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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:39 .\ndrwxr-xr-x 128 simon-pierreboucher staff 4096 oct. 30 01:05 ..\n-rw-r--r-- 1 simon-pierreboucher staff 600 oct. 29 23:39 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:37 data\n-rw-r--r-- 1 simon-pierreboucher staff 1736 oct. 29 23:39 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 4631 oct. 29 23:39 TASK.md\ndata:\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":false}]},"uuid":"050428b3-0ff8-4f65-a560-9a78a210cb7d","timestamp":"2026-10-30T05:05:36.245Z","toolUseResult":{"stdout":"# F2-conf-04 — Okun's law in U.S. quarterly data with autocorrelation-robust uncertainty\n\n## Scientific question\nEstimate the Okun's-law relation between changes in the U.S. unemployment rate and real GDP growth, with uncertainty that is valid under serially correlated disturbances. The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 32\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:39 .\ndrwxr-xr-x 128 simon-pierreboucher staff 4096 oct. 30 01:05 ..\n-rw-r--r-- 1 simon-pierreboucher staff 600 oct. 29 23:39 bundle.json\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:37 data\n-rw-r--r-- 1 simon-pierreboucher staff 1736 oct. 29 23:39 gate_spec.json\n-rw-r--r-- 1 simon-pierreboucher staff 4631 oct. 29 23:39 TASK.md\ndata:\nGDPC1.csv\nREADME.md\nUNRATE.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"acee5269-8a06-484c-83e3-a71be4f5b087","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","hash":"cdae58a544292e0d7753885d894b240e33d86cf4f1aeb7c9eec34eb6b8fbcdb8","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01AaDceUyJHb3Nb1fteTKu9h","name":"Bash","input":{"command":"cat TASK.md; 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cd data; shasum -a 256 *.csv; wc -l *.csv; head -3 *.csv; tail -3 *.csv; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['UNRATE','GDPC1']:\n d=pd.read_csv(f+'.csv'); print(f,d.shape,d.dtypes.to_dict(),d.isna().sum().to_dict(),d.observation_date.duplicated().sum())\n d['observation_date']=pd.to_datetime(d.observation_date); print(d.observation_date.diff().value_counts().head()); print(d.describe())\n x=pd.to_numeric(d[f],errors='coerce'); print('nonnum',x.isna().sum())\n\"","description":"Audit data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2311,"cache_read_input_tokens":3713,"output_tokens":334,"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":2311},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqeKvNTfaccRE9PAizE","requestRef":"9ee1d734-36ed-43f2-9d35-84616f7c7b10","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a543d46c-4a20-4ab7-bda0-1e91b007defe","timestamp":"2026-10-30T05:05:39.496Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}18{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a543d46c-4a20-4ab7-bda0-1e91b007defe","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61"}19{"type":"atis-latch","atis":"","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61"}20{"parentUuid":"a543d46c-4a20-4ab7-bda0-1e91b007defe","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01R54kMzdbpz5y5ig2f24MMm","type":"tool_result","content":"# Data provenance — F2-conf-04\n\nBoth files are unmodified downloads from FRED (Federal Reserve Bank of St. Louis), retrieved on 2026-10-30 03:32:42 UTC with\n`curl -L \"https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>\"`. They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv\n 320 GDPC1.csv\n 946 UNRATE.csv\n 1266 total\n==> GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')} {'observation_date': 0, 'UNRATE': 1} 0\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64\n observation_date UNRATE\ncount 945 944.000000\nmean 1987-05-02 04:20:34.285714304 5.652966\nmin 1948-01-01 00:00:00 2.500000\n25% 1967-09-01 00:00:00 4.300000\n50% 1987-05-01 00:00:00 5.450000\n75% 2007-01-01 00:00:00 6.700000\nmax 2026-09-01 00:00:00 14.800000\nstd NaN 1.703534\nnonnum 1\nGDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')} {'observation_date': 0, 'GDPC1': 0} 0\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64\n observation_date GDPC1\ncount 319 319.000000\nmean 1986-10-01 00:45:08.463949824 10458.210204\nmin 1947-01-01 00:00:00 2172.432000\n25% 1966-11-16 00:00:00 4848.918000\n50% 1986-10-01 00:00:00 8920.193000\n75% 2006-08-16 00:00:00 16340.058000\nmax 2026-07-01 00:00:00 24611.077000\nstd NaN 6485.042448\nnonnum 0","is_error":false}]},"uuid":"acd165c8-0b10-487c-94e3-6aaf8df0e8aa","timestamp":"2026-10-30T05:05:39.744Z","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\".\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv\n 320 GDPC1.csv\n 946 UNRATE.csv\n 1266 total\n==> GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')} {'observation_date': 0, 'UNRATE': 1} 0\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64\n observation_date UNRATE\ncount 945 944.000000\nmean 1987-05-02 04:20:34.285714304 5.652966\nmin 1948-01-01 00:00:00 2.500000\n25% 1967-09-01 00:00:00 4.300000\n50% 1987-05-01 00:00:00 5.450000\n75% 2007-01-01 00:00:00 6.700000\nmax 2026-09-01 00:00:00 14.800000\nstd NaN 1.703534\nnonnum 1\nGDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')} {'observation_date': 0, 'GDPC1': 0} 0\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64\n observation_date GDPC1\ncount 319 319.000000\nmean 1986-10-01 00:45:08.463949824 10458.210204\nmin 1947-01-01 00:00:00 2172.432000\n25% 1966-11-16 00:00:00 4848.918000\n50% 1986-10-01 00:00:00 8920.193000\n75% 2006-08-16 00:00:00 16340.058000\nmax 2026-07-01 00:00:00 24611.077000\nstd NaN 6485.042448\nnonnum 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/Users/simon-pierreboucher/wp29/runs/RC0224 && mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in U.S. quarterly data: how does the quarterly change in the unemployment rate co-move with quarterly real GDP growth, with inference robust to serial correlation?\n\n**Estimand.** β in the linear projection Δu_t = α + β g_t + e_t, 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}) (percent, not annualised). Sample 1960Q1–2019Q4 (240 quarters; 1959Q4 levels used for first differences). β is a linear-projection coefficient (descriptive, not causal); it is meaningful under covariance stationarity of (Δu, g) over the sample. HAC SE valid under weak dependence / mixing, with bandwidth growing slowly.\n\n**Outputs.** out/results.json keys: task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/figure1.png.\n\n**Information rules.** Only data/ files; current vintage, treated as given. Observations outside 1960Q1–2019Q4 used only for 1959Q4 levels; window not extended.\n\n**Invalidity/impossibility.** Missing monthly values or GDP inside 1959Q4–2019Q4 (incomplete quarters), wrong sample size (≠240), annualised growth, using data outside window in regression.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1249,"cache_read_input_tokens":7955,"output_tokens":1352,"output_tokens_details":{"thinking_tokens":352},"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":1249},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqeqDyrhvdtfPdteZEr","requestRef":"f1ed9884-2589-485b-b39c-bf5243831b5f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5bafd6c5-0274-4d5d-8556-805ceb136691","timestamp":"2026-10-30T05:05:55.232Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224/data","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"5bafd6c5-0274-4d5d-8556-805ceb136691","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01WMuT8ZF5nYCxohxv3tQdG5","type":"tool_result","content":"(Bash completed with no 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/Users/simon-pierreboucher/wp29/runs/RC0224 && mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in U.S. quarterly data: how does the quarterly change in the unemployment rate co-move with quarterly real GDP growth, with inference robust to serial correlation?\n\n**Estimand.** β in the linear projection Δu_t = α + β g_t + e_t, 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}) (percent, not annualised). Sample 1960Q1–2019Q4 (240 quarters; 1959Q4 levels used for first differences). β is a linear-projection coefficient (descriptive, not causal); it is meaningful under covariance stationarity of (Δu, g) over the sample. HAC SE valid under weak dependence / mixing, with bandwidth growing slowly.\n\n**Outputs.** out/results.json keys: task_id, beta_hat, alpha_hat, r_squared, se_iid, se_hac, hac_lags, beta_ci_low, beta_ci_high, okun_negative_5pct, mean_growth, first_quarter, last_quarter, n_analysed, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed. Also out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues), out/analysis.py, out/claims.json, out/figure1.png.\n\n**Information rules.** Only data/ files; current vintage, treated as given. Observations outside 1960Q1–2019Q4 used only for 1959Q4 levels; window not extended.\n\n**Invalidity/impossibility.** Missing monthly values or GDP inside 1959Q4–2019Q4 (incomplete quarters), wrong sample size (≠240), annualised growth, using data outside window in regression.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully; contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}31{"type":"api-request","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","id":"489e58b4-78c0-4164-8d53-d5069182c28d","timestamp":"2026-10-30T05:05:55.286Z","version":"2.1.314","querySource":"sdk","shapeHash":"1b2d89bcce2a9abf5376556142180e8efa18b27ec6c087cdb15f347ce33c5948","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":"f1ed9884-2589-485b-b39c-bf5243831b5f","keep":8,"tail":["c7fded2907f48f0a3cb59c7ff70fba252c11e5282ac337101663eb6fd42a4c10","b7feac91b90ac385760e9810842bfde05ee9898fbd11481ac67da502b480a5fe"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"11a6747135bf0250fa6e89714ed223027e8c4f645fe0f6751b3198d206c4a16a"}32{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5a308ff9-28e1-4138-995f-0f6edc359384","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61"}33{"type":"atis-latch","atis":"","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61"}34{"parentUuid":"5a308ff9-28e1-4138-995f-0f6edc359384","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfjcBL8G7Jsk9uguFg","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script (handles audit, exclusions, baseline, HAC, figure)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1367,"cache_read_input_tokens":9204,"output_tokens":3431,"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":1367},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqfjKZgmM6ihtSbzvqU","requestRef":"489e58b4-78c0-4164-8d53-d5069182c28d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"507cf7e8-9895-40e8-8f26-705a6c1bfbde","timestamp":"2026-10-30T05:05:56.432Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}35{"parentUuid":"507cf7e8-9895-40e8-8f26-705a6c1bfbde","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqfjcBL8G7Jsk9uguFg","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01G6PZwDD3Tuwi6NdYwWEKT9","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, U.S. quarterly 1960Q1-2019Q4, OLS + Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/exclusions.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math\nfrom pathlib import Path\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 = Path(__file__).resolve().parent.parent\nDATA, OUT = ROOT / \"data\", ROOT / \"out\"\nOUT.mkdir(exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1 # 1959Q4\n\nun = pd.read_csv(DATA / \"UNRATE.csv\")\ngd = pd.read_csv(DATA / \"GDPC1.csv\")\nn_input_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\ndata_issues = []\nexcl = []\n\n# --- unemployment: monthly -> quarterly average (require all 3 months) ---\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nassert not un[\"date\"].duplicated().any()\nmiss = un[un[\"UNRATE\"].isna()]\nfor d in miss[\"observation_date\"]:\n p = pd.Period(d, \"M\").asfreq(\"Q\")\n inside = LEVEL_START <= p <= LAST\n data_issues.append({\"issue\": f\"UNRATE missing value at {d} (empty field)\",\n \"handling\": (\"inside the needed 1959Q4-2019Q4 range; affected quarter would be dropped\"\n if inside else\n \"outside the 1959Q4-2019Q4 range needed for the regression; dropped, no effect on results\")})\nn0 = len(un); un = un.dropna(subset=[\"UNRATE\"])\nexcl.append((\"U1\", \"drop UNRATE rows with missing value\", n0, len(un), \"empty field in source file\"))\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nn0 = len(un); un = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)]\nexcl.append((\"U2\", \"keep months in 1959Q4-2019Q4\", n0, len(un), \"window rule: only 1959Q4 levels used outside sample\"))\nqu = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nassert (qu[\"count\"] == 3).all(), \"incomplete quarter in window\"\nu = qu[\"mean\"]\n\n# --- GDP ---\ngd[\"q\"] = pd.to_datetime(gd[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gd[\"q\"].duplicated().any() and gd[\"GDPC1\"].notna().all() and (gd[\"GDPC1\"] > 0).all()\nn0 = len(gd); gd = gd[(gd[\"q\"] >= LEVEL_START) & (gd[\"q\"] <= LAST)]\nexcl.append((\"G1\", \"keep quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: only 1959Q4 level used outside sample\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\ndf = pd.DataFrame({\"u\": u, \"gdp\": y}).sort_index()\nassert df.index.is_monotonic_increasing and len(df) == 241 and df.notna().all().all()\nassert (df.index[1:] - df.index[:-1] == 1 * df.index.freq).all() if df.index.freq else True\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"gdp\"] / df[\"gdp\"].shift(1))\nn0 = len(df); reg = df.loc[FIRST:LAST].dropna()\nexcl.append((\"Q1\", \"drop 1959Q4 (levels-only quarter used for first differences)\", n0, len(reg), \"differencing\"))\nassert len(reg) == 240\n\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(OUT / \"exclusions.csv\", index=False)\n\n# --- OLS ---\nT = len(reg)\nX = sm.add_constant(reg[\"g\"].values)\nY = reg[\"du\"].values\nols = sm.OLS(Y, X).fit()\n# Newey-West (1994) plug-in rule of thumb for Bartlett kernel: floor(4 (T/100)^(2/9))\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\", \"use_correction\": False})\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_hac, beta + z * se_hac)\nt_hac = beta / se_hac\np_hac = 2 * stats.norm.sf(abs(t_hac))\n\n# sensitivity over bandwidths (not required keys; reported as extras)\nsens = {}\nfor L in [0, 1, 2, 4, 8, 12, int(math.floor(0.75 * T ** (1 / 3)))]:\n r = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": False})\n sens[str(L)] = float(r.bse[1])\nhac_dfc = float(sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"use_correction\": True}).bse[1])\nresid_ac1 = float(np.corrcoef(ols.resid[1:], ols.resid[:-1])[0, 1])\n\nfq = lambda p: f\"{p.year}Q{p.quarter}\"\nres = {\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": hac_lags,\n \"beta_ci_low\": float(ci[0]), \"beta_ci_high\": float(ci[1]),\n \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n \"mean_growth\": float(reg[\"g\"].mean()),\n \"first_quarter\": fq(reg.index[0]), \"last_quarter\": fq(reg.index[-1]),\n \"n_analysed\": int(T),\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"random_seed\": None,\n # extras\n \"baseline_ci_iid_low\": float(beta - z * se_iid), \"baseline_ci_iid_high\": float(beta + z * se_iid),\n \"t_stat_hac\": float(t_hac), \"p_value_hac\": float(p_hac), \"z_crit\": float(z),\n \"se_hac_small_sample_corrected\": hac_dfc,\n \"se_hac_by_lags\": sens, \"resid_lag1_autocorr\": resid_ac1,\n \"mean_du\": float(reg[\"du\"].mean()),\n \"hac_lag_rule\": \"floor(4*(T/100)^(2/9)) (Newey-West 1994 rule of thumb, Bartlett kernel)\",\n}\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2) + \"\\n\")\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(reg[\"g\"], reg[\"du\"], s=12, alpha=0.7)\ngg = np.linspace(reg[\"g\"].min(), reg[\"g\"].max(), 50)\nax.plot(gg, alpha + beta * gg, color=\"C3\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Change in unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout(); fig.savefig(OUT / \"figure1.png\", dpi=150)\n\nprint(f\"beta={beta:.4f} alpha={alpha:.4f} R2={ols.rsquared:.3f} se_iid={se_iid:.4f} \"\n f\"se_hac(L={hac_lags})={se_hac:.4f} CI=[{ci[0]:.4f},{ci[1]:.4f}] neg5%={res['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":1367,"cache_read_input_tokens":9204,"output_tokens":3431,"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":1367},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqfjKZgmM6ihtSbzvqU","requestRef":"489e58b4-78c0-4164-8d53-d5069182c28d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b882306b-d92b-4655-b095-cb176b7a774f","timestamp":"2026-10-30T05:06:21.435Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"b882306b-d92b-4655-b095-cb176b7a774f","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G6PZwDD3Tuwi6NdYwWEKT9","type":"tool_result","content":"beta=-0.2558 alpha=0.1844 R2=0.408 se_iid=0.0200 se_hac(L=4)=0.0358 CI=[-0.3260,-0.1855] neg5%=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 value at 2025-10-01 (empty field)\",\n \"handling\": \"outside the 1959Q4-2019Q4 range needed for the regression; dropped, no effect on results\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945,\n \"GDPC1.csv\": 319\n },\n \"random_seed\": null,\n \"baseline_ci_iid_low\": -0.2948887909900262,\n \"baseline_ci_iid_high\": -0.21664666450743272,\n \"t_stat_hac\": -7.137445096377235,\n \"p_value_hac\": 9.508157930953398e-13,\n \"z_crit\": 1.959963984540054,\n \"se_hac_small_sample_corrected\": 0.03598488399675887,\n \"se_hac_by_lags\": {\n \"0\": 0.028836223207270188,\n \"1\": 0.032358596534706116,\n \"2\": 0.034227320236154,\n \"4\": 0.03583463330296578,\n \"8\": 0.03713550417540173,\n \"12\": 0.03640885110228042\n },\n \"resid_lag1_autocorr\": 0.38935129370212984,\n \"mean_du\": -0.008333333333333335,\n \"hac_lag_rule\": \"floor(4*(T/100)^(2/9)) (Newey-West 1994 rule of thumb, Bartlett kernel)\"\n}\nstep,rule,n_before,n_after,n_removed,rationale\nU1,drop UNRATE rows with missing value,945,944,1,empty field in source file\nU2,keep months in 1959Q4-2019Q4,944,723,221,window rule: only 1959Q4 levels used outside sample\nG1,keep quarters in 1959Q4-2019Q4,319,241,78,window rule: only 1959Q4 level used outside sample\nQ1,drop 1959Q4 (levels-only quarter used for first differences),241,240,1,differencing","is_error":false}]},"uuid":"f6bd3199-daf3-408e-8044-1036984ad310","timestamp":"2026-10-30T05:06:22.272Z","toolUseResult":{"stdout":"beta=-0.2558 alpha=0.1844 R2=0.408 se_iid=0.0200 se_hac(L=4)=0.0358 CI=[-0.3260,-0.1855] neg5%=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 value at 2025-10-01 (empty field)\",\n \"handling\": \"outside the 1959Q4-2019Q4 range needed for the regression; dropped, no effect on results\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945,\n \"GDPC1.csv\": 319\n },\n \"random_seed\": null,\n \"baseline_ci_iid_low\": -0.2948887909900262,\n \"baseline_ci_iid_high\": -0.21664666450743272,\n \"t_stat_hac\": -7.137445096377235,\n \"p_value_hac\": 9.508157930953398e-13,\n \"z_crit\": 1.959963984540054,\n \"se_hac_small_sample_corrected\": 0.03598488399675887,\n \"se_hac_by_lags\": {\n \"0\": 0.028836223207270188,\n \"1\": 0.032358596534706116,\n \"2\": 0.034227320236154,\n \"4\": 0.03583463330296578,\n \"8\": 0.03713550417540173,\n \"12\": 0.03640885110228042\n },\n \"resid_lag1_autocorr\": 0.38935129370212984,\n \"mean_du\": -0.008333333333333335,\n \"hac_lag_rule\": \"floor(4*(T/100)^(2/9)) (Newey-West 1994 rule of thumb, Bartlett kernel)\"\n}\nstep,rule,n_before,n_after,n_removed,rationale\nU1,drop UNRATE rows with missing value,945,944,1,empty field in source file\nU2,keep months in 1959Q4-2019Q4,944,723,221,window rule: only 1959Q4 levels used outside sample\nG1,keep quarters in 1959Q4-2019Q4,319,241,78,window rule: only 1959Q4 level used outside sample\nQ1,drop 1959Q4 (levels-only quarter used for first differences),241,240,1,differencing","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"b882306b-d92b-4655-b095-cb176b7a774f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}37{"type":"api-request-blob","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","hash":"953ee16a0b268511a05b928cd70c6ce29d952c0a3b9bcd7d4232ab9f4e431b7b","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script (handles audit, exclusions, baseline, HAC, figure)."},{"type":"tool_use","id":"toolu_01G6PZwDD3Tuwi6NdYwWEKT9","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F2-conf-04: Okun's law, U.S. quarterly 1960Q1-2019Q4, OLS + Newey-West HAC SE.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/exclusions.csv, out/figure1.png. No randomness.\"\"\"\nimport json, math\nfrom pathlib import Path\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 = Path(__file__).resolve().parent.parent\nDATA, OUT = ROOT / \"data\", ROOT / \"out\"\nOUT.mkdir(exist_ok=True)\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nLEVEL_START = FIRST - 1 # 1959Q4\n\nun = pd.read_csv(DATA / \"UNRATE.csv\")\ngd = pd.read_csv(DATA / \"GDPC1.csv\")\nn_input_rows = {\"UNRATE.csv\": int(len(un)), \"GDPC1.csv\": int(len(gd))}\ndata_issues = []\nexcl = []\n\n# --- unemployment: monthly -> quarterly average (require all 3 months) ---\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nassert not un[\"date\"].duplicated().any()\nmiss = un[un[\"UNRATE\"].isna()]\nfor d in miss[\"observation_date\"]:\n p = pd.Period(d, \"M\").asfreq(\"Q\")\n inside = LEVEL_START <= p <= LAST\n data_issues.append({\"issue\": f\"UNRATE missing value at {d} (empty field)\",\n \"handling\": (\"inside the needed 1959Q4-2019Q4 range; affected quarter would be dropped\"\n if inside else\n \"outside the 1959Q4-2019Q4 range needed for the regression; dropped, no effect on results\")})\nn0 = len(un); un = un.dropna(subset=[\"UNRATE\"])\nexcl.append((\"U1\", \"drop UNRATE rows with missing value\", n0, len(un), \"empty field in source file\"))\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nn0 = len(un); un = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)]\nexcl.append((\"U2\", \"keep months in 1959Q4-2019Q4\", n0, len(un), \"window rule: only 1959Q4 levels used outside sample\"))\nqu = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nassert (qu[\"count\"] == 3).all(), \"incomplete quarter in window\"\nu = qu[\"mean\"]\n\n# --- GDP ---\ngd[\"q\"] = pd.to_datetime(gd[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gd[\"q\"].duplicated().any() and gd[\"GDPC1\"].notna().all() and (gd[\"GDPC1\"] > 0).all()\nn0 = len(gd); gd = gd[(gd[\"q\"] >= LEVEL_START) & (gd[\"q\"] <= LAST)]\nexcl.append((\"G1\", \"keep quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: only 1959Q4 level used outside sample\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\ndf = pd.DataFrame({\"u\": u, \"gdp\": y}).sort_index()\nassert df.index.is_monotonic_increasing and len(df) == 241 and df.notna().all().all()\nassert (df.index[1:] - df.index[:-1] == 1 * df.index.freq).all() if df.index.freq else True\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"gdp\"] / df[\"gdp\"].shift(1))\nn0 = len(df); reg = df.loc[FIRST:LAST].dropna()\nexcl.append((\"Q1\", \"drop 1959Q4 (levels-only quarter used for first differences)\", n0, len(reg), \"differencing\"))\nassert len(reg) == 240\n\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(OUT / \"exclusions.csv\", index=False)\n\n# --- OLS ---\nT = len(reg)\nX = sm.add_constant(reg[\"g\"].values)\nY = reg[\"du\"].values\nols = sm.OLS(Y, X).fit()\n# Newey-West (1994) plug-in rule of thumb for Bartlett kernel: floor(4 (T/100)^(2/9))\nhac_lags = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"kernel\": \"bartlett\", \"use_correction\": False})\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_iid, se_hac = float(ols.bse[1]), float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_hac, beta + z * se_hac)\nt_hac = beta / se_hac\np_hac = 2 * stats.norm.sf(abs(t_hac))\n\n# sensitivity over bandwidths (not required keys; reported as extras)\nsens = {}\nfor L in [0, 1, 2, 4, 8, 12, int(math.floor(0.75 * T ** (1 / 3)))]:\n r = sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": False})\n sens[str(L)] = float(r.bse[1])\nhac_dfc = float(sm.OLS(Y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac_lags, \"use_correction\": True}).bse[1])\nresid_ac1 = float(np.corrcoef(ols.resid[1:], ols.resid[:-1])[0, 1])\n\nfq = lambda p: f\"{p.year}Q{p.quarter}\"\nres = {\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n \"se_iid\": se_iid, \"se_hac\": se_hac, \"hac_lags\": hac_lags,\n \"beta_ci_low\": float(ci[0]), \"beta_ci_high\": float(ci[1]),\n \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n \"mean_growth\": float(reg[\"g\"].mean()),\n \"first_quarter\": fq(reg.index[0]), \"last_quarter\": fq(reg.index[-1]),\n \"n_analysed\": int(T),\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"random_seed\": None,\n # extras\n \"baseline_ci_iid_low\": float(beta - z * se_iid), \"baseline_ci_iid_high\": float(beta + z * se_iid),\n \"t_stat_hac\": float(t_hac), \"p_value_hac\": float(p_hac), \"z_crit\": float(z),\n \"se_hac_small_sample_corrected\": hac_dfc,\n \"se_hac_by_lags\": sens, \"resid_lag1_autocorr\": resid_ac1,\n \"mean_du\": float(reg[\"du\"].mean()),\n \"hac_lag_rule\": \"floor(4*(T/100)^(2/9)) (Newey-West 1994 rule of thumb, Bartlett kernel)\",\n}\n(OUT / \"results.json\").write_text(json.dumps(res, indent=2) + \"\\n\")\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(reg[\"g\"], reg[\"du\"], s=12, alpha=0.7)\ngg = np.linspace(reg[\"g\"].min(), reg[\"g\"].max(), 50)\nax.plot(gg, alpha + beta * gg, color=\"C3\", label=f\"OLS: Δu = {alpha:.3f} {beta:+.3f}·g\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Change in unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1–2019Q4 (n=240)\")\nax.legend(); fig.tight_layout(); fig.savefig(OUT / \"figure1.png\", dpi=150)\n\nprint(f\"beta={beta:.4f} alpha={alpha:.4f} R2={ols.rsquared:.3f} se_iid={se_iid:.4f} \"\n f\"se_hac(L={hac_lags})={se_hac:.4f} CI=[{ci[0]:.4f},{ci[1]:.4f}] neg5%={res['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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- <<'EOF'\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''n0 = len(un); un = un.dropna(subset=[\"UNRATE\"])\nexcl.append((\"U1\", \"drop UNRATE rows with missing value\", n0, len(un), \"empty field in source file\"))\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nn0 = len(un); un = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)]\nexcl.append((\"U2\", \"keep months in 1959Q4-2019Q4\", n0, len(un), \"window rule: only 1959Q4 levels used outside sample\"))\n''','''# monthly UNRATE is an input to the quarterly panel, not the unit of analysis; the exclusion\n# ledger follows the quarterly panel (unit of analysis). Monthly rows: window filter then dropna.\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nun = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)].dropna(subset=[\"UNRATE\"])\n''')\ns=s.replace('''excl.append((\"G1\", \"keep quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: only 1959Q4 level used outside sample\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\ndf = pd.DataFrame({\"u\": u, \"gdp\": y}).sort_index()''','''excl.append((\"G1\", \"keep GDPC1 quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: outside 1960Q1-2019Q4 only 1959Q4 levels are used\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\nn0 = len(y)\ndf = pd.concat({\"u\": u, \"gdp\": y}, axis=1, join=\"inner\").sort_index()\nexcl.append((\"M1\", \"inner-join with quarters having 3 non-missing UNRATE months\", n0, len(df), \"u_t requires complete quarter (none missing in window)\"))''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cp out/results.json /tmp/r1.json","description":"Restructure exclusion ledger to single quarterly chain"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4414,"cache_read_input_tokens":10571,"output_tokens":1693,"output_tokens_details":{"thinking_tokens":794},"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":4414},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqhiiQ2y9QegM63D6UG","requestRef":"e5df1b62-9bc9-4a76-a346-86352fbedb86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7f28fb8e-0b70-4264-a8f3-59c9fcb82e04","timestamp":"2026-10-30T05:06:37.164Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"7f28fb8e-0b70-4264-a8f3-59c9fcb82e04","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XLFb41Wr8W4zgBGyRjkzcs","type":"tool_result","content":"beta=-0.2558 alpha=0.1844 R2=0.408 se_iid=0.0200 se_hac(L=4)=0.0358 CI=[-0.3260,-0.1855] neg5%=True\nstep,rule,n_before,n_after,n_removed,rationale\nG1,keep GDPC1 quarters in 1959Q4-2019Q4,319,241,78,window rule: outside 1960Q1-2019Q4 only 1959Q4 levels are used\nM1,inner-join with quarters having 3 non-missing UNRATE months,241,241,0,u_t requires complete quarter (none missing in window)\nQ1,drop 1959Q4 (levels-only quarter used for first differences),241,240,1,differencing","is_error":false}]},"uuid":"f6fc8993-6a2d-4cf8-9bba-56f0cb56e486","timestamp":"2026-10-30T05:06:38.034Z","toolUseResult":{"stdout":"beta=-0.2558 alpha=0.1844 R2=0.408 se_iid=0.0200 se_hac(L=4)=0.0358 CI=[-0.3260,-0.1855] neg5%=True\nstep,rule,n_before,n_after,n_removed,rationale\nG1,keep GDPC1 quarters in 1959Q4-2019Q4,319,241,78,window rule: outside 1960Q1-2019Q4 only 1959Q4 levels are used\nM1,inner-join with quarters having 3 non-missing UNRATE months,241,241,0,u_t requires complete quarter (none missing in window)\nQ1,drop 1959Q4 (levels-only quarter used for first 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Monthly rows: window filter then dropna.\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nun = un[(un[\"q\"] >= LEVEL_START) & (un[\"q\"] <= LAST)].dropna(subset=[\"UNRATE\"])\n''')\ns=s.replace('''excl.append((\"G1\", \"keep quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: only 1959Q4 level used outside sample\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\ndf = pd.DataFrame({\"u\": u, \"gdp\": y}).sort_index()''','''excl.append((\"G1\", \"keep GDPC1 quarters in 1959Q4-2019Q4\", n0, len(gd), \"window rule: outside 1960Q1-2019Q4 only 1959Q4 levels are used\"))\ny = gd.set_index(\"q\")[\"GDPC1\"]\n\nn0 = len(y)\ndf = pd.concat({\"u\": u, \"gdp\": y}, axis=1, join=\"inner\").sort_index()\nexcl.append((\"M1\", \"inner-join with quarters having 3 non-missing UNRATE months\", n0, len(df), \"u_t requires complete quarter (none missing in window)\"))''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cp out/results.json 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P1 audit doc, then P4 verification (clean rerun + independent numpy recomputation)."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1915,"cache_read_input_tokens":14985,"output_tokens":2042,"output_tokens_details":{"thinking_tokens":40},"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":1915},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqit7V8y3r2CdsyS1Ki","requestRef":"74fd8f43-a8a0-42d9-a30d-82372d98cc8f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"df03d6d1-d8e3-43d3-bad0-77986f6db750","timestamp":"2026-10-30T05:06:39.889Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"df03d6d1-d8e3-43d3-bad0-77986f6db750","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqitPqnwhR3tiuRmHxS","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01FAbJ4WLU6bbWTiWJzjDP1L","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 | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | 945 | 2 (observation_date, UNRATE float, percent) | 1948-01 to 2026-09, monthly, no gaps in dates | 1 (2025-10-01 empty field; likely 2025 federal shutdown) | 0 |\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | 319 | 2 (observation_date, GDPC1 float, bn chained 2017 $ SAAR) | 1947Q1 to 2026Q3, quarterly, no gaps | 0 | 0 |\n\n- UNRATE range 2.5–14.8 %, GDPC1 range 2172–24611; all positive, no anomalies.\n- The single missing UNRATE value (2025-10) lies outside the 1959Q4–2019Q4 range needed: no effect. Recorded in `data_issues`.\n- Information rule: files are current vintage (allowed, \"treat as given\"). Observations after 2019Q4 and before 1959Q4 are filtered out by the script before any computation; no violation.\n- Exclusion ledger (`out/exclusions.csv`) follows the quarterly panel (unit of analysis): 319 GDPC1 quarters − 78 (outside 1959Q4–2019Q4) − 0 (incomplete UNRATE quarter) − 1 (1959Q4 differencing) = 240 = n_analysed. Monthly UNRATE rows in window: 723 (=241×3), all non-missing.\nEOF\nrm out/results.json out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,numpy as np,pandas as pd,csv\nr=json.load(open('out/results.json'))\nu=pd.read_csv('data/UNRATE.csv',parse_dates=[0]); g=pd.read_csv('data/GDPC1.csv',parse_dates=[0])\nu['q']=u.observation_date.dt.to_period('Q'); uq=u.groupby('q').UNRATE.mean()\ng['q']=g.observation_date.dt.to_period('Q'); gq=g.set_index('q').GDPC1\ndu=uq.diff(); gr=100*np.log(gq/gq.shift())\nidx=pd.period_range('1960Q1','2019Q4',freq='Q')\ny=du.loc[idx].values; x=gr.loc[idx].values; T=len(y)\nX=np.c_[np.ones(T),x]; b=np.linalg.solve(X.T@X,X.T@y); e=y-X@b\nXtXi=np.linalg.inv(X.T@X)\nse_iid=np.sqrt(e@e/(T-2)*XtXi[1,1])\nL=4; S=(X*e[:,None]).T@(X*e[:,None])\nfor l in range(1,L+1):\n w=1-l/(L+1); G=(X[l:]*e[l:,None]).T@(X[:-l]*e[:-l,None]); S+=w*(G+G.T)\nV=XtXi@S@XtXi; se_hac=np.sqrt(V[1,1])\nr2=1-e@e/((y-y.mean())@(y-y.mean()))\nchk=lambda n,a,b_: print(n, 'PASS' if abs(a-b_)<1e-9*max(1,abs(b_)) else 'FAIL', a, b_)\nchk('beta',b[1],r['beta_hat']); chk('alpha',b[0],r['alpha_hat']); chk('r2',r2,r['r_squared'])\nchk('se_iid',se_iid,r['se_iid']); chk('se_hac',se_hac,r['se_hac']); chk('mean_g',x.mean(),r['mean_growth'])\nprint('n240',T==r['n_analysed']==240)\nprint('se>0',r['se_iid']>0 and r['se_hac']>0)\nprint('ci contains',r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'], 'symmetric',abs((r['beta_ci_high']-r['beta_hat'])-(r['beta_hat']-r['beta_ci_low']))<1e-12)\nprint('flag consistent', r['okun_negative_5pct']==(r['beta_hat']<0 and r['beta_ci_high']<0))\nex=list(csv.DictReader(open('out/exclusions.csv')))\nprint('ledger rows ok',all(int(a['n_before'])-int(a['n_after'])==int(a['n_removed']) for a in ex),\n 'chain',all(ex[i]['n_after']==ex[i+1]['n_before'] for i in range(len(ex)-1)),\n 'sum',319-sum(int(a['n_removed']) for a in ex)==r['n_analysed'])\nprint('magnitude: mean g annualised ~',4*x.mean(),'% ; hac/iid ratio',r['se_hac']/r['se_iid'])\nEOF","description":"Write audit, clean rerun, independent verification"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1915,"cache_read_input_tokens":14985,"output_tokens":2042,"output_tokens_details":{"thinking_tokens":40},"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":1915},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXqit7V8y3r2CdsyS1Ki","requestRef":"74fd8f43-a8a0-42d9-a30d-82372d98cc8f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"65763cae-0de9-4eda-9180-a64ae64d744e","timestamp":"2026-10-30T05:06:55.583Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}49{"parentUuid":"65763cae-0de9-4eda-9180-a64ae64d744e","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01FAbJ4WLU6bbWTiWJzjDP1L","type":"tool_result","content":"IDENTICAL\nbeta PASS -0.25576772774872936 -0.25576772774872947\nalpha PASS 0.18438835418210958 0.18438835418210964\nr2 PASS 0.40825082845620597 0.40825082845620597\nse_iid PASS 0.019960092914910018 0.019960092914910015\nse_hac PASS 0.03583463330296579 0.03583463330296578\nmean_g PASS 0.7535027550652363 0.7535027550652363\nn240 True\nse>0 True\nci contains True symmetric True\nflag consistent True\nledger rows ok True chain True sum True\nmagnitude: mean g annualised ~ 3.0140110202609454 % ; hac/iid ratio 1.7953139524815351","is_error":false}]},"uuid":"5297fb20-3ef9-4d05-892f-56c91ed12510","timestamp":"2026-10-30T05:06:56.608Z","toolUseResult":{"stdout":"IDENTICAL\nbeta PASS -0.25576772774872936 -0.25576772774872947\nalpha PASS 0.18438835418210958 0.18438835418210964\nr2 PASS 0.40825082845620597 0.40825082845620597\nse_iid PASS 0.019960092914910018 0.019960092914910015\nse_hac PASS 0.03583463330296579 0.03583463330296578\nmean_g PASS 0.7535027550652363 0.7535027550652363\nn240 True\nse>0 True\nci contains True symmetric True\nflag consistent True\nledger rows ok True chain True sum True\nmagnitude: mean g annualised ~ 3.0140110202609454 % ; hac/iid ratio 1.7953139524815351","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"65763cae-0de9-4eda-9180-a64ae64d744e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}50{"type":"api-request-blob","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","hash":"9230aa3ff700851f5206c5bc0f1ee9798b60f1a493091ac066477731d897839f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"text","text":"Now P1 audit doc, then P4 verification (clean rerun + independent numpy recomputation)."},{"type":"tool_use","id":"toolu_01FAbJ4WLU6bbWTiWJzjDP1L","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 | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | 945 | 2 (observation_date, UNRATE float, percent) | 1948-01 to 2026-09, monthly, no gaps in dates | 1 (2025-10-01 empty field; likely 2025 federal shutdown) | 0 |\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | 319 | 2 (observation_date, GDPC1 float, bn chained 2017 $ SAAR) | 1947Q1 to 2026Q3, quarterly, no gaps | 0 | 0 |\n\n- UNRATE range 2.5–14.8 %, GDPC1 range 2172–24611; all positive, no anomalies.\n- The single missing UNRATE value (2025-10) lies outside the 1959Q4–2019Q4 range needed: no effect. Recorded in `data_issues`.\n- Information rule: files are current vintage (allowed, \"treat as given\"). Observations after 2019Q4 and before 1959Q4 are filtered out by the script before any computation; no violation.\n- Exclusion ledger (`out/exclusions.csv`) follows the quarterly panel (unit of analysis): 319 GDPC1 quarters − 78 (outside 1959Q4–2019Q4) − 0 (incomplete UNRATE quarter) − 1 (1959Q4 differencing) = 240 = n_analysed. Monthly UNRATE rows in window: 723 (=241×3), all non-missing.\nEOF\nrm out/results.json out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp out/results.json /tmp/r1.json && echo IDENTICAL\n~/wp29/venv/bin/python - <<'EOF'\nimport json,numpy as np,pandas as pd,csv\nr=json.load(open('out/results.json'))\nu=pd.read_csv('data/UNRATE.csv',parse_dates=[0]); g=pd.read_csv('data/GDPC1.csv',parse_dates=[0])\nu['q']=u.observation_date.dt.to_period('Q'); uq=u.groupby('q').UNRATE.mean()\ng['q']=g.observation_date.dt.to_period('Q'); gq=g.set_index('q').GDPC1\ndu=uq.diff(); gr=100*np.log(gq/gq.shift())\nidx=pd.period_range('1960Q1','2019Q4',freq='Q')\ny=du.loc[idx].values; x=gr.loc[idx].values; T=len(y)\nX=np.c_[np.ones(T),x]; b=np.linalg.solve(X.T@X,X.T@y); e=y-X@b\nXtXi=np.linalg.inv(X.T@X)\nse_iid=np.sqrt(e@e/(T-2)*XtXi[1,1])\nL=4; S=(X*e[:,None]).T@(X*e[:,None])\nfor l in range(1,L+1):\n w=1-l/(L+1); G=(X[l:]*e[l:,None]).T@(X[:-l]*e[:-l,None]); S+=w*(G+G.T)\nV=XtXi@S@XtXi; se_hac=np.sqrt(V[1,1])\nr2=1-e@e/((y-y.mean())@(y-y.mean()))\nchk=lambda n,a,b_: print(n, 'PASS' if abs(a-b_)<1e-9*max(1,abs(b_)) else 'FAIL', a, b_)\nchk('beta',b[1],r['beta_hat']); chk('alpha',b[0],r['alpha_hat']); chk('r2',r2,r['r_squared'])\nchk('se_iid',se_iid,r['se_iid']); chk('se_hac',se_hac,r['se_hac']); chk('mean_g',x.mean(),r['mean_growth'])\nprint('n240',T==r['n_analysed']==240)\nprint('se>0',r['se_iid']>0 and r['se_hac']>0)\nprint('ci contains',r['beta_ci_low']<r['beta_hat']<r['beta_ci_high'], 'symmetric',abs((r['beta_ci_high']-r['beta_hat'])-(r['beta_hat']-r['beta_ci_low']))<1e-12)\nprint('flag consistent', r['okun_negative_5pct']==(r['beta_hat']<0 and r['beta_ci_high']<0))\nex=list(csv.DictReader(open('out/exclusions.csv')))\nprint('ledger rows ok',all(int(a['n_before'])-int(a['n_after'])==int(a['n_removed']) for a in ex),\n 'chain',all(ex[i]['n_after']==ex[i+1]['n_before'] for i in range(len(ex)-1)),\n 'sum',319-sum(int(a['n_removed']) for a in ex)==r['n_analysed'])\nprint('magnitude: mean g annualised ~',4*x.mean(),'% ; hac/iid ratio',r['se_hac']/r['se_iid'])\nEOF","description":"Write audit, clean rerun, independent verification"}}]}}51{"type":"api-request","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","id":"48f8e251-99ae-49e2-8595-ef2f06daf653","timestamp":"2026-10-30T05:06:56.610Z","version":"2.1.314","querySource":"sdk","shapeHash":"1b2d89bcce2a9abf5376556142180e8efa18b27ec6c087cdb15f347ce33c5948","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":"74fd8f43-a8a0-42d9-a30d-82372d98cc8f","keep":14,"tail":["9230aa3ff700851f5206c5bc0f1ee9798b60f1a493091ac066477731d897839f","02bcd2d88250b655890cb48c7b95522408f9399614e0be4bae0f321ac6113b30"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"17a695c484012a9c680e1914e5ee6463a85e730be1e235c6a1cefc05dfcc7f76"}52{"parentUuid":"5297fb20-3ef9-4d05-892f-56c91ed12510","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkFnjc4NysYZXr7D9z","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XBCciVQ1oACZPVmJANVQA3","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n| check | outcome |\n|---|---|\n| Clean rerun: deleted results.json/figure1.png/exclusions.csv, reran analysis.py, `cmp` with previous results.json | PASS (byte-identical) |\n| Independent numpy recomputation (raw-file aggregation, OLS, iid SE, Bartlett NW L=4 no df-correction, R², mean g) | PASS all to <1e-9 relative |\n| n_analysed = 240, first 1960Q1, last 2019Q4 | PASS |\n| se_iid > 0, se_hac > 0 | PASS |\n| CI contains beta_hat; symmetric around beta_hat | PASS |\n| okun_negative_5pct consistent with beta<0 and CI excluding 0 | PASS |\n| Exclusion ledger: n_before−n_after=n_removed per row; chained; 319−79=240 | PASS |\n| Window/info rule: only 1959Q4–2019Q4 levels used (script filters before computing) | PASS |\n| Magnitude: mean g 0.754 %/q ≈ 3.0 % annualised (plausible US 1960–2019); β ≈ −0.26 in line with conventional Okun estimates (~−0.3 to −0.5 for Δu on q/q growth, differs by spec); HAC/iid SE ratio 1.80 consistent with residual AC(1)=0.39 | PASS (plausible) |\n| Bandwidth sensitivity: se_hac for L∈{0,1,2,4,8,12} in 0.0288–0.0371; conclusion unchanged | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README for both files\",\"row/col counts, ranges, missing (1 UNRATE, outside window), duplicates (0) recorded in data_audit.md\",\"exclusion ledger written and sums 319-79=240\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed: se_iid and baseline_ci_iid_* in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, no randomness (random_seed null), writes results.json, exclusions.csv, figure1.png\"],\"notes\":\"HAC lags = floor(4(T/100)^(2/9)) = 4\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"independent numpy recomputation matches\",\"CI/SE/ledger/flag consistency checks pass (see checks.md)\"],\"notes\":\"\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed (audit). P2 passed (iid baseline). P3 passed (analysis.py, NW L=4). P4 passed (rerun identical, independent recomputation).\" >> out/state/log.md","description":"Record checks and update phase ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2296,"cache_read_input_tokens":16900,"output_tokens":1167,"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":2296},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqkFWssRxABDaSXrGGK","requestRef":"48f8e251-99ae-49e2-8595-ef2f06daf653","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d537ac31-5fd9-49e9-a5a1-30adde1b48ac","timestamp":"2026-10-30T05:07:07.113Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"d537ac31-5fd9-49e9-a5a1-30adde1b48ac","isSidechain":false,"promptId":"2571757c-f927-4602-b1fe-3d4223e3b254","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XBCciVQ1oACZPVmJANVQA3","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"3e5424ed-191f-4566-b5ae-7d2883faa960","timestamp":"2026-10-30T05:07:07.180Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d537ac31-5fd9-49e9-a5a1-30adde1b48ac","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","hash":"3b53713fae4ef9fc7a2352e40a232b5ce87211ea1e2830aa459fb2a6804baf22","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01XBCciVQ1oACZPVmJANVQA3","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4) — 2026-10-30\n| check | outcome |\n|---|---|\n| Clean rerun: deleted results.json/figure1.png/exclusions.csv, reran analysis.py, `cmp` with previous results.json | PASS (byte-identical) |\n| Independent numpy recomputation (raw-file aggregation, OLS, iid SE, Bartlett NW L=4 no df-correction, R², mean g) | PASS all to <1e-9 relative |\n| n_analysed = 240, first 1960Q1, last 2019Q4 | PASS |\n| se_iid > 0, se_hac > 0 | PASS |\n| CI contains beta_hat; symmetric around beta_hat | PASS |\n| okun_negative_5pct consistent with beta<0 and CI excluding 0 | PASS |\n| Exclusion ledger: n_before−n_after=n_removed per row; chained; 319−79=240 | PASS |\n| Window/info rule: only 1959Q4–2019Q4 levels used (script filters before computing) | PASS |\n| Magnitude: mean g 0.754 %/q ≈ 3.0 % annualised (plausible US 1960–2019); β ≈ −0.26 in line with conventional Okun estimates (~−0.3 to −0.5 for Δu on q/q growth, differs by spec); HAC/iid SE ratio 1.80 consistent with residual AC(1)=0.39 | PASS (plausible) |\n| Bandwidth sensitivity: se_hac for L∈{0,1,2,4,8,12} in 0.0288–0.0371; conclusion unchanged | PASS |\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 computed and matches README for both files\",\"row/col counts, ranges, missing (1 UNRATE, outside window), duplicates (0) recorded in data_audit.md\",\"exclusion ledger written and sums 319-79=240\"],\"notes\":\"\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline OLS with iid SE computed: se_iid and baseline_ci_iid_* in results.json\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads data/ only, no randomness (random_seed null), writes results.json, exclusions.csv, figure1.png\"],\"notes\":\"HAC lags = floor(4(T/100)^(2/9)) = 4\"}\np['P4']={\"status\":\"passed\",\"checks\":[\"clean rerun byte-identical\",\"independent numpy recomputation matches\",\"CI/SE/ledger/flag consistency checks pass (see checks.md)\"],\"notes\":\"\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1 passed (audit). P2 passed (iid baseline). P3 passed (analysis.py, NW L=4). P4 passed (rerun identical, independent recomputation).\" >> out/state/log.md","description":"Record checks and update phase ledger"}}]}}55{"type":"api-request","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","id":"0d8bf747-e02d-43f0-989f-43d2188beb3c","timestamp":"2026-10-30T05:07:07.183Z","version":"2.1.314","querySource":"sdk","shapeHash":"1b2d89bcce2a9abf5376556142180e8efa18b27ec6c087cdb15f347ce33c5948","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":"48f8e251-99ae-49e2-8595-ef2f06daf653","keep":16,"tail":["3b53713fae4ef9fc7a2352e40a232b5ce87211ea1e2830aa459fb2a6804baf22","2c2696bee02fb0b7d409ef1b9eb82bb1f5dd255772a88ceb4c050c6901eb03ef"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"c97bd464b9aa201edf243b01f1648f5d33be6167b0881a6871bd1404797dee16"}56{"parentUuid":"3e5424ed-191f-4566-b5ae-7d2883faa960","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqm32pHTyNi2JYudnj7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019giz1f6bqKmbSzqp5UHJbK","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4, with HAC uncertainty (F2-conf-04)\n\n## Question\nEstimate β in the linear projection Δu_t = α + β g_t + e_t. Here u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}), i.e. quarter-on-quarter growth in percent, not annualised. The sample is 1960Q1–2019Q4. The task asks whether β is negative and significantly different from zero at the 5% level, using a Newey–West standard error.\n\n## Data and exclusions\n- Inputs: `data/UNRATE.csv` (945 monthly rows) and `data/GDPC1.csv` (319 quarterly rows) (`n_input_rows`). Both SHA-256 checksums match `data/README.md`; see `out/state/data_audit.md`.\n- Data issue: one UNRATE value is missing (2025-10-01). It lies outside the range the regression needs, so it has no effect (`data_issues`).\n- Exclusions (`out/exclusions.csv`), counted on the quarterly panel:\n - 319 GDPC1 quarters to start.\n - −78 quarters outside 1959Q4–2019Q4.\n - −0 quarters dropped for lacking 3 UNRATE months (none in the window).\n - −1 for 1959Q4, which is used only as the level for the first differences.\n - This leaves 240 quarters (`n_analysed`), from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4.\n\n## Method\n- I estimated the regression by OLS of Δu_t on a constant and g_t (statsmodels).\n- Naïve baseline: the conventional OLS standard error, which assumes independent, equal-variance errors (`se_iid`).\n- Robust standard error: Newey–West HAC with a Bartlett kernel, without a small-sample degrees-of-freedom correction (`se_hac`).\n - Bandwidth: `hac_lags` = 4. This comes from the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240. The alternative rule floor(0.75·T^{1/3}) also gives 4.\n - Choosing a HAC correction at all is justified: the OLS residuals have a first-order autocorrelation of 0.39 (`resid_lag1_autocorr`).\n- The 95% interval is β̂ ± 1.96·`se_hac` (normal critical value, `z_crit`).\n- The script uses no randomness (`random_seed` = null).\n- Figure: `out/figure1.png` plots Δu_t against g_t with the fitted line.\n\n## Results\n- Okun coefficient: `beta_hat` = −0.2558. Each extra percentage point of quarterly real GDP growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter.\n- Intercept: `alpha_hat` = 0.1844 pp.\n- Fit: `r_squared` = 0.408.\n- Implied break-even growth, where predicted Δu = 0: −α̂/β̂ ≈ 0.72 % per quarter, about 2.9 % annualised. This is derived arithmetically from `alpha_hat` and `beta_hat`.\n- Average growth: `mean_growth` = 0.754 % per quarter.\n- Conclusion: `okun_negative_5pct` = true. β̂ is negative, and the HAC t-statistic is −7.14 (`t_stat_hac`, two-sided p ≈ 1e-12, `p_value_hac`).\n\n## Uncertainty\n- `se_iid` = 0.0200; `se_hac` = 0.0358 (L = 4). The HAC standard error is about 1.8 times the naïve one, so ignoring serial correlation understates the uncertainty substantially.\n- 95% HAC interval: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve interval is [−0.295, −0.217] (`baseline_ci_iid_low`, `baseline_ci_iid_high`).\n- Sensitivity to the bandwidth (`se_hac_by_lags`): the HAC standard error ranges from 0.029 (L = 0, which is White's heteroskedasticity-robust SE) to 0.037 (L = 8). It is 0.0364 at L = 12.\n- Applying the small-sample correction n/(n−k) gives 0.0360 (`se_hac_small_sample_corrected`).\n- Under every one of these choices the interval stays well below zero.\n\n## Limitations and open issues\n- β is a descriptive linear-projection coefficient, not a causal effect. It assumes a stable relation over 1960–2019; Okun coefficients are known to drift over time, and I did not test for structural breaks because the task does not ask for it.\n- The data are the current vintage, with GDP revised and in chained 2017 dollars, not the real-time data available at each date.\n- Normal-approximation inference with a Bartlett HAC estimator can over-reject in finite samples when residuals are persistent. Given |t| ≈ 7, this does not threaten the conclusion.\n- No part of the task was infeasible (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (240 quarters) is about -0.26 pp of unemployment per percent of quarterly real GDP growth, with intercept 0.18 pp and R^2 0.41.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Descriptive linear projection on current-vintage data; assumes a stable relation over the sample.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) standard error is 0.036, about 1.8 times the iid OLS standard error of 0.020, consistent with residual autocorrelation of 0.39.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_lag1_autocorr\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb; no small-sample correction.\"},\n {\"id\":\"C3\",\"text\":\"The 95% HAC confidence interval for beta is [-0.326, -0.186]; beta is negative and significantly different from zero at the 5% level.\",\"results_keys\":[\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal critical values; finite-sample HAC size distortions possible but irrelevant at |t| about 7.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths 0-12 and to the small-sample correction (SE range 0.029-0.037).\",\"results_keys\":[\"se_hac_by_lags\",\"se_hac_small_sample_corrected\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel examined.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754 percent.\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nbad=[k for x in c for k in x['results_keys'] if k not in r]; print('missing keys',bad)\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written. 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Here u_t is the quarterly average of monthly UNRATE (pp) and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}), i.e. quarter-on-quarter growth in percent, not annualised. The sample is 1960Q1–2019Q4. The task asks whether β is negative and significantly different from zero at the 5% level, using a Newey–West standard error.\n\n## Data and exclusions\n- Inputs: `data/UNRATE.csv` (945 monthly rows) and `data/GDPC1.csv` (319 quarterly rows) (`n_input_rows`). Both SHA-256 checksums match `data/README.md`; see `out/state/data_audit.md`.\n- Data issue: one UNRATE value is missing (2025-10-01). It lies outside the range the regression needs, so it has no effect (`data_issues`).\n- Exclusions (`out/exclusions.csv`), counted on the quarterly panel:\n - 319 GDPC1 quarters to start.\n - −78 quarters outside 1959Q4–2019Q4.\n - −0 quarters dropped for lacking 3 UNRATE months (none in the window).\n - −1 for 1959Q4, which is used only as the level for the first differences.\n - This leaves 240 quarters (`n_analysed`), from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4.\n\n## Method\n- I estimated the regression by OLS of Δu_t on a constant and g_t (statsmodels).\n- Naïve baseline: the conventional OLS standard error, which assumes independent, equal-variance errors (`se_iid`).\n- Robust standard error: Newey–West HAC with a Bartlett kernel, without a small-sample degrees-of-freedom correction (`se_hac`).\n - Bandwidth: `hac_lags` = 4. This comes from the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240. The alternative rule floor(0.75·T^{1/3}) also gives 4.\n - Choosing a HAC correction at all is justified: the OLS residuals have a first-order autocorrelation of 0.39 (`resid_lag1_autocorr`).\n- The 95% interval is β̂ ± 1.96·`se_hac` (normal critical value, `z_crit`).\n- The script uses no randomness (`random_seed` = null).\n- Figure: `out/figure1.png` plots Δu_t against g_t with the fitted line.\n\n## Results\n- Okun coefficient: `beta_hat` = −0.2558. Each extra percentage point of quarterly real GDP growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter.\n- Intercept: `alpha_hat` = 0.1844 pp.\n- Fit: `r_squared` = 0.408.\n- Implied break-even growth, where predicted Δu = 0: −α̂/β̂ ≈ 0.72 % per quarter, about 2.9 % annualised. This is derived arithmetically from `alpha_hat` and `beta_hat`.\n- Average growth: `mean_growth` = 0.754 % per quarter.\n- Conclusion: `okun_negative_5pct` = true. β̂ is negative, and the HAC t-statistic is −7.14 (`t_stat_hac`, two-sided p ≈ 1e-12, `p_value_hac`).\n\n## Uncertainty\n- `se_iid` = 0.0200; `se_hac` = 0.0358 (L = 4). The HAC standard error is about 1.8 times the naïve one, so ignoring serial correlation understates the uncertainty substantially.\n- 95% HAC interval: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186]. The naïve interval is [−0.295, −0.217] (`baseline_ci_iid_low`, `baseline_ci_iid_high`).\n- Sensitivity to the bandwidth (`se_hac_by_lags`): the HAC standard error ranges from 0.029 (L = 0, which is White's heteroskedasticity-robust SE) to 0.037 (L = 8). It is 0.0364 at L = 12.\n- Applying the small-sample correction n/(n−k) gives 0.0360 (`se_hac_small_sample_corrected`).\n- Under every one of these choices the interval stays well below zero.\n\n## Limitations and open issues\n- β is a descriptive linear-projection coefficient, not a causal effect. It assumes a stable relation over 1960–2019; Okun coefficients are known to drift over time, and I did not test for structural breaks because the task does not ask for it.\n- The data are the current vintage, with GDP revised and in chained 2017 dollars, not the real-time data available at each date.\n- Normal-approximation inference with a Bartlett HAC estimator can over-reject in finite samples when residuals are persistent. Given |t| ≈ 7, this does not threaten the conclusion.\n- No part of the task was infeasible (`feasibility` = \"complete\").\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The OLS Okun coefficient over 1960Q1-2019Q4 (240 quarters) is about -0.26 pp of unemployment per percent of quarterly real GDP growth, with intercept 0.18 pp and R^2 0.41.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Descriptive linear projection on current-vintage data; assumes a stable relation over the sample.\"},\n {\"id\":\"C2\",\"text\":\"The Newey-West (Bartlett, 4 lags) standard error is 0.036, about 1.8 times the iid OLS standard error of 0.020, consistent with residual autocorrelation of 0.39.\",\"results_keys\":[\"se_hac\",\"se_iid\",\"hac_lags\",\"resid_lag1_autocorr\"],\"status\":\"supported\",\"caveat\":\"Bandwidth from the Newey-West (1994) rule of thumb; no small-sample correction.\"},\n {\"id\":\"C3\",\"text\":\"The 95% HAC confidence interval for beta is [-0.326, -0.186]; beta is negative and significantly different from zero at the 5% level.\",\"results_keys\":[\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Normal critical values; finite-sample HAC size distortions possible but irrelevant at |t| about 7.\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to HAC bandwidths 0-12 and to the small-sample correction (SE range 0.029-0.037).\",\"results_keys\":[\"se_hac_by_lags\",\"se_hac_small_sample_corrected\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel examined.\"},\n {\"id\":\"C5\",\"text\":\"Mean quarterly real GDP growth over the sample is 0.754 percent.\",\"results_keys\":[\"mean_growth\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\nr=json.load(open('out/results.json')); c=json.load(open('out/claims.json'))\nbad=[k for x in c for k in x['results_keys'] if k not in r]; print('missing keys',bad)\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md has 6 required sections\",\"claims.json keys all exist in results.json\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\ngrep '^## ' out/report.md; echo \"- 2026-10-30 P5 passed: report and claims written. 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Each extra percentage point of quarterly real GDP growth goes with a fall of about 0.26 percentage points in the unemployment rate in the same quarter.\n\nThe regression uses the 240 quarters from 1960Q1 to 2019Q4. I re-ran the script from scratch and got byte-identical results, and a separate calculation in plain numpy matched every number.\n\n| | Value |\n|---|---|\n| β̂ (Okun coefficient) | −0.2558 |\n| α̂ (intercept, pp) | 0.1844 |\n| R² | 0.408 |\n| Standard error assuming independent errors | 0.0200 |\n| Newey–West standard error, 4 lags | 0.0358 |\n| 95% interval from the Newey–West standard error | [−0.326, −0.186] |\n| t-statistic | −7.14 |\n| Mean quarterly GDP growth | 0.754 % (about 3.0 % a year) |\n\n- **Bandwidth:** 4 lags comes from the standard Newey–West rule of thumb floor(4·(T/100)^{2/9}) with T = 240. A second common rule, floor(0.75·T^{1/3}), also gives 4.\n- **Why a robust standard error matters:** the regression errors are noticeably autocorrelated (first-order autocorrelation 0.39). The robust standard error is about 1.8 times the naïve one, so ignoring this would overstate precision.\n- **Sensitivity:** across 0 to 12 lags, and with a small-sample correction, the robust standard error stays between 0.029 and 0.037. The interval excludes zero in every case.\n- **Data issue:** UNRATE has one missing month (2025-10). It falls outside the period the regression uses, so it has no effect.\n- **Exclusions:** the 319 GDP quarters drop to 240. 78 lie outside 1959Q4–2019Q4, and 1959Q4 itself is used only as the starting level for the first differences.\n\n**Limitations:** β is a descriptive relationship, not a causal effect, and it assumes the relation was stable over 1960–2019. I did not test for structural breaks because the task doesn't ask for it. The data are today's revised figures, not what was known at each date. The interval uses normal critical values; with a t-statistic around −7, small-sample size distortions won't change the conclusion.\n\nEverything is in `out/`; open `report.md` first, and `analysis.py` regenerates the results from `data/`.\n- results.json\n- report.md\n- analysis.py\n- claims.json\n- figure1.png\n- exclusions.csv\n- state/ (working notes and verification checks)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2939,"cache_read_input_tokens":20378,"output_tokens":923,"output_tokens_details":{"thinking_tokens":18},"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":2939},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqnjE49sHvhaoe1556g","requestRef":"1b7f2f98-73be-401c-a0ab-9f0673271e2b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0f9d310b-4075-4e40-a1d3-9cdb126a5aac","timestamp":"2026-10-30T05:07:40.716Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0224","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","version":"2.1.314","gitBranch":"HEAD"}64{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"0f9d310b-4075-4e40-a1d3-9cdb126a5aac","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61"}65{"type":"cost-state","sessionId":"8c9200c8-e1bc-41a9-80b2-f4a1e7f4ab61","totalCostUSD":0.41197900000000004,"totalAPIDuration":124055,"totalAPIDurationWithoutRetries":124052,"totalToolDuration":3046,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":127228,"startTime":1793336733515,"modelUsage":{"claude-opus-5-5":{"inputTokens":22,"outputTokens":14037,"thinkingTokens":1224,"cacheReadInputTokens":110430,"cacheCreationInputTokens":21813,"cacheCreationEphemeral5mInputTokens":21813,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.41197900000000004}},"hasUnknownModelCost":false}66