spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T05:05:14.182Z","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T05:05:14.188Z","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}4{"parentUuid":null,"isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":"Begin"},"uuid":"c6092288-7dc0-4403-8c29-5e23053be352","timestamp":"2026-10-30T05:05:14.192Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","shapeHash":"074721cf65d261e798c36c2072ad74b9b6fcd23d5b46c8d5dc8f120f0b887b82","timestamp":"2026-10-30T05:05:14.206Z","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/RC0227\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}12{"parentUuid":"73d916be-8bf7-45cd-bce8-33be7fd81c48","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqcmtkuS4RmymDLfxSh","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSugUKEAgTGAI4AUIIdGhpbmtpbmcSDHaWITUb/CWu5z7ashoMIVn0SHSHX+1HbFuvIjBRwsG9rOn8aUrNHqB5llNq19ZrbJ72UT8H19BYufGQ+80C3O5NhQYGxvyCEjhu5coq1wRDx8djRJG1fSGDX4Gsj8JIB6j6jIYIq4A3kC40jMxebAbwqMZ0sMgvOBvg+/sc+galmOV+xTyGUb2c53WtKo1MekKcR2Gw+h++4wnWXI2qzlLZUoewDNHj7RPTUgTWan/pXDGRpxk0DlgJBFWifvaDtBfMw6pS3lYCuCRJB0pSFiC1lMA4WBBYjHEcCH41Cg9ruQXT72VEspoYXEZp9qh+8wvX6mEe5Ikj+/IRdfiknmUscTPkEpOOVESuE6bvzR1Ztbt8kBrtZtDtFhVoUZRclW0gJxJxMFMc5gf7SYVjkqhje2GkR7hPrCLNZ7/jq6Zq4R/ioEx+swhtYXBacMn31tcNoAEuoZUVhIpEDpBE6DEfbqRlKqAiPw4FSK/M/YRFQNxkNzqQ1lAaH3UcK28/IBPez0ZpPo9hFk04HiFfTlSH+K0h9AhCwI8AC5F/4crU1aVZbvbZHPdVN0d1vpywmklCo265yzYedh4sqGfFhRdtusSS4TuHxAkXf9OhVEfKSjwflXtKiNhL7qxSfMfHxMx5T+IqPUbDLkewACgYOiNwIq5j1NiP6nmcVxClr/Dp1Z3PYozDtgi26e8FEX+7Y92tzytiIW3eT+t8vWje5PDC3qgZUJrO2Y+Efzcef7Mq/x/8CU0qk8EZujdrDUNvabzUEC0MmT5T0YATof+WwtuRwgOPUYrImvRqmrwzkjw/I0wXnAmbJBzF/KdUs4iUjlCCA3lCZJfphbMX5EDpP5UzzKRS7Ox59vI8iF8DCc2TeD5dv2bYic17ilS8OAB5SzM47D1hvhgB"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":137,"output_tokens_details":{"thinking_tokens":28},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":635,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqcmfs1tqNv3KFmeXPj","requestRef":"e76f7258-b7e3-40f8-8961-5499cac6fc05","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"37a714df-c208-4abd-a064-ab952d036fc4","timestamp":"2026-10-30T05:05:16.154Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"37a714df-c208-4abd-a064-ab952d036fc4","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqcmtkuS4RmymDLfxSh","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_015YXgoZhgFkCGQyicYgpaAT","name":"Bash","input":{"command":"cat TASK.md; 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The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":true,"tool_use_id":"toolu_015YXgoZhgFkCGQyicYgpaAT"}]},"uuid":"8c2ee114-dc1d-46dc-b6ab-8214b5c555da","timestamp":"2026-10-30T05:05:16.752Z","toolUseResult":"Error: Exit code 1\n# F2-conf-04 — Okun's law in U.S. quarterly data with autocorrelation-robust uncertainty\n\n## Scientific question\nEstimate the Okun's-law relation between changes in the U.S. unemployment rate and real GDP growth, with uncertainty that is valid under serially correlated disturbances. The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). 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They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n  \"task_id\": \"F2-conf-04\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n    \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n    \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n    \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n    \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n    \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n    \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n    \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n    \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n  \"positive_keys\": [\"se_iid\", \"se_hac\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n==> 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     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv","is_error":false}]},"uuid":"e74a7b78-17e9-44b5-8126-08596566952c","timestamp":"2026-10-30T05:05:18.413Z","toolUseResult":{"stdout":"# Data provenance — F2-conf-04\n\nBoth files are unmodified downloads from FRED (Federal Reserve Bank of St. Louis), retrieved on 2026-10-30 03:32:42 UTC with\n`curl -L \"https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>\"`. They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n  \"task_id\": \"F2-conf-04\",\n  \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n  \"results_keys\": {\n    \"task_id\": {\"type\": \"string\", \"nullable\": false},\n    \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n    \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n    \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n    \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n    \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n    \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n    \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n    \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n    \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n    \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n    \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n    \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n    \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n    \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n    \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n    \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n  },\n  \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n  \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n  \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n  \"positive_keys\": [\"se_iid\", \"se_hac\"],\n  \"chronology_pairs\": [],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-6\n}\n==> 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     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\ncb177a40e235651c0792ccb9a038f2b6bed53740c22ac902f9e6ae4e47856e3c  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  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spec"}}]}}22{"type":"api-request","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","id":"8f1225d2-4d7a-407e-a076-21ae56be0992","timestamp":"2026-10-30T05:05:18.416Z","version":"2.1.314","querySource":"sdk","shapeHash":"074721cf65d261e798c36c2072ad74b9b6fcd23d5b46c8d5dc8f120f0b887b82","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":"86ecdbb4-c2be-4714-b2c5-a48853b33e92","keep":4,"tail":["bb65feea1d74b079c32b3f2f6149eb5bafadaa8c61f22cd6e3d05add949d3a76","d5907e773d7d9c4bdf032c4417b5e435a0c7b3b55a41e57ec08f9f70c6dc211b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f680129c6084e3724a64fcc8b50ad6f7c7675b4b7de46fb49ffe64701399798e"}23{"parentUuid":"e74a7b78-17e9-44b5-8126-08596566952c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqd2sumDGu9AWvsWvmp","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":1894,"cache_read_input_tokens":6420,"output_tokens":375,"output_tokens_details":{"thinking_tokens":30},"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":1894},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":427,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqd2cYNvD5HBuA8vDkQ","requestRef":"8f1225d2-4d7a-407e-a076-21ae56be0992","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6e27d26f-a9b4-4ba0-b050-753a00160280","timestamp":"2026-10-30T05:05:19.702Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"6e27d26f-a9b4-4ba0-b050-753a00160280","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqd2sumDGu9AWvsWvmp","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0191V2N1hUcb2DsVvWic6Htr","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['UNRATE','GDPC1']:\n    d=pd.read_csv(f'data/{f}.csv',dtype=str,keep_default_na=False)\n    print(f,d.shape,d.observation_date.min(),d.observation_date.max(),'dupdates',d.observation_date.duplicated().sum(),'empty',(d[f]=='').sum())\n    v=pd.to_numeric(d[f],errors='coerce');print(' nonnum',v.isna().sum(),v.describe().to_dict())\n    dt=pd.to_datetime(d.observation_date);print(' gaps',(dt.diff().dt.days.dropna().agg(['min','max'])).tolist())\n    print(d[v.isna()])\n\"","description":"Audit input data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1894,"cache_read_input_tokens":6420,"output_tokens":375,"output_tokens_details":{"thinking_tokens":30},"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":1894},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqd2cYNvD5HBuA8vDkQ","requestRef":"8f1225d2-4d7a-407e-a076-21ae56be0992","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b7ded3de-e2fc-49df-b8d1-a5f82c4c8f40","timestamp":"2026-10-30T05:05:21.829Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"b7ded3de-e2fc-49df-b8d1-a5f82c4c8f40","isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0191V2N1hUcb2DsVvWic6Htr","type":"tool_result","content":"UNRATE (945, 2) 1948-01-01 2026-09-01 dupdates 0 empty 1\n nonnum 1 {'count': 944.0, 'mean': 5.652966101694915, 'std': 1.703533753130343, 'min': 2.5, '25%': 4.3, '50%': 5.45, '75%': 6.7, 'max': 14.8}\n gaps [28.0, 31.0]\n    observation_date UNRATE\n933       2025-10-01       \nGDPC1 (319, 2) 1947-01-01 2026-07-01 dupdates 0 empty 0\n nonnum 0 {'count': 319.0, 'mean': 10458.210203761757, 'std': 6485.0424476807175, 'min': 2172.432, '25%': 4848.918, '50%': 8920.193, '75%': 16340.058, 'max': 24611.077}\n gaps [90.0, 92.0]\nEmpty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: []","is_error":false}]},"uuid":"d0fe2cdd-9202-42f7-9528-ab231d9b911b","timestamp":"2026-10-30T05:05:22.084Z","toolUseResult":{"stdout":"UNRATE (945, 2) 1948-01-01 2026-09-01 dupdates 0 empty 1\n nonnum 1 {'count': 944.0, 'mean': 5.652966101694915, 'std': 1.703533753130343, 'min': 2.5, '25%': 4.3, '50%': 5.45, '75%': 6.7, 'max': 14.8}\n gaps [28.0, 31.0]\n    observation_date UNRATE\n933       2025-10-01       \nGDPC1 (319, 2) 1947-01-01 2026-07-01 dupdates 0 empty 0\n nonnum 0 {'count': 319.0, 'mean': 10458.210203761757, 'std': 6485.0424476807175, 'min': 2172.432, '25%': 4848.918, '50%': 8920.193, '75%': 16340.058, 'max': 24611.077}\n gaps [90.0, 92.0]\nEmpty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: 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-c \"\nimport pandas as pd\nfor f in ['UNRATE','GDPC1']:\n    d=pd.read_csv(f'data/{f}.csv',dtype=str,keep_default_na=False)\n    print(f,d.shape,d.observation_date.min(),d.observation_date.max(),'dupdates',d.observation_date.duplicated().sum(),'empty',(d[f]=='').sum())\n    v=pd.to_numeric(d[f],errors='coerce');print(' nonnum',v.isna().sum(),v.describe().to_dict())\n    dt=pd.to_datetime(d.observation_date);print(' gaps',(dt.diff().dt.days.dropna().agg(['min','max'])).tolist())\n    print(d[v.isna()])\n\"","description":"Audit input data 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Inference: Newey–West Bartlett HAC SE, valid under weakly dependent (serially correlated, heteroskedastic) disturbances; bandwidth must be justified.\n**Sample:** exactly 240 quarters, 1960Q1–2019Q4; 1959Q4 levels used only for differences.\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 protocol files (state/, exclusions.csv).\n**Information rules:** only data/ files, current vintage as given; no observations outside 1960Q1–2019Q4 except 1959Q4 levels; do not extend window.\n**Invalid/impossible if:** missing months/quarters in 1959Q4–2019Q4 (incomplete quarterly averages), checksum mismatch, sample ≠ 240 quarters.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question:** Okun's law in US quarterly data 1960Q1–2019Q4: how does the change in the unemployment rate co-move with real GDP growth?\n**Estimand:** slope β of the linear projection Δu_t = α + β g_t + e_t, with u_t = quarterly mean of 3 monthly UNRATE values (pp), Δu_t = u_t − u_{t−1}, g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (quarterly, not annualised). β is a best-linear-projection coefficient (meaningful under covariance stationarity / finite second moments); not a structural causal effect. Inference: Newey–West Bartlett HAC SE, valid under weakly dependent (serially correlated, heteroskedastic) disturbances; bandwidth must be justified.\n**Sample:** exactly 240 quarters, 1960Q1–2019Q4; 1959Q4 levels used only for differences.\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 protocol files (state/, exclusions.csv).\n**Information rules:** only data/ files, current vintage as given; no observations outside 1960Q1–2019Q4 except 1959Q4 levels; do not extend window.\n**Invalid/impossible if:** missing months/quarters in 1959Q4–2019Q4 (incomplete quarterly averages), checksum mismatch, sample ≠ 240 quarters.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > log.md","description":"Write contract, phase ledger, log"}}]}}32{"type":"api-request","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","id":"0def2f16-e48c-4cb3-bc70-dc067f3491ff","timestamp":"2026-10-30T05:05:32.430Z","version":"2.1.314","querySource":"sdk","shapeHash":"074721cf65d261e798c36c2072ad74b9b6fcd23d5b46c8d5dc8f120f0b887b82","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":"c8903bf6-246e-4395-ad9d-51be8b3f59d3","keep":8,"tail":["7382f5bc49978107d0885fabc4c646bedcf586627f60eb8e365b556f43bb1518","2b0f8b7e782c2ead5b5ee98c5ec72bddcbb7a72f2d6c0c2e54b0120645edaf34"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"69804e3138ed8e9f37153eb72c4c204b1520f70b46c7c00d4baa62b1c69df996"}33{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"3f048098-0a2a-491c-8a2b-12e4d4ac5c7f","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}34{"type":"atis-latch","atis":"","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}35{"parentUuid":"3f048098-0a2a-491c-8a2b-12e4d4ac5c7f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqe4rFozDm8mY3HyCKv","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (includes baseline se_iid, audit, exclusions)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1217,"cache_read_input_tokens":9038,"output_tokens":3130,"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":1217},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqe4XeuVdgLrmsfc4C7","requestRef":"0def2f16-e48c-4cb3-bc70-dc067f3491ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"877abf6b-e5dc-4355-a17a-d0ef5549a88f","timestamp":"2026-10-30T05:05:33.324Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227/out/state","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"877abf6b-e5dc-4355-a17a-d0ef5549a88f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqe4rFozDm8mY3HyCKv","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Bnce25Ub3fZHBsg4RXbfER","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/exclusions.csv. No randomness.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.getcwd(); OUT = os.path.join(ROOT, \"out\"); os.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nEXPECTED_SHA = {\"UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n                \"GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\"}\nissues, excl = [], []\n\ndef sha(p):\n    return hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n\nfor f, h in EXPECTED_SHA.items():\n    if sha(os.path.join(\"data\", f)) != h:\n        issues.append({\"issue\": f\"{f} SHA-256 differs from data/README.md\", \"handling\": \"used as given; flagged\"})\n\nu = pd.read_csv(\"data/UNRATE.csv\", dtype={\"UNRATE\": str}, keep_default_na=False)\ng = pd.read_csv(\"data/GDPC1.csv\", dtype={\"GDPC1\": str}, keep_default_na=False)\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nu[\"UNRATE\"] = pd.to_numeric(u.UNRATE.replace(\"\", np.nan)); g[\"GDPC1\"] = pd.to_numeric(g.GDPC1.replace(\"\", np.nan))\n\n# Monthly unemployment -> quarterly averages\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n    issues.append({\"issue\": \"UNRATE missing (empty) for month(s) \" + \", \".join(miss.observation_date) +\n                   \" (outside the 1959Q4-2019Q4 window needed)\",\n                   \"handling\": \"row dropped; affects no quarter used in the analysis\"})\nn0 = len(u); u = u.dropna(subset=[\"UNRATE\"])\nexcl.append((\"1\", \"drop UNRATE months with missing value\", n0, len(u), \"empty field in source file\"))\nu[\"q\"] = u.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ng[\"q\"] = g.date.dt.to_period(\"Q\"); gq = g.set_index(\"q\").GDPC1\nassert g.q.is_unique and u.date.is_unique\n\nneed = pd.period_range(FIRST - 1, LAST, freq=\"Q\")\nassert all(uq.reindex(need)[\"count\"] == 3), \"incomplete quarter of monthly UNRATE in window\"\nassert gq.reindex(need).notna().all(), \"missing GDPC1 in window\"\n\ndf = pd.DataFrame({\"u\": uq[\"mean\"], \"Y\": gq}).reindex(need)\ndf[\"du\"] = df.u.diff(); df[\"g\"] = 100 * np.log(df.Y / df.Y.shift(1))\nnq_total = len(pd.period_range(min(uq.index.min(), gq.index.min()), max(uq.index.max(), gq.index.max()), freq=\"Q\"))\nn_before = nq_total\nsample = df.loc[FIRST:LAST].dropna()\nexcl.append((\"2\", \"restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences)\",\n             n_before, len(sample), \"window fixed by TASK.md\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n    ].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nn = len(sample); assert n == 240\nX = sm.add_constant(sample.g.values); y = sample.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) rule of thumb for the Bartlett kernel, floor(4*(T/100)^(2/9))\nL = int(np.floor(4 * (n / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nb, a = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = float(stats.norm.ppf(0.975))\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(b / se_hac)))\nsens = {}\nfor lag in [0, 1, 2, 4, 6, 8, 12]:\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    sens[str(lag)] = float(r.bse[1])\nfrom statsmodels.tsa.stattools import acf\nres_acf = acf(ols.resid, nlags=4, fft=False)[1:]\n\nresults = {\n    \"task_id\": \"F2-conf-04\", \"beta_hat\": b, \"alpha_hat\": a, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n    \"mean_growth\": float(sample.g.mean()),\n    \"first_quarter\": str(sample.index[0]), \"last_quarter\": str(sample.index[-1]),\n    \"n_analysed\": int(n), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary (not required by TASK.md)\n    \"t_stat_hac\": float(b / se_hac), \"p_value_hac\": p_hac, \"z_crit_95\": z,\n    \"baseline_ci_iid_low\": float(b - z * se_iid), \"baseline_ci_iid_high\": float(b + z * se_iid),\n    \"se_hac_by_lag\": sens, \"resid_acf_lags1to4\": [float(x) for x in res_acf],\n    \"mean_du\": float(sample.du.mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(results, fh, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(sample.g, sample.du, s=12, alpha=0.7)\nxs = np.linspace(sample.g.min(), sample.g.max(), 50); ax.plot(xs, a + b * xs, \"r-\",\n        label=f\"OLS: Δu = {a:.3f} + ({b:.3f}) g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\"); ax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}))\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":1217,"cache_read_input_tokens":9038,"output_tokens":3130,"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":1217},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqe4XeuVdgLrmsfc4C7","requestRef":"0def2f16-e48c-4cb3-bc70-dc067f3491ff","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"276127d2-cac1-4a92-96c5-0a0e23d9237c","timestamp":"2026-10-30T05:05:56.967Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227/out/state","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"276127d2-cac1-4a92-96c5-0a0e23d9237c","isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0227/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01Bnce25Ub3fZHBsg4RXbfER"}]},"uuid":"28f36836-d82f-4e82-923c-596d5381b9b3","timestamp":"2026-10-30T05:05:57.001Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0227/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"276127d2-cac1-4a92-96c5-0a0e23d9237c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227/out/state","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","hash":"d95934fa05063181e918ae647c0433d967c4ff7367a3d99cca37d8ee0d52d6e2","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (includes baseline se_iid, audit, exclusions)."},{"type":"tool_use","id":"toolu_01Bnce25Ub3fZHBsg4RXbfER","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/exclusions.csv. No randomness.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.getcwd(); OUT = os.path.join(ROOT, \"out\"); os.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nEXPECTED_SHA = {\"UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n                \"GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\"}\nissues, excl = [], []\n\ndef sha(p):\n    return hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n\nfor f, h in EXPECTED_SHA.items():\n    if sha(os.path.join(\"data\", f)) != h:\n        issues.append({\"issue\": f\"{f} SHA-256 differs from data/README.md\", \"handling\": \"used as given; flagged\"})\n\nu = pd.read_csv(\"data/UNRATE.csv\", dtype={\"UNRATE\": str}, keep_default_na=False)\ng = pd.read_csv(\"data/GDPC1.csv\", dtype={\"GDPC1\": str}, keep_default_na=False)\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nu[\"UNRATE\"] = pd.to_numeric(u.UNRATE.replace(\"\", np.nan)); g[\"GDPC1\"] = pd.to_numeric(g.GDPC1.replace(\"\", np.nan))\n\n# Monthly unemployment -> quarterly averages\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n    issues.append({\"issue\": \"UNRATE missing (empty) for month(s) \" + \", \".join(miss.observation_date) +\n                   \" (outside the 1959Q4-2019Q4 window needed)\",\n                   \"handling\": \"row dropped; affects no quarter used in the analysis\"})\nn0 = len(u); u = u.dropna(subset=[\"UNRATE\"])\nexcl.append((\"1\", \"drop UNRATE months with missing value\", n0, len(u), \"empty field in source file\"))\nu[\"q\"] = u.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ng[\"q\"] = g.date.dt.to_period(\"Q\"); gq = g.set_index(\"q\").GDPC1\nassert g.q.is_unique and u.date.is_unique\n\nneed = pd.period_range(FIRST - 1, LAST, freq=\"Q\")\nassert all(uq.reindex(need)[\"count\"] == 3), \"incomplete quarter of monthly UNRATE in window\"\nassert gq.reindex(need).notna().all(), \"missing GDPC1 in window\"\n\ndf = pd.DataFrame({\"u\": uq[\"mean\"], \"Y\": gq}).reindex(need)\ndf[\"du\"] = df.u.diff(); df[\"g\"] = 100 * np.log(df.Y / df.Y.shift(1))\nnq_total = len(pd.period_range(min(uq.index.min(), gq.index.min()), max(uq.index.max(), gq.index.max()), freq=\"Q\"))\nn_before = nq_total\nsample = df.loc[FIRST:LAST].dropna()\nexcl.append((\"2\", \"restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences)\",\n             n_before, len(sample), \"window fixed by TASK.md\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n    ].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nn = len(sample); assert n == 240\nX = sm.add_constant(sample.g.values); y = sample.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) rule of thumb for the Bartlett kernel, floor(4*(T/100)^(2/9))\nL = int(np.floor(4 * (n / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nb, a = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = float(stats.norm.ppf(0.975))\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(b / se_hac)))\nsens = {}\nfor lag in [0, 1, 2, 4, 6, 8, 12]:\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    sens[str(lag)] = float(r.bse[1])\nfrom statsmodels.tsa.stattools import acf\nres_acf = acf(ols.resid, nlags=4, fft=False)[1:]\n\nresults = {\n    \"task_id\": \"F2-conf-04\", \"beta_hat\": b, \"alpha_hat\": a, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n    \"mean_growth\": float(sample.g.mean()),\n    \"first_quarter\": str(sample.index[0]), \"last_quarter\": str(sample.index[-1]),\n    \"n_analysed\": int(n), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary (not required by TASK.md)\n    \"t_stat_hac\": float(b / se_hac), \"p_value_hac\": p_hac, \"z_crit_95\": z,\n    \"baseline_ci_iid_low\": float(b - z * se_iid), \"baseline_ci_iid_high\": float(b + z * se_iid),\n    \"se_hac_by_lag\": sens, \"resid_acf_lags1to4\": [float(x) for x in res_acf],\n    \"mean_du\": float(sample.du.mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(results, fh, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(sample.g, sample.du, s=12, alpha=0.7)\nxs = np.linspace(sample.g.min(), sample.g.max(), 50); ax.plot(xs, a + b * xs, \"r-\",\n        label=f\"OLS: Δu = {a:.3f} + ({b:.3f}) g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\"); ax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Write and run analysis 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> out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/exclusions.csv. No randomness.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import acf\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.getcwd(); OUT = os.path.join(ROOT, \"out\"); os.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nEXPECTED_SHA = {\"UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n                \"GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\"}\nissues, excl = [], []\n\ndef sha(p):\n    return hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n\nfor f, h in EXPECTED_SHA.items():\n    if sha(os.path.join(\"data\", f)) != h:\n        issues.append({\"issue\": f\"{f} SHA-256 differs from data/README.md\", \"handling\": \"used as given; flagged\"})\n\nu = pd.read_csv(\"data/UNRATE.csv\", dtype={\"UNRATE\": str}, keep_default_na=False)\ng = pd.read_csv(\"data/GDPC1.csv\", dtype={\"GDPC1\": str}, keep_default_na=False)\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nu[\"UNRATE\"] = pd.to_numeric(u.UNRATE.replace(\"\", np.nan)); g[\"GDPC1\"] = pd.to_numeric(g.GDPC1.replace(\"\", np.nan))\nassert u.date.is_unique and g.date.is_unique\n\n# Step 1: drop months with missing UNRATE (monthly rows)\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n    issues.append({\"issue\": \"UNRATE missing (empty field) for month(s) \" + \", \".join(miss.observation_date) +\n                   \"; outside the 1959Q4-2019Q4 window used\",\n                   \"handling\": \"row dropped; affects no quarter used in the analysis\"})\nn0 = len(u); u = u.dropna(subset=[\"UNRATE\"])\nexcl.append((\"1_monthly\", \"drop UNRATE monthly rows with missing value\", n0, len(u),\n             \"empty field in source file (unit: monthly rows; separate from quarterly ledger below)\"))\n\n# Quarterly aggregation\nu[\"q\"] = u.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ng[\"q\"] = g.date.dt.to_period(\"Q\"); gq = g.set_index(\"q\").GDPC1\nneed = pd.period_range(FIRST - 1, LAST, freq=\"Q\")\nassert (uq[\"count\"].reindex(need) == 3).all(), \"incomplete quarter of monthly UNRATE in window\"\nassert gq.reindex(need).notna().all(), \"missing GDPC1 in window\"\n\n# Step 2: build quarterly panel over the union of quarters, then restrict to the window\nallq = pd.period_range(min(uq.index.min(), gq.index.min()), max(uq.index.max(), gq.index.max()), freq=\"Q\")\nfull = pd.DataFrame({\"u\": uq[\"mean\"], \"Y\": gq}).reindex(allq)\nfull[\"du\"] = full.u.diff(); full[\"g\"] = 100 * np.log(full.Y / full.Y.shift(1))\nsample = full.loc[FIRST:LAST]\nexcl.append((\"2_quarterly\", \"restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences)\",\n             len(full), len(sample), \"window fixed by TASK.md; quarters 1947Q1-1959Q4 and 2020Q1-2026Q3 out of window\"))\nn_b = len(sample); sample = sample.dropna(subset=[\"du\", \"g\"])\nexcl.append((\"3_quarterly\", \"drop window quarters with missing du or g\", n_b, len(sample), \"none expected\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n    ].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nn = len(sample); assert n == 240\nX = sm.add_constant(sample.g.values); y = sample.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) rule of thumb for the Bartlett kernel, floor(4*(T/100)^(2/9))\nL = int(np.floor(4 * (n / 100) ** (2 / 9)))\ndef hac_se(lag):\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    return float(r.bse[1])\nb, a = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), hac_se(L)\nz = float(stats.norm.ppf(0.975))\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(b / se_hac)))\nsens = {str(l): hac_se(l) for l in [0, 1, 2, 4, 6, 8, 12]}\nres_acf = acf(ols.resid, nlags=4, fft=False)[1:]\n\nresults = {\n    \"task_id\": \"F2-conf-04\", \"beta_hat\": b, \"alpha_hat\": a, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n    \"mean_growth\": float(sample.g.mean()),\n    \"first_quarter\": str(sample.index[0]), \"last_quarter\": str(sample.index[-1]),\n    \"n_analysed\": int(n), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary keys (not required by TASK.md)\n    \"t_stat_hac\": float(b / se_hac), \"p_value_hac\": p_hac, \"z_crit_95\": z,\n    \"baseline_ci_iid_low\": float(b - z * se_iid), \"baseline_ci_iid_high\": float(b + z * se_iid),\n    \"se_hac_by_lag\": sens, \"resid_acf_lags1to4\": [float(x) for x in res_acf],\n    \"mean_du\": float(sample.du.mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(results, fh, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(sample.g, sample.du, s=12, alpha=0.7)\nxs = np.linspace(sample.g.min(), sample.g.max(), 50)\nax.plot(xs, a + b * xs, \"r-\", label=f\"OLS: Δu = {a:.3f} + ({b:.3f}) g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\"); ax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}))\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":183,"cache_read_input_tokens":13547,"output_tokens":3247,"output_tokens_details":{"thinking_tokens":23},"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":183},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqfyjjCj5truKLU5NL6","requestRef":"b1741e82-94b2-4658-9353-d9a2e5e8d732","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0b500b40-a518-4047-b2d2-61cf8e9c1b4b","timestamp":"2026-10-30T05:06:22.417Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}47{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"0b500b40-a518-4047-b2d2-61cf8e9c1b4b","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}48{"type":"atis-latch","atis":"","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}49{"parentUuid":"0b500b40-a518-4047-b2d2-61cf8e9c1b4b","isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_018E5PeYU5PrnBhCfYxiUGTF","type":"tool_result","content":"{\"beta_hat\": -0.25576772774872947, \"se_iid\": 0.019960092914910015, \"se_hac\": 0.03583463330296578, \"hac_lags\": 4, \"beta_ci_low\": -0.326002318421742, \"beta_ci_high\": -0.18553313707571695, \"okun_negative_5pct\": true}\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 field) for month(s) 2025-10-01; outside the 1959Q4-2019Q4 window used\",\n      \"handling\": \"row dropped; affects no quarter used in the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_stat_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"z_crit_95\": 1.959963984540054,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272,\n  \"se_hac_by_lag\": {\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  \"resid_acf_lags1to4\": [\n    0.38926161159297024,\n    0.29039314373832453,\n    0.13912378513356943,\n    0.07040439437283147\n  ],\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n1_monthly,drop UNRATE monthly rows with missing value,945,944,1,empty field in source file (unit: monthly rows; separate from quarterly ledger below)\n2_quarterly,restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences),319,240,79,window fixed by TASK.md; quarters 1947Q1-1959Q4 and 2020Q1-2026Q3 out of window\n3_quarterly,drop window quarters with missing du or g,240,240,0,none expected","is_error":false}]},"uuid":"58db4482-bca8-46ab-8558-d5e1f821fa4a","timestamp":"2026-10-30T05:06:23.542Z","toolUseResult":{"stdout":"{\"beta_hat\": -0.25576772774872947, \"se_iid\": 0.019960092914910015, \"se_hac\": 0.03583463330296578, \"hac_lags\": 4, \"beta_ci_low\": -0.326002318421742, \"beta_ci_high\": -0.18553313707571695, \"okun_negative_5pct\": true}\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 field) for month(s) 2025-10-01; outside the 1959Q4-2019Q4 window used\",\n      \"handling\": \"row dropped; affects no quarter used in the analysis\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"UNRATE.csv\": 945,\n    \"GDPC1.csv\": 319\n  },\n  \"random_seed\": null,\n  \"t_stat_hac\": -7.137445096377235,\n  \"p_value_hac\": 9.508157930953398e-13,\n  \"z_crit_95\": 1.959963984540054,\n  \"baseline_ci_iid_low\": -0.2948887909900262,\n  \"baseline_ci_iid_high\": -0.21664666450743272,\n  \"se_hac_by_lag\": {\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  \"resid_acf_lags1to4\": [\n    0.38926161159297024,\n    0.29039314373832453,\n    0.13912378513356943,\n    0.07040439437283147\n  ],\n  \"mean_du\": -0.008333333333333335\n}step,rule,n_before,n_after,n_removed,rationale\n1_monthly,drop UNRATE monthly rows with missing value,945,944,1,empty field in source file (unit: monthly rows; separate from quarterly ledger below)\n2_quarterly,restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences),319,240,79,window fixed by TASK.md; quarters 1947Q1-1959Q4 and 2020Q1-2026Q3 out of window\n3_quarterly,drop window quarters with missing du or g,240,240,0,none expected","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0b500b40-a518-4047-b2d2-61cf8e9c1b4b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","hash":"54ceef8ea63720f48f78338f3b3cfe7411441d9254751dce56ea20de270725fc","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_018E5PeYU5PrnBhCfYxiUGTF","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, US quarterly 1960Q1-2019Q4, OLS with Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/figure1.png, out/exclusions.csv. No randomness.\"\"\"\nimport json, os, hashlib\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom statsmodels.tsa.stattools import acf\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nROOT = os.getcwd(); OUT = os.path.join(ROOT, \"out\"); os.makedirs(OUT, exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nEXPECTED_SHA = {\"UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n                \"GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\"}\nissues, excl = [], []\n\ndef sha(p):\n    return hashlib.sha256(open(p, \"rb\").read()).hexdigest()\n\nfor f, h in EXPECTED_SHA.items():\n    if sha(os.path.join(\"data\", f)) != h:\n        issues.append({\"issue\": f\"{f} SHA-256 differs from data/README.md\", \"handling\": \"used as given; flagged\"})\n\nu = pd.read_csv(\"data/UNRATE.csv\", dtype={\"UNRATE\": str}, keep_default_na=False)\ng = pd.read_csv(\"data/GDPC1.csv\", dtype={\"GDPC1\": str}, keep_default_na=False)\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nu[\"UNRATE\"] = pd.to_numeric(u.UNRATE.replace(\"\", np.nan)); g[\"GDPC1\"] = pd.to_numeric(g.GDPC1.replace(\"\", np.nan))\nassert u.date.is_unique and g.date.is_unique\n\n# Step 1: drop months with missing UNRATE (monthly rows)\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n    issues.append({\"issue\": \"UNRATE missing (empty field) for month(s) \" + \", \".join(miss.observation_date) +\n                   \"; outside the 1959Q4-2019Q4 window used\",\n                   \"handling\": \"row dropped; affects no quarter used in the analysis\"})\nn0 = len(u); u = u.dropna(subset=[\"UNRATE\"])\nexcl.append((\"1_monthly\", \"drop UNRATE monthly rows with missing value\", n0, len(u),\n             \"empty field in source file (unit: monthly rows; separate from quarterly ledger below)\"))\n\n# Quarterly aggregation\nu[\"q\"] = u.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ng[\"q\"] = g.date.dt.to_period(\"Q\"); gq = g.set_index(\"q\").GDPC1\nneed = pd.period_range(FIRST - 1, LAST, freq=\"Q\")\nassert (uq[\"count\"].reindex(need) == 3).all(), \"incomplete quarter of monthly UNRATE in window\"\nassert gq.reindex(need).notna().all(), \"missing GDPC1 in window\"\n\n# Step 2: build quarterly panel over the union of quarters, then restrict to the window\nallq = pd.period_range(min(uq.index.min(), gq.index.min()), max(uq.index.max(), gq.index.max()), freq=\"Q\")\nfull = pd.DataFrame({\"u\": uq[\"mean\"], \"Y\": gq}).reindex(allq)\nfull[\"du\"] = full.u.diff(); full[\"g\"] = 100 * np.log(full.Y / full.Y.shift(1))\nsample = full.loc[FIRST:LAST]\nexcl.append((\"2_quarterly\", \"restrict to regression window 1960Q1-2019Q4 (1959Q4 levels used only for differences)\",\n             len(full), len(sample), \"window fixed by TASK.md; quarters 1947Q1-1959Q4 and 2020Q1-2026Q3 out of window\"))\nn_b = len(sample); sample = sample.dropna(subset=[\"du\", \"g\"])\nexcl.append((\"3_quarterly\", \"drop window quarters with missing du or g\", n_b, len(sample), \"none expected\"))\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"rationale\"]).assign(\n    n_removed=lambda d: d.n_before - d.n_after)[[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]\n    ].to_csv(os.path.join(OUT, \"exclusions.csv\"), index=False)\n\nn = len(sample); assert n == 240\nX = sm.add_constant(sample.g.values); y = sample.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) rule of thumb for the Bartlett kernel, floor(4*(T/100)^(2/9))\nL = int(np.floor(4 * (n / 100) ** (2 / 9)))\ndef hac_se(lag):\n    r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    return float(r.bse[1])\nb, a = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), hac_se(L)\nz = float(stats.norm.ppf(0.975))\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(b / se_hac)))\nsens = {str(l): hac_se(l) for l in [0, 1, 2, 4, 6, 8, 12]}\nres_acf = acf(ols.resid, nlags=4, fft=False)[1:]\n\nresults = {\n    \"task_id\": \"F2-conf-04\", \"beta_hat\": b, \"alpha_hat\": a, \"r_squared\": float(ols.rsquared),\n    \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": L,\n    \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n    \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n    \"mean_growth\": float(sample.g.mean()),\n    \"first_quarter\": str(sample.index[0]), \"last_quarter\": str(sample.index[-1]),\n    \"n_analysed\": int(n), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n    # supplementary keys (not required by TASK.md)\n    \"t_stat_hac\": float(b / se_hac), \"p_value_hac\": p_hac, \"z_crit_95\": z,\n    \"baseline_ci_iid_low\": float(b - z * se_iid), \"baseline_ci_iid_high\": float(b + z * se_iid),\n    \"se_hac_by_lag\": sens, \"resid_acf_lags1to4\": [float(x) for x in res_acf],\n    \"mean_du\": float(sample.du.mean()),\n}\nwith open(os.path.join(OUT, \"results.json\"), \"w\") as fh:\n    json.dump(results, fh, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(sample.g, sample.du, s=12, alpha=0.7)\nxs = np.linspace(sample.g.min(), sample.g.max(), 50)\nax.plot(xs, a + b * xs, \"r-\", label=f\"OLS: Δu = {a:.3f} + ({b:.3f}) g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"g_t: real GDP growth, % q/q (not annualised)\"); ax.set_ylabel(\"Δu_t: change in unemployment rate, pp\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(os.path.join(OUT, \"figure1.png\"), dpi=120)\nprint(json.dumps({k: results[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Write and run analysis 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   # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n147:                nb, na, nr = int(float(r[\"n_before\"])), int(float(r[\"n_after\"])), int(float(r[\"n_removed\"]))\n149:                    bad.append(f\"row {i}: n_before - n_removed != n_after\")\n151:                    bad.append(f\"row {i}: n_before != previous n_after\")\n153:            add(\"exclusion_ledger\", not bad, \"; \".join(bad) if bad else f\"{len(rows)} steps consistent\")\n155:            add(\"exclusion_ledger\", False, f\"unreadable: {e}\")","is_error":false}]},"uuid":"63278285-ae19-46de-970c-a6451d7f2ea8","timestamp":"2026-10-30T05:06:27.190Z","toolUseResult":{"stdout":"139:    # 6. exclusion ledger\n140:    ex_path = os.path.join(ws, \"out\", \"exclusions.csv\")\n141:    if \"exclusion_ledger_sums\" in fam and os.path.exists(ex_path):\n147:                nb, na, nr = int(float(r[\"n_before\"])), 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Writing state documents, report and claims."},{"type":"tool_use","id":"toolu_019w66MSjzuVE6HfvMUCLqez","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit (P1)\n| file | SHA-256 (computed) | matches README | data rows | cols | date range | missing | dup dates |\n|---|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fa…a06c | yes | 945 | 2 (observation_date, UNRATE) | 1948-01-01 … 2026-09-01, monthly (gaps 28–31 days) | 1 (2025-10-01 empty) | 0 |\n| GDPC1.csv | 1c6c6e9f…3343 | yes | 319 | 2 (observation_date, GDPC1) | 1947-01-01 … 2026-07-01, quarterly (gaps 90–92 days) | 0 | 0 |\n| README.md | cb177a40…6e3c | – | – | – | – | – | – |\n\nUnits: UNRATE percent (SA); GDPC1 billions of chained 2017 USD, SAAR. Ranges: UNRATE 2.5–14.8; GDPC1 2172–24611. No non-numeric values except the one empty UNRATE field.\n\nAnomaly: UNRATE for 2025-10 is missing (empty). It lies outside the window 1959Q4–2019Q4, so no quarter used is affected; documented in results.json `data_issues`. Every quarter 1959Q4–2019Q4 has exactly 3 monthly UNRATE values and one GDPC1 value (asserted in analysis.py).\n\nInformation availability: data are current vintage (not real-time) — accepted as given per TASK.md. Observations outside 1959Q4–2019Q4 are not used (post-2019 and pre-1959Q4 quarters excluded; ledger in out/exclusions.csv, quarterly units: 319 → 240).\nEOF\ncat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Determinism: re-ran out/analysis.py; out/results.json byte-identical (cmp) — PASS.\n2. Independent re-implementation (raw numpy OLS + hand-coded Bartlett NW with L=4, no small-sample correction): beta, alpha, se_iid, se_hac, R², mean growth agree to <1e-14 — PASS.\n3. n_analysed = 240; first_quarter 1960Q1, last_quarter 2019Q4 — PASS.\n4. Exclusion ledger: 319 quarters − 79 − 0 = 240 = n_analysed — PASS.\n5. SEs positive (se_iid 0.0200, se_hac 0.0358) — PASS. CI [-0.326, -0.186] contains beta_hat -0.256 and is symmetric — PASS.\n6. okun_negative_5pct consistent: beta<0 and |t_hac|=7.14>1.96 (CI excludes 0) — PASS.\n7. Magnitude vs baseline: HAC SE ≈1.8× iid SE, consistent with positive residual autocorrelation (ACF lag1 0.39, lag2 0.29); beta ≈ −0.26 in the usual Okun range (≈ −0.3 to −0.5 for annualised-scale rules ÷… plausible for quarterly q/q growth) — PASS.\n8. Bandwidth sensitivity: se_hac over lags 0–12 ranges 0.029–0.037; conclusion unchanged for all — PASS.\n9. Identity check: alpha_hat + beta_hat*mean_growth = mean_du (0.1844 − 0.2558·0.7535 = −0.0083) — PASS.\n10. Information rule: only quarters 1959Q4–2019Q4 enter the computation (enforced via window slicing) — PASS.\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(r['alpha_hat']+r['beta_hat']*r['mean_growth'],r['mean_du'])\"","description":"Write data audit and checks"}}]}}79{"type":"api-request","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","id":"fba44fd0-9f76-4919-a468-9a88ad472748","timestamp":"2026-10-30T05:06:55.124Z","version":"2.1.314","querySource":"sdk","shapeHash":"074721cf65d261e798c36c2072ad74b9b6fcd23d5b46c8d5dc8f120f0b887b82","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":"fa3fa222-a40f-4a7e-b9eb-612197a286cb","keep":24,"tail":["fd73506eaf26eb9a56576a51b8340bcad7c16d8079f11351ca6043c4e49c67fb","e308f652d86f1e2d3f9a3d5725a5cf883714c27636880da2bd8cebb286284bf7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d89016df850b9ae7c8e61bb66bc894d48e94773d67c04929f16e0f9a6aa1d9ec"}80{"parentUuid":"32338a7e-629b-48ae-ab08-84f24b1daf94","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkANZhbNzazjydvbcB","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":1486,"cache_read_input_tokens":20486,"output_tokens":821,"output_tokens_details":{"thinking_tokens":24},"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":1486},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":386,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqkA7BqP6pxKGG5NDbS","requestRef":"fba44fd0-9f76-4919-a468-9a88ad472748","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"00ad61d7-693a-4e20-8c87-dd059197b3a1","timestamp":"2026-10-30T05:06:56.357Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}81{"parentUuid":"00ad61d7-693a-4e20-8c87-dd059197b3a1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkANZhbNzazjydvbcB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01QjP3hiUTpEwqKRzwRRwaZY","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\np='out/state/checks.md';s=open(p).read()\ns=s.replace(\"beta ≈ −0.26 in the usual Okun range (≈ −0.3 to −0.5 for annualised-scale rules ÷… plausible for quarterly q/q growth)\",\"beta ≈ −0.26 is negative and of a plausible order of magnitude for a quarterly-difference Okun regression (no external reference values used)\")\nopen(p,'w').write(s)\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of both CSVs match README\",\"row counts 945/319\",\"missing/duplicate/gap audit done\",\"1959Q4-2019Q4 completeness asserted\",\"exclusion ledger chains 319->240\"],\"notes\":\"one missing UNRATE month (2025-10) outside window\"},\n\"P2\":{\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed (se_iid, baseline_ci_iid_*)\"],\"notes\":\"\"},\n\"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, figure1.png, exclusions.csv; no randomness (random_seed null)\"],\"notes\":\"NW bandwidth floor(4*(T/100)^(2/9))=4\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"rerun byte-identical\",\"independent numpy reimplementation agrees <1e-14\",\"see out/state/checks.md (10 checks, all pass)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P1-P3 passed; decision: NW bandwidth L=4 by Newey-West (1994) rule of thumb; normal critical value for CI. P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -30","description":"Update ledger and run gate (P4)"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1486,"cache_read_input_tokens":20486,"output_tokens":821,"output_tokens_details":{"thinking_tokens":24},"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":1486},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqkA7BqP6pxKGG5NDbS","requestRef":"fba44fd0-9f76-4919-a468-9a88ad472748","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"89035af3-0af2-4165-b827-9956a808cde2","timestamp":"2026-10-30T05:07:01.862Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}82{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"89035af3-0af2-4165-b827-9956a808cde2","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}83{"type":"atis-latch","atis":"","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085"}84{"parentUuid":"89035af3-0af2-4165-b827-9956a808cde2","isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01QjP3hiUTpEwqKRzwRRwaZY","type":"tool_result","content":"  [ok] key:r_squared: ok\n  [ok] key:se_iid: ok\n  [ok] key:se_hac: ok\n  [ok] key:hac_lags: ok\n  [ok] key:beta_ci_low: ok\n  [ok] key:beta_ci_high: ok\n  [ok] key:okun_negative_5pct: ok\n  [ok] key:mean_growth: ok\n  [ok] key:first_quarter: ok\n  [ok] key:last_quarter: ok\n  [ok] key:n_analysed: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n  [ok] positive:se_iid: 0.019960092914910015\n  [ok] positive:se_hac: 0.03583463330296578\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0227/out/state/gate_receipts/20261030T050702.986444Z.json","is_error":false}]},"uuid":"536bff19-b25d-4d2f-9c33-971ac949b991","timestamp":"2026-10-30T05:07:02.993Z","toolUseResult":{"stdout":"  [ok] key:r_squared: ok\n  [ok] key:se_iid: ok\n  [ok] key:se_hac: ok\n  [ok] key:hac_lags: ok\n  [ok] key:beta_ci_low: ok\n  [ok] key:beta_ci_high: ok\n  [ok] key:okun_negative_5pct: ok\n  [ok] key:mean_growth: ok\n  [ok] key:first_quarter: ok\n  [ok] key:last_quarter: ok\n  [ok] key:n_analysed: ok\n  [ok] key:feasibility: ok\n  [ok] key:feasibility_reason: ok\n  [ok] key:data_issues: ok\n  [ok] key:n_input_rows: ok\n  [ok] key:random_seed: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 2 steps consistent\n  [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n  [ok] positive:se_iid: 0.019960092914910015\n  [ok] positive:se_hac: 0.03583463330296578\n  [ok] reproducibility: regenerated in 1.1s; 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no randomness (random_seed null)\"],\"notes\":\"NW bandwidth floor(4*(T/100)^(2/9))=4\"},\n\"P4\":{\"status\":\"running\",\"checks\":[\"rerun byte-identical\",\"independent numpy reimplementation agrees <1e-14\",\"see out/state/checks.md (10 checks, all pass)\"],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P1-P3 passed; decision: NW bandwidth L=4 by Newey-West (1994) rule of thumb; normal critical value for CI. P4 checks done, running gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | tail -30","description":"Update ledger and run gate (P4)"}}]}}86{"type":"api-request","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","id":"00bff278-cda3-441a-be2b-3d80675db824","timestamp":"2026-10-30T05:07:02.997Z","version":"2.1.314","querySource":"sdk","shapeHash":"074721cf65d261e798c36c2072ad74b9b6fcd23d5b46c8d5dc8f120f0b887b82","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":"fba44fd0-9f76-4919-a468-9a88ad472748","keep":26,"tail":["f0e6cd3ef90b16da3d9621e3f50e8d4cb2187e41ca6ec68d6b47005bbce499af","30a11431c6bd0f604f0028dfbcf7763ef633e901b2b9abab3b075185ba03c46b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f73efcad21dc83a09f5d730b2d33aedb086b968b4afbef819e32e9f1369afa76"}87{"parentUuid":"536bff19-b25d-4d2f-9c33-971ac949b991","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkk477198yxwZPgz89","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":1457,"cache_read_input_tokens":21972,"output_tokens":2870,"output_tokens_details":{"thinking_tokens":47},"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":1457},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":497,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXqkjkWAvMuufLGAAY3z","requestRef":"00bff278-cda3-441a-be2b-3d80675db824","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"cc81ae3a-9999-4de6-8d2e-b01cd789ca80","timestamp":"2026-10-30T05:07:04.404Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}88{"parentUuid":"cc81ae3a-9999-4de6-8d2e-b01cd789ca80","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkk477198yxwZPgz89","type":"message","role":"assistant","content":[{"type":"text","text":"P4-relevant checks pass (remaining failures are P5 deliverables). Writing report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1457,"cache_read_input_tokens":21972,"output_tokens":2870,"output_tokens_details":{"thinking_tokens":47},"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":1457},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqkjkWAvMuufLGAAY3z","requestRef":"00bff278-cda3-441a-be2b-3d80675db824","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"49a8bf77-ceba-4292-8470-71d4bd41d69a","timestamp":"2026-10-30T05:07:04.405Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}89{"parentUuid":"49a8bf77-ceba-4292-8470-71d4bd41d69a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkk477198yxwZPgz89","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XRrbNcTr92S8mzBfVUPqmi","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4 (F2-conf-04)\n\n## Question\nHow strongly does the change in the U.S. unemployment rate co-move with quarterly real GDP growth? We estimate the slope β of the linear projection Δu_t = α + β g_t + e_t, where u_t is the quarterly mean of monthly UNRATE (pp), Δu_t = u_t − u_{t−1}, and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (percent, quarter-on-quarter, not annualised), and ask whether β is negative and significantly different from zero at 5% using autocorrelation-robust (Newey–West) inference.\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`. Current vintage, used as given.\n- One data issue (`data_issues`): UNRATE for 2025-10 is empty. It lies outside the window used and is dropped without affecting any analysed quarter. Every quarter 1959Q4–2019Q4 has all three monthly unemployment observations and a GDP value (asserted in the script).\n- Exclusions (`out/exclusions.csv`, quarterly units): 319 quarters in the union of both series → 240 after restricting to 1960Q1–2019Q4 (79 removed; 1959Q4 levels used only to form the 1960Q1 differences) → 240 after dropping missing differences (0 removed). Sample: `first_quarter` = 1960Q1, `last_quarter` = 2019Q4, `n_analysed` = 240.\n\n## Method\n- OLS of Δu_t on a constant and g_t (statsmodels). The conventional homoskedastic/independent SE (`se_iid`) is the naïve baseline.\n- Newey–West HAC SE with Bartlett kernel (`se_hac`), no small-sample degrees-of-freedom correction. Bandwidth `hac_lags` = 4 from the Newey–West (1994) rule of thumb for the Bartlett kernel, L = floor(4·(T/100)^{2/9}) with T = 240 (= floor(4.86)); the alternative common rule 0.75·T^{1/3} ≈ 4.66 gives the same order. The choice is supported by the residual autocorrelations, which are sizeable at lags 1–2 and small by lag 4 (`resid_acf_lags1to4`).\n- 95% CI = β̂ ± 1.96·`se_hac` (normal critical value, `z_crit_95`); two-sided test of H0: β = 0 with the HAC t-statistic.\n- Deterministic; no random numbers (`random_seed` = null). Results were verified by an independent numpy re-implementation (agreement < 1e-14) and by a byte-identical re-run (`out/state/checks.md`).\n\n## Results\n- Okun coefficient `beta_hat` = −0.256: a 1 percentage point higher quarterly (non-annualised) real GDP growth is associated with a 0.256 pp larger fall in the unemployment rate in the same quarter.\n- Intercept `alpha_hat` = 0.184 pp; `r_squared` = 0.408. Mean quarterly growth `mean_growth` = 0.754%, so the implied growth rate consistent with unchanged unemployment is about −α̂/β̂ ≈ 0.72% per quarter (derived from `alpha_hat` and `beta_hat`).\n- `okun_negative_5pct` = true: β̂ < 0 and the HAC t-statistic is −7.14 (`t_stat_hac`, p ≈ 1e-12, `p_value_hac`).\n- Figure: `out/figure1.png` (scatter of Δu_t against g_t with the fitted OLS line).\n\n## Uncertainty\n- `se_iid` = 0.0200 (baseline) vs `se_hac` = 0.0358 (L = 4): the HAC SE is about 1.8 times the naïve SE, consistent with positively autocorrelated residuals (lag-1 autocorrelation 0.39, `resid_acf_lags1to4`).\n- 95% HAC CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve interval would be [−0.295, −0.217] (`baseline_ci_iid_low`, `baseline_ci_iid_high`), i.e. too narrow.\n- Bandwidth sensitivity (`se_hac_by_lag`): SE ranges from 0.029 (L = 0, heteroskedasticity-robust only) to 0.037 (L = 8); with L = 12 it is 0.036. At the largest of these (≈0.037) |t| ≈ 6.9, so the significance conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- β is a reduced-form linear projection coefficient, not a causal effect; it is a full-sample average and ignores possible structural change over 1960–2019 and asymmetries between expansions and recessions.\n- Data are the current vintage, not real-time; GDP revisions mean results describe today's view of history.\n- Newey–West inference is asymptotic; with strong persistence, fixed-b critical values would widen the CI somewhat, but given |t| ≈ 7 the conclusion would not change. This was not computed.\n- The bandwidth is a rule of thumb rather than a data-dependent (Andrews/Newey–West automatic) selection; sensitivity over 0–12 lags is reported instead.\n- No other limitations: the task was completed as specified (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient for 1960Q1-2019Q4 (240 quarters) is about -0.256 pp of unemployment per 1% of quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Reduced-form linear projection, full-sample average; not causal.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) standard error is 0.0358, about 1.8 times the conventional SE of 0.0200, reflecting positive residual autocorrelation.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_acf_lags1to4\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb, not an automatic selector.\"},\n {\"id\":\"C3\",\"text\":\"The 95% HAC confidence interval for beta is [-0.326, -0.186]; the Okun coefficient is negative and significantly different from zero at the 5% level (HAC t = -7.14).\",\"results_keys\":[\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Asymptotic normal critical value; fixed-b corrections not computed.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths from 0 to 12 lags (SE between 0.029 and 0.037).\",\"results_keys\":[\"se_hac_by_lag\",\"beta_hat\"],\"status\":\"supported\",\"caveat\":\"Only lags 0,1,2,4,6,8,12 evaluated.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754%; the only data issue (missing UNRATE for 2025-10) lies outside the analysis window.\",\"results_keys\":[\"mean_growth\",\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, not real-time.\"}\n]\nEOF\necho ok","description":"Write report and claims ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1457,"cache_read_input_tokens":21972,"output_tokens":2870,"output_tokens_details":{"thinking_tokens":47},"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":1457},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqkjkWAvMuufLGAAY3z","requestRef":"00bff278-cda3-441a-be2b-3d80675db824","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0a6b2f14-f911-45f5-a98a-5cffe727e745","timestamp":"2026-10-30T05:07:26.138Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}90{"parentUuid":"0a6b2f14-f911-45f5-a98a-5cffe727e745","isSidechain":false,"promptId":"28339e5a-a0d8-41d9-a248-d0990a7ec218","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XRrbNcTr92S8mzBfVUPqmi","type":"tool_result","content":"ok","is_error":false}]},"uuid":"e2e8fada-06fd-4eba-97b4-707cad62422d","timestamp":"2026-10-30T05:07:26.160Z","toolUseResult":{"stdout":"ok","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0a6b2f14-f911-45f5-a98a-5cffe727e745","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0227","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","version":"2.1.314","gitBranch":"HEAD"}91{"type":"api-request-blob","sessionId":"b3616e3b-b8e9-4998-a665-a73378bfa085","hash":"c7c8202faa792d42bad3597bd90f7dcf8a0ab19d3a47cc165080467b9517682a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"P4-relevant checks pass (remaining failures are P5 deliverables). Writing report and claims."},{"type":"tool_use","id":"toolu_01XRrbNcTr92S8mzBfVUPqmi","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4 (F2-conf-04)\n\n## Question\nHow strongly does the change in the U.S. unemployment rate co-move with quarterly real GDP growth? We estimate the slope β of the linear projection Δu_t = α + β g_t + e_t, where u_t is the quarterly mean of monthly UNRATE (pp), Δu_t = u_t − u_{t−1}, and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (percent, quarter-on-quarter, not annualised), and ask whether β is negative and significantly different from zero at 5% using autocorrelation-robust (Newey–West) inference.\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`. Current vintage, used as given.\n- One data issue (`data_issues`): UNRATE for 2025-10 is empty. It lies outside the window used and is dropped without affecting any analysed quarter. Every quarter 1959Q4–2019Q4 has all three monthly unemployment observations and a GDP value (asserted in the script).\n- Exclusions (`out/exclusions.csv`, quarterly units): 319 quarters in the union of both series → 240 after restricting to 1960Q1–2019Q4 (79 removed; 1959Q4 levels used only to form the 1960Q1 differences) → 240 after dropping missing differences (0 removed). Sample: `first_quarter` = 1960Q1, `last_quarter` = 2019Q4, `n_analysed` = 240.\n\n## Method\n- OLS of Δu_t on a constant and g_t (statsmodels). The conventional homoskedastic/independent SE (`se_iid`) is the naïve baseline.\n- Newey–West HAC SE with Bartlett kernel (`se_hac`), no small-sample degrees-of-freedom correction. Bandwidth `hac_lags` = 4 from the Newey–West (1994) rule of thumb for the Bartlett kernel, L = floor(4·(T/100)^{2/9}) with T = 240 (= floor(4.86)); the alternative common rule 0.75·T^{1/3} ≈ 4.66 gives the same order. The choice is supported by the residual autocorrelations, which are sizeable at lags 1–2 and small by lag 4 (`resid_acf_lags1to4`).\n- 95% CI = β̂ ± 1.96·`se_hac` (normal critical value, `z_crit_95`); two-sided test of H0: β = 0 with the HAC t-statistic.\n- Deterministic; no random numbers (`random_seed` = null). Results were verified by an independent numpy re-implementation (agreement < 1e-14) and by a byte-identical re-run (`out/state/checks.md`).\n\n## Results\n- Okun coefficient `beta_hat` = −0.256: a 1 percentage point higher quarterly (non-annualised) real GDP growth is associated with a 0.256 pp larger fall in the unemployment rate in the same quarter.\n- Intercept `alpha_hat` = 0.184 pp; `r_squared` = 0.408. Mean quarterly growth `mean_growth` = 0.754%, so the implied growth rate consistent with unchanged unemployment is about −α̂/β̂ ≈ 0.72% per quarter (derived from `alpha_hat` and `beta_hat`).\n- `okun_negative_5pct` = true: β̂ < 0 and the HAC t-statistic is −7.14 (`t_stat_hac`, p ≈ 1e-12, `p_value_hac`).\n- Figure: `out/figure1.png` (scatter of Δu_t against g_t with the fitted OLS line).\n\n## Uncertainty\n- `se_iid` = 0.0200 (baseline) vs `se_hac` = 0.0358 (L = 4): the HAC SE is about 1.8 times the naïve SE, consistent with positively autocorrelated residuals (lag-1 autocorrelation 0.39, `resid_acf_lags1to4`).\n- 95% HAC CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve interval would be [−0.295, −0.217] (`baseline_ci_iid_low`, `baseline_ci_iid_high`), i.e. too narrow.\n- Bandwidth sensitivity (`se_hac_by_lag`): SE ranges from 0.029 (L = 0, heteroskedasticity-robust only) to 0.037 (L = 8); with L = 12 it is 0.036. At the largest of these (≈0.037) |t| ≈ 6.9, so the significance conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- β is a reduced-form linear projection coefficient, not a causal effect; it is a full-sample average and ignores possible structural change over 1960–2019 and asymmetries between expansions and recessions.\n- Data are the current vintage, not real-time; GDP revisions mean results describe today's view of history.\n- Newey–West inference is asymptotic; with strong persistence, fixed-b critical values would widen the CI somewhat, but given |t| ≈ 7 the conclusion would not change. This was not computed.\n- The bandwidth is a rule of thumb rather than a data-dependent (Andrews/Newey–West automatic) selection; sensitivity over 0–12 lags is reported instead.\n- No other limitations: the task was completed as specified (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient for 1960Q1-2019Q4 (240 quarters) is about -0.256 pp of unemployment per 1% of quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Reduced-form linear projection, full-sample average; not causal.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) standard error is 0.0358, about 1.8 times the conventional SE of 0.0200, reflecting positive residual autocorrelation.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_acf_lags1to4\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb, not an automatic selector.\"},\n {\"id\":\"C3\",\"text\":\"The 95% HAC confidence interval for beta is [-0.326, -0.186]; the Okun coefficient is negative and significantly different from zero at the 5% level (HAC t = -7.14).\",\"results_keys\":[\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Asymptotic normal critical value; fixed-b corrections not computed.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths from 0 to 12 lags (SE between 0.029 and 0.037).\",\"results_keys\":[\"se_hac_by_lag\",\"beta_hat\"],\"status\":\"supported\",\"caveat\":\"Only lags 0,1,2,4,6,8,12 evaluated.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754%; the only data issue (missing UNRATE for 2025-10) lies outside the analysis window.\",\"results_keys\":[\"mean_growth\",\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data, not real-time.\"}\n]\nEOF\necho ok","description":"Write report and claims 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- <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']['status']='passed'; p['P4']['checks'].append('gate run at end of P4: receipt out/state/gate_receipts/20261030T050702.986444Z.json (contract keys, CI, positive SE, exclusion ledger, reproducibility ok; only P5 deliverables missing at that time)')\np['P5']={'status':'passed','checks':['report.md with 6 required sections, every number cites a results key','claims.json C1-C5 reference existing keys','final gate run (see receipt list)'],'notes':''}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed (gate receipt 20261030T050702); P5 report+claims written; running final gate.\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -v \"\\[ok\\]\"","description":"Finalize ledger and run final 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The final gate passed (0 of 38 checks failed), and re-running `out/analysis.py` reproduces `out/results.json` exactly.\n\n**Results (240 quarters, 1960Q1–2019Q4):**\n\n| Quantity | Value |\n|---|---|\n| β̂ (Okun coefficient) | −0.256 |\n| α̂ (intercept) | 0.184 |\n| R² | 0.408 |\n| Conventional standard error (the simple baseline) | 0.0200 |\n| Newey–West standard error, 4 lags | 0.0358 |\n| 95% confidence interval | [−0.326, −0.186] |\n| t-statistic | −7.14 |\n| Mean quarterly GDP growth | 0.754% |\n\n- **Interpretation:** one extra percentage point of quarterly real GDP growth (not annualised) goes with a 0.256-point larger fall in the unemployment rate in the same quarter.\n- **Effect of correlated errors:** the Newey–West standard error is about 1.8 times the conventional one. This fits the positively correlated residuals (correlation 0.39 with the previous quarter). The conventional interval, [−0.295, −0.217], would be too narrow.\n- **Lag choice:** I used 4 lags, from the standard Newey–West (1994) rule of thumb, floor(4·(240/100)^(2/9)) = 4. With anywhere from 0 to 12 lags the standard error stays between 0.029 and 0.037, so the conclusion is the same.\n- **Checks:** a separate hand-coded version of the regression and the standard errors matches the script to within 1e-14.\n\n**Data:** both files match the checksums in `data/README.md`. The only problem is a missing unemployment value for October 2025, which is outside the sample and affects no quarter used.\n\n**Limitations:**\n- The estimate is a whole-period association, not a causal effect. It does not allow for the relationship changing over time or differing between recessions and expansions.\n- The data are today's revised figures, not what was known at the time.\n- The confidence interval uses large-sample normal critical values. 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