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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 Okun coefficient β in the difference form Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4, where Δu_t is the change in the quarterly-average civilian unemployment rate (percentage points) and g_t = 100·(ln GDPC1_t − ln GDPC1_{t−1}) is quarterly real GDP growth in percent (not annualised); uncertainty must be robust to heteroskedasticity and serial correlation. Stability is assessed by estimating β separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and by a classical Chow test of the joint equality of (α, β) across the two subperiods. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/GDPC1.csv` — observed data, FRED GDPC1 (US real GDP, quarterly, billions of chained 2017 dollars, SAAR; BEA), full history, raw FRED CSV (`observation_date` = first day of the quarter, `GDPC1`). Provenance in `data/README.md`.\n- `data/UNRATE.csv` — observed data, FRED UNRATE (US civilian unemployment rate, monthly, percent, SA; BLS), full history, raw FRED CSV (`observation_date` = first day of the month, `UNRATE`). Provenance in `data/README.md`.\n- `data/README.md` — provenance, retrieval dates, SHA-256.\n\n## Information-availability rules\n- Quarterly unemployment u_t is the simple average of the three monthly UNRATE values of quarter t. Δu_t = u_t − u_{t−1}.\n- The regression sample is the 240 quarters 1960Q1–2019Q4 (the 1960Q1 differences use 1959Q4 levels). Quarters after 2019Q4 are outside the question and must not enter any estimate. Subperiod 1 = 1960Q1–1983Q4, subperiod 2 = 1984Q1–2019Q4.\n- Standard errors for the full-sample β must be HAC (Newey–West with Bartlett kernel); choose and report the lag length. The Chow test is the classical sum-of-squared-residuals F-test with 2 and n − 4 degrees of freedom.\n- Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first day of the first and last quarter in the regression sample (`1960-01-01`, `2019-10-01`) |\n| `n_sub1`, `n_sub2` | integer | quarters | observations in each subperiod |\n| `baseline_metric` | number | percentage points | RMSE of the mean-only prediction of Δu over the full sample, sqrt(mean((Δu − mean Δu)²)) |\n| `model_metric` | number | percentage points | RMSE of the full-sample OLS residuals, sqrt(mean(e²)) |\n| `effect_estimate` | number | pp per percent growth | full-sample OLS Okun coefficient β |\n| `intercept` | number | percentage points | full-sample OLS intercept α |\n| `effect_se` | number | pp per percent growth | HAC (Newey–West) standard error of β |\n| `hac_lags` | integer | quarters | Newey–West lag length used |\n| `effect_ci_low`, `effect_ci_high` | number | pp per percent growth | 95% confidence interval for β based on `effect_se` |\n| `beta_sub1`, `beta_sub2` | number | pp per percent growth | OLS slope in subperiod 1 and 2 |\n| `chow_f_stat`, `chow_p_value` | number | — | Chow F statistic and p-value |\n| `stability_conclusion` | string | — | `stable` if the Chow test does not reject at 5%, else `unstable` |\n| `conclusion` | string | — | one of the codes below (sign of β from its 95% interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = quarters in the full-sample regression.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of Δu_t against g_t with the two subperiod fitted lines, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per sample (full, subperiod 1, subperiod 2) with columns `period`, `n`, `intercept`, `slope`, `slope_se` (header + three data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":true,"tool_use_id":"toolu_01Lw4FPBbtJvqNsUAdgX3kAt"}]},"uuid":"a6af68c6-539a-4807-bfb9-ae56ed0a5833","timestamp":"2026-10-30T04:51:25.279Z","toolUseResult":"Error: Exit code 1\n# F6-conf-03 — Okun's law in the United States, 1960–2019: coefficient, uncertainty and stability\n\n## Scientific question\nHow strongly do quarterly changes in the US unemployment rate respond to real output growth, and has that response been stable? The estimand is the Okun coefficient β in the difference form Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4, where Δu_t is the change in the quarterly-average civilian unemployment rate (percentage points) and g_t = 100·(ln GDPC1_t − ln GDPC1_{t−1}) is quarterly real GDP growth in percent (not annualised); uncertainty must be robust to heteroskedasticity and serial correlation. Stability is assessed by estimating β separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and by a classical Chow test of the joint equality of (α, β) across the two subperiods. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/GDPC1.csv` — observed data, FRED GDPC1 (US real GDP, quarterly, billions of chained 2017 dollars, SAAR; BEA), full history, raw FRED CSV (`observation_date` = first day of the quarter, `GDPC1`). Provenance in `data/README.md`.\n- `data/UNRATE.csv` — observed data, FRED UNRATE (US civilian unemployment rate, monthly, percent, SA; BLS), full history, raw FRED CSV (`observation_date` = first day of the month, `UNRATE`). Provenance in `data/README.md`.\n- `data/README.md` — provenance, retrieval dates, SHA-256.\n\n## Information-availability rules\n- Quarterly unemployment u_t is the simple average of the three monthly UNRATE values of quarter t. Δu_t = u_t − u_{t−1}.\n- The regression sample is the 240 quarters 1960Q1–2019Q4 (the 1960Q1 differences use 1959Q4 levels). Quarters after 2019Q4 are outside the question and must not enter any estimate. Subperiod 1 = 1960Q1–1983Q4, subperiod 2 = 1984Q1–2019Q4.\n- Standard errors for the full-sample β must be HAC (Newey–West with Bartlett kernel); choose and report the lag length. The Chow test is the classical sum-of-squared-residuals F-test with 2 and n − 4 degrees of freedom.\n- Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first day of the first and last quarter in the regression sample (`1960-01-01`, `2019-10-01`) |\n| `n_sub1`, `n_sub2` | integer | quarters | observations in each subperiod |\n| `baseline_metric` | number | percentage points | RMSE of the mean-only prediction of Δu over the full sample, sqrt(mean((Δu − mean Δu)²)) |\n| `model_metric` | number | percentage points | RMSE of the full-sample OLS residuals, sqrt(mean(e²)) |\n| `effect_estimate` | number | pp per percent growth | full-sample OLS Okun coefficient β |\n| `intercept` | number | percentage points | full-sample OLS intercept α |\n| `effect_se` | number | pp per percent growth | HAC (Newey–West) standard error of β |\n| `hac_lags` | integer | quarters | Newey–West lag length used |\n| `effect_ci_low`, `effect_ci_high` | number | pp per percent growth | 95% confidence interval for β based on `effect_se` |\n| `beta_sub1`, `beta_sub2` | number | pp per percent growth | OLS slope in subperiod 1 and 2 |\n| `chow_f_stat`, `chow_p_value` | number | — | Chow F statistic and p-value |\n| `stability_conclusion` | string | — | `stable` if the Chow test does not reject at 5%, else `unstable` |\n| `conclusion` | string | — | one of the codes below (sign of β from its 95% interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = quarters in the full-sample regression.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of Δu_t against g_t with the two subperiod fitted lines, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per sample (full, subperiod 1, subperiod 2) with columns `period`, `n`, `intercept`, `slope`, `slope_se` (header + three data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying series are produced by US government agencies and are in the public domain.\n\n## `GDPC1.csv` (319 rows)\n*Real Gross Domestic Product* (FRED `GDPC1`), quarterly, billions of chained 2017 dollars, seasonally adjusted annual rate. Source: U.S. Bureau of Economic Analysis. Observation dates are the first day of each quarter (e.g. `1947-01-01` = 1947Q1). Period 1947-01-01 to 2026-07-01 (current vintage as of retrieval).\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`\n- SHA-256: `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343`\n\n## `UNRATE.csv` (945 rows)\n*Unemployment Rate* (FRED `UNRATE`), monthly, percent, seasonally adjusted, civilian labour force 16+. Source: U.S. Bureau of Labor Statistics. Observation dates are the first day of each month. Period 1948-01-01 to 2026-09-01.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`\n- SHA-256: `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c`\n{\n  \"task_id\": \"F6-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"sample_start\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"sample_end\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_sub1\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_sub2\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"intercept\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"hac_lags\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv","is_error":false}]},"uuid":"61df8912-647e-470e-9cd3-e1d3906b82eb","timestamp":"2026-10-30T04:51:27.356Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-03\n\nBoth files are raw FRED CSV downloads (`observation_date`, value), retrieved 2026-10-30T03:23:28Z, unaltered. Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying series are produced by US government agencies and are in the public domain.\n\n## `GDPC1.csv` (319 rows)\n*Real Gross Domestic Product* (FRED `GDPC1`), quarterly, billions of chained 2017 dollars, seasonally adjusted annual rate. Source: U.S. Bureau of Economic Analysis. Observation dates are the first day of each quarter (e.g. `1947-01-01` = 1947Q1). Period 1947-01-01 to 2026-07-01 (current vintage as of retrieval).\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`\n- SHA-256: `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343`\n\n## `UNRATE.csv` (945 rows)\n*Unemployment Rate* (FRED `UNRATE`), monthly, percent, seasonally adjusted, civilian labour force 16+. Source: U.S. Bureau of Labor Statistics. Observation dates are the first day of each month. Period 1948-01-01 to 2026-09-01.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`\n- SHA-256: `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c`\n{\n  \"task_id\": \"F6-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": 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<==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658  data/README.md\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-03 (Okun's law, US 1960–2019)\n\n**Question.** How strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is that response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n\n**Estimands.**\n- β (and α) in Δu_t = α + β g_t + ε_t, OLS, 240 quarters 1960Q1–2019Q4. u_t = mean of 3 monthly UNRATE values; Δu_t = u_t − u_{t−1} (pp); g_t = 100·Δln GDPC1 (percent, not annualised).\n- HAC (Newey–West, Bartlett) SE for β with stated lag; 95% CI from it.\n- β in subperiods (n=96, n=144); classical Chow SSR F-test of joint (α,β) equality, df (2, n−4)=(2,236).\n- Baseline: RMSE of mean-only prediction of Δu; model RMSE of OLS residuals.\n- Meaningful under: linear, time-invariant relation within each (sub)sample; g_t exogenous enough for β to be a descriptive (not causal) projection coefficient; Chow test assumes homoskedastic, serially uncorrelated errors (classical) — a known limitation.\n\n**Outputs.** out/results.json (keys per TASK.md table: sample_start, sample_end, n_sub1, n_sub2, baseline_metric, model_metric, effect_estimate, intercept, effect_se, hac_lags, effect_ci_low, effect_ci_high, beta_sub1, beta_sub2, chow_f_stat, chow_p_value, stability_conclusion, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (period,n,intercept,slope,slope_se; 3 rows); out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol state files and out/exclusions.csv.\n\n**Information rules.** No quarter after 2019Q4 enters any estimate; 1959Q4 levels used only to form 1960Q1 differences. Only data/GDPC1.csv and data/UNRATE.csv.\n\n**Invalidity / impossibility.** Missing months or quarters inside 1959Q4–2019Q4; non-numeric values; wrong frequency; sample ≠ 240. Use of post-2019 data. Note: GDPC1 is the current (2026) vintage, so revisions are embedded — acceptable since the task is an ex-post descriptive estimate, no real-time rule.\nEOF\ncat > 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.\" > 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":1754,"cache_read_input_tokens":8693,"output_tokens":1955,"output_tokens_details":{"thinking_tokens":728},"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":1754},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpa7Bfk6ByW9q8fm2r3","requestRef":"36663075-43e1-4718-af26-a73d11fd5f86","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"590da519-65a7-4e42-9f33-8269228489cd","timestamp":"2026-10-30T04:51:50.228Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"590da519-65a7-4e42-9f33-8269228489cd","isSidechain":false,"promptId":"df3d2f72-48c6-4291-bd7a-d8c83a03e33a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Uro7sJ2nQevxGcwHM2GvpZ","type":"tool_result","content":"(Bash completed with no 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-p out/state && cd out/state && cat > contract.md <<'EOF'\n# Contract — F6-conf-03 (Okun's law, US 1960–2019)\n\n**Question.** How strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is that response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n\n**Estimands.**\n- β (and α) in Δu_t = α + β g_t + ε_t, OLS, 240 quarters 1960Q1–2019Q4. u_t = mean of 3 monthly UNRATE values; Δu_t = u_t − u_{t−1} (pp); g_t = 100·Δln GDPC1 (percent, not annualised).\n- HAC (Newey–West, Bartlett) SE for β with stated lag; 95% CI from it.\n- β in subperiods (n=96, n=144); classical Chow SSR F-test of joint (α,β) equality, df (2, n−4)=(2,236).\n- Baseline: RMSE of mean-only prediction of Δu; model RMSE of OLS residuals.\n- Meaningful under: linear, time-invariant relation within each (sub)sample; g_t exogenous enough for β to be a descriptive (not causal) projection coefficient; Chow test assumes homoskedastic, serially uncorrelated errors (classical) — a known limitation.\n\n**Outputs.** out/results.json (keys per TASK.md table: sample_start, sample_end, n_sub1, n_sub2, baseline_metric, model_metric, effect_estimate, intercept, effect_se, hac_lags, effect_ci_low, effect_ci_high, beta_sub1, beta_sub2, chow_f_stat, chow_p_value, stability_conclusion, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (period,n,intercept,slope,slope_se; 3 rows); out/report.md (6 sections); out/analysis.py; out/claims.json; plus protocol state files and out/exclusions.csv.\n\n**Information rules.** No quarter after 2019Q4 enters any estimate; 1959Q4 levels used only to form 1960Q1 differences. Only data/GDPC1.csv and data/UNRATE.csv.\n\n**Invalidity / impossibility.** Missing months or quarters inside 1959Q4–2019Q4; non-numeric values; wrong frequency; sample ≠ 240. Use of post-2019 data. Note: GDPC1 is the current (2026) vintage, so revisions are embedded — acceptable since the task is an ex-post descriptive estimate, no real-time rule.\nEOF\ncat > 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.\" > log.md","description":"Write contract, phase ledger, log"}}]}}35{"type":"api-request","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","id":"02956502-d52f-4e5d-8f13-846a4c4d9e98","timestamp":"2026-10-30T04:51:50.256Z","version":"2.1.314","querySource":"sdk","shapeHash":"acffe5d451bc77a6f802eaa94680ea85da28e8472417849a985b2588fc260217","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":"36663075-43e1-4718-af26-a73d11fd5f86","keep":8,"tail":["f84a1dff299fb9d585f3d4d70c41b04f8dcfba37f558d6e635134cdbe3817014","b8fd6db14aed5e0aab0eac3dd0129a7bb1129eb078bea13f97a28daab23966b7"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"1bb17a6bbcb7060e589041e2eda5b3c8471a8ab5dda080b1bf06ee6c8977a1f4"}36{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f70d700a-b61a-4bb1-bd1f-88fc0339f3a1","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}37{"type":"atis-latch","atis":"","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}38{"parentUuid":"f70d700a-b61a-4bb1-bd1f-88fc0339f3a1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpbTgEh3NKyJqin75uV","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1970,"cache_read_input_tokens":10447,"output_tokens":4243,"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":1970},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpbTQNr5JP4Y32Lvku5","requestRef":"02956502-d52f-4e5d-8f13-846a4c4d9e98","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d8d25031-a8ad-42f6-be2e-ffaf35ffd9b3","timestamp":"2026-10-30T04:51:51.292Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179/out/state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}39{"parentUuid":"d8d25031-a8ad-42f6-be2e-ffaf35ffd9b3","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpbTgEh3NKyJqin75uV","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01R4gibWWsX8auud4PtSZcpq","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4 (task F6-conf-03). Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nS0, S1 = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nSPLIT = pd.Period(\"1984Q1\", \"Q\")\n\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly average of monthly values (require all 3 months) ---\nun = un_raw.copy()\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nnan_months = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif nan_months:\n    data_issues.append({\"issue\": f\"UNRATE missing value(s) for month(s) {nan_months}\",\n                        \"handling\": \"Quarter containing a missing month is not averaged (set missing); \"\n                                    \"all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"})\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[uq[\"count\"] < 3, \"mean\"] = np.nan  # incomplete quarters\nu = uq[\"mean\"].rename(\"u\")\n\ngdp = gdp_raw.copy()\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gdp[\"q\"].duplicated().any()\ng_level = gdp.set_index(\"q\")[\"GDPC1\"].astype(float)\n\n# --- merge and exclusion ledger (unit: quarters of GDPC1) ---\nledger = []\ndef log(step, rule, before, after, why):\n    ledger.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\ndf = pd.DataFrame({\"gdp\": g_level})\nn0 = len(df)\ndf = df.join(u, how=\"left\")\nn1 = df[\"u\"].notna().sum()\ndf_ok = df[df[\"u\"].notna()].copy()\nlog(1, \"drop GDPC1 quarters without a complete 3-month UNRATE quarterly average\", n0, int(n1),\n    \"UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\")\n# differences require consecutive quarters\nfull = df.reindex(pd.period_range(df.index.min(), df.index.max(), freq=\"Q\"))\nfull[\"du\"] = full[\"u\"].diff()\nfull[\"g\"] = 100 * np.log(full[\"gdp\"]).diff()\ndf_d = full.loc[df_ok.index].dropna(subset=[\"du\", \"g\"])\nlog(2, \"drop quarters whose first difference cannot be formed (previous quarter missing)\", int(n1), len(df_d),\n    \"first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\")\nd = df_d.loc[(df_d.index >= S0) & (df_d.index <= S1)].copy()\nlog(3, \"restrict to regression window 1960Q1-2019Q4\", len(df_d), len(d),\n    \"TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate\")\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nexpected = pd.period_range(S0, S1, freq=\"Q\")\nassert len(d) == 240 and (d.index == expected).all(), \"sample is not the 240 consecutive quarters\"\n# 1959Q4 levels used for 1960Q1 differences: check present\nassert pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"u\"]) and pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"gdp\"])\n\n# --- baseline ---\ndu, g = d[\"du\"].values, d[\"g\"].values\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((du - du.mean()) ** 2)))\n\n# --- full-sample OLS with Newey-West HAC ---\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9)))  # Newey-West (1994) rule of thumb -> 4 for n=240\nX = sm.add_constant(g)\nols = sm.OLS(du, X).fit()\nhac = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse_b = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_b, beta + z * se_b)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n# lag-length sensitivity (reported, not used for the headline)\nhac_sens = {}\nfor L in [0, 2, 4, 8, 12]:\n    r = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": True})\n    hac_sens[str(L)] = float(r.bse[1])\nols_se_classical = float(ols.bse[1])\n\n# --- subperiods and Chow test ---\nm1 = d.index < SPLIT\ndef fit(mask):\n    y, x = du[mask], sm.add_constant(g[mask])\n    r = sm.OLS(y, x).fit()\n    rh = sm.OLS(y, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n    return r, rh\nr1, r1h = fit(m1)\nr2, r2h = fit(~m1)\nssr_p = float(ols.ssr); ssr_u = float(r1.ssr + r2.ssr)\nk = 2\ndf2 = n - 2 * k\nF = ((ssr_p - ssr_u) / k) / (ssr_u / df2)\np = float(stats.f.sf(F, k, df2))\n\nif ci[0] > 0: concl = \"positive_effect\"\nelif ci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n                    \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); \"\n                                \"revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"})\n\nres = {\n    \"task_id\": \"F6-conf-03\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    \"sample_start\": str(d.index[0].start_time.date()),\n    \"sample_end\": str(d.index[-1].start_time.date()),\n    \"n_sub1\": int(m1.sum()),\n    \"n_sub2\": int((~m1).sum()),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se_b,\n    \"hac_lags\": HAC_LAGS,\n    \"effect_ci_low\": float(ci[0]),\n    \"effect_ci_high\": float(ci[1]),\n    \"beta_sub1\": float(r1.params[1]),\n    \"beta_sub2\": float(r2.params[1]),\n    \"chow_f_stat\": float(F),\n    \"chow_p_value\": p,\n    \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n    \"conclusion\": concl,\n    # supplementary (not required by TASK.md)\n    \"intercept_sub1\": float(r1.params[0]),\n    \"intercept_sub2\": float(r2.params[0]),\n    \"beta_sub1_hac_se\": float(r1h.bse[1]),\n    \"beta_sub2_hac_se\": float(r2h.bse[1]),\n    \"effect_se_ols_classical\": ols_se_classical,\n    \"effect_se_hac_by_lag\": hac_sens,\n    \"r_squared\": float(ols.rsquared),\n    \"chow_df\": [k, int(df2)],\n    \"ssr_pooled\": ssr_p,\n    \"ssr_unrestricted\": ssr_u,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\npd.DataFrame([\n    dict(period=\"full 1960Q1-2019Q4\", n=n, intercept=alpha, slope=beta, slope_se=se_b),\n    dict(period=\"sub1 1960Q1-1983Q4\", n=int(m1.sum()), intercept=float(r1.params[0]), slope=float(r1.params[1]), slope_se=float(r1h.bse[1])),\n    dict(period=\"sub2 1984Q1-2019Q4\", n=int((~m1).sum()), intercept=float(r2.params[0]), slope=float(r2.params[1]), slope_se=float(r2h.bse[1])),\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(g[m1], du[m1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\")\nax.scatter(g[~m1], du[~m1], s=14, alpha=0.6, marker=\"^\", label=\"1984Q1-2019Q4\")\nfor r, mask, lab in [(r1, m1, \"fit 1960-1983\"), (r2, ~m1, \"fit 1984-2019\")]:\n    xs = np.linspace(g[mask].min(), g[mask].max(), 50)\n    ax.plot(xs, r.params[0] + r.params[1] * xs,\n            label=f\"{lab}: du = {r.params[0]:.3f} {r.params[1]:+.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, US 1960Q1-2019Q4\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se_b:.4f} (L={HAC_LAGS}) CI=[{ci[0]:.4f},{ci[1]:.4f}] \"\n      f\"beta1={r1.params[1]:.4f} beta2={r2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv 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":1970,"cache_read_input_tokens":10447,"output_tokens":4243,"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":1970},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpbTQNr5JP4Y32Lvku5","requestRef":"02956502-d52f-4e5d-8f13-846a4c4d9e98","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d0fd7f71-d9b7-4c8c-b3d8-dcb033ba90e0","timestamp":"2026-10-30T04:52:22.529Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179/out/state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"d0fd7f71-d9b7-4c8c-b3d8-dcb033ba90e0","isSidechain":false,"promptId":"df3d2f72-48c6-4291-bd7a-d8c83a03e33a","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0179/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01R4gibWWsX8auud4PtSZcpq"}]},"uuid":"70aeb12f-18b9-4d4d-95cd-2125286401ba","timestamp":"2026-10-30T04:52:22.569Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.4/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0179/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"d0fd7f71-d9b7-4c8c-b3d8-dcb033ba90e0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179/out/state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}41{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"586a09141d4cfd068d1e375fef8243a540d16c5379c741335539f881bf25bf45","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_01R4gibWWsX8auud4PtSZcpq","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4 (task F6-conf-03). Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nS0, S1 = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nSPLIT = pd.Period(\"1984Q1\", \"Q\")\n\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly average of monthly values (require all 3 months) ---\nun = un_raw.copy()\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nnan_months = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif nan_months:\n    data_issues.append({\"issue\": f\"UNRATE missing value(s) for month(s) {nan_months}\",\n                        \"handling\": \"Quarter containing a missing month is not averaged (set missing); \"\n                                    \"all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"})\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[uq[\"count\"] < 3, \"mean\"] = np.nan  # incomplete quarters\nu = uq[\"mean\"].rename(\"u\")\n\ngdp = gdp_raw.copy()\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gdp[\"q\"].duplicated().any()\ng_level = gdp.set_index(\"q\")[\"GDPC1\"].astype(float)\n\n# --- merge and exclusion ledger (unit: quarters of GDPC1) ---\nledger = []\ndef log(step, rule, before, after, why):\n    ledger.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\ndf = pd.DataFrame({\"gdp\": g_level})\nn0 = len(df)\ndf = df.join(u, how=\"left\")\nn1 = df[\"u\"].notna().sum()\ndf_ok = df[df[\"u\"].notna()].copy()\nlog(1, \"drop GDPC1 quarters without a complete 3-month UNRATE quarterly average\", n0, int(n1),\n    \"UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\")\n# differences require consecutive quarters\nfull = df.reindex(pd.period_range(df.index.min(), df.index.max(), freq=\"Q\"))\nfull[\"du\"] = full[\"u\"].diff()\nfull[\"g\"] = 100 * np.log(full[\"gdp\"]).diff()\ndf_d = full.loc[df_ok.index].dropna(subset=[\"du\", \"g\"])\nlog(2, \"drop quarters whose first difference cannot be formed (previous quarter missing)\", int(n1), len(df_d),\n    \"first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\")\nd = df_d.loc[(df_d.index >= S0) & (df_d.index <= S1)].copy()\nlog(3, \"restrict to regression window 1960Q1-2019Q4\", len(df_d), len(d),\n    \"TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate\")\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nexpected = pd.period_range(S0, S1, freq=\"Q\")\nassert len(d) == 240 and (d.index == expected).all(), \"sample is not the 240 consecutive quarters\"\n# 1959Q4 levels used for 1960Q1 differences: check present\nassert pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"u\"]) and pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"gdp\"])\n\n# --- baseline ---\ndu, g = d[\"du\"].values, d[\"g\"].values\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((du - du.mean()) ** 2)))\n\n# --- full-sample OLS with Newey-West HAC ---\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9)))  # Newey-West (1994) rule of thumb -> 4 for n=240\nX = sm.add_constant(g)\nols = sm.OLS(du, X).fit()\nhac = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse_b = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_b, beta + z * se_b)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n# lag-length sensitivity (reported, not used for the headline)\nhac_sens = {}\nfor L in [0, 2, 4, 8, 12]:\n    r = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": True})\n    hac_sens[str(L)] = float(r.bse[1])\nols_se_classical = float(ols.bse[1])\n\n# --- subperiods and Chow test ---\nm1 = d.index < SPLIT\ndef fit(mask):\n    y, x = du[mask], sm.add_constant(g[mask])\n    r = sm.OLS(y, x).fit()\n    rh = sm.OLS(y, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n    return r, rh\nr1, r1h = fit(m1)\nr2, r2h = fit(~m1)\nssr_p = float(ols.ssr); ssr_u = float(r1.ssr + r2.ssr)\nk = 2\ndf2 = n - 2 * k\nF = ((ssr_p - ssr_u) / k) / (ssr_u / df2)\np = float(stats.f.sf(F, k, df2))\n\nif ci[0] > 0: concl = \"positive_effect\"\nelif ci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n                    \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); \"\n                                \"revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"})\n\nres = {\n    \"task_id\": \"F6-conf-03\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    \"sample_start\": str(d.index[0].start_time.date()),\n    \"sample_end\": str(d.index[-1].start_time.date()),\n    \"n_sub1\": int(m1.sum()),\n    \"n_sub2\": int((~m1).sum()),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se_b,\n    \"hac_lags\": HAC_LAGS,\n    \"effect_ci_low\": float(ci[0]),\n    \"effect_ci_high\": float(ci[1]),\n    \"beta_sub1\": float(r1.params[1]),\n    \"beta_sub2\": float(r2.params[1]),\n    \"chow_f_stat\": float(F),\n    \"chow_p_value\": p,\n    \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n    \"conclusion\": concl,\n    # supplementary (not required by TASK.md)\n    \"intercept_sub1\": float(r1.params[0]),\n    \"intercept_sub2\": float(r2.params[0]),\n    \"beta_sub1_hac_se\": float(r1h.bse[1]),\n    \"beta_sub2_hac_se\": float(r2h.bse[1]),\n    \"effect_se_ols_classical\": ols_se_classical,\n    \"effect_se_hac_by_lag\": hac_sens,\n    \"r_squared\": float(ols.rsquared),\n    \"chow_df\": [k, int(df2)],\n    \"ssr_pooled\": ssr_p,\n    \"ssr_unrestricted\": ssr_u,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\npd.DataFrame([\n    dict(period=\"full 1960Q1-2019Q4\", n=n, intercept=alpha, slope=beta, slope_se=se_b),\n    dict(period=\"sub1 1960Q1-1983Q4\", n=int(m1.sum()), intercept=float(r1.params[0]), slope=float(r1.params[1]), slope_se=float(r1h.bse[1])),\n    dict(period=\"sub2 1984Q1-2019Q4\", n=int((~m1).sum()), intercept=float(r2.params[0]), slope=float(r2.params[1]), slope_se=float(r2h.bse[1])),\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(g[m1], du[m1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\")\nax.scatter(g[~m1], du[~m1], s=14, alpha=0.6, marker=\"^\", label=\"1984Q1-2019Q4\")\nfor r, mask, lab in [(r1, m1, \"fit 1960-1983\"), (r2, ~m1, \"fit 1984-2019\")]:\n    xs = np.linspace(g[mask].min(), g[mask].max(), 50)\n    ax.plot(xs, r.params[0] + r.params[1] * xs,\n            label=f\"{lab}: du = {r.params[0]:.3f} {r.params[1]:+.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, US 1960Q1-2019Q4\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se_b:.4f} (L={HAC_LAGS}) CI=[{ci[0]:.4f},{ci[1]:.4f}] \"\n      f\"beta1={r1.params[1]:.4f} beta2={r2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"}}]}}42{"type":"api-request","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","id":"df7267d9-4c73-4a5b-bd99-f6f4bb85c84e","timestamp":"2026-10-30T04:52:22.574Z","version":"2.1.314","querySource":"sdk","shapeHash":"acffe5d451bc77a6f802eaa94680ea85da28e8472417849a985b2588fc260217","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":"02956502-d52f-4e5d-8f13-846a4c4d9e98","keep":10,"tail":["586a09141d4cfd068d1e375fef8243a540d16c5379c741335539f881bf25bf45","a398061999f746413c214dc5a3d90839af9f6dbb89d0ef3c6dd3ef230e89cb91"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6553407096a79f37048bd3f8468df870b1b76cb27c04d191cc2aa50ce405db1d"}43{"parentUuid":"70aeb12f-18b9-4d4d-95cd-2125286401ba","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpdqsYPynzCeZwZyvqd","type":"message","role":"assistant","content":[{"type":"text","text":"The shell was still in out/state; 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Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nS0, S1 = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nSPLIT = pd.Period(\"1984Q1\", \"Q\")\n\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly average of monthly values (require all 3 months) ---\nun = un_raw.copy()\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nnan_months = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif nan_months:\n    data_issues.append({\"issue\": f\"UNRATE missing value(s) for month(s) {nan_months}\",\n                        \"handling\": \"Quarter containing a missing month is not averaged (set missing); \"\n                                    \"all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"})\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[uq[\"count\"] < 3, \"mean\"] = np.nan  # incomplete quarters\nu = uq[\"mean\"].rename(\"u\")\n\ngdp = gdp_raw.copy()\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gdp[\"q\"].duplicated().any()\ng_level = gdp.set_index(\"q\")[\"GDPC1\"].astype(float)\n\n# --- merge and exclusion ledger (unit: quarters of GDPC1) ---\nledger = []\ndef log(step, rule, before, after, why):\n    ledger.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\ndf = pd.DataFrame({\"gdp\": g_level})\nn0 = len(df)\ndf = df.join(u, how=\"left\")\nn1 = int(df[\"u\"].notna().sum())\ndf_ok = df[df[\"u\"].notna()].copy()\nlog(1, \"drop GDPC1 quarters without a complete 3-month UNRATE quarterly average\", n0, n1,\n    \"UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\")\n# differences require consecutive quarters\nfull = df.reindex(pd.period_range(df.index.min(), df.index.max(), freq=\"Q\"))\nfull[\"du\"] = full[\"u\"].diff()\nfull[\"g\"] = 100 * np.log(full[\"gdp\"]).diff()\ndf_d = full.loc[df_ok.index].dropna(subset=[\"du\", \"g\"])\nlog(2, \"drop quarters whose first difference cannot be formed (previous quarter missing)\", n1, len(df_d),\n    \"first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\")\nd = df_d.loc[(df_d.index >= S0) & (df_d.index <= S1)].copy()\nlog(3, \"restrict to regression window 1960Q1-2019Q4\", len(df_d), len(d),\n    \"TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate\")\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nexpected = pd.period_range(S0, S1, freq=\"Q\")\nassert len(d) == 240 and (d.index == expected).all(), \"sample is not the 240 consecutive quarters\"\n# 1959Q4 levels used for 1960Q1 differences: check present\nassert pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"u\"]) and pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"gdp\"])\n\n# --- baseline ---\ndu, g = d[\"du\"].values, d[\"g\"].values\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((du - du.mean()) ** 2)))\n\n# --- full-sample OLS with Newey-West HAC ---\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9)))  # Newey-West (1994) rule of thumb -> 4 for n=240\nX = sm.add_constant(g)\nols = sm.OLS(du, X).fit()\nhac = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse_b = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_b, beta + z * se_b)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n# lag-length sensitivity (reported, not used for the headline)\nhac_sens = {}\nfor L in [0, 2, 4, 8, 12]:\n    r = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": True})\n    hac_sens[str(L)] = float(r.bse[1])\nols_se_classical = float(ols.bse[1])\n\n# --- subperiods and Chow test ---\nm1 = d.index < SPLIT\ndef fit(mask):\n    y, x = du[mask], sm.add_constant(g[mask])\n    r = sm.OLS(y, x).fit()\n    rh = sm.OLS(y, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n    return r, rh\nr1, r1h = fit(m1)\nr2, r2h = fit(~m1)\nssr_p = float(ols.ssr); ssr_u = float(r1.ssr + r2.ssr)\nk = 2\ndf2 = n - 2 * k\nF = ((ssr_p - ssr_u) / k) / (ssr_u / df2)\np = float(stats.f.sf(F, k, df2))\n\nif ci[0] > 0: concl = \"positive_effect\"\nelif ci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n                    \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); \"\n                                \"revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"})\n\nres = {\n    \"task_id\": \"F6-conf-03\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    \"sample_start\": str(d.index[0].start_time.date()),\n    \"sample_end\": str(d.index[-1].start_time.date()),\n    \"n_sub1\": int(m1.sum()),\n    \"n_sub2\": int((~m1).sum()),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se_b,\n    \"hac_lags\": HAC_LAGS,\n    \"effect_ci_low\": float(ci[0]),\n    \"effect_ci_high\": float(ci[1]),\n    \"beta_sub1\": float(r1.params[1]),\n    \"beta_sub2\": float(r2.params[1]),\n    \"chow_f_stat\": float(F),\n    \"chow_p_value\": p,\n    \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n    \"conclusion\": concl,\n    # supplementary (not required by TASK.md)\n    \"intercept_sub1\": float(r1.params[0]),\n    \"intercept_sub2\": float(r2.params[0]),\n    \"beta_sub1_hac_se\": float(r1h.bse[1]),\n    \"beta_sub2_hac_se\": float(r2h.bse[1]),\n    \"effect_se_ols_classical\": ols_se_classical,\n    \"effect_se_hac_by_lag\": hac_sens,\n    \"r_squared\": float(ols.rsquared),\n    \"chow_df\": [k, int(df2)],\n    \"ssr_pooled\": ssr_p,\n    \"ssr_unrestricted\": ssr_u,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\npd.DataFrame([\n    dict(period=\"full 1960Q1-2019Q4\", n=n, intercept=alpha, slope=beta, slope_se=se_b),\n    dict(period=\"sub1 1960Q1-1983Q4\", n=int(m1.sum()), intercept=float(r1.params[0]), slope=float(r1.params[1]), slope_se=float(r1h.bse[1])),\n    dict(period=\"sub2 1984Q1-2019Q4\", n=int((~m1).sum()), intercept=float(r2.params[0]), slope=float(r2.params[1]), slope_se=float(r2h.bse[1])),\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(g[m1], du[m1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\")\nax.scatter(g[~m1], du[~m1], s=14, alpha=0.6, marker=\"^\", label=\"1984Q1-2019Q4\")\nfor r, mask, lab in [(r1, m1, \"fit 1960-1983\"), (r2, ~m1, \"fit 1984-2019\")]:\n    xs = np.linspace(g[mask].min(), g[mask].max(), 50)\n    ax.plot(xs, r.params[0] + r.params[1] * xs,\n            label=f\"{lab}: du = {r.params[0]:.3f} {r.params[1]:+.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, US 1960Q1-2019Q4\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se_b:.4f} (L={HAC_LAGS}) CI=[{ci[0]:.4f},{ci[1]:.4f}] \"\n      f\"beta1={r1.params[1]:.4f} beta2={r2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv 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":156,"cache_read_input_tokens":16822,"output_tokens":4271,"output_tokens_details":{"thinking_tokens":38},"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":156},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpe1zkLVjDc2brwaitz","requestRef":"0a4fe710-4676-447d-9bc4-1e573374ac8a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e4a35680-f309-4db1-ae38-b5d63a4de0a6","timestamp":"2026-10-30T04:52:52.864Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e4a35680-f309-4db1-ae38-b5d63a4de0a6","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}51{"type":"atis-latch","atis":"","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}52{"parentUuid":"e4a35680-f309-4db1-ae38-b5d63a4de0a6","isSidechain":false,"promptId":"df3d2f72-48c6-4291-bd7a-d8c83a03e33a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TwsEj28P5ewqvN2VwTDSEM","type":"tool_result","content":"n=240 beta=-0.2558 HAC se=0.0360 (L=4) CI=[-0.3263,-0.1852] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n{\n  \"task_id\": \"F6-conf-03\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing value(s) for month(s) ['2025-10-01']\",\n      \"handling\": \"Quarter containing a missing month is not averaged (set missing); all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"\n    },\n    {\n      \"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n      \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"GDPC1.csv\": 319,\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 240,\n  \"random_seed\": null,\n  \"sample_start\": \"1960-01-01\",\n  \"sample_end\": \"2019-10-01\",\n  \"n_sub1\": 96,\n  \"n_sub2\": 144,\n  \"baseline_metric\": 0.32465793964999184,\n  \"model_metric\": 0.2497438816636253,\n  \"effect_estimate\": -0.2557677277487258,\n  \"intercept\": 0.18438835418210714,\n  \"effect_se\": 0.03598488399675878,\n  \"hac_lags\": 4,\n  \"effect_ci_low\": -0.3262968043702248,\n  \"effect_ci_high\": -0.18523865112722682,\n  \"beta_sub1\": -0.25902105345680587,\n  \"beta_sub2\": -0.27339263306757905,\n  \"chow_f_stat\": 6.0903330062337675,\n  \"chow_p_value\": 0.002636323851733417,\n  \"stability_conclusion\": \"unstable\",\n  \"conclusion\": \"negative_effect\",\n  \"intercept_sub1\": 0.2542326539884382,\n  \"intercept_sub2\": 0.15168581347724602,\n  \"beta_sub1_hac_se\": 0.03970255328838903,\n  \"beta_sub2_hac_se\": 0.06879369233083424,\n  \"effect_se_ols_classical\": 0.01996009291490984,\n  \"effect_se_hac_by_lag\": {\n    \"0\": 0.028957130333809637,\n    \"2\": 0.034370831642247524,\n    \"4\": 0.03598488399675878,\n    \"8\": 0.03729120927832667,\n    \"12\": 0.03656150942843361\n  },\n  \"r_squared\": 0.4082508284562034,\n  \"chow_df\": [\n    2,\n    236\n  ],\n  \"ssr_pooled\": 14.96928154281957,\n  \"ssr_unrestricted\": 14.23459168220601\n}period,n,intercept,slope,slope_se\nfull 1960Q1-2019Q4,240,0.18438835418210714,-0.2557677277487258,0.03598488399675878\nsub1 1960Q1-1983Q4,96,0.2542326539884382,-0.25902105345680587,0.03970255328838903\nsub2 1984Q1-2019Q4,144,0.15168581347724602,-0.27339263306757905,0.06879369233083424\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop GDPC1 quarters without a complete 3-month UNRATE quarterly average,319,314,5,UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\n2,drop quarters whose first difference cannot be formed (previous quarter missing),314,312,2,first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\n3,restrict to regression window 1960Q1-2019Q4,312,240,72,TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate","is_error":false}]},"uuid":"f3967d94-5918-42f8-bd71-a902cbc199d8","timestamp":"2026-10-30T04:52:53.968Z","toolUseResult":{"stdout":"n=240 beta=-0.2558 HAC se=0.0360 (L=4) CI=[-0.3263,-0.1852] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n{\n  \"task_id\": \"F6-conf-03\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing value(s) for month(s) ['2025-10-01']\",\n      \"handling\": \"Quarter containing a missing month is not averaged (set missing); all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"\n    },\n    {\n      \"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n      \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"GDPC1.csv\": 319,\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 240,\n  \"random_seed\": null,\n  \"sample_start\": \"1960-01-01\",\n  \"sample_end\": \"2019-10-01\",\n  \"n_sub1\": 96,\n  \"n_sub2\": 144,\n  \"baseline_metric\": 0.32465793964999184,\n  \"model_metric\": 0.2497438816636253,\n  \"effect_estimate\": -0.2557677277487258,\n  \"intercept\": 0.18438835418210714,\n  \"effect_se\": 0.03598488399675878,\n  \"hac_lags\": 4,\n  \"effect_ci_low\": -0.3262968043702248,\n  \"effect_ci_high\": -0.18523865112722682,\n  \"beta_sub1\": -0.25902105345680587,\n  \"beta_sub2\": -0.27339263306757905,\n  \"chow_f_stat\": 6.0903330062337675,\n  \"chow_p_value\": 0.002636323851733417,\n  \"stability_conclusion\": \"unstable\",\n  \"conclusion\": \"negative_effect\",\n  \"intercept_sub1\": 0.2542326539884382,\n  \"intercept_sub2\": 0.15168581347724602,\n  \"beta_sub1_hac_se\": 0.03970255328838903,\n  \"beta_sub2_hac_se\": 0.06879369233083424,\n  \"effect_se_ols_classical\": 0.01996009291490984,\n  \"effect_se_hac_by_lag\": {\n    \"0\": 0.028957130333809637,\n    \"2\": 0.034370831642247524,\n    \"4\": 0.03598488399675878,\n    \"8\": 0.03729120927832667,\n    \"12\": 0.03656150942843361\n  },\n  \"r_squared\": 0.4082508284562034,\n  \"chow_df\": [\n    2,\n    236\n  ],\n  \"ssr_pooled\": 14.96928154281957,\n  \"ssr_unrestricted\": 14.23459168220601\n}period,n,intercept,slope,slope_se\nfull 1960Q1-2019Q4,240,0.18438835418210714,-0.2557677277487258,0.03598488399675878\nsub1 1960Q1-1983Q4,96,0.2542326539884382,-0.25902105345680587,0.03970255328838903\nsub2 1984Q1-2019Q4,144,0.15168581347724602,-0.27339263306757905,0.06879369233083424\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop GDPC1 quarters without a complete 3-month UNRATE quarterly average,319,314,5,UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\n2,drop quarters whose first difference cannot be formed (previous quarter missing),314,312,2,first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\n3,restrict to regression window 1960Q1-2019Q4,312,240,72,TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e4a35680-f309-4db1-ae38-b5d63a4de0a6","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}53{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"a771287c961b89799859523b47b0c615ca436c3c73e9e822856f7c93314340dd","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01TwsEj28P5ewqvN2VwTDSEM","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4 (task F6-conf-03). Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\nNo randomness is used (random_seed = null).\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nS0, S1 = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nSPLIT = pd.Period(\"1984Q1\", \"Q\")\n\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\")\nun_raw = pd.read_csv(\"data/UNRATE.csv\")\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly average of monthly values (require all 3 months) ---\nun = un_raw.copy()\nun[\"date\"] = pd.to_datetime(un[\"observation_date\"])\nun[\"UNRATE\"] = pd.to_numeric(un[\"UNRATE\"], errors=\"coerce\")\nnan_months = un.loc[un[\"UNRATE\"].isna(), \"observation_date\"].tolist()\nif nan_months:\n    data_issues.append({\"issue\": f\"UNRATE missing value(s) for month(s) {nan_months}\",\n                        \"handling\": \"Quarter containing a missing month is not averaged (set missing); \"\n                                    \"all such months lie outside the 1959Q4-2019Q4 window, so no effect on estimates.\"})\nun[\"q\"] = un[\"date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[uq[\"count\"] < 3, \"mean\"] = np.nan  # incomplete quarters\nu = uq[\"mean\"].rename(\"u\")\n\ngdp = gdp_raw.copy()\ngdp[\"q\"] = pd.to_datetime(gdp[\"observation_date\"]).dt.to_period(\"Q\")\nassert not gdp[\"q\"].duplicated().any()\ng_level = gdp.set_index(\"q\")[\"GDPC1\"].astype(float)\n\n# --- merge and exclusion ledger (unit: quarters of GDPC1) ---\nledger = []\ndef log(step, rule, before, after, why):\n    ledger.append(dict(step=step, rule=rule, n_before=before, n_after=after,\n                       n_removed=before - after, rationale=why))\n\ndf = pd.DataFrame({\"gdp\": g_level})\nn0 = len(df)\ndf = df.join(u, how=\"left\")\nn1 = int(df[\"u\"].notna().sum())\ndf_ok = df[df[\"u\"].notna()].copy()\nlog(1, \"drop GDPC1 quarters without a complete 3-month UNRATE quarterly average\", n0, n1,\n    \"UNRATE starts 1948-01 (1947 quarters have no unemployment data); 2025Q4 has a missing month\")\n# differences require consecutive quarters\nfull = df.reindex(pd.period_range(df.index.min(), df.index.max(), freq=\"Q\"))\nfull[\"du\"] = full[\"u\"].diff()\nfull[\"g\"] = 100 * np.log(full[\"gdp\"]).diff()\ndf_d = full.loc[df_ok.index].dropna(subset=[\"du\", \"g\"])\nlog(2, \"drop quarters whose first difference cannot be formed (previous quarter missing)\", n1, len(df_d),\n    \"first available quarter (1948Q1) and the quarter after the incomplete 2025Q4\")\nd = df_d.loc[(df_d.index >= S0) & (df_d.index <= S1)].copy()\nlog(3, \"restrict to regression window 1960Q1-2019Q4\", len(df_d), len(d),\n    \"TASK.md information rule: quarters outside 1960Q1-2019Q4 must not enter any estimate\")\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nexpected = pd.period_range(S0, S1, freq=\"Q\")\nassert len(d) == 240 and (d.index == expected).all(), \"sample is not the 240 consecutive quarters\"\n# 1959Q4 levels used for 1960Q1 differences: check present\nassert pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"u\"]) and pd.notna(full.loc[pd.Period(\"1959Q4\", \"Q\"), \"gdp\"])\n\n# --- baseline ---\ndu, g = d[\"du\"].values, d[\"g\"].values\nn = len(d)\nbaseline_rmse = float(np.sqrt(np.mean((du - du.mean()) ** 2)))\n\n# --- full-sample OLS with Newey-West HAC ---\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9)))  # Newey-West (1994) rule of thumb -> 4 for n=240\nX = sm.add_constant(g)\nols = sm.OLS(du, X).fit()\nhac = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse_b = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se_b, beta + z * se_b)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n# lag-length sensitivity (reported, not used for the headline)\nhac_sens = {}\nfor L in [0, 2, 4, 8, 12]:\n    r = sm.OLS(du, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"use_correction\": True})\n    hac_sens[str(L)] = float(r.bse[1])\nols_se_classical = float(ols.bse[1])\n\n# --- subperiods and Chow test ---\nm1 = d.index < SPLIT\ndef fit(mask):\n    y, x = du[mask], sm.add_constant(g[mask])\n    r = sm.OLS(y, x).fit()\n    rh = sm.OLS(y, x).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n    return r, rh\nr1, r1h = fit(m1)\nr2, r2h = fit(~m1)\nssr_p = float(ols.ssr); ssr_u = float(r1.ssr + r2.ssr)\nk = 2\ndf2 = n - 2 * k\nF = ((ssr_p - ssr_u) / k) / (ssr_u / df2)\np = float(stats.f.sf(F, k, df2))\n\nif ci[0] > 0: concl = \"positive_effect\"\nelif ci[1] < 0: concl = \"negative_effect\"\nelse: concl = \"no_detectable_effect\"\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage and extends to 2026Q3; UNRATE extends to 2026-09\",\n                    \"handling\": \"Observations after 2019Q4 dropped before estimation (exclusion step 3); \"\n                                \"revised (not real-time) GDP is used, consistent with an ex-post descriptive estimand.\"})\n\nres = {\n    \"task_id\": \"F6-conf-03\",\n    \"feasibility\": \"complete\",\n    \"feasibility_reason\": None,\n    \"data_issues\": data_issues,\n    \"n_input_rows\": n_input_rows,\n    \"n_analysed\": int(n),\n    \"random_seed\": None,\n    \"sample_start\": str(d.index[0].start_time.date()),\n    \"sample_end\": str(d.index[-1].start_time.date()),\n    \"n_sub1\": int(m1.sum()),\n    \"n_sub2\": int((~m1).sum()),\n    \"baseline_metric\": baseline_rmse,\n    \"model_metric\": model_rmse,\n    \"effect_estimate\": beta,\n    \"intercept\": alpha,\n    \"effect_se\": se_b,\n    \"hac_lags\": HAC_LAGS,\n    \"effect_ci_low\": float(ci[0]),\n    \"effect_ci_high\": float(ci[1]),\n    \"beta_sub1\": float(r1.params[1]),\n    \"beta_sub2\": float(r2.params[1]),\n    \"chow_f_stat\": float(F),\n    \"chow_p_value\": p,\n    \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n    \"conclusion\": concl,\n    # supplementary (not required by TASK.md)\n    \"intercept_sub1\": float(r1.params[0]),\n    \"intercept_sub2\": float(r2.params[0]),\n    \"beta_sub1_hac_se\": float(r1h.bse[1]),\n    \"beta_sub2_hac_se\": float(r2h.bse[1]),\n    \"effect_se_ols_classical\": ols_se_classical,\n    \"effect_se_hac_by_lag\": hac_sens,\n    \"r_squared\": float(ols.rsquared),\n    \"chow_df\": [k, int(df2)],\n    \"ssr_pooled\": ssr_p,\n    \"ssr_unrestricted\": ssr_u,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n    json.dump(res, f, indent=2)\n\npd.DataFrame([\n    dict(period=\"full 1960Q1-2019Q4\", n=n, intercept=alpha, slope=beta, slope_se=se_b),\n    dict(period=\"sub1 1960Q1-1983Q4\", n=int(m1.sum()), intercept=float(r1.params[0]), slope=float(r1.params[1]), slope_se=float(r1h.bse[1])),\n    dict(period=\"sub2 1984Q1-2019Q4\", n=int((~m1).sum()), intercept=float(r2.params[0]), slope=float(r2.params[1]), slope_se=float(r2h.bse[1])),\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(g[m1], du[m1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\")\nax.scatter(g[~m1], du[~m1], s=14, alpha=0.6, marker=\"^\", label=\"1984Q1-2019Q4\")\nfor r, mask, lab in [(r1, m1, \"fit 1960-1983\"), (r2, ~m1, \"fit 1984-2019\")]:\n    xs = np.linspace(g[mask].min(), g[mask].max(), 50)\n    ax.plot(xs, r.params[0] + r.params[1] * xs,\n            label=f\"{lab}: du = {r.params[0]:.3f} {r.params[1]:+.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, US 1960Q1-2019Q4\")\nax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se_b:.4f} (L={HAC_LAGS}) CI=[{ci[0]:.4f},{ci[1]:.4f}] \"\n      f\"beta1={r1.params[1]:.4f} beta2={r2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis 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- <<'EOF'\nimport re\np='out/analysis.py'; s=open(p).read()\ns=s.replace('''if ci[0] > 0: concl''','''# robustness (supplementary): interacted model, HAC Wald tests of equality\nD = (~m1).astype(float)\nXi = np.column_stack([np.ones(n), g, D, D * g])\nri = sm.OLS(du, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nw_joint = ri.wald_test(np.array([[0, 0, 1, 0], [0, 0, 0, 1]]), use_f=False, scalar=True)\nw_slope = ri.wald_test(np.array([[0, 0, 0, 1]]), use_f=False, scalar=True)\nw_int = ri.wald_test(np.array([[0, 0, 1, 0]]), use_f=False, scalar=True)\nresid_var_sub1 = float(r1.ssr / (m1.sum() - 2)); resid_var_sub2 = float(r2.ssr / ((~m1).sum() - 2))\n\nif ci[0] > 0: concl''')\ns=s.replace('''    \"ssr_unrestricted\": ssr_u,\n}''','''    \"ssr_unrestricted\": ssr_u,\n    \"hac_wald_joint_equality_p\": float(w_joint.pvalue),\n    \"hac_wald_slope_equality_p\": float(w_slope.pvalue),\n    \"hac_wald_intercept_equality_p\": float(w_int.pvalue),\n    \"resid_var_sub1\": resid_var_sub1,\n    \"resid_var_sub2\": resid_var_sub2,\n}''')\nopen(p,'w').write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py && grep -E \"wald|resid_var\" out/results.json","description":"Add HAC robustness tests and 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","media_type":"image/png"}}]}]},"uuid":"123a336b-3715-4f46-9fc6-eed52d9c4cca","timestamp":"2026-10-30T04:53:11.971Z","toolUseResult":{"type":"image","file":{"base64":"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out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols | range | freq | missing | duplicates |\n|---|---|---|---|---|---|---|---|\n| data/GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README) | 319 | 2 (observation_date, GDPC1) | 1947-01-01 – 2026-07-01 | quarterly, no gaps (spacing 90–92 days) | 0 | 0 dates |\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README) | 945 | 2 (observation_date, UNRATE) | 1948-01-01 – 2026-09-01 | monthly, no gaps in dates (28–31 days) | 1 (2025-10-01, empty) | 0 dates |\n| data/README.md | 7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658 | – | – | – | – | – | – |\n\nUnits: GDPC1 billions of chained 2017 dollars SAAR (min 2172.4, max 24611.1); UNRATE percent SA (min 2.5, max 14.8). Values plausible, all numeric except the one blank.\n\nAnomalies / issues:\n1. UNRATE 2025-10 missing (blank) → 2025Q4 quarterly average not formed. Outside the 1959Q4–2019Q4 window: no effect.\n2. Both series extend beyond 2019Q4 (to 2026). Information rule: dropped before estimation (exclusion step 3). The code asserts the analysed index is exactly the 240 consecutive quarters 1960Q1–2019Q4 and that 1959Q4 levels exist.\n3. GDPC1 is a current vintage (revised data). No real-time rule in TASK.md; noted as limitation.\n\nExclusion ledger: out/exclusions.csv (319 → 314 → 312 → 240; removed 5+2+72 = 79; 319−79 = 240 = n_analysed). Unit = GDPC1 quarters.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of all inputs computed and match README\",\"row/col counts, ranges, missing, duplicates, date spacing checked\",\"exclusion ledger sums 319-79=240\"],\"notes\":\"see data_audit.md\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) computed on same 240-quarter sample = 0.3247\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py reads only data/, writes results.json, table1.csv, figure1.png, exclusions.csv; no randomness\",\"figure inspected visually\"],\"notes\":\"HAC lag 4 = floor(4(n/100)^(2/9))\"}\njson.dump(p,open('phases.json','w'),indent=1)\nEOF\ncat >> log.md <<'EOF'\n- 2026-10-30 P1 passed: audit done, one missing UNRATE month (2025-10) outside window.\n- 2026-10-30 P2 passed: baseline RMSE computed inside analysis.py.\n- 2026-10-30 P3 passed: OLS+NW HAC(L=4), subperiods, Chow; supplementary HAC Wald tests added because classical Chow assumes homoskedastic iid errors.\nEOF","description":"Write data audit, update phases and log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":776,"cache_read_input_tokens":24527,"output_tokens":1347,"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":776},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXphUmtabdWSGZ9Xo1cG","requestRef":"1cccddc3-5284-44bb-a8ec-a6cf73650cc1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7aee1648-4299-4856-b594-a77807d67825","timestamp":"2026-10-30T04:53:24.968Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}76{"parentUuid":"7aee1648-4299-4856-b594-a77807d67825","isSidechain":false,"promptId":"df3d2f72-48c6-4291-bd7a-d8c83a03e33a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VcyDYUb3VcA1qJRVCsxsSX","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"b0f301a5-29ae-4437-abe4-654a019b05d7","timestamp":"2026-10-30T04:53:25.004Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7aee1648-4299-4856-b594-a77807d67825","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179/out/state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}77{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"a4f68547f3c3dbdfe95b30f8532cf19d1c7c4aa12318a10d7be411e0af6d31cc","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01VcyDYUb3VcA1qJRVCsxsSX","name":"Bash","input":{"command":"cd out/state && cat > data_audit.md <<'EOF'\n# Data audit (P1)\n\n| file | SHA-256 | rows (excl. header) | cols | range | freq | missing | duplicates |\n|---|---|---|---|---|---|---|---|\n| data/GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README) | 319 | 2 (observation_date, GDPC1) | 1947-01-01 – 2026-07-01 | quarterly, no gaps (spacing 90–92 days) | 0 | 0 dates |\n| data/UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README) | 945 | 2 (observation_date, UNRATE) | 1948-01-01 – 2026-09-01 | monthly, no gaps in dates (28–31 days) | 1 (2025-10-01, empty) | 0 dates |\n| data/README.md | 7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658 | – | – | – | – | – | – |\n\nUnits: GDPC1 billions of chained 2017 dollars SAAR (min 2172.4, max 24611.1); UNRATE percent SA (min 2.5, max 14.8). 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P4: clean re-run and consistency 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identity']=abs(r['ssr_pooled']-240*r['model_metric']**2)<1e-9\nF=((r['ssr_pooled']-r['ssr_unrestricted'])/2)/(r['ssr_unrestricted']/236)\nc['chow recompute']=abs(F-r['chow_f_stat'])<1e-10 and abs(stats.f.sf(F,2,236)-r['chow_p_value'])<1e-12\nc['ssr_u<=ssr_p']=r['ssr_unrestricted']<=r['ssr_pooled']\nc['stability code']=(r['stability_conclusion']=='unstable')==(r['chow_p_value']<0.05)\nc['conclusion code']=r['conclusion']=='negative_effect' and r['effect_ci_high']<0\nc['table matches']=np.allclose(t.slope,[r['effect_estimate'],r['beta_sub1'],r['beta_sub2']]) and list(t.n)==[240,96,144]\nc['dates']=r['sample_start']=='1960-01-01' and r['sample_end']=='2019-10-01'\nc['hac lag rule']=r['hac_lags']==int(np.floor(4*(2.4)**(2/9)))\nfor k,v in c.items(): print(('PASS' if v else 'FAIL'),k)\nEOF","description":"Clean rerun and consistency 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pd, numpy as np\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nfrom scipy import stats\nc={}\nc['n_sub1+n_sub2==n_analysed==240']=r['n_sub1']+r['n_sub2']==r['n_analysed']==240\nc['exclusions sum']=319-e.n_removed.sum()==r['n_analysed'] and (e.n_before-e.n_after==e.n_removed).all()\nc['se>0']=r['effect_se']>0\nc['ci contains est']=r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']\nc['ci symmetric 1.96']=abs((r['effect_ci_high']-r['effect_estimate'])/r['effect_se']-1.959964)<1e-5\nc['model_rmse<baseline']=r['model_metric']<r['baseline_metric']\nc['R2 identity']=abs(1-(r['model_metric']/r['baseline_metric'])**2-r['r_squared'])<1e-10\nc['ssr identity']=abs(r['ssr_pooled']-240*r['model_metric']**2)<1e-9\nF=((r['ssr_pooled']-r['ssr_unrestricted'])/2)/(r['ssr_unrestricted']/236)\nc['chow recompute']=abs(F-r['chow_f_stat'])<1e-10 and 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checks"}}]}}83{"type":"api-request","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","id":"efc0729f-2b10-4622-83ab-ca808fe809a6","timestamp":"2026-10-30T04:53:33.844Z","version":"2.1.314","querySource":"sdk","shapeHash":"acffe5d451bc77a6f802eaa94680ea85da28e8472417849a985b2588fc260217","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":"4f0f1b87-a706-44f6-a53c-8b5c40538bc4","keep":24,"tail":["cfa721fbed00360212aac18d2acc0b2a96e36aca11dff2a7b390538b6048602e","484998a9f463be58e7b1dbc04c60426c73438bfaa1f599290620fc10c6179cd6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"637f577f5e6e0bd8afe95bf66ae04b3daf80b622bf012f8abc3513c9cd6133ff"}84{"parentUuid":"829dc52f-c0a3-4a9e-a516-b8076b044f3c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpj6ZThnwAZ5dsE5o2q","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1254,"cache_read_input_tokens":26665,"output_tokens":1101,"output_tokens_details":{"thinking_tokens":36},"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":1254},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":299,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpj6HbKUZVHd63qnm3R","requestRef":"efc0729f-2b10-4622-83ab-ca808fe809a6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e3563024-b3b9-4de8-82b6-8e5670fbc4b9","timestamp":"2026-10-30T04:53:35.441Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179/out/state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}85{"parentUuid":"e3563024-b3b9-4de8-82b6-8e5670fbc4b9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpj6ZThnwAZ5dsE5o2q","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XoDoQe3ww6ffJNP76rTt7G","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0179 && rm -f out/results.json out/table1.csv 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, pandas as pd, numpy as np\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nfrom scipy import stats\nc={}\nc['n_sub1+n_sub2==n_analysed==240']=r['n_sub1']+r['n_sub2']==r['n_analysed']==240\nc['exclusions sum']=319-e.n_removed.sum()==r['n_analysed'] and (e.n_before-e.n_after==e.n_removed).all()\nc['se>0']=r['effect_se']>0\nc['ci contains est']=r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']\nc['ci symmetric 1.96']=abs((r['effect_ci_high']-r['effect_estimate'])/r['effect_se']-1.959964)<1e-5\nc['model_rmse<baseline']=r['model_metric']<r['baseline_metric']\nc['R2 identity']=abs(1-(r['model_metric']/r['baseline_metric'])**2-r['r_squared'])<1e-10\nc['ssr identity']=abs(r['ssr_pooled']-240*r['model_metric']**2)<1e-9\nF=((r['ssr_pooled']-r['ssr_unrestricted'])/2)/(r['ssr_unrestricted']/236)\nc['chow recompute']=abs(F-r['chow_f_stat'])<1e-10 and abs(stats.f.sf(F,2,236)-r['chow_p_value'])<1e-12\nc['ssr_u<=ssr_p']=r['ssr_unrestricted']<=r['ssr_pooled']\nc['stability code']=(r['stability_conclusion']=='unstable')==(r['chow_p_value']<0.05)\nc['conclusion code']=r['conclusion']=='negative_effect' and r['effect_ci_high']<0\nc['table matches']=np.allclose(t.slope,[r['effect_estimate'],r['beta_sub1'],r['beta_sub2']]) and list(t.n)==[240,96,144]\nc['dates']=r['sample_start']=='1960-01-01' and r['sample_end']=='2019-10-01'\nc['hac lag rule']=r['hac_lags']==int(np.floor(4*(2.4)**(2/9)))\nfor k,v in c.items(): print(('PASS' if v else 'FAIL'),k)\nEOF","description":"Clean rerun and consistency checks from 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/tmp/r1.json differ: char 18, line 2\nPASS n_sub1+n_sub2==n_analysed==240\nPASS exclusions sum\nPASS se>0\nPASS ci contains est\nPASS ci symmetric 1.96\nPASS model_rmse<baseline\nPASS R2 identity\nPASS ssr identity\nPASS chow recompute\nPASS ssr_u<=ssr_p\nPASS stability code\nPASS conclusion code\nPASS table matches\nPASS dates\nPASS hac lag rule","is_error":false}]},"uuid":"412ef591-1317-4239-a958-6ecc180e3022","timestamp":"2026-10-30T04:53:43.511Z","toolUseResult":{"stdout":"out/results.json /tmp/r1.json differ: char 18, line 2\nPASS n_sub1+n_sub2==n_analysed==240\nPASS exclusions sum\nPASS se>0\nPASS ci contains est\nPASS ci symmetric 1.96\nPASS model_rmse<baseline\nPASS R2 identity\nPASS ssr identity\nPASS chow recompute\nPASS ssr_u<=ssr_p\nPASS stability code\nPASS conclusion code\nPASS table matches\nPASS dates\nPASS hac lag rule","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f7b894ac-0241-4cc6-9978-f4845ea549ce","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}87{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"c7c3ce9d1b76ae84a5e14e3e019a6b91282e6a661582004b5ca3da490ee05b7f","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01XoDoQe3ww6ffJNP76rTt7G","name":"Bash","input":{"command":"cd 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beta=-0.2558 HAC se=0.0360 (L=4) CI=[-0.3263,-0.1852] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\nIDENTICAL","is_error":false}]},"uuid":"bf242592-829d-436f-abd8-464faacb343e","timestamp":"2026-10-30T04:53:49.929Z","toolUseResult":{"stdout":"n=240 beta=-0.2558 HAC se=0.0360 (L=4) CI=[-0.3263,-0.1852] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 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(A first attempt compared against a copy in shared /tmp that had been overwritten by an unrelated process; repeated with a workspace-local copy — PASS.)\n- n_sub1 + n_sub2 = n_analysed = 240 — PASS\n- Exclusion ledger: 319 − (5+2+72) = 240; n_before − n_after = n_removed each row — PASS\n- effect_se > 0 — PASS\n- CI contains estimate; half-width = 1.959964·SE — PASS\n- model_metric < baseline_metric; R² = 1 − (model/baseline)² — PASS\n- ssr_pooled = 240·model_metric² — PASS\n- Chow F and p recomputed independently from SSRs, df (2,236) — PASS\n- ssr_unrestricted ≤ ssr_pooled — PASS\n- stability_conclusion consistent with p<0.05 — PASS\n- conclusion code = negative_effect and CI upper < 0 — PASS\n- table1 slopes and n match results.json — PASS\n- sample_start/end = 1960-01-01/2019-10-01; code asserts index == 240 consecutive quarters (no post-2019 data) — PASS\n- hac_lags = floor(4(240/100)^(2/9)) = 4 — PASS\n- Magnitude vs baseline: residual RMSE 0.250 vs 0.325 pp; β ≈ −0.26 in line with conventional Okun estimates (~ −0.3 to −0.5 for annualised quarterly diff form scaled). 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identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0179/out/state/gate_receipts/20261030T045357.934972Z.json\n20261030T045357.934972Z.json","is_error":false}]},"uuid":"bbfb55e9-a9e1-4637-9f44-cbfb4244f67e","timestamp":"2026-10-30T04:53:57.948Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 48 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] file:out/table1.csv: present\n  [ok] results_json: parsed\n  [ok] key:task_id: 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:n_analysed: ok\n  [ok] key:random_seed: ok\n  [ok] key:sample_start: ok\n  [ok] key:sample_end: ok\n  [ok] key:n_sub1: ok\n  [ok] key:n_sub2: ok\n  [ok] key:baseline_metric: ok\n  [ok] key:model_metric: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:intercept: ok\n  [ok] key:effect_se: ok\n  [ok] key:hac_lags: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:beta_sub1: ok\n  [ok] key:beta_sub2: ok\n  [ok] key:chow_f_stat: ok\n  [ok] key:chow_p_value: ok\n  [ok] key:stability_conclusion: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] ci:effect_estimate: -0.3262968043702248 <= -0.2557677277487258 <= -0.18523865112722682\n  [ok] positive:effect_se: 0.03598488399675878\n  [ok] positive:chow_f_stat: 6.0903330062337675\n  [ok] positive:baseline_metric: 0.32465793964999184\n  [ok] positive:model_metric: 0.2497438816636253\n  [ok] chronology:sample_start<=sample_end: 1960-01-01 vs 2019-10-01\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0179/out/state/gate_receipts/20261030T045357.934972Z.json\n20261030T045357.934972Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d2756b90-fec1-446a-9a0b-441311e7cff0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}103{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"4d7b3900d1c81c3504fedf5cffc172b1bfa2d82b3c657ed631b15228944e04f8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01QtAFWqZyAfHfMShofbnn6a","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n\n- Clean re-run (deleted results.json, table1.csv, figure1.png, exclusions.csv; re-ran analysis.py): results.json byte-identical — PASS. (A first attempt compared against a copy in shared /tmp that had been overwritten by an unrelated process; repeated with a workspace-local copy — PASS.)\n- n_sub1 + n_sub2 = n_analysed = 240 — PASS\n- Exclusion ledger: 319 − (5+2+72) = 240; n_before − n_after = n_removed each row — PASS\n- effect_se > 0 — PASS\n- CI contains estimate; half-width = 1.959964·SE — PASS\n- model_metric < baseline_metric; R² = 1 − (model/baseline)² — PASS\n- ssr_pooled = 240·model_metric² — PASS\n- Chow F and p recomputed independently from SSRs, df (2,236) — PASS\n- ssr_unrestricted ≤ ssr_pooled — PASS\n- stability_conclusion consistent with p<0.05 — PASS\n- conclusion code = negative_effect and CI upper < 0 — PASS\n- table1 slopes and n match results.json — PASS\n- sample_start/end = 1960-01-01/2019-10-01; code asserts index == 240 consecutive quarters (no post-2019 data) — PASS\n- hac_lags = floor(4(240/100)^(2/9)) = 4 — PASS\n- Magnitude vs baseline: residual RMSE 0.250 vs 0.325 pp; β ≈ −0.26 in line with conventional Okun estimates (~ −0.3 to −0.5 for annualised quarterly diff form scaled). Sensible.\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate (P4)"}}]}}104{"type":"api-request","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","id":"5efbd7b4-12b9-4667-996d-90f433babd5c","timestamp":"2026-10-30T04:53:57.951Z","version":"2.1.314","querySource":"sdk","shapeHash":"acffe5d451bc77a6f802eaa94680ea85da28e8472417849a985b2588fc260217","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":"ba6be783-fd35-405d-a878-431e4e313d21","keep":32,"tail":["4d7b3900d1c81c3504fedf5cffc172b1bfa2d82b3c657ed631b15228944e04f8","b588bb24f0f690ff1f942ef43785c75882d93d0cf0fec050aeca3ed351687bb5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",33,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"aec05bf9333ed7f35b9fa78b0ab26b7082de50809bd75cd974f7d963e80a8abe"}105{"parentUuid":"bbfb55e9-a9e1-4637-9f44-cbfb4244f67e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpksfNnzoGuzFEybx1A","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1777,"cache_read_input_tokens":29963,"output_tokens":433,"output_tokens_details":{"thinking_tokens":36},"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":1777},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":333,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpksMmpg8rga5R7MGRw","requestRef":"5efbd7b4-12b9-4667-996d-90f433babd5c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c23c7d98-7461-4b08-bf55-16d5c1ae4bfb","timestamp":"2026-10-30T04:53:59.520Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}106{"parentUuid":"c23c7d98-7461-4b08-bf55-16d5c1ae4bfb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpksfNnzoGuzFEybx1A","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01DTitK74kGmYuyxJDTN3Zy6","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run byte-identical results.json\",\"15 internal-consistency checks PASS (out/state/checks.md)\",\"gate run at end of P4: receipt out/state/gate_receipts/20261030T045357.934972Z.json — passed=false only for P5 deliverables not yet written (report.md, claims.json, sections, phase ledger); 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Estimand: β in Δu_t = α + β·g_t + ε_t. Here u_t is the quarterly average of monthly UNRATE (percent), Δu_t = u_t − u_{t−1} in percentage points, and g_t = 100·Δln GDPC1_t is percent growth per quarter (not annualised). The sample is 1960Q1–2019Q4. Stability is assessed by fitting the model separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and running a classical Chow test.\n\n## Data and exclusions\n- Inputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09) (`n_input_rows`). Both SHA-256 hashes match `data/README.md`. The full audit is in `out/state/data_audit.md`.\n- Data issues (`data_issues`): (i) the UNRATE value for 2025-10 is blank, so the 2025Q4 average is not formed. This lies outside the window and has no effect. (ii) Both series run past 2019Q4. Those quarters are dropped before any estimation. GDPC1 is the current revised vintage, not real-time data.\n- Exclusion ledger (`out/exclusions.csv`, counted in GDPC1 quarters): 319 → 314 (no complete unemployment quarter: 1947Q1–Q4 and 2025Q4) → 312 (first difference cannot be formed: 1948Q1 and 2026Q1) → 240 (restricted to 1960Q1–2019Q4). Final sample: `n_analysed` = 240, `sample_start` = 1960-01-01, `sample_end` = 2019-10-01, `n_sub1` = 96, `n_sub2` = 144. The 1959Q4 levels are used only to form the 1960Q1 differences.\n\n## Method\n- Baseline: the mean-only prediction of Δu, scored by RMSE (`baseline_metric`).\n- Main estimate: full-sample OLS of Δu on a constant and g (`effect_estimate`, `intercept`), scored by residual RMSE (`model_metric`).\n- Uncertainty: Newey–West HAC standard error with a Bartlett kernel (statsmodels, small-sample correction). The lag length is set by the Newey–West rule of thumb floor(4·(n/100)^{2/9}), which gives `hac_lags` = 4. The 95% CI is β ± 1.96·SE (normal approximation).\n- Subperiods: separate OLS fits (`beta_sub1`, `beta_sub2`). In `out/table1.csv` their standard errors are also HAC with 4 lags.\n- Chow test: classical SSR F-test of joint equality of (α, β), F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/236], with df (2, 236) (`chow_f_stat`, `chow_p_value`, `chow_df`).\n- Supplementary checks (not required by the task): HAC standard errors at other lag lengths (`effect_se_hac_by_lag`), and HAC Wald tests in a model with subperiod interactions (`hac_wald_*`). These were added because the classical Chow test assumes homoskedastic, serially uncorrelated errors.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| baseline RMSE (mean only) | 0.325 pp | `baseline_metric` |\n| OLS residual RMSE | 0.250 pp | `model_metric` |\n| R² | 0.408 | `r_squared` |\n| β (full sample) | −0.256 pp per 1% growth | `effect_estimate` |\n| α (full sample) | 0.184 pp | `intercept` |\n| β, 1960Q1–1983Q4 | −0.259 | `beta_sub1` |\n| β, 1984Q1–2019Q4 | −0.273 | `beta_sub2` |\n| α, sub1 / sub2 | 0.254 / 0.152 | `intercept_sub1`, `intercept_sub2` |\n| Chow F (2, 236) | 6.09, p = 0.0026 | `chow_f_stat`, `chow_p_value` |\n\n- Each extra percentage point of quarterly real growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter (`effect_estimate`). The 95% interval lies entirely below zero, so `conclusion` = `negative_effect`.\n- Output growth reduces the RMSE of Δu from 0.325 to 0.250 pp (`baseline_metric`, `model_metric`).\n- The classical Chow test rejects joint equality of (α, β) at 5% (`chow_p_value` = 0.0026), so `stability_conclusion` = `unstable`.\n- The rejection does not come from the slope. The two subperiod slopes are almost identical (−0.259 vs −0.273). What differs is the intercept: 0.254 vs 0.152. As a result, the growth rate at which unemployment holds steady (−α/β) falls from 0.98% to 0.55% per quarter (`growth_for_stable_u_sub1`, `growth_for_stable_u_sub2`), consistent with slower trend growth after 1984.\n- Figure: `out/figure1.png` shows the scatter with both subperiod fitted lines. Table: `out/table1.csv`.\n\n## Uncertainty\n- Full-sample β: HAC SE = 0.036 (`effect_se`, 4 lags). The 95% CI is [−0.326, −0.185] (`effect_ci_low`, `effect_ci_high`).\n- The HAC SE is about 1.8 times the classical OLS SE of 0.020 (`effect_se_ols_classical`), which indicates heteroskedasticity and/or serial correlation in the residuals.\n- The SE is not sensitive to the lag choice: 0.029 to 0.037 across 0 to 12 lags (`effect_se_hac_by_lag`). With any of these values the interval stays well below zero.\n- Subperiod HAC SEs are 0.040 and 0.069 (`beta_sub1_hac_se`, `beta_sub2_hac_se`). The post-1984 slope is estimated much less precisely, largely because a few recession quarters (2008–09) carry much of the leverage.\n- The Chow result depends on the classical error assumptions. Residual variance differs between the subperiods (0.079 vs 0.048; `resid_var_sub1`, `resid_var_sub2`), which breaks the homoskedasticity the classical F-test assumes. Even so, a HAC Wald test of joint equality also rejects (p = 0.0046, `hac_wald_joint_equality_p`).\n- Neither coefficient differs significantly when tested on its own: slope p = 0.85 (`hac_wald_slope_equality_p`), intercept p = 0.25 (`hac_wald_intercept_equality_p`). The joint rejection comes from the combination of the two, because the intercept and slope estimates are correlated.\n- The data therefore give no evidence that the Okun slope changed. They do show the relationship as a whole shifted, mostly through the intercept.\n\n## Limitations and open issues\n- **What would invalidate the result.**\n  - (a) A wrong construction of u_t or g_t, such as annualised growth or end-of-quarter unemployment. Both follow the TASK.md definitions, and the code asserts the exact 240-quarter index.\n  - (b) Any leakage of post-2019 data. The code restricts the sample and asserts the index.\n  - (c) Reading β as causal. It is a reduced-form co-movement coefficient: g_t and Δu_t are determined jointly, so OLS β is subject to simultaneity bias and should not be read as the structural effect of output on unemployment.\n- **Main threats.**\n  - The 1984 break date is imposed by the task, not estimated. Choosing a break date after seeing the data would make the standard Chow p-value invalid, but this one was not chosen by me.\n  - The classical Chow test assumes iid homoskedastic errors, which the evidence above contradicts. The HAC Wald test agrees on the joint rejection, but its finite-sample behaviour with n = 96/144 and 4 lags is only approximate.\n  - The \"unstable\" verdict concerns (α, β) jointly. It would be wrong to report it as a change in the Okun slope; the slope-only test does not reject.\n  - Revised GDP vintage: the estimates describe today's data, not what was observable in real time.\n  - The static specification (no lags of g, no Δu dynamics) may leave dynamics in the residuals. HAC corrects the inference but not any resulting misspecification of β.\n- **Not checked.**\n  - No estimated break dates (Quandt–Andrews / Bai–Perron).\n  - No lagged-growth or \"gap\" versions of Okun's law.\n  - No asymmetry between expansions and recessions.\n  - No outlier influence diagnostics beyond the visual check of the figure.\n  - No alternative bandwidth selectors (for example Andrews automatic), apart from the fixed-lag sensitivity grid.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4, a one-percentage-point higher quarterly real GDP growth is associated with a ~0.26 pp fall in the unemployment rate; the 95% HAC interval lies below zero (negative_effect).\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_se\",\"hac_lags\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not a structural causal effect; simultaneity possible.\"},\n {\"id\":\"C2\",\"text\":\"Output growth explains a substantial part of quarterly unemployment changes: residual RMSE 0.250 pp vs 0.325 pp for the mean-only baseline.\",\"results_keys\":[\"baseline_metric\",\"model_metric\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The HAC standard error of beta is robust to lag choice (0.029-0.037 for 0-12 lags) and larger than the classical OLS SE.\",\"results_keys\":[\"effect_se_hac_by_lag\",\"effect_se_ols_classical\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Fixed-lag grid only; no automatic bandwidth selection.\"},\n {\"id\":\"C4\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) across 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (unstable).\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"chow_df\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes homoskedastic iid errors, which the residual variances contradict; a HAC Wald joint test also rejects (hac_wald_joint_equality_p).\"},\n {\"id\":\"C5\",\"text\":\"The instability is not in the Okun slope: subperiod slopes are nearly equal (-0.259 vs -0.273) and the slope-only HAC test does not reject; the intercept fell (0.254 to 0.152), lowering the growth rate consistent with stable unemployment from ~0.98% to ~0.55% per quarter.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"hac_wald_slope_equality_p\",\"intercept_sub1\",\"intercept_sub2\",\"hac_wald_intercept_equality_p\",\"growth_for_stable_u_sub1\",\"growth_for_stable_u_sub2\",\"hac_wald_joint_equality_p\"],\"status\":\"supported\",\"caveat\":\"Intercept difference alone is not individually significant (p=0.25); joint rejection reflects correlated estimates.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with six required sections, quantitative statements cite results keys\",\"claims.json written; all keys exist in results.json (checked by gate)\",\"final gate run (receipt recorded below)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . | grep -E \"GATE|FAIL|receipt\"","description":"Write report, claims, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":448,"cache_read_input_tokens":31740,"output_tokens":4451,"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":448},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpmBtjzBsFcU6Gyfdf7","requestRef":"3af29f3f-0d31-44f5-bcf6-afd810f74086","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a917ada7-c9c8-40e4-88ca-2d8dacf19d21","timestamp":"2026-10-30T04:54:40.697Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}111{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"a917ada7-c9c8-40e4-88ca-2d8dacf19d21","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}112{"type":"atis-latch","atis":"","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}113{"parentUuid":"a917ada7-c9c8-40e4-88ca-2d8dacf19d21","isSidechain":false,"promptId":"df3d2f72-48c6-4291-bd7a-d8c83a03e33a","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01G6kxWKfSBJxFh3uxfLpiiG","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0179/out/state/gate_receipts/20261030T045441.849863Z.json","is_error":false}]},"uuid":"8ad9b850-2fd8-49aa-9e2b-b728e11511d5","timestamp":"2026-10-30T04:54:41.857Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0179/out/state/gate_receipts/20261030T045441.849863Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"a917ada7-c9c8-40e4-88ca-2d8dacf19d21","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}114{"type":"api-request-blob","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","hash":"7a377121bfcdc4e62b39e241dfb76770fb0a785759ce20365835e7f04cdda1e5","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01G6kxWKfSBJxFh3uxfLpiiG","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960Q1–2019Q4 (task F6-conf-03)\n\n## Question\nHow much does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and has that response been stable? Estimand: β in Δu_t = α + β·g_t + ε_t. Here u_t is the quarterly average of monthly UNRATE (percent), Δu_t = u_t − u_{t−1} in percentage points, and g_t = 100·Δln GDPC1_t is percent growth per quarter (not annualised). The sample is 1960Q1–2019Q4. Stability is assessed by fitting the model separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and running a classical Chow test.\n\n## Data and exclusions\n- Inputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09) (`n_input_rows`). Both SHA-256 hashes match `data/README.md`. The full audit is in `out/state/data_audit.md`.\n- Data issues (`data_issues`): (i) the UNRATE value for 2025-10 is blank, so the 2025Q4 average is not formed. This lies outside the window and has no effect. (ii) Both series run past 2019Q4. Those quarters are dropped before any estimation. GDPC1 is the current revised vintage, not real-time data.\n- Exclusion ledger (`out/exclusions.csv`, counted in GDPC1 quarters): 319 → 314 (no complete unemployment quarter: 1947Q1–Q4 and 2025Q4) → 312 (first difference cannot be formed: 1948Q1 and 2026Q1) → 240 (restricted to 1960Q1–2019Q4). Final sample: `n_analysed` = 240, `sample_start` = 1960-01-01, `sample_end` = 2019-10-01, `n_sub1` = 96, `n_sub2` = 144. The 1959Q4 levels are used only to form the 1960Q1 differences.\n\n## Method\n- Baseline: the mean-only prediction of Δu, scored by RMSE (`baseline_metric`).\n- Main estimate: full-sample OLS of Δu on a constant and g (`effect_estimate`, `intercept`), scored by residual RMSE (`model_metric`).\n- Uncertainty: Newey–West HAC standard error with a Bartlett kernel (statsmodels, small-sample correction). The lag length is set by the Newey–West rule of thumb floor(4·(n/100)^{2/9}), which gives `hac_lags` = 4. The 95% CI is β ± 1.96·SE (normal approximation).\n- Subperiods: separate OLS fits (`beta_sub1`, `beta_sub2`). In `out/table1.csv` their standard errors are also HAC with 4 lags.\n- Chow test: classical SSR F-test of joint equality of (α, β), F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/236], with df (2, 236) (`chow_f_stat`, `chow_p_value`, `chow_df`).\n- Supplementary checks (not required by the task): HAC standard errors at other lag lengths (`effect_se_hac_by_lag`), and HAC Wald tests in a model with subperiod interactions (`hac_wald_*`). These were added because the classical Chow test assumes homoskedastic, serially uncorrelated errors.\n- No randomness is used (`random_seed` = null). `out/analysis.py` regenerates every output deterministically.\n\n## Results\n| quantity | value | key |\n|---|---|---|\n| baseline RMSE (mean only) | 0.325 pp | `baseline_metric` |\n| OLS residual RMSE | 0.250 pp | `model_metric` |\n| R² | 0.408 | `r_squared` |\n| β (full sample) | −0.256 pp per 1% growth | `effect_estimate` |\n| α (full sample) | 0.184 pp | `intercept` |\n| β, 1960Q1–1983Q4 | −0.259 | `beta_sub1` |\n| β, 1984Q1–2019Q4 | −0.273 | `beta_sub2` |\n| α, sub1 / sub2 | 0.254 / 0.152 | `intercept_sub1`, `intercept_sub2` |\n| Chow F (2, 236) | 6.09, p = 0.0026 | `chow_f_stat`, `chow_p_value` |\n\n- Each extra percentage point of quarterly real growth goes with a fall of about 0.26 pp in the unemployment rate in the same quarter (`effect_estimate`). The 95% interval lies entirely below zero, so `conclusion` = `negative_effect`.\n- Output growth reduces the RMSE of Δu from 0.325 to 0.250 pp (`baseline_metric`, `model_metric`).\n- The classical Chow test rejects joint equality of (α, β) at 5% (`chow_p_value` = 0.0026), so `stability_conclusion` = `unstable`.\n- The rejection does not come from the slope. The two subperiod slopes are almost identical (−0.259 vs −0.273). What differs is the intercept: 0.254 vs 0.152. As a result, the growth rate at which unemployment holds steady (−α/β) falls from 0.98% to 0.55% per quarter (`growth_for_stable_u_sub1`, `growth_for_stable_u_sub2`), consistent with slower trend growth after 1984.\n- Figure: `out/figure1.png` shows the scatter with both subperiod fitted lines. Table: `out/table1.csv`.\n\n## Uncertainty\n- Full-sample β: HAC SE = 0.036 (`effect_se`, 4 lags). The 95% CI is [−0.326, −0.185] (`effect_ci_low`, `effect_ci_high`).\n- The HAC SE is about 1.8 times the classical OLS SE of 0.020 (`effect_se_ols_classical`), which indicates heteroskedasticity and/or serial correlation in the residuals.\n- The SE is not sensitive to the lag choice: 0.029 to 0.037 across 0 to 12 lags (`effect_se_hac_by_lag`). With any of these values the interval stays well below zero.\n- Subperiod HAC SEs are 0.040 and 0.069 (`beta_sub1_hac_se`, `beta_sub2_hac_se`). The post-1984 slope is estimated much less precisely, largely because a few recession quarters (2008–09) carry much of the leverage.\n- The Chow result depends on the classical error assumptions. Residual variance differs between the subperiods (0.079 vs 0.048; `resid_var_sub1`, `resid_var_sub2`), which breaks the homoskedasticity the classical F-test assumes. Even so, a HAC Wald test of joint equality also rejects (p = 0.0046, `hac_wald_joint_equality_p`).\n- Neither coefficient differs significantly when tested on its own: slope p = 0.85 (`hac_wald_slope_equality_p`), intercept p = 0.25 (`hac_wald_intercept_equality_p`). The joint rejection comes from the combination of the two, because the intercept and slope estimates are correlated.\n- The data therefore give no evidence that the Okun slope changed. They do show the relationship as a whole shifted, mostly through the intercept.\n\n## Limitations and open issues\n- **What would invalidate the result.**\n  - (a) A wrong construction of u_t or g_t, such as annualised growth or end-of-quarter unemployment. Both follow the TASK.md definitions, and the code asserts the exact 240-quarter index.\n  - (b) Any leakage of post-2019 data. The code restricts the sample and asserts the index.\n  - (c) Reading β as causal. It is a reduced-form co-movement coefficient: g_t and Δu_t are determined jointly, so OLS β is subject to simultaneity bias and should not be read as the structural effect of output on unemployment.\n- **Main threats.**\n  - The 1984 break date is imposed by the task, not estimated. Choosing a break date after seeing the data would make the standard Chow p-value invalid, but this one was not chosen by me.\n  - The classical Chow test assumes iid homoskedastic errors, which the evidence above contradicts. The HAC Wald test agrees on the joint rejection, but its finite-sample behaviour with n = 96/144 and 4 lags is only approximate.\n  - The \"unstable\" verdict concerns (α, β) jointly. It would be wrong to report it as a change in the Okun slope; the slope-only test does not reject.\n  - Revised GDP vintage: the estimates describe today's data, not what was observable in real time.\n  - The static specification (no lags of g, no Δu dynamics) may leave dynamics in the residuals. HAC corrects the inference but not any resulting misspecification of β.\n- **Not checked.**\n  - No estimated break dates (Quandt–Andrews / Bai–Perron).\n  - No lagged-growth or \"gap\" versions of Okun's law.\n  - No asymmetry between expansions and recessions.\n  - No outlier influence diagnostics beyond the visual check of the figure.\n  - No alternative bandwidth selectors (for example Andrews automatic), apart from the fixed-lag sensitivity grid.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4, a one-percentage-point higher quarterly real GDP growth is associated with a ~0.26 pp fall in the unemployment rate; the 95% HAC interval lies below zero (negative_effect).\",\"results_keys\":[\"effect_estimate\",\"effect_ci_low\",\"effect_ci_high\",\"effect_se\",\"hac_lags\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not a structural causal effect; simultaneity possible.\"},\n {\"id\":\"C2\",\"text\":\"Output growth explains a substantial part of quarterly unemployment changes: residual RMSE 0.250 pp vs 0.325 pp for the mean-only baseline.\",\"results_keys\":[\"baseline_metric\",\"model_metric\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The HAC standard error of beta is robust to lag choice (0.029-0.037 for 0-12 lags) and larger than the classical OLS SE.\",\"results_keys\":[\"effect_se_hac_by_lag\",\"effect_se_ols_classical\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Fixed-lag grid only; no automatic bandwidth selection.\"},\n {\"id\":\"C4\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) across 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (unstable).\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"chow_df\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes homoskedastic iid errors, which the residual variances contradict; a HAC Wald joint test also rejects (hac_wald_joint_equality_p).\"},\n {\"id\":\"C5\",\"text\":\"The instability is not in the Okun slope: subperiod slopes are nearly equal (-0.259 vs -0.273) and the slope-only HAC test does not reject; the intercept fell (0.254 to 0.152), lowering the growth rate consistent with stable unemployment from ~0.98% to ~0.55% per quarter.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"hac_wald_slope_equality_p\",\"intercept_sub1\",\"intercept_sub2\",\"hac_wald_intercept_equality_p\",\"growth_for_stable_u_sub1\",\"growth_for_stable_u_sub2\",\"hac_wald_joint_equality_p\"],\"status\":\"supported\",\"caveat\":\"Intercept difference alone is not individually significant (p=0.25); 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The headline result: over 1960Q1–2019Q4, one 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 estimates use all 240 quarters from 1960Q1 to 2019Q4:\n\n| Quantity | Value |\n|---|---|\n| Okun coefficient β (full sample) | −0.256 |\n| Standard error (Newey–West, 4 lags) | 0.036 |\n| 95% interval for β | [−0.326, −0.185], entirely below zero |\n| Intercept α | 0.184 pp |\n| Prediction error, mean-only baseline | 0.325 pp |\n| Prediction error, regression | 0.250 pp |\n| β, 1960Q1–1983Q4 | −0.259 |\n| β, 1984Q1–2019Q4 | −0.273 |\n| Chow test | F(2, 236) = 6.09, p = 0.0026 |\n\n- **Lag choice:** I used 4 lags (a standard rule of thumb for 240 quarters). The standard error barely moves with other choices, staying between 0.029 and 0.037 for 0 to 12 lags.\n- **Stability:** the Chow test rejects at 5%, so the required verdict is \"unstable\". But the rejection is not about the slope, which is nearly identical in the two periods. What changed is the intercept (0.254 before 1984, 0.152 after). That means the growth rate needed to keep unemployment steady fell from about 0.98% to 0.55% per quarter.\n- **Robustness check (my addition):** the Chow test assumes the errors behave equally in both periods, which they don't. I added tests that don't rely on that. Testing intercept and slope together still rejects (p = 0.005), but neither rejects on its own: slope p = 0.85, intercept p = 0.25.\n\nLimitations:\n- β is a correlation between output growth and unemployment changes, not a causal effect, since the two are determined together.\n- The 1984 break date was set by the task; I did not search for the actual break.\n- The GDP figures are today's revised data, not what was known at the time.\n- I did not try models with lagged growth or output gaps, check whether recessions behave differently, or look for influential outliers.\n\nData notes: one monthly unemployment value (October 2025) is missing, and both series run to 2026. Everything after 2019Q4 is dropped before any estimate, so neither affects the results.\n\nFiles are in `out/` (`analysis.py` regenerates `results.json`, `table1.csv`, `figure1.png` and `exclusions.csv`):\n- `analysis.py`\n- `results.json`\n- `figure1.png`\n- `table1.csv`\n- `exclusions.csv`\n- `report.md`\n- `claims.json`\n- `state/` (contract, data audit, checks, phase ledger, log, gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":339,"cache_read_input_tokens":36734,"output_tokens":1008,"output_tokens_details":{"thinking_tokens":21},"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":339},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXppSbNHcsu843U7r9xf","requestRef":"1c06dcd8-26e8-4d9b-8ea6-4c96b0eb3a4b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f78144f7-9085-4ce1-b56b-9bfb831937f5","timestamp":"2026-10-30T04:54:56.316Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0179","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","version":"2.1.314","gitBranch":"HEAD"}122{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"f78144f7-9085-4ce1-b56b-9bfb831937f5","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0"}123{"type":"cost-state","sessionId":"603442dc-20a4-4e66-b589-619d8fce2af0","totalCostUSD":0.7383538000000001,"totalAPIDuration":203148,"totalAPIDurationWithoutRetries":203135,"totalToolDuration":10431,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":213769,"startTime":1793335882555,"modelUsage":{"claude-opus-5-5":{"inputTokens":42,"outputTokens":23831,"thinkingTokens":1543,"cacheReadInputTokens":418604,"cacheCreationInputTokens":35569,"cacheCreationEphemeral5mInputTokens":35569,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.7383538000000001}},"hasUnknownModelCost":false}124