQ","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 big is the Okun coefficient β in the linear projection Δu_t = α + β g_t + e_t? Here u_t is the quarterly average of the monthly unemployment rate (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is quarter-on-quarter real GDP growth in percent, not annualised. The disturbances may be serially correlated, so I report a standard error that stays valid in that case. I also test whether β is negative and significantly different from zero at the 5% level.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows. `data/GDPC1.csv`: 319 quarterly rows (`n_input_rows`). Both SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data issue was found: UNRATE is empty for 2025-10. That month is outside the window, so it does not affect the analysis (`data_issues`). Every quarter from 1959Q4 to 2019Q4 has all 3 monthly observations.\n- Exclusions (`out/exclusions.csv`), counted in quarters starting from the 319 GDP quarters:\n  - Step 1: keep the levels window 1959Q4–2019Q4. This removes 78 quarters and leaves 241.\n  - Step 2: drop 1959Q4. It only supplies the lagged levels for the 1960Q1 differences, so 1 quarter is removed and 240 remain.\n- The regression sample therefore runs from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4, with `n_analysed` = 240.\n\n## Method\n- **Estimation:** OLS of Δu_t on a constant and g_t (statsmodels).\n- **Baseline uncertainty:** the conventional iid/homoskedastic standard error (`se_iid`).\n- **Main uncertainty:** a Newey–West HAC standard error with Bartlett kernel (`se_hac`).\n  - Bandwidth `hac_lags` = 4. This comes from the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240, fixed before looking at results.\n  - No small-sample degrees-of-freedom correction is applied, which is the classic Newey–West estimator.\n- **Confidence interval and test:** the 95% CI is β̂ ± 1.96·se_hac. `okun_negative_5pct` is true only if β̂ < 0 and the two-sided normal test using se_hac has p < 0.05.\n- **Reproducibility:** the procedure is deterministic, so no seed is needed (`random_seed` = null). Running `out/analysis.py` regenerates `results.json`, `exclusions.csv` and `figure1.png`.\n\n## Results\n- β̂ = −0.2558 (`beta_hat`): each extra 1% of quarterly real GDP growth goes with an unemployment rate about 0.26 pp lower in that quarter. In annualised-growth units that is roughly −0.064.\n- α̂ = 0.1844 pp (`alpha_hat`) and R² = 0.408 (`r_squared`).\n- Together these imply that unemployment is stable when quarterly growth is about 0.72%, i.e. −α̂/β̂ computed from `alpha_hat` and `beta_hat`.\n- Mean growth over the sample is 0.754% per quarter (`mean_growth`).\n- The Okun coefficient is negative and statistically significant at the 5% level (`okun_negative_5pct` = true).\n- `out/figure1.png` shows the scatter of Δu_t against g_t with the fitted line.\n\n## Uncertainty\n| | SE | 95% CI |\n|---|---|---|\n| iid baseline | 0.0200 (`se_iid`) | [−0.2949, −0.2166] (`baseline_ci_iid_low`, `baseline_ci_iid_high`) |\n| Newey–West, 4 lags | 0.0358 (`se_hac`) | [−0.3260, −0.1855] (`beta_ci_low`, `beta_ci_high`) |\n\n- The OLS residuals are positively autocorrelated, with lag-1 autocorrelation 0.39 (`residual_autocorr_lag1`). As a result the HAC SE is about 1.8 times the iid SE.\n- With the HAC SE, t = −7.14 (`t_hac`) and p ≈ 1e−12 (`p_value_hac`).\n- **Bandwidth sensitivity** (`se_hac_by_lags`): the HAC SE is 0.029 at 0 lags (heteroskedasticity-robust only), 0.032–0.037 for 1–12 lags, and 0.036 with the degrees-of-freedom correction (`se_hac_lag4_dfcorrected`). The significance conclusion does not change for any bandwidth tried.\n\n## Limitations and open issues\n- **Data vintage:** the series are current-vintage, revised data, not real-time data, as the task allows.\n- **Interpretation:** β is a linear-projection coefficient, not a causal or structural parameter.\n- **Stability over time:** the relation may have shifted within 1960–2019. I did not test for this because the task does not ask for it.\n- **Asymptotic inference:** the inference relies on asymptotic normality. Bartlett-kernel HAC estimators with small bandwidths can understate uncertainty when persistence is strong. Here the result is far from the significance boundary, so this does not affect the conclusion.\n- **Completeness:** no part of the task was infeasible (`feasibility` = complete).\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 recorded and match README\",\"row/col counts, ranges, missing, duplicates audited (data_audit.md)\",\"exclusion ledger sums: 319-79=240\"],\"notes\":\"UNRATE 2025-10 missing, outside window\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed: se_iid, baseline_ci_iid_*\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, writes results.json, exclusions.csv, figure1.png\"],\"notes\":\"no random seed needed\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical\",\"CI contains estimate\",\"SEs positive\",\"exclusion ledger sums\",\"independent manual recomputation matches\",\"see checks.md\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has all 6 sections citing results keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nr=json.load(open('out/results.json'))\nprint(all(k in r for c in json.load(open('out/claims.json')) for k in c['results_keys']))\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30: P1 passed (audit; 2025-10 UNRATE missing, outside window). Decision: NW bandwidth floor(4(T/100)^(2/9))=4, no df correction, normal critical values.\n- 2026-10-30: P2/P3 passed (analysis.py). P4 passed (identical re-run, manual recomputation). P5 passed (report, claims). Task complete.\nEOF","description":"Write report, finalize phase ledger and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2080,"cache_read_input_tokens":15821,"output_tokens":2676,"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":2080},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqY3WUQUYQucbQAa7qQ","requestRef":"3735451b-a6b5-4ca1-b960-00f66a09c40d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"67eb0d26-1413-41f7-8eb5-861c7984f8b8","timestamp":"2026-10-30T05:04:33.750Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}55{"parentUuid":"67eb0d26-1413-41f7-8eb5-861c7984f8b8","isSidechain":false,"promptId":"f61206b2-af08-4f95-ac86-8a0ab1423ba6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011vMwxH2XFx1ABbxNhggZWQ","type":"tool_result","content":"True","is_error":false}]},"uuid":"10204af7-0422-4382-a168-f79af033c607","timestamp":"2026-10-30T05:04:33.814Z","toolUseResult":{"stdout":"True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"67eb0d26-1413-41f7-8eb5-861c7984f8b8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}56{"type":"api-request-blob","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","hash":"b2e19428abbc57dd17ad3f0be7fae14a201f16892a4003c787a324c9d27a02e7","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011vMwxH2XFx1ABbxNhggZWQ","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 big is the Okun coefficient β in the linear projection Δu_t = α + β g_t + e_t? Here u_t is the quarterly average of the monthly unemployment rate (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is quarter-on-quarter real GDP growth in percent, not annualised. The disturbances may be serially correlated, so I report a standard error that stays valid in that case. I also test whether β is negative and significantly different from zero at the 5% level.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows. `data/GDPC1.csv`: 319 quarterly rows (`n_input_rows`). Both SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data issue was found: UNRATE is empty for 2025-10. That month is outside the window, so it does not affect the analysis (`data_issues`). Every quarter from 1959Q4 to 2019Q4 has all 3 monthly observations.\n- Exclusions (`out/exclusions.csv`), counted in quarters starting from the 319 GDP quarters:\n  - Step 1: keep the levels window 1959Q4–2019Q4. This removes 78 quarters and leaves 241.\n  - Step 2: drop 1959Q4. It only supplies the lagged levels for the 1960Q1 differences, so 1 quarter is removed and 240 remain.\n- The regression sample therefore runs from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4, with `n_analysed` = 240.\n\n## Method\n- **Estimation:** OLS of Δu_t on a constant and g_t (statsmodels).\n- **Baseline uncertainty:** the conventional iid/homoskedastic standard error (`se_iid`).\n- **Main uncertainty:** a Newey–West HAC standard error with Bartlett kernel (`se_hac`).\n  - Bandwidth `hac_lags` = 4. This comes from the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240, fixed before looking at results.\n  - No small-sample degrees-of-freedom correction is applied, which is the classic Newey–West estimator.\n- **Confidence interval and test:** the 95% CI is β̂ ± 1.96·se_hac. `okun_negative_5pct` is true only if β̂ < 0 and the two-sided normal test using se_hac has p < 0.05.\n- **Reproducibility:** the procedure is deterministic, so no seed is needed (`random_seed` = null). Running `out/analysis.py` regenerates `results.json`, `exclusions.csv` and `figure1.png`.\n\n## Results\n- β̂ = −0.2558 (`beta_hat`): each extra 1% of quarterly real GDP growth goes with an unemployment rate about 0.26 pp lower in that quarter. In annualised-growth units that is roughly −0.064.\n- α̂ = 0.1844 pp (`alpha_hat`) and R² = 0.408 (`r_squared`).\n- Together these imply that unemployment is stable when quarterly growth is about 0.72%, i.e. −α̂/β̂ computed from `alpha_hat` and `beta_hat`.\n- Mean growth over the sample is 0.754% per quarter (`mean_growth`).\n- The Okun coefficient is negative and statistically significant at the 5% level (`okun_negative_5pct` = true).\n- `out/figure1.png` shows the scatter of Δu_t against g_t with the fitted line.\n\n## Uncertainty\n| | SE | 95% CI |\n|---|---|---|\n| iid baseline | 0.0200 (`se_iid`) | [−0.2949, −0.2166] (`baseline_ci_iid_low`, `baseline_ci_iid_high`) |\n| Newey–West, 4 lags | 0.0358 (`se_hac`) | [−0.3260, −0.1855] (`beta_ci_low`, `beta_ci_high`) |\n\n- The OLS residuals are positively autocorrelated, with lag-1 autocorrelation 0.39 (`residual_autocorr_lag1`). As a result the HAC SE is about 1.8 times the iid SE.\n- With the HAC SE, t = −7.14 (`t_hac`) and p ≈ 1e−12 (`p_value_hac`).\n- **Bandwidth sensitivity** (`se_hac_by_lags`): the HAC SE is 0.029 at 0 lags (heteroskedasticity-robust only), 0.032–0.037 for 1–12 lags, and 0.036 with the degrees-of-freedom correction (`se_hac_lag4_dfcorrected`). The significance conclusion does not change for any bandwidth tried.\n\n## Limitations and open issues\n- **Data vintage:** the series are current-vintage, revised data, not real-time data, as the task allows.\n- **Interpretation:** β is a linear-projection coefficient, not a causal or structural parameter.\n- **Stability over time:** the relation may have shifted within 1960–2019. I did not test for this because the task does not ask for it.\n- **Asymptotic inference:** the inference relies on asymptotic normality. Bartlett-kernel HAC estimators with small bandwidths can understate uncertainty when persistence is strong. Here the result is far from the significance boundary, so this does not affect the conclusion.\n- **Completeness:** no part of the task was infeasible (`feasibility` = complete).\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 recorded and match README\",\"row/col counts, ranges, missing, duplicates audited (data_audit.md)\",\"exclusion ledger sums: 319-79=240\"],\"notes\":\"UNRATE 2025-10 missing, outside window\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed: se_iid, baseline_ci_iid_*\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, deterministic, writes results.json, exclusions.csv, figure1.png\"],\"notes\":\"no random seed needed\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical\",\"CI contains estimate\",\"SEs positive\",\"exclusion ledger sums\",\"independent manual recomputation matches\",\"see checks.md\"],\"notes\":\"\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has all 6 sections citing results keys\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nr=json.load(open('out/results.json'))\nprint(all(k in r for c in json.load(open('out/claims.json')) for k in c['results_keys']))\nEOF\ncat >> out/state/log.md <<'EOF'\n- 2026-10-30: P1 passed (audit; 2025-10 UNRATE missing, outside window). Decision: NW bandwidth floor(4(T/100)^(2/9))=4, no df correction, normal critical values.\n- 2026-10-30: P2/P3 passed (analysis.py). P4 passed (identical re-run, manual recomputation). P5 passed (report, claims). Task complete.\nEOF","description":"Write report, finalize phase ledger and log"}}]}}57{"type":"api-request","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","id":"6a151ebc-b763-4746-891e-0e53cb06c88d","timestamp":"2026-10-30T05:04:33.818Z","version":"2.1.314","querySource":"sdk","shapeHash":"064f3b601ed04355154274a6057a8fdf9f86bdea3ad023add3a12a18a2e01759","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":"3735451b-a6b5-4ca1-b960-00f66a09c40d","keep":16,"tail":["b2e19428abbc57dd17ad3f0be7fae14a201f16892a4003c787a324c9d27a02e7","a3b73772b93076981403cf3fda422e6902e59982b070502117b518a340f1d349"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"989b4f26ee0b6f745afb779a132f98cc7f337ddd3bf63ffdbf5620a26f6eb8d5"}58{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"10204af7-0422-4382-a168-f79af033c607","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}59{"type":"atis-latch","atis":"","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}60{"parentUuid":"10204af7-0422-4382-a168-f79af033c607","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqZjNNMxSwKxqfJidmc","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2682,"cache_read_input_tokens":17901,"output_tokens":795,"output_tokens_details":{"thinking_tokens":31},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2682},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6053,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqZj5GQsCfFbRxmRQjP","requestRef":"6a151ebc-b763-4746-891e-0e53cb06c88d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f86a506c-71cb-40a0-ab44-7d5cc9571a2e","timestamp":"2026-10-30T05:04:41.411Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"f86a506c-71cb-40a0-ab44-7d5cc9571a2e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqZjNNMxSwKxqfJidmc","type":"message","role":"assistant","content":[{"type":"text","text":"I estimated the Okun coefficient for 1960Q1–2019Q4 (240 quarters). It is β̂ = −0.256, meaning each extra 1% of quarterly real GDP growth goes with an unemployment rate about 0.26 percentage points lower that quarter. It is negative and significant at the 5% level using the Newey–West standard error (`okun_negative_5pct` = true).\n\n| | Value |\n|---|---|\n| Intercept α̂ | 0.184 pp |\n| R² | 0.408 |\n| Conventional standard error (baseline) | 0.0200 |\n| Newey–West standard error, 4 lags | 0.0358 |\n| 95% confidence interval | [−0.326, −0.186] |\n| t-statistic | −7.1 |\n| Mean quarterly growth | 0.754% |\n\n- **Why the robust error is larger:** the regression residuals are positively correlated over time (lag-1 correlation 0.39), so the Newey–West error is about 1.8 times the conventional one.\n- **Bandwidth choice:** I used 4 lags, from the standard Newey–West (1994) rule floor(4·(T/100)^(2/9)) with T = 240. I applied no small-sample correction and used normal critical values.\n- **Sensitivity:** across 0 to 12 lags the standard error ranges from 0.029 to 0.037, and the coefficient stays significant at every setting.\n- **Checks:** re-running `out/analysis.py` from scratch reproduces `out/results.json` byte for byte. A separate hand calculation gives the same coefficient and standard error.\n\n**Limitations:**\n- **Revised data:** the data are the current revised series, not what was known in real time; the task allows this.\n- **No causal reading:** the coefficient is a linear fit, not a causal estimate.\n- **Stability not tested:** I didn't test whether the relationship shifted within 1960–2019, since the task doesn't ask for it.\n- **Missing month:** the unemployment file has one missing month (October 2025). It falls outside the sample window, so it has no effect.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- claims.json\n- figure1.png\n- exclusions.csv\n- state/ (contract, phase ledger, data audit, checks, log)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2682,"cache_read_input_tokens":17901,"output_tokens":795,"output_tokens_details":{"thinking_tokens":31},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":2682},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqZj5GQsCfFbRxmRQjP","requestRef":"6a151ebc-b763-4746-891e-0e53cb06c88d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c10535fd-359a-433a-ba43-0fb326a28ecd","timestamp":"2026-10-30T05:04:41.414Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0217","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","version":"2.1.314","gitBranch":"HEAD"}62{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"c10535fd-359a-433a-ba43-0fb326a28ecd","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85"}63{"type":"cost-state","sessionId":"304ec8f1-1257-4cf1-939a-55cb2f42ba85","totalCostUSD":0.3445104,"totalAPIDuration":111823,"totalAPIDurationWithoutRetries":111817,"totalToolDuration":3320,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":115327,"startTime":1793336566094,"modelUsage":{"claude-opus-5-5":{"inputTokens":20,"outputTokens":11567,"thinkingTokens":554,"cacheReadInputTokens":88477,"cacheCreationInputTokens":19079,"cacheCreationEphemeral5mInputTokens":19079,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.3445104}},"hasUnknownModelCost":false}64