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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\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. 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The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":true,"tool_use_id":"toolu_01LmbHrnjfh1ynoG2TVX2e9m"}]},"uuid":"cd652228-e777-4981-9f28-e86f4197ba2d","timestamp":"2026-10-30T05:03:56.148Z","toolUseResult":"Error: Exit code 1\n# F2-conf-04 — Okun's law in U.S. quarterly data with autocorrelation-robust uncertainty\n\n## Scientific question\nEstimate the Okun's-law relation between changes in the U.S. unemployment rate and real GDP growth, with uncertainty that is valid under serially correlated disturbances. The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). 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They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n \"task_id\": \"F2-conf-04\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n \"positive_keys\": [\"se_iid\", \"se_hac\"],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\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","is_error":false}]},"uuid":"6c310306-8394-48d5-b102-5b39c3ebed05","timestamp":"2026-10-30T05:03:58.075Z","toolUseResult":{"stdout":"# Data provenance — F2-conf-04\n\nBoth files are unmodified downloads from FRED (Federal Reserve Bank of St. Louis), retrieved on 2026-10-30 03:32:42 UTC with\n`curl -L \"https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>\"`. They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n{\n \"task_id\": \"F2-conf-04\",\n \"required_files\": [\"out/results.json\", \"out/report.md\", \"out/analysis.py\", \"out/claims.json\", \"out/figure1.png\"],\n \"results_keys\": {\n \"task_id\": {\"type\": \"string\", \"nullable\": false},\n \"beta_hat\": {\"type\": \"number\", \"nullable\": false},\n \"alpha_hat\": {\"type\": \"number\", \"nullable\": false},\n \"r_squared\": {\"type\": \"number\", \"nullable\": false},\n \"se_iid\": {\"type\": \"number\", \"nullable\": false},\n \"se_hac\": {\"type\": \"number\", \"nullable\": false},\n \"hac_lags\": {\"type\": \"integer\", \"nullable\": false},\n \"beta_ci_low\": {\"type\": \"number\", \"nullable\": false},\n \"beta_ci_high\": {\"type\": \"number\", \"nullable\": false},\n \"okun_negative_5pct\": {\"type\": \"boolean\", \"nullable\": false},\n \"mean_growth\": {\"type\": \"number\", \"nullable\": false},\n \"first_quarter\": {\"type\": \"string\", \"nullable\": false},\n \"last_quarter\": {\"type\": \"string\", \"nullable\": false},\n \"n_analysed\": {\"type\": \"integer\", \"nullable\": false},\n \"feasibility\": {\"type\": \"string\", \"nullable\": false},\n \"feasibility_reason\": {\"type\": \"string\", \"nullable\": true},\n \"data_issues\": {\"type\": \"array\", \"nullable\": false},\n \"n_input_rows\": {\"type\": \"object\", \"nullable\": false},\n \"random_seed\": {\"type\": \"integer\", \"nullable\": true}\n },\n \"report_sections\": [\"Question\", \"Data and exclusions\", \"Method\", \"Results\", \"Uncertainty\", \"Limitations and open issues\"],\n \"family_checks\": [\"ci_contains_estimate\", \"positive_se\", \"claims_reference_existing_keys\", \"phase_ledger_consistent\", \"exclusion_ledger_sums\"],\n \"ci_pairs\": [[\"beta_hat\", \"beta_ci_low\", \"beta_ci_high\"]],\n \"positive_keys\": [\"se_iid\", \"se_hac\"],\n \"chronology_pairs\": [],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-6\n}\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3247ba28-5488-44a2-9672-64c61abf585a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218/data","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","hash":"dbbb94e8fbdc3476f9581e075482fdde76017f2c3c5109ea67fcdb4d265a1761","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01GWfyeWMpE3qxC44JsU9y7H","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; cd data; shasum -a 256 *.csv; wc -l *.csv; head -3 *.csv; tail -3 *.csv","description":"Inspect data and gate 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-p out/state && ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f,c in [('UNRATE.csv','UNRATE'),('GDPC1.csv','GDPC1')]:\n d=pd.read_csv('data/'+f,dtype=str)\n print(f,d.shape,d.columns.tolist())\n v=pd.to_numeric(d[c],errors='coerce'); dt=pd.to_datetime(d.observation_date,errors='coerce')\n print(' missing/nonnum',v.isna().sum(),'bad dates',dt.isna().sum(),'dup dates',dt.duplicated().sum(),'range',dt.min(),dt.max(),'val',v.min(),v.max())\n print(' monotonic',dt.is_monotonic_increasing, 'day!=1',(dt.dt.day!=1).sum())\n per=dt.dt.to_period('M' if c=='UNRATE' else 'Q'); print(' gaps',(per.diff().iloc[1:].apply(lambda x:x.n)!=(1)).sum())\n\"","description":"Audit input files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1938,"cache_read_input_tokens":6425,"output_tokens":658,"output_tokens_details":{"thinking_tokens":209},"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":1938},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqX6M7CTCJSqmXBGYz7","requestRef":"73904c7b-0c87-4318-aae3-70895af4df6d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4d103348-3e6d-414f-b061-884c5fb692e8","timestamp":"2026-10-30T05:04:04.236Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218/data","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}25{"parentUuid":"4d103348-3e6d-414f-b061-884c5fb692e8","isSidechain":false,"promptId":"b3330770-2dac-4c5f-9d37-8aa07450de36","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 4, in <module>\n d=pd.read_csv('data/'+f,dtype=str)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","is_error":true,"tool_use_id":"toolu_01Y7LD7FLxexESbbfhXPHVoX"}]},"uuid":"276be1fb-58c9-47ea-b1f7-692337fd31cd","timestamp":"2026-10-30T05:04:04.537Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 4, in <module>\n d=pd.read_csv('data/'+f,dtype=str)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","sandboxed":false,"sourceToolAssistantUUID":"4d103348-3e6d-414f-b061-884c5fb692e8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218/data","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}26{"type":"api-request-blob","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","hash":"e89ced376575feca9f7187bc52213ed9150bbef71d223a051c656ded4522475a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Y7LD7FLxexESbbfhXPHVoX","name":"Bash","input":{"command":"mkdir 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files"}}]}}27{"type":"api-request","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","id":"bcb97fcf-fcd4-469d-897c-9c6fcbe5cb17","timestamp":"2026-10-30T05:04:04.540Z","version":"2.1.314","querySource":"sdk","shapeHash":"51ba678ab59eba93828bc9952ae405c2a25c1c7e7554885fb53a61d69dd8766d","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":"73904c7b-0c87-4318-aae3-70895af4df6d","keep":6,"tail":["e89ced376575feca9f7187bc52213ed9150bbef71d223a051c656ded4522475a","2c9ead2218a1fbaac37000fc1f6bc6ef5eb9441bd1e502c532a70b22f39d6aed"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"bc02bda751311afb6594622ea01b10e3d49d51081dd73f20e817d6999d3502d5"}28{"parentUuid":"276be1fb-58c9-47ea-b1f7-692337fd31cd","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqXaDQCABRKCEoWbLKf","type":"message","role":"assistant","content":[{"type":"text","text":"Earlier 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> 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 uncertainty robust to serial correlation?\n\n**Estimand.** β in the linear projection Δu_t = α + β g_t + e_t, u_t = mean of the 3 monthly UNRATE values of quarter t (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 feed the first differences). β is a linear-projection coefficient (descriptive association, not causal); OLS is consistent for it under stationarity/weak dependence with finite moments. HAC (Newey–West, Bartlett) SE is valid under serially correlated, heteroskedastic e_t with bandwidth growing slowly with T.\n\n**Outputs.** out/results.json with 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; out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png (scatter + fitted line); plus protocol files (out/state/*, out/exclusions.csv).\n\n**Information rules.** Only data/ files; current vintage taken as given; observations outside 1960Q1–2019Q4 only used for 1959Q4 levels; no window extension.\n\n**Invalidating conditions.** Missing/incomplete months in a sample quarter (or 1959Q4), missing GDP quarters, wrong growth definition (annualised), sample ≠ 240 quarters, using data beyond 2019Q4.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 (computed) | matches README | data rows | cols | date range | missing | dup dates | gaps |\n|---|---|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | yes | 945 | 2 (observation_date, UNRATE) | 1948-01-01 – 2026-09-01 monthly | 1 (2025-10-01 empty field) | 0 | 0 |\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | yes | 319 | 2 (observation_date, GDPC1) | 1947-01-01 – 2026-07-01 quarterly | 0 | 0 | 0 |\n\nUnits: UNRATE percent (SA); GDPC1 billions chained 2017 $, SAAR. Ranges: UNRATE 2.5–14.8; GDPC1 2172.4–24611.1. All dates are first-of-period, monotonic increasing.\n\nAnomalies: UNRATE 2025-10 is missing (empty field; no October 2025 household survey release). It lies outside the 1959Q4–2019Q4 span needed, so it has no effect; it is recorded in `data_issues` and removed by the window filter. analysis.py additionally asserts every quarter in 1959Q4–2019Q4 has exactly 3 non-missing months.\n\nInformation availability: files contain data after 2019Q4 (and before 1959Q4); these are excluded by the window filter (see out/exclusions.csv). No other violations.\nEOF\necho \"- 2026-10-30 P0 passed (contract written); P1 audit done: checksums match README, one missing UNRATE month (2025-10) outside window.\" > out/state/log.md","description":"Write contract, audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":313,"cache_read_input_tokens":10476,"output_tokens":1546,"output_tokens_details":{"thinking_tokens":44},"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":313},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqYDVkC3NDZ88Ly2bsh","requestRef":"8efe51de-672c-497d-9957-79eb932401f0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0a96ba44-9e87-489f-853e-7e9d015c3b53","timestamp":"2026-10-30T05:04:27.143Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"0a96ba44-9e87-489f-853e-7e9d015c3b53","isSidechain":false,"promptId":"b3330770-2dac-4c5f-9d37-8aa07450de36","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_012aYA5TPq12eKoydbp2p9wM","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"24a254e7-f0bf-4f35-ab99-a9aaf54a0c0e","timestamp":"2026-10-30T05:04:27.179Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"0a96ba44-9e87-489f-853e-7e9d015c3b53","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","hash":"c7aa5872df3fc3326022afd7da053e5e5cecb9c1ce51da5086ff1a2afbf463b2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_012aYA5TPq12eKoydbp2p9wM","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F2-conf-04\n**Question.** Okun's law in U.S. quarterly data: how does the quarterly change in the unemployment rate co-move with quarterly real GDP growth, with uncertainty robust to serial correlation?\n\n**Estimand.** β in the linear projection Δu_t = α + β g_t + e_t, u_t = mean of the 3 monthly UNRATE values of quarter t (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 feed the first differences). β is a linear-projection coefficient (descriptive association, not causal); OLS is consistent for it under stationarity/weak dependence with finite moments. HAC (Newey–West, Bartlett) SE is valid under serially correlated, heteroskedastic e_t with bandwidth growing slowly with T.\n\n**Outputs.** out/results.json with 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; out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json; out/figure1.png (scatter + fitted line); plus protocol files (out/state/*, out/exclusions.csv).\n\n**Information rules.** Only data/ files; current vintage taken as given; observations outside 1960Q1–2019Q4 only used for 1959Q4 levels; no window extension.\n\n**Invalidating conditions.** Missing/incomplete months in a sample quarter (or 1959Q4), missing GDP quarters, wrong growth definition (annualised), sample ≠ 240 quarters, using data beyond 2019Q4.\nEOF\ncat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 (computed) | matches README | data rows | cols | date range | missing | dup dates | gaps |\n|---|---|---|---|---|---|---|---|---|\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c | yes | 945 | 2 (observation_date, UNRATE) | 1948-01-01 – 2026-09-01 monthly | 1 (2025-10-01 empty field) | 0 | 0 |\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 | yes | 319 | 2 (observation_date, GDPC1) | 1947-01-01 – 2026-07-01 quarterly | 0 | 0 | 0 |\n\nUnits: UNRATE percent (SA); GDPC1 billions chained 2017 $, SAAR. Ranges: UNRATE 2.5–14.8; GDPC1 2172.4–24611.1. All dates are first-of-period, monotonic increasing.\n\nAnomalies: UNRATE 2025-10 is missing (empty field; no October 2025 household survey release). It lies outside the 1959Q4–2019Q4 span needed, so it has no effect; it is recorded in `data_issues` and removed by the window filter. analysis.py additionally asserts every quarter in 1959Q4–2019Q4 has exactly 3 non-missing months.\n\nInformation availability: files contain data after 2019Q4 (and before 1959Q4); these are excluded by the window filter (see out/exclusions.csv). No other violations.\nEOF\necho \"- 2026-10-30 P0 passed (contract written); P1 audit done: checksums match README, one missing UNRATE month (2025-10) outside window.\" > out/state/log.md","description":"Write contract, audit, 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un5","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, U.S. 1960Q1-2019Q4, OLS with Newey-West HAC SE. Run from workspace root.\"\"\"\nimport json, math\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\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nissues, excl = [], []\n\nu_raw = pd.read_csv(\"data/UNRATE.csv\")\ng_raw = pd.read_csv(\"data/GDPC1.csv\")\nn_rows = {\"UNRATE.csv\": int(len(u_raw)), \"GDPC1.csv\": int(len(g_raw))}\n\n# --- unemployment: monthly -> quarterly average\nu = u_raw.copy()\nu[\"date\"] = pd.to_datetime(u[\"observation_date\"])\nu[\"UNRATE\"] = pd.to_numeric(u[\"UNRATE\"], errors=\"coerce\")\nmiss = u.loc[u[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif miss:\n issues.append({\"issue\": f\"UNRATE missing value(s) at {', '.join(miss)}\",\n \"handling\": \"outside the 1959Q4-2019Q4 span needed; dropped by window filter, no effect on estimates\"})\nu[\"q\"] = u[\"date\"].dt.to_period(\"Q\")\nn0 = len(u)\nuw = u[(u[\"q\"] >= FIRST - 1) & (u[\"q\"] <= LAST)]\nexcl.append((\"unrate_window\", \"keep months in 1959Q4-2019Q4 (1959Q4 only for differencing)\", n0, len(uw), n0 - len(uw),\n \"information rule: sample window; months outside not used (includes missing 2025-10)\"))\ncnt = uw.groupby(\"q\")[\"UNRATE\"].count()\nassert (cnt == 3).all() and len(cnt) == 241, \"incomplete quarter in window\"\nuq = uw.groupby(\"q\")[\"UNRATE\"].mean()\n\n# --- GDP\ng = g_raw.copy()\ng[\"q\"] = pd.to_datetime(g[\"observation_date\"]).dt.to_period(\"Q\")\ng[\"GDPC1\"] = pd.to_numeric(g[\"GDPC1\"], errors=\"coerce\")\nn0 = len(g)\ngw = g[(g[\"q\"] >= FIRST - 1) & (g[\"q\"] <= LAST)].set_index(\"q\")[\"GDPC1\"]\nexcl.append((\"gdp_window\", \"keep quarters in 1959Q4-2019Q4 (1959Q4 only for differencing)\", n0, len(gw), n0 - len(gw),\n \"information rule: sample window\"))\nassert gw.notna().all() and len(gw) == 241\n\ndf = pd.DataFrame({\"u\": uq, \"y\": gw}).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"y\"] / df[\"y\"].shift(1))\nn0 = len(df)\ndf = df.loc[FIRST:LAST].dropna()\nexcl.append((\"differencing\", \"drop 1959Q4 (levels used only to form 1960Q1 differences)\", n0, len(df), n0 - len(df),\n \"first difference unavailable for base quarter\"))\nT = len(df)\nassert T == 240\n\nX = sm.add_constant(df[\"g\"].values)\ny = df[\"du\"].values\nols = sm.OLS(y, X).fit()\n# Newey-West (1994) plug-in rule of thumb: floor(4*(T/100)^(2/9))\nL = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\n\n# manual check of HAC SE\ne = ols.resid; Xe = X * e[:, None]\nS = Xe.T @ Xe\nfor j in range(1, L + 1):\n w = 1 - j / (L + 1); G = Xe[j:].T @ Xe[:-j]; S += w * (G + G.T)\nXtXi = np.linalg.inv(X.T @ X)\nse_manual = float(np.sqrt((XtXi @ S @ XtXi)[1, 1]))\nassert abs(se_manual - hac.bse[1]) < 1e-10\n\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_hac = float(hac.bse[1]); z = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_hac, beta + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(beta / se_hac)))\n\nsens = {}\nfor l in [0, 1, 2, 4, 6, 8, 12]:\n r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": l, \"kernel\": \"bartlett\", \"use_correction\": False})\n sens[str(l)] = float(r.bse[1])\n\nres = {\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n \"se_iid\": float(ols.bse[1]), \"se_hac\": se_hac, \"hac_lags\": L,\n \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n \"mean_growth\": float(df[\"g\"].mean()),\n \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n \"n_analysed\": int(T),\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n # supplementary\n \"t_stat_hac\": float(beta / se_hac), \"p_value_hac\": p_hac,\n \"residual_acf_lag1\": float(pd.Series(e).autocorr(1)),\n \"se_hac_by_lags\": sens,\n \"beta_ci_iid_low\": float(beta - z * ols.bse[1]), \"beta_ci_iid_high\": float(beta + z * ols.bse[1]),\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\"out/exclusions.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\nxs = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 100)\nax.plot(xs, alpha + beta * xs, color=\"red\", label=f\"OLS: du = {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 du_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1-2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps({k: res[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json 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":1532,"cache_read_input_tokens":10789,"output_tokens":3128,"output_tokens_details":{"thinking_tokens":307},"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":1532},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqZEmAiseRP2RRVJ1jd","requestRef":"7e9f4d36-37f5-446f-b59d-4f4174b927ee","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"42508d00-df46-4e09-960a-c12893290b23","timestamp":"2026-10-30T05:04:51.495Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}47{"parentUuid":"42508d00-df46-4e09-960a-c12893290b23","isSidechain":false,"promptId":"b3330770-2dac-4c5f-9d37-8aa07450de36","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017dPTs6KggFoReLuy5XRun5","type":"tool_result","content":"{\n \"beta_hat\": -0.25576772774872947,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true\n}\n{\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": -0.25576772774872947,\n \"alpha_hat\": 0.18438835418210964,\n \"r_squared\": 0.40825082845620597,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true,\n \"mean_growth\": 0.7535027550652363,\n \"first_quarter\": \"1960Q1\",\n \"last_quarter\": \"2019Q4\",\n \"n_analysed\": 240,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE missing value(s) at 2025-10-01\",\n \"handling\": \"outside the 1959Q4-2019Q4 span needed; dropped by window filter, no effect on estimates\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945,\n \"GDPC1.csv\": 319\n },\n \"random_seed\": null,\n \"t_stat_hac\": -7.137445096377235,\n \"p_value_hac\": 9.508157930953398e-13,\n \"residual_acf_lag1\": 0.38935129370212984,\n \"se_hac_by_lags\": {\n \"0\": 0.028836223207270188,\n \"1\": 0.032358596534706116,\n \"2\": 0.034227320236154,\n \"4\": 0.03583463330296578,\n \"6\": 0.03687902855135959,\n \"8\": 0.03713550417540173,\n \"12\": 0.03640885110228042\n },\n \"beta_ci_iid_low\": -0.2948887909900262,\n \"beta_ci_iid_high\": -0.21664666450743272\n}step,rule,n_before,n_after,n_removed,rationale\nunrate_window,keep months in 1959Q4-2019Q4 (1959Q4 only for differencing),945,723,222,information rule: sample window; months outside not used (includes missing 2025-10)\ngdp_window,keep quarters in 1959Q4-2019Q4 (1959Q4 only for differencing),319,241,78,information rule: sample window\ndifferencing,drop 1959Q4 (levels used only to form 1960Q1 differences),241,240,1,first difference unavailable for base quarter","is_error":false}]},"uuid":"fcaa8b8e-6fd1-4a6b-afa4-21a4b9bbddd6","timestamp":"2026-10-30T05:04:52.663Z","toolUseResult":{"stdout":"{\n \"beta_hat\": -0.25576772774872947,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true\n}\n{\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": -0.25576772774872947,\n \"alpha_hat\": 0.18438835418210964,\n \"r_squared\": 0.40825082845620597,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true,\n \"mean_growth\": 0.7535027550652363,\n \"first_quarter\": \"1960Q1\",\n \"last_quarter\": \"2019Q4\",\n \"n_analysed\": 240,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE missing value(s) at 2025-10-01\",\n \"handling\": \"outside the 1959Q4-2019Q4 span needed; dropped by window filter, no effect on estimates\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945,\n \"GDPC1.csv\": 319\n },\n \"random_seed\": null,\n \"t_stat_hac\": -7.137445096377235,\n \"p_value_hac\": 9.508157930953398e-13,\n \"residual_acf_lag1\": 0.38935129370212984,\n \"se_hac_by_lags\": {\n \"0\": 0.028836223207270188,\n \"1\": 0.032358596534706116,\n \"2\": 0.034227320236154,\n \"4\": 0.03583463330296578,\n \"6\": 0.03687902855135959,\n \"8\": 0.03713550417540173,\n \"12\": 0.03640885110228042\n },\n \"beta_ci_iid_low\": -0.2948887909900262,\n \"beta_ci_iid_high\": -0.21664666450743272\n}step,rule,n_before,n_after,n_removed,rationale\nunrate_window,keep months in 1959Q4-2019Q4 (1959Q4 only for differencing),945,723,222,information rule: sample window; months outside not used (includes missing 2025-10)\ngdp_window,keep quarters in 1959Q4-2019Q4 (1959Q4 only for differencing),319,241,78,information rule: sample window\ndifferencing,drop 1959Q4 (levels used only to form 1960Q1 differences),241,240,1,first difference unavailable for base quarter","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"42508d00-df46-4e09-960a-c12893290b23","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0218","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","version":"2.1.314","gitBranch":"HEAD"}48{"type":"api-request-blob","sessionId":"c7dd64e3-2f86-44fd-bf44-e208b569b084","hash":"ff961f5d622c8303c1798cb2409e4a5f4d1b648c7f97d3cef1888e832c56279f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_017dPTs6KggFoReLuy5XRun5","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, U.S. 1960Q1-2019Q4, OLS with Newey-West HAC SE. Run from workspace root.\"\"\"\nimport json, math\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\nFIRST, LAST = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nissues, excl = [], []\n\nu_raw = pd.read_csv(\"data/UNRATE.csv\")\ng_raw = pd.read_csv(\"data/GDPC1.csv\")\nn_rows = {\"UNRATE.csv\": int(len(u_raw)), \"GDPC1.csv\": int(len(g_raw))}\n\n# --- unemployment: monthly -> quarterly average\nu = u_raw.copy()\nu[\"date\"] = pd.to_datetime(u[\"observation_date\"])\nu[\"UNRATE\"] = pd.to_numeric(u[\"UNRATE\"], errors=\"coerce\")\nmiss = u.loc[u[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif miss:\n issues.append({\"issue\": f\"UNRATE missing value(s) at {', '.join(miss)}\",\n \"handling\": \"outside the 1959Q4-2019Q4 span needed; dropped by window filter, no effect on estimates\"})\nu[\"q\"] = u[\"date\"].dt.to_period(\"Q\")\nn0 = len(u)\nuw = u[(u[\"q\"] >= FIRST - 1) & (u[\"q\"] <= LAST)]\nexcl.append((\"unrate_window\", \"keep months in 1959Q4-2019Q4 (1959Q4 only for differencing)\", n0, len(uw), n0 - len(uw),\n \"information rule: sample window; months outside not used (includes missing 2025-10)\"))\ncnt = uw.groupby(\"q\")[\"UNRATE\"].count()\nassert (cnt == 3).all() and len(cnt) == 241, \"incomplete quarter in window\"\nuq = uw.groupby(\"q\")[\"UNRATE\"].mean()\n\n# --- GDP\ng = g_raw.copy()\ng[\"q\"] = pd.to_datetime(g[\"observation_date\"]).dt.to_period(\"Q\")\ng[\"GDPC1\"] = pd.to_numeric(g[\"GDPC1\"], errors=\"coerce\")\nn0 = len(g)\ngw = g[(g[\"q\"] >= FIRST - 1) & (g[\"q\"] <= LAST)].set_index(\"q\")[\"GDPC1\"]\nexcl.append((\"gdp_window\", \"keep quarters in 1959Q4-2019Q4 (1959Q4 only for differencing)\", n0, len(gw), n0 - len(gw),\n \"information rule: sample window\"))\nassert gw.notna().all() and len(gw) == 241\n\ndf = pd.DataFrame({\"u\": uq, \"y\": gw}).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"y\"] / df[\"y\"].shift(1))\nn0 = len(df)\ndf = df.loc[FIRST:LAST].dropna()\nexcl.append((\"differencing\", \"drop 1959Q4 (levels used only to form 1960Q1 differences)\", n0, len(df), n0 - len(df),\n \"first difference unavailable for base quarter\"))\nT = len(df)\nassert T == 240\n\nX = sm.add_constant(df[\"g\"].values)\ny = df[\"du\"].values\nols = sm.OLS(y, X).fit()\n# Newey-West (1994) plug-in rule of thumb: floor(4*(T/100)^(2/9))\nL = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\n\n# manual check of HAC SE\ne = ols.resid; Xe = X * e[:, None]\nS = Xe.T @ Xe\nfor j in range(1, L + 1):\n w = 1 - j / (L + 1); G = Xe[j:].T @ Xe[:-j]; S += w * (G + G.T)\nXtXi = np.linalg.inv(X.T @ X)\nse_manual = float(np.sqrt((XtXi @ S @ XtXi)[1, 1]))\nassert abs(se_manual - hac.bse[1]) < 1e-10\n\nbeta, alpha = float(ols.params[1]), float(ols.params[0])\nse_hac = float(hac.bse[1]); z = stats.norm.ppf(0.975)\nlo, hi = beta - z * se_hac, beta + z * se_hac\np_hac = float(2 * stats.norm.sf(abs(beta / se_hac)))\n\nsens = {}\nfor l in [0, 1, 2, 4, 6, 8, 12]:\n r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": l, \"kernel\": \"bartlett\", \"use_correction\": False})\n sens[str(l)] = float(r.bse[1])\n\nres = {\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": beta, \"alpha_hat\": alpha, \"r_squared\": float(ols.rsquared),\n \"se_iid\": float(ols.bse[1]), \"se_hac\": se_hac, \"hac_lags\": L,\n \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n \"okun_negative_5pct\": bool(beta < 0 and p_hac < 0.05),\n \"mean_growth\": float(df[\"g\"].mean()),\n \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n \"n_analysed\": int(T),\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n # supplementary\n \"t_stat_hac\": float(beta / se_hac), \"p_value_hac\": p_hac,\n \"residual_acf_lag1\": float(pd.Series(e).autocorr(1)),\n \"se_hac_by_lags\": sens,\n \"beta_ci_iid_low\": float(beta - z * ols.bse[1]), \"beta_ci_iid_high\": float(beta + z * ols.bse[1]),\n}\nwith open(\"out/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\npd.DataFrame(excl, columns=[\"step\", \"rule\", \"n_before\", \"n_after\", \"n_removed\", \"rationale\"]).to_csv(\"out/exclusions.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(6.5, 4.5))\nax.scatter(df[\"g\"], df[\"du\"], s=12, alpha=0.6)\nxs = np.linspace(df[\"g\"].min(), df[\"g\"].max(), 100)\nax.plot(xs, alpha + beta * xs, color=\"red\", label=f\"OLS: du = {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 du_t (pp)\")\nax.set_title(\"Okun's law, U.S. 1960Q1-2019Q4 (n=240)\"); ax.legend()\nfig.tight_layout(); fig.savefig(\"out/figure1.png\", dpi=120)\nprint(json.dumps({k: res[k] for k in [\"beta_hat\", \"se_iid\", \"se_hac\", \"hac_lags\", \"beta_ci_low\", \"beta_ci_high\", \"okun_negative_5pct\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/exclusions.csv","description":"Write and run analysis 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\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .","description":"Reproducibility check and first gate 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code 1\nIDENTICAL\nPGAR GATE pgar_gate/1.0 — FAILED (11 of 38 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/figure1.png: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:beta_hat: ok\n [ok] key:alpha_hat: ok\n [ok] key:r_squared: ok\n [ok] key:se_iid: ok\n [ok] key:se_hac: ok\n [ok] key:hac_lags: ok\n [ok] key:beta_ci_low: ok\n [ok] key:beta_ci_high: ok\n [ok] key:okun_negative_5pct: ok\n [ok] key:mean_growth: ok\n [ok] key:first_quarter: ok\n [ok] key:last_quarter: ok\n [ok] key:n_analysed: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [FAIL] exclusion_ledger: row 1: n_before != previous n_after\n [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n [ok] positive:se_iid: 0.019960092914910015\n [ok] positive:se_hac: 0.03583463330296578\n [ok] reproducibility: regenerated in 1.2s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0218/out/state/gate_receipts/20261030T050501.095806Z.json","is_error":true,"tool_use_id":"toolu_01FKeR1BbCYuVnKmgceC1LPZ"}]},"uuid":"29cd2a7b-9e88-4b64-bb8d-7485088495c6","timestamp":"2026-10-30T05:05:01.103Z","toolUseResult":"Error: Exit code 1\nIDENTICAL\nPGAR GATE pgar_gate/1.0 — FAILED (11 of 38 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/figure1.png: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:beta_hat: ok\n [ok] key:alpha_hat: ok\n [ok] key:r_squared: ok\n [ok] key:se_iid: ok\n [ok] key:se_hac: ok\n [ok] key:hac_lags: ok\n [ok] key:beta_ci_low: ok\n [ok] key:beta_ci_high: ok\n [ok] key:okun_negative_5pct: ok\n [ok] key:mean_growth: ok\n [ok] key:first_quarter: ok\n [ok] key:last_quarter: ok\n [ok] key:n_analysed: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n [FAIL] exclusion_ledger: row 1: n_before != previous n_after\n [ok] ci:beta_hat: -0.326002318421742 <= -0.25576772774872947 <= -0.18553313707571695\n [ok] positive:se_iid: 0.019960092914910015\n [ok] positive:se_hac: 0.03583463330296578\n [ok] reproducibility: regenerated in 1.2s; identical within tolerance\nreceipt: 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out/results.json /tmp/r1.json && rm out/results.json out/figure1.png out/exclusions.csv && ~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp /tmp/r1.json out/results.json && echo IDENTICAL\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"contract.md written covering question, estimand, outputs, info rules, invalidating conditions\"],\"notes\":\"\"},\n \"P1\":{\"status\":\"passed\",\"checks\":[\"SHA-256 of both files match README\",\"row counts 945/319 match README\",\"missing/dup/gap scan done\",\"exclusions.csv written\"],\"notes\":\"one missing UNRATE month 2025-10 outside window\"},\n \"P2\":{\"status\":\"passed\",\"checks\":[\"baseline se_iid computed with same OLS fit\"],\"notes\":\"\"},\n \"P3\":{\"status\":\"passed\",\"checks\":[\"analysis.py runs, writes results.json, figure1.png, exclusions.csv\"],\"notes\":\"no randomness; random_seed null\"},\n \"P4\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n 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GDPC1 quarters with a complete (3-month, non-missing) UNRATE quarterly average\",319,314,5,\"UNRATE (945 monthly rows) averaged to quarters; 1947 has no UNRATE, 2025Q4 lacks Oct-2025 value\"\nwindow,keep 1959Q4-2019Q4 (1959Q4 only for differencing),314,241,73,\"information rule: fixed sample window, no extension\"\ndifferencing,drop 1959Q4 (levels used only to form 1960Q1 differences),241,240,1,first difference unavailable for base quarter","is_error":false}]},"uuid":"2fc642d6-06a8-405c-a689-e8a90d4dbbb7","timestamp":"2026-10-30T05:05:12.730Z","toolUseResult":{"stdout":"SAME\nstep,rule,n_before,n_after,n_removed,rationale\nmerge_unrate,\"keep GDPC1 quarters with a complete (3-month, non-missing) UNRATE quarterly average\",319,314,5,\"UNRATE (945 monthly rows) averaged to quarters; 1947 has no UNRATE, 2025Q4 lacks Oct-2025 value\"\nwindow,keep 1959Q4-2019Q4 (1959Q4 only for differencing),314,241,73,\"information rule: fixed sample window, no extension\"\ndifferencing,drop 1959Q4 (levels used only to form 1960Q1 differences),241,240,1,first difference unavailable for base 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- <<'EOF'\np='out/analysis.py'; s=open(p).read()\nold_start=s.index('u[\"q\"] = u[\"date\"].dt.to_period(\"Q\")')\nold_end=s.index('df[\"du\"] = df[\"u\"].diff()')\nnew='''u[\"q\"] = u[\"date\"].dt.to_period(\"Q\")\n# quarterly average only for quarters with all 3 monthly values present\ncnt_all = u.groupby(\"q\")[\"UNRATE\"].count()\nuq_all = u.groupby(\"q\")[\"UNRATE\"].mean()[cnt_all == 3]\n\n# --- GDP\ng = g_raw.copy()\ng[\"q\"] = pd.to_datetime(g[\"observation_date\"]).dt.to_period(\"Q\")\ng[\"GDPC1\"] = pd.to_numeric(g[\"GDPC1\"], errors=\"coerce\")\nassert g[\"GDPC1\"].notna().all() and not g[\"q\"].duplicated().any()\ngq = g.set_index(\"q\")[\"GDPC1\"]\n\n# Exclusion ledger in quarterly units, starting from the 319 GDPC1 quarters\npanel = pd.DataFrame({\"y\": gq})\nn0 = len(panel)\npanel[\"u\"] = uq_all.reindex(panel.index)\npanel = panel.dropna()\nexcl.append((\"merge_unrate\", \"keep GDPC1 quarters with a complete (3-month, non-missing) UNRATE quarterly average\",\n n0, len(panel), n0 - len(panel),\n \"UNRATE (945 monthly rows) averaged to quarters; 1947 has no UNRATE, 2025Q4 lacks Oct-2025 value\"))\nn0 = len(panel)\npanel = panel.loc[FIRST - 1:LAST]\nexcl.append((\"window\", \"keep 1959Q4-2019Q4 (1959Q4 only for differencing)\", n0, len(panel), n0 - len(panel),\n \"information rule: fixed sample window, no extension\"))\nassert len(panel) == 241 and (cnt_all.loc[FIRST - 1:LAST] == 3).all()\ndf = panel.sort_index()\n'''\ns=s[:old_start]+new+s[old_end:]\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && cmp <(grep -v '\"' /tmp/r1.json) <(grep -v '\"' out/results.json); diff /tmp/r1.json out/results.json && echo SAME; cat out/exclusions.csv","description":"Restructure exclusion ledger as quarterly 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, figure1.png, exclusions.csv) regenerates byte-identical results.json | PASS (cmp identical) |\n| 2 | Re-run after exclusion-ledger refactor gives identical results.json | PASS (diff empty) |\n| 3 | Exclusion ledger: 319 − 5 − 73 − 1 = 240 = n_analysed; each n_before = previous n_after | PASS |\n| 4 | Every quarter 1959Q4–2019Q4 has 3 non-missing UNRATE months; 241 GDP levels; T = 240 | PASS (asserts in analysis.py) |\n| 5 | first_quarter = 1960Q1, last_quarter = 2019Q4 (no data beyond window used in regression) | PASS |\n| 6 | Manual Bartlett NW sandwich equals statsmodels HAC SE to 1e-10 | PASS (assert) |\n| 7 | se_iid > 0, se_hac > 0 | PASS (0.01996, 0.03583) |\n| 8 | CI [−0.3260, −0.1855] contains beta_hat −0.2558; symmetric: (lo+hi)/2 = beta_hat | PASS |\n| 9 | se_hac > se_iid consistent with positive residual lag-1 autocorrelation (0.389) | 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0.0\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 38 checks failed)\n [FAIL] file:out/report.md: missing or empty\n [FAIL] file:out/claims.json: missing or empty\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0218/out/state/gate_receipts/20261030T050524.640519Z.json","is_error":false}]},"uuid":"e0ee87ae-3e60-4cf5-bb88-98944552b388","timestamp":"2026-10-30T05:05:24.648Z","toolUseResult":{"stdout":"0.7209211099660543 0.0\nPGAR GATE pgar_gate/1.0 — FAILED (10 of 38 checks failed)\n [FAIL] file:out/report.md: missing or empty\n [FAIL] file:out/claims.json: missing or empty\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\nreceipt: 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| # | check | outcome |\n|---|---|---|\n| 1 | Clean re-run (deleted results.json, figure1.png, exclusions.csv) regenerates byte-identical results.json | PASS (cmp identical) |\n| 2 | Re-run after exclusion-ledger refactor gives identical results.json | PASS (diff empty) |\n| 3 | Exclusion ledger: 319 − 5 − 73 − 1 = 240 = n_analysed; each n_before = previous n_after | PASS |\n| 4 | Every quarter 1959Q4–2019Q4 has 3 non-missing UNRATE months; 241 GDP levels; T = 240 | PASS (asserts in analysis.py) |\n| 5 | first_quarter = 1960Q1, last_quarter = 2019Q4 (no data beyond window used in regression) | PASS |\n| 6 | Manual Bartlett NW sandwich equals statsmodels HAC SE to 1e-10 | PASS (assert) |\n| 7 | se_iid > 0, se_hac > 0 | PASS (0.01996, 0.03583) |\n| 8 | CI [−0.3260, −0.1855] contains beta_hat −0.2558; symmetric: (lo+hi)/2 = beta_hat | PASS |\n| 9 | se_hac > se_iid consistent with positive residual lag-1 autocorrelation (0.389) | PASS (plausible direction) |\n| 10 | okun_negative_5pct = (beta<0 and p_hac<0.05): beta<0, p≈9.5e-13 → true | PASS |\n| 11 | Magnitudes: mean_growth 0.754 %/q (~3.0 % annualised), alpha 0.184 pp, implied growth for stable u = 0.72 %/q — plausible for U.S. 1960–2019 | PASS |\n| 12 | Bandwidth sensitivity: se_hac in [0.0288, 0.0371] for L ∈ {0,…,12}; inference unchanged | PASS |\n| 13 | Input checksums match README | PASS |\nEOF\n~/wp29/venv/bin/python -c \"\nimport json;r=json.load(open('out/results.json'));print(-r['alpha_hat']/r['beta_hat'], (r['beta_ci_low']+r['beta_ci_high'])/2-r['beta_hat'])\"\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E 'FAIL|receipt|GATE'","description":"Write checks and run gate 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> out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4 (F2-conf-04)\n\n## Question\nHow strongly does the quarterly change in the U.S. unemployment rate move with quarterly real GDP growth? The target is the slope β in the linear projection Δu_t = α + β g_t + e_t. Here u_t is the average of the three monthly UNRATE values in quarter t (in pp), and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is growth in percent, quarter on quarter and not annualised. The sample is 1960Q1–2019Q4. The standard error must stay valid when the errors are serially correlated. β describes an association. It is not a causal effect.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 monthly rows and `data/GDPC1.csv` has 319 quarterly rows (`n_input_rows`). Both SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data problem was found: UNRATE for 2025-10 is an empty field. It falls outside the window, so it does not affect the estimates (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`, counted in quarters):\n - Start with the 319 GDPC1 quarters. Keep only quarters with a complete 3-month UNRATE average: 5 removed (1947Q1–Q4 and 2025Q4), 314 left.\n - Restrict to 1959Q4–2019Q4: 73 removed, 241 left.\n - Drop the 1959Q4 base quarter after differencing: 1 removed, 240 left.\n- The regression uses `n_analysed` = 240 quarters, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4. No observation after 2019Q4 enters the regression.\n\n## Method\n- **Baseline:** OLS of Δu_t on a constant and g_t, with the conventional homoskedastic, independent-errors standard error (`se_iid`).\n- **Main inference:** the same OLS point estimate with a Newey–West HAC standard error (Bartlett kernel), no small-sample degrees-of-freedom correction (`se_hac`).\n- **Bandwidth:** `hac_lags` = 4. This is the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240, fixed before looking at results. A lag window of about one year also fits quarterly macro data.\n- **Confidence interval and test:** the 95% CI is β̂ ± 1.96·`se_hac`, using the asymptotic normal critical value. `okun_negative_5pct` is true only if β̂ < 0 and the two-sided HAC test of β = 0 has p < 0.05.\n- **Code check:** the HAC sandwich was also computed by hand in `out/analysis.py` and asserted equal to the statsmodels result.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` also produces `out/figure1.png` (scatter of Δu_t against g_t with the fitted line).\n\n## Results\n- **Slope:** `beta_hat` = −0.2558. One extra percentage point of quarterly real GDP growth goes with a 0.256 pp smaller quarterly change in the unemployment rate.\n- **Intercept and fit:** `alpha_hat` = 0.1844 pp and `r_squared` = 0.408.\n- **Mean growth:** `mean_growth` = 0.754 percent per quarter.\n- **Significance:** the Okun coefficient is negative and significant at the 5% level (`okun_negative_5pct` = true). The HAC t-statistic is −7.14 (`t_stat_hac`), with p ≈ 9.5e−13 (`p_value_hac`).\n\n## Uncertainty\n- **Standard errors:** `se_iid` = 0.0200 and `se_hac` = 0.0358 (`hac_lags` = 4).\n- **Why they differ:** the OLS residuals are positively autocorrelated, with lag-1 autocorrelation 0.39 (`residual_acf_lag1`). Because of that, the HAC SE is about 1.8 times the naive SE.\n- **Confidence intervals:**\n - 95% HAC CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186].\n - Naive 95% CI, shown for comparison only: [`beta_ci_iid_low`, `beta_ci_iid_high`] = [−0.295, −0.217]. It is too narrow.\n- **Bandwidth sensitivity** (`se_hac_by_lags`): across L ∈ {0, 1, 2, 4, 6, 8, 12} the HAC SE ranges from 0.0288 to 0.0371. Every choice gives a CI well away from zero, so the conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- **Data vintage:** this is the current data vintage, not real-time data. Revisions to GDP could change the estimate in other vintages.\n- **Interpretation:** β is a reduced-form projection coefficient, not a structural or causal parameter. It assumes the relationship is stable over 1960–2019, and the possibility of breaks across decades was not examined, because the task fixes the window.\n- **Inference choices:** the HAC inference is asymptotic, with a normal critical value and no small-sample correction. Fixed-b critical values or a small-sample correction would widen the interval slightly. With |t| > 7, this would not change the conclusion.\n- **Measurement:** Δu is built from quarterly averages of monthly data. This time-averaging itself creates serial correlation, which is one reason to use HAC errors.\n- Nothing in 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 -0.256 pp of unemployment change per percent of quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; reduced-form projection, not causal.\"},\n {\"id\":\"C2\",\"text\":\"Using a Newey-West Bartlett HAC SE with 4 lags (0.0358), the 95% CI is [-0.326, -0.186]; the coefficient is negative and significantly different from zero at 5%.\",\"results_keys\":[\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Asymptotic normal critical value, no small-sample correction.\"},\n {\"id\":\"C3\",\"text\":\"The naive iid SE (0.0200) understates uncertainty: residuals have lag-1 autocorrelation 0.39 and the HAC SE is about 1.8 times larger.\",\"results_keys\":[\"se_iid\",\"se_hac\",\"residual_acf_lag1\",\"beta_ci_iid_low\",\"beta_ci_iid_high\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to the HAC bandwidth over 0-12 lags (HAC SE between 0.0288 and 0.0371).\",\"results_keys\":[\"se_hac_by_lags\",\"beta_hat\"],\"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\np=json.load(open('out/phases.json' if False else 'out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"13 consistency checks in out/state/checks.md all PASS\",\"gate run 20261030T050524.640519Z: all P4-relevant checks ok (exclusion ledger, CI, positive SE, reproducibility); remaining failures were P5 deliverables not yet written\"],\"notes\":\"gate receipts: out/state/gate_receipts/20261030T050501.095806Z.json (exclusion ledger chain failure, fixed), 20261030T050524.640519Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, quantities cite results keys\",\"claims.json with 5 claims all referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed (beta=-0.256, se_hac=0.0358 L=4). Exclusion ledger refactored to a quarterly chain after gate failure; results unchanged. P4 passed. 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The target is the slope β in the linear projection Δu_t = α + β g_t + e_t. Here u_t is the average of the three monthly UNRATE values in quarter t (in pp), and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) is growth in percent, quarter on quarter and not annualised. The sample is 1960Q1–2019Q4. The standard error must stay valid when the errors are serially correlated. β describes an association. It is not a causal effect.\n\n## Data and exclusions\n- `data/UNRATE.csv` has 945 monthly rows and `data/GDPC1.csv` has 319 quarterly rows (`n_input_rows`). Both SHA-256 checksums match `data/README.md` (see `out/state/data_audit.md`).\n- One data problem was found: UNRATE for 2025-10 is an empty field. It falls outside the window, so it does not affect the estimates (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`, counted in quarters):\n - Start with the 319 GDPC1 quarters. Keep only quarters with a complete 3-month UNRATE average: 5 removed (1947Q1–Q4 and 2025Q4), 314 left.\n - Restrict to 1959Q4–2019Q4: 73 removed, 241 left.\n - Drop the 1959Q4 base quarter after differencing: 1 removed, 240 left.\n- The regression uses `n_analysed` = 240 quarters, from `first_quarter` = 1960Q1 to `last_quarter` = 2019Q4. No observation after 2019Q4 enters the regression.\n\n## Method\n- **Baseline:** OLS of Δu_t on a constant and g_t, with the conventional homoskedastic, independent-errors standard error (`se_iid`).\n- **Main inference:** the same OLS point estimate with a Newey–West HAC standard error (Bartlett kernel), no small-sample degrees-of-freedom correction (`se_hac`).\n- **Bandwidth:** `hac_lags` = 4. This is the Newey–West (1994) rule of thumb floor(4·(T/100)^{2/9}) with T = 240, fixed before looking at results. A lag window of about one year also fits quarterly macro data.\n- **Confidence interval and test:** the 95% CI is β̂ ± 1.96·`se_hac`, using the asymptotic normal critical value. `okun_negative_5pct` is true only if β̂ < 0 and the two-sided HAC test of β = 0 has p < 0.05.\n- **Code check:** the HAC sandwich was also computed by hand in `out/analysis.py` and asserted equal to the statsmodels result.\n- No randomness is involved (`random_seed` = null). `out/analysis.py` also produces `out/figure1.png` (scatter of Δu_t against g_t with the fitted line).\n\n## Results\n- **Slope:** `beta_hat` = −0.2558. One extra percentage point of quarterly real GDP growth goes with a 0.256 pp smaller quarterly change in the unemployment rate.\n- **Intercept and fit:** `alpha_hat` = 0.1844 pp and `r_squared` = 0.408.\n- **Mean growth:** `mean_growth` = 0.754 percent per quarter.\n- **Significance:** the Okun coefficient is negative and significant at the 5% level (`okun_negative_5pct` = true). The HAC t-statistic is −7.14 (`t_stat_hac`), with p ≈ 9.5e−13 (`p_value_hac`).\n\n## Uncertainty\n- **Standard errors:** `se_iid` = 0.0200 and `se_hac` = 0.0358 (`hac_lags` = 4).\n- **Why they differ:** the OLS residuals are positively autocorrelated, with lag-1 autocorrelation 0.39 (`residual_acf_lag1`). Because of that, the HAC SE is about 1.8 times the naive SE.\n- **Confidence intervals:**\n - 95% HAC CI: [`beta_ci_low`, `beta_ci_high`] = [−0.326, −0.186].\n - Naive 95% CI, shown for comparison only: [`beta_ci_iid_low`, `beta_ci_iid_high`] = [−0.295, −0.217]. It is too narrow.\n- **Bandwidth sensitivity** (`se_hac_by_lags`): across L ∈ {0, 1, 2, 4, 6, 8, 12} the HAC SE ranges from 0.0288 to 0.0371. Every choice gives a CI well away from zero, so the conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- **Data vintage:** this is the current data vintage, not real-time data. Revisions to GDP could change the estimate in other vintages.\n- **Interpretation:** β is a reduced-form projection coefficient, not a structural or causal parameter. It assumes the relationship is stable over 1960–2019, and the possibility of breaks across decades was not examined, because the task fixes the window.\n- **Inference choices:** the HAC inference is asymptotic, with a normal critical value and no small-sample correction. Fixed-b critical values or a small-sample correction would widen the interval slightly. With |t| > 7, this would not change the conclusion.\n- **Measurement:** Δu is built from quarterly averages of monthly data. This time-averaging itself creates serial correlation, which is one reason to use HAC errors.\n- Nothing in 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 -0.256 pp of unemployment change per percent of quarterly real GDP growth, with intercept 0.184 and R^2 0.408.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"Current-vintage data; reduced-form projection, not causal.\"},\n {\"id\":\"C2\",\"text\":\"Using a Newey-West Bartlett HAC SE with 4 lags (0.0358), the 95% CI is [-0.326, -0.186]; the coefficient is negative and significantly different from zero at 5%.\",\"results_keys\":[\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\",\"t_stat_hac\",\"p_value_hac\"],\"status\":\"supported\",\"caveat\":\"Asymptotic normal critical value, no small-sample correction.\"},\n {\"id\":\"C3\",\"text\":\"The naive iid SE (0.0200) understates uncertainty: residuals have lag-1 autocorrelation 0.39 and the HAC SE is about 1.8 times larger.\",\"results_keys\":[\"se_iid\",\"se_hac\",\"residual_acf_lag1\",\"beta_ci_iid_low\",\"beta_ci_iid_high\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The significance conclusion is robust to the HAC bandwidth over 0-12 lags (HAC SE between 0.0288 and 0.0371).\",\"results_keys\":[\"se_hac_by_lags\",\"beta_hat\"],\"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\np=json.load(open('out/phases.json' if False else 'out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"13 consistency checks in out/state/checks.md all PASS\",\"gate run 20261030T050524.640519Z: all P4-relevant checks ok (exclusion ledger, CI, positive SE, reproducibility); remaining failures were P5 deliverables not yet written\"],\"notes\":\"gate receipts: out/state/gate_receipts/20261030T050501.095806Z.json (exclusion ledger chain failure, fixed), 20261030T050524.640519Z.json\"}\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 required sections, quantities cite results keys\",\"claims.json with 5 claims all referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P2/P3 passed (beta=-0.256, se_hac=0.0358 L=4). Exclusion ledger refactored to a quarterly chain after gate failure; results unchanged. P4 passed. 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The final gate check passed with no failures, and re-running `out/analysis.py` reproduces `out/results.json` exactly.\n\n**Results (240 quarters)**\n- **Okun coefficient:** −0.256. One extra percentage point of quarterly real GDP growth goes with a 0.256 percentage-point smaller quarterly change in the unemployment rate.\n- **Intercept and fit:** intercept 0.184, R² 0.408. Mean quarterly growth is 0.754%.\n- **Standard errors:** the conventional standard error is 0.0200. The Newey–West standard error (4 lags, set by the standard rule of thumb for 240 quarters) is 0.0358, about 1.8 times larger. That fits the regression residuals being positively autocorrelated (lag-1 autocorrelation 0.39).\n- **Confidence interval:** the 95% interval from the Newey–West standard error is [−0.326, −0.186]. 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