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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"}],"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/`. 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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/`. 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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 \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"beta_sub1\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"beta_sub2\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"chow_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"chow_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"stability_conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\",\n \"chronology\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"effect_se\",\n \"chow_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [\n [\n \"sample_start\",\n \"sample_end\"\n ]\n ],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-03\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.725294+00:00\",\n \"files\": {\n \"TASK.md\": \"64afb4744b89e1ab9b45cefc3fad325b1857216f04a3d3cc54ed3b1681db2a0e\",\n \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n \"data/README.md\": \"7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658\",\n \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n \"gate_spec.json\": \"bced13a911d728cc853d5a88f4412cf86338d82c325eb7891ba2f5fd86317f61\"\n }\n}==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\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":"5e40ef68-33f5-423b-bb93-d372df0282a0","timestamp":"2026-10-30T04:48:27.905Z","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\": \"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 \"type\": \"number\",\n \"nullable\": false\n },\n \"effect_ci_high\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"beta_sub1\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"beta_sub2\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"chow_f_stat\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"chow_p_value\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"stability_conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"conclusion\": {\n \"type\": \"string\",\n \"nullable\": false\n }\n },\n \"report_sections\": [\n \"Question\",\n \"Data and exclusions\",\n \"Method\",\n \"Results\",\n \"Uncertainty\",\n \"Limitations and open issues\"\n ],\n \"family_checks\": [\n \"ci_contains_estimate\",\n \"positive_se\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\n \"exclusion_ledger_sums\",\n \"chronology\"\n ],\n \"ci_pairs\": [\n [\n \"effect_estimate\",\n \"effect_ci_low\",\n \"effect_ci_high\"\n ]\n ],\n \"positive_keys\": [\n \"effect_se\",\n \"chow_f_stat\",\n \"baseline_metric\",\n \"model_metric\"\n ],\n \"chronology_pairs\": [\n [\n \"sample_start\",\n \"sample_end\"\n ]\n ],\n \"analysis_timeout_s\": 300,\n \"reproducibility_tolerance_rel\": 1e-06\n}{\n \"task_id\": \"F6-conf-03\",\n \"family\": \"F6\",\n \"split\": \"conf\",\n \"version\": \"1.0\",\n \"created_utc\": \"2026-10-30T03:42:41.725294+00:00\",\n \"files\": {\n \"TASK.md\": \"64afb4744b89e1ab9b45cefc3fad325b1857216f04a3d3cc54ed3b1681db2a0e\",\n \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n \"data/README.md\": \"7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658\",\n \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n \"gate_spec.json\": \"bced13a911d728cc853d5a88f4412cf86338d82c325eb7891ba2f5fd86317f61\"\n }\n}==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\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","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1f093532-5e8c-49d7-8f46-b1203eec572f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"0522c5c2b853d5ad96a1578a57de533efece372c500a028b457ae05fb883f21e","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01B4nHjz4gBcvtBtSuTbfdeL","name":"Bash","input":{"command":"cat data/README.md gate_spec.json bundle.json; head -3 data/*.csv; tail -3 data/*.csv; wc -l data/*.csv; shasum -a 256 data/*","description":"Inspect data, provenance, gate 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out/state\ncat > contract.md <<'EOF'\n# Contract — F6-conf-03\n**Question.** How strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth (Okun's law, difference form), 1960Q1–2019Q4, and is the response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n\n**Estimands.** β (and α) in Δu_t = α + β g_t + ε_t, OLS; u_t = mean of 3 monthly UNRATE values; g_t = 100·Δln GDPC1 (quarterly, not annualised). Meaningful as a reduced-form conditional-mean association (not causal), under stationarity of (Δu, g) within the sample and a linear, time-invariant relation (the latter is tested). Uncertainty: Newey–West HAC (Bartlett) SE for full-sample β, 95% CI (normal critical value). Stability: subperiod OLS slopes and classical Chow SSR F-test, df (2, n−4).\n\n**Outputs.** out/results.json (keys: 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 state ledgers and exclusions.csv.\n\n**Information rules.** Only data/GDPC1.csv and data/UNRATE.csv. Sample 240 quarters 1960Q1–2019Q4; 1959Q4 levels used for the first differences; nothing after 2019Q4 enters any estimate.\n\n**Invalidity / impossibility.** Missing monthly UNRATE or GDPC1 values within 1959Q4–2019Q4; sample not equal to 240 quarters; using post-2019 data; non-HAC full-sample SE.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README) | 319 | 2 (observation_date, GDPC1) | 1947-01-01 .. 2026-07-01, quarterly, no gaps | 0 | 0 |\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README) | 945 | 2 (observation_date, UNRATE) | 1948-01-01 .. 2026-09-01, monthly, no gaps in dates | 1 (2025-10-01, empty value) | 0 |\n\nUnits: GDPC1 billions chained 2017 USD SAAR (only log-differences used, so units/annualisation of the level drop out); UNRATE percent SA. GDPC1 range 2172–24611; UNRATE range 2.5–14.8 — plausible.\n\nAnomalies: UNRATE 2025-10-01 blank (BLS did not publish October 2025 during the federal shutdown). Outside the 1959Q4–2019Q4 window, so irrelevant; handled by dropping out-of-window observations.\n\nInformation availability: both files extend beyond 2019Q4 (current vintage, 2026). Post-2019Q4 rows are excluded before any computation. Note the GDPC1 vintage is 2026 (chained 2017 dollars), i.e. revised data, not real-time — acceptable since the task is a historical estimate, but noted as a limitation.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1: audited data; hashes match README; one missing UNRATE value (2025-10) outside window. Decision: restrict to 1959Q4–2019Q4 levels; HAC lag rule floor(4(T/100)^(2/9)) = 4 for T=240.\nEOF","description":"Write contract, phase ledger, audit, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":166,"cache_read_input_tokens":10691,"output_tokens":1801,"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":166},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpLyXBffuCeBHirJs6Q","requestRef":"6e15c2ea-f3d3-4ad5-9291-534a0502f983","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1bab6b66-8933-4e46-8cd1-d6ffd3bd4780","timestamp":"2026-10-30T04:48:50.321Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"1bab6b66-8933-4e46-8cd1-d6ffd3bd4780","isSidechain":false,"promptId":"f1abe550-c839-453e-86a3-50749a1a8dd0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017qsmfCkfW43nsdgoGaJpbk","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"59a93665-219c-4025-8961-5d3bc01e536e","timestamp":"2026-10-30T04:48:50.380Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1bab6b66-8933-4e46-8cd1-d6ffd3bd4780","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169/out/state","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"6dac17c39888d9a1885a159e5c031d8be86ffdfd61d91fed7e141466f4b42b3c","message":{"role":"assistant","content":[{"type":"text","text":"Now P0/P1 state files."},{"type":"tool_use","id":"toolu_017qsmfCkfW43nsdgoGaJpbk","name":"Bash","input":{"command":"cd out/state\ncat > contract.md <<'EOF'\n# Contract — F6-conf-03\n**Question.** How strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth (Okun's law, difference form), 1960Q1–2019Q4, and is the response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n\n**Estimands.** β (and α) in Δu_t = α + β g_t + ε_t, OLS; u_t = mean of 3 monthly UNRATE values; g_t = 100·Δln GDPC1 (quarterly, not annualised). Meaningful as a reduced-form conditional-mean association (not causal), under stationarity of (Δu, g) within the sample and a linear, time-invariant relation (the latter is tested). Uncertainty: Newey–West HAC (Bartlett) SE for full-sample β, 95% CI (normal critical value). Stability: subperiod OLS slopes and classical Chow SSR F-test, df (2, n−4).\n\n**Outputs.** out/results.json (keys: 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 state ledgers and exclusions.csv.\n\n**Information rules.** Only data/GDPC1.csv and data/UNRATE.csv. Sample 240 quarters 1960Q1–2019Q4; 1959Q4 levels used for the first differences; nothing after 2019Q4 enters any estimate.\n\n**Invalidity / impossibility.** Missing monthly UNRATE or GDPC1 values within 1959Q4–2019Q4; sample not equal to 240 quarters; using post-2019 data; non-HAC full-sample SE.\nEOF\ncat > phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\n\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\ncat > data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README) | 319 | 2 (observation_date, GDPC1) | 1947-01-01 .. 2026-07-01, quarterly, no gaps | 0 | 0 |\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README) | 945 | 2 (observation_date, UNRATE) | 1948-01-01 .. 2026-09-01, monthly, no gaps in dates | 1 (2025-10-01, empty value) | 0 |\n\nUnits: GDPC1 billions chained 2017 USD SAAR (only log-differences used, so units/annualisation of the level drop out); UNRATE percent SA. GDPC1 range 2172–24611; UNRATE range 2.5–14.8 — plausible.\n\nAnomalies: UNRATE 2025-10-01 blank (BLS did not publish October 2025 during the federal shutdown). Outside the 1959Q4–2019Q4 window, so irrelevant; handled by dropping out-of-window observations.\n\nInformation availability: both files extend beyond 2019Q4 (current vintage, 2026). Post-2019Q4 rows are excluded before any computation. Note the GDPC1 vintage is 2026 (chained 2017 dollars), i.e. revised data, not real-time — acceptable since the task is a historical estimate, but noted as a limitation.\nEOF\ncat > log.md <<'EOF'\n# Log\n- 2026-10-30 P0 passed: contract written.\n- 2026-10-30 P1: audited data; hashes match README; one missing UNRATE value (2025-10) outside window. Decision: restrict to 1959Q4–2019Q4 levels; HAC lag rule floor(4(T/100)^(2/9)) = 4 for T=240.\nEOF","description":"Write contract, phase ledger, audit, log"}}]}}39{"type":"api-request","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","id":"0eb3b63c-7dd6-4bc6-8984-50d44cd97c8d","timestamp":"2026-10-30T04:48:50.383Z","version":"2.1.314","querySource":"sdk","shapeHash":"c3c94937d96da46f69f6d4c09f4ff501be59660c9cdf3cce35bc2dad0b838ad3","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":"6e15c2ea-f3d3-4ad5-9291-534a0502f983","keep":10,"tail":["6dac17c39888d9a1885a159e5c031d8be86ffdfd61d91fed7e141466f4b42b3c","b9e40313792b5851267f2167c67bc088ac73133144ad4d064080a935a98aef51"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a753dc9d26d9645b3721a8ff6ef3402bbe5a1f1aa72f154028ef5b30504a5780"}40{"parentUuid":"59a93665-219c-4025-8961-5d3bc01e536e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpNBgTgrhxeP4ZB8qm6","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS0gkKEAgTGAI4AUIIdGhpbmtpbmcSDI68+5yaqCP8MvxOJhoMGjhbg/nZhvG4I37AIjAORzfCvNJr4oKW4YNb35K/MQzwF2615E3bFhxH7iH8k58tGWFseheJj7KRS0A9hUUq7wje1HE4GnfSQwM8mW5fFjQ6ZPPrNu+V0bxhMB7PZP5wPelrtIssUaUAJVRZo1AY28K90jWWC8SKNuPYT5VUOYmpWDh5a359MtHjQru/0mOeJBPa3n5Yq9nkiy2IAEcEM/MVTpSL1YKkxppPwMR5VpdQLIeWqt6gIlCYIlkR4kb7ZSrYClTpuRKywtsyyX2WoeDphiqK5swlou3EuBNIZcVtnm/OkmgJXdxp/YYtBSaXhwDV2tVipQjyAAvIobSdYdGOcF0PoG7eiluI/xgkubOFx3OQzXJuUuZeFtjR73B/jfBhn4svArB6vGtGW50ZWCNg0UdG757oYutwOOlL9hCmiQdEJ1Rld7Yg+v6wgIs8I072puv097R+0Vccf9+gBjSFa5T92Sp+y/F3XVOIst2+acRSBacDGuetqaDJdzvgK2JGNOpTjOfnQYYU7cqJUJ29ullUVXTfGvD/RdHiUJ9e03NakZsFzcNKNUqeLiSBIZ5KYMUs8nH8MUiO+/nfzu9GKBKMVFXYN42Tj+iq9PHNf4v+SbRTMCeT5REPGzggf2Fbs201ORtDp6uCgUy2ltT5qXnRYididRr/hrNxd19+Dsfdxrhd/CdaWaW3I3mLykc9rqd7kd/ml86NWLhmCg5Mk4HrG1SQ0edqUM6y1Rdugq5S8pEOQwH7X4LOSb7I6bMHwIcIZsVAwhLDVusoRUtQIY1Qk5PKeAZ/ITnX0gtQhbn2blvX0szYEtqZZPWlbT68ZaL1JmCw+wx+/bPY7+W1uqH2B6O/ZORnHm3Wzp38x0cV+jC0G0KrdfM+QF2sNlwXq+oD9DOrZnRsbxRfIrowiQ/qanTBtEJ1c9inhaCiCOxswjzObP3DnAueh90oZZ7mTxc8+R7u/2k0LSViWwa5t2r5Ca/G85chPUPdmuMRMhiiRpJ3bP95gHOoguV+/Ii4nFLd5dj7tvfPnMzx+8luvAvEgOgTU6ZVw2qIGbx6q+adVk4oE4YLKJ8p/wtZo0T8MwK9z7mWy5haH07XXiIQC2c3+yhvpEYq7YFUbsXUaaMseFNP8nzNEKP97lAF7ulFQXzAqSNofVeDlomC+8ugpD6pTPU8cqHkfSrRLR8Gtm2FBQvkZOtHIuZFByTGq4S5oco0JVYyqO4DrdABT1gmIIAhC7ZelTcg0CR5fvYqN8EX56EH0WAeTnmfzdmJSmyQgf/YrVUxXLO70eGV8WJG+0/cKGsjx3Vddi2fAzDVTwLao/ZHbEd9YnVyl20JKxml7H18ZjIgEaGSYzckE4UdXUhzS55YFEwHghiLtjJrsyKc6GyStK89D4MrkgOrDT/XRkKxIVV8UPctA166M/0gYWEw51bOBuxJx6YSLajfS//F2oDmpNABl+jFLnBgNqEjlt3h4NNfQVuM1ZAJyGnfX3tiYGhovGRkdJwL5kOp9RFQnHebf4KEs+HDn6okf2Bn9egUDhiu2q2kkLD3bi3ETc6UwOPKGCx0YbeRtaqIK9XUAMPkljuEyAofak6AGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1816,"cache_read_input_tokens":10857,"output_tokens":3801,"output_tokens_details":{"thinking_tokens":237},"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":1816},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2208,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpNBVYN6tnw3vo7Bq93","requestRef":"0eb3b63c-7dd6-4bc6-8984-50d44cd97c8d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4aea0513-6b05-4d84-8241-658afcac1c7d","timestamp":"2026-10-30T04:48:54.352Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169/out/state","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"4aea0513-6b05-4d84-8241-658afcac1c7d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpNBgTgrhxeP4ZB8qm6","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01UkrBZ2K8UDH6nmS9ty24Bh","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. 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, 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)\ngdp = pd.read_csv(\"data/GDPC1.csv\", na_values=[\".\"])\nun = pd.read_csv(\"data/UNRATE.csv\", na_values=[\".\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(un))}\ngdp[\"observation_date\"] = pd.to_datetime(gdp[\"observation_date\"])\nun[\"observation_date\"] = pd.to_datetime(un[\"observation_date\"])\n\ndata_issues = []\nmiss = un[un[\"UNRATE\"].isna()][\"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nif miss:\n data_issues.append({\"issue\": f\"UNRATE missing value(s) at {miss} (blank in raw FRED CSV)\",\n \"handling\": \"outside the 1959Q4-2019Q4 window; dropped with all post-2019Q4 observations, no effect on estimates\"})\ndata_issues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are the 2026 vintage (revised, not real-time)\",\n \"handling\": \"all observations after 2019Q4 excluded before computation; vintage noted as limitation\"})\n\n# Quarterly unemployment = mean of 3 months; require all 3 months present\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ndf = gdp.set_index(\"q\")[[\"GDPC1\"]].join(uq, how=\"left\")\n\nexcl = []\ndef log(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nn0 = len(df)\nwin = df.loc[pd.Period(\"1959Q4\"):pd.Period(\"2019Q4\")].copy()\nlog(1, \"keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit)\", n0, len(win),\n \"task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\")\nassert (win[\"count\"] == 3).all() and win[\"GDPC1\"].notna().all(), \"incomplete data inside window\"\nwin[\"u\"] = win[\"mean\"]\nwin[\"du\"] = win[\"u\"].diff()\nwin[\"g\"] = 100 * np.log(win[\"GDPC1\"]).diff()\nreg = win.dropna(subset=[\"du\", \"g\"]).copy()\nlog(2, \"drop 1959Q4 (no lagged level, differences undefined)\", len(win), len(reg),\n \"first-difference construction\")\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert len(reg) == 240 and str(reg.index[0]) == \"1960Q1\" and str(reg.index[-1]) == \"2019Q4\"\n\ny, X = reg[\"du\"].values, sm.add_constant(reg[\"g\"].values)\nn = len(y)\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n# Full sample OLS with Newey-West HAC (Bartlett) SE\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nols = sm.OLS(y, X).fit()\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se, beta + z * se)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n\n# Subperiods\ns1 = reg.index <= pd.Period(\"1983Q4\")\ns2 = ~s1\nfits = {}\nfor name, m in [(\"sub1\", s1), (\"sub2\", s2)]:\n Xs = sm.add_constant(reg[\"g\"].values[m])\n f = sm.OLS(y[m], Xs).fit()\n fh = sm.OLS(y[m], Xs).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n fits[name] = (f, fh, int(m.sum()))\nssr_p = ols.ssr\nssr_u = fits[\"sub1\"][0].ssr + fits[\"sub2\"][0].ssr\nk = 2\nchow_f = float(((ssr_p - ssr_u) / k) / (ssr_u / (n - 2 * k)))\nchow_p = float(stats.f.sf(chow_f, k, n - 2 * k))\n\nconclusion = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\nresults = {\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\": reg.index[0].start_time.strftime(\"%Y-%m-%d\"),\n \"sample_end\": reg.index[-1].start_time.strftime(\"%Y-%m-%d\"),\n \"n_sub1\": fits[\"sub1\"][2],\n \"n_sub2\": fits[\"sub2\"][2],\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(ci[0]),\n \"effect_ci_high\": float(ci[1]),\n \"beta_sub1\": float(fits[\"sub1\"][0].params[1]),\n \"beta_sub2\": float(fits[\"sub2\"][0].params[1]),\n \"chow_f_stat\": chow_f,\n \"chow_p_value\": chow_p,\n \"stability_conclusion\": \"stable\" if chow_p >= 0.05 else \"unstable\",\n \"conclusion\": conclusion,\n # supplementary (not required)\n \"ols_classical_se_full\": float(ols.bse[1]),\n \"r_squared_full\": float(ols.rsquared),\n \"intercept_sub1\": float(fits[\"sub1\"][0].params[0]),\n \"intercept_sub2\": float(fits[\"sub2\"][0].params[0]),\n \"beta_sub1_hac_se\": float(fits[\"sub1\"][1].bse[1]),\n \"beta_sub2_hac_se\": float(fits[\"sub2\"][1].bse[1]),\n \"implied_zero_du_growth_full\": float(-alpha / beta),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh_:\n json.dump(results, fh_, indent=2)\n\nrows = [(\"full_1960Q1_2019Q4\", n, alpha, beta, se)]\nfor lab, key in [(\"sub1_1960Q1_1983Q4\", \"sub1\"), (\"sub2_1984Q1_2019Q4\", \"sub2\")]:\n f, fh, nn = fits[key]\n rows.append((lab, nn, float(f.params[0]), float(f.params[1]), float(fh.bse[1])))\npd.DataFrame(rows, columns=[\"period\", \"n\", \"intercept\", \"slope\", \"slope_se\"]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ng = reg[\"g\"].values\nax.scatter(g[s1], y[s1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\", color=\"tab:blue\")\nax.scatter(g[s2], y[s2], s=14, alpha=0.6, label=\"1984Q1-2019Q4\", color=\"tab:orange\")\nxx = np.linspace(g.min(), g.max(), 50)\nfor key, c in [(\"sub1\", \"tab:blue\"), (\"sub2\", \"tab:orange\")]:\n a, b = fits[key][0].params\n ax.plot(xx, a + b * xx, color=c, lw=2, label=f\"fit {key}: du = {a:.2f} {b:+.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()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: results[k] for k in [\"effect_estimate\", \"effect_se\", \"effect_ci_low\", \"effect_ci_high\",\n \"beta_sub1\", \"beta_sub2\", \"chow_f_stat\", \"chow_p_value\", \"baseline_metric\", \"model_metric\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat 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":1816,"cache_read_input_tokens":10857,"output_tokens":3801,"output_tokens_details":{"thinking_tokens":237},"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":1816},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpNBVYN6tnw3vo7Bq93","requestRef":"0eb3b63c-7dd6-4bc6-8984-50d44cd97c8d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3b202c0b-e459-4c29-8d36-a2b809ffebb8","timestamp":"2026-10-30T04:49:19.679Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169/out/state","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}42{"parentUuid":"3b202c0b-e459-4c29-8d36-a2b809ffebb8","isSidechain":false,"promptId":"f1abe550-c839-453e-86a3-50749a1a8dd0","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@3.14/3.14.5/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0169/out/state/out/analysis.py': [Errno 2] No such file or directory","is_error":true,"tool_use_id":"toolu_01UkrBZ2K8UDH6nmS9ty24Bh"}]},"uuid":"80a30751-c143-47e9-b8c5-0cb445e6cc77","timestamp":"2026-10-30T04:49:19.740Z","toolUseResult":"Error: Exit code 2\n(eval):1: no such file or directory: out/analysis.py\n/opt/homebrew/Cellar/python@3.14/3.14.5/Frameworks/Python.framework/Versions/3.14/Resources/Python.app/Contents/MacOS/Python: can't open file '/Users/simon-pierreboucher/wp29/runs/RC0169/out/state/out/analysis.py': [Errno 2] No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"3b202c0b-e459-4c29-8d36-a2b809ffebb8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169/out/state","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}43{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"5c8f17645ef435c4b9d19e76769b455f297280a8620c34d04bb1f61374921e3b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS0gkKEAgTGAI4AUIIdGhpbmtpbmcSDI68+5yaqCP8MvxOJhoMGjhbg/nZhvG4I37AIjAORzfCvNJr4oKW4YNb35K/MQzwF2615E3bFhxH7iH8k58tGWFseheJj7KRS0A9hUUq7wje1HE4GnfSQwM8mW5fFjQ6ZPPrNu+V0bxhMB7PZP5wPelrtIssUaUAJVRZo1AY28K90jWWC8SKNuPYT5VUOYmpWDh5a359MtHjQru/0mOeJBPa3n5Yq9nkiy2IAEcEM/MVTpSL1YKkxppPwMR5VpdQLIeWqt6gIlCYIlkR4kb7ZSrYClTpuRKywtsyyX2WoeDphiqK5swlou3EuBNIZcVtnm/OkmgJXdxp/YYtBSaXhwDV2tVipQjyAAvIobSdYdGOcF0PoG7eiluI/xgkubOFx3OQzXJuUuZeFtjR73B/jfBhn4svArB6vGtGW50ZWCNg0UdG757oYutwOOlL9hCmiQdEJ1Rld7Yg+v6wgIs8I072puv097R+0Vccf9+gBjSFa5T92Sp+y/F3XVOIst2+acRSBacDGuetqaDJdzvgK2JGNOpTjOfnQYYU7cqJUJ29ullUVXTfGvD/RdHiUJ9e03NakZsFzcNKNUqeLiSBIZ5KYMUs8nH8MUiO+/nfzu9GKBKMVFXYN42Tj+iq9PHNf4v+SbRTMCeT5REPGzggf2Fbs201ORtDp6uCgUy2ltT5qXnRYididRr/hrNxd19+Dsfdxrhd/CdaWaW3I3mLykc9rqd7kd/ml86NWLhmCg5Mk4HrG1SQ0edqUM6y1Rdugq5S8pEOQwH7X4LOSb7I6bMHwIcIZsVAwhLDVusoRUtQIY1Qk5PKeAZ/ITnX0gtQhbn2blvX0szYEtqZZPWlbT68ZaL1JmCw+wx+/bPY7+W1uqH2B6O/ZORnHm3Wzp38x0cV+jC0G0KrdfM+QF2sNlwXq+oD9DOrZnRsbxRfIrowiQ/qanTBtEJ1c9inhaCiCOxswjzObP3DnAueh90oZZ7mTxc8+R7u/2k0LSViWwa5t2r5Ca/G85chPUPdmuMRMhiiRpJ3bP95gHOoguV+/Ii4nFLd5dj7tvfPnMzx+8luvAvEgOgTU6ZVw2qIGbx6q+adVk4oE4YLKJ8p/wtZo0T8MwK9z7mWy5haH07XXiIQC2c3+yhvpEYq7YFUbsXUaaMseFNP8nzNEKP97lAF7ulFQXzAqSNofVeDlomC+8ugpD6pTPU8cqHkfSrRLR8Gtm2FBQvkZOtHIuZFByTGq4S5oco0JVYyqO4DrdABT1gmIIAhC7ZelTcg0CR5fvYqN8EX56EH0WAeTnmfzdmJSmyQgf/YrVUxXLO70eGV8WJG+0/cKGsjx3Vddi2fAzDVTwLao/ZHbEd9YnVyl20JKxml7H18ZjIgEaGSYzckE4UdXUhzS55YFEwHghiLtjJrsyKc6GyStK89D4MrkgOrDT/XRkKxIVV8UPctA166M/0gYWEw51bOBuxJx6YSLajfS//F2oDmpNABl+jFLnBgNqEjlt3h4NNfQVuM1ZAJyGnfX3tiYGhovGRkdJwL5kOp9RFQnHebf4KEs+HDn6okf2Bn9egUDhiu2q2kkLD3bi3ETc6UwOPKGCx0YbeRtaqIK9XUAMPkljuEyAofak6AGAE="},{"type":"tool_use","id":"toolu_01UkrBZ2K8UDH6nmS9ty24Bh","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. 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, 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)\ngdp = pd.read_csv(\"data/GDPC1.csv\", na_values=[\".\"])\nun = pd.read_csv(\"data/UNRATE.csv\", na_values=[\".\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(un))}\ngdp[\"observation_date\"] = pd.to_datetime(gdp[\"observation_date\"])\nun[\"observation_date\"] = pd.to_datetime(un[\"observation_date\"])\n\ndata_issues = []\nmiss = un[un[\"UNRATE\"].isna()][\"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nif miss:\n data_issues.append({\"issue\": f\"UNRATE missing value(s) at {miss} (blank in raw FRED CSV)\",\n \"handling\": \"outside the 1959Q4-2019Q4 window; dropped with all post-2019Q4 observations, no effect on estimates\"})\ndata_issues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are the 2026 vintage (revised, not real-time)\",\n \"handling\": \"all observations after 2019Q4 excluded before computation; vintage noted as limitation\"})\n\n# Quarterly unemployment = mean of 3 months; require all 3 months present\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ndf = gdp.set_index(\"q\")[[\"GDPC1\"]].join(uq, how=\"left\")\n\nexcl = []\ndef log(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nn0 = len(df)\nwin = df.loc[pd.Period(\"1959Q4\"):pd.Period(\"2019Q4\")].copy()\nlog(1, \"keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit)\", n0, len(win),\n \"task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\")\nassert (win[\"count\"] == 3).all() and win[\"GDPC1\"].notna().all(), \"incomplete data inside window\"\nwin[\"u\"] = win[\"mean\"]\nwin[\"du\"] = win[\"u\"].diff()\nwin[\"g\"] = 100 * np.log(win[\"GDPC1\"]).diff()\nreg = win.dropna(subset=[\"du\", \"g\"]).copy()\nlog(2, \"drop 1959Q4 (no lagged level, differences undefined)\", len(win), len(reg),\n \"first-difference construction\")\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert len(reg) == 240 and str(reg.index[0]) == \"1960Q1\" and str(reg.index[-1]) == \"2019Q4\"\n\ny, X = reg[\"du\"].values, sm.add_constant(reg[\"g\"].values)\nn = len(y)\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n# Full sample OLS with Newey-West HAC (Bartlett) SE\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nols = sm.OLS(y, X).fit()\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se, beta + z * se)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n\n# Subperiods\ns1 = reg.index <= pd.Period(\"1983Q4\")\ns2 = ~s1\nfits = {}\nfor name, m in [(\"sub1\", s1), (\"sub2\", s2)]:\n Xs = sm.add_constant(reg[\"g\"].values[m])\n f = sm.OLS(y[m], Xs).fit()\n fh = sm.OLS(y[m], Xs).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n fits[name] = (f, fh, int(m.sum()))\nssr_p = ols.ssr\nssr_u = fits[\"sub1\"][0].ssr + fits[\"sub2\"][0].ssr\nk = 2\nchow_f = float(((ssr_p - ssr_u) / k) / (ssr_u / (n - 2 * k)))\nchow_p = float(stats.f.sf(chow_f, k, n - 2 * k))\n\nconclusion = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\nresults = {\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\": reg.index[0].start_time.strftime(\"%Y-%m-%d\"),\n \"sample_end\": reg.index[-1].start_time.strftime(\"%Y-%m-%d\"),\n \"n_sub1\": fits[\"sub1\"][2],\n \"n_sub2\": fits[\"sub2\"][2],\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(ci[0]),\n \"effect_ci_high\": float(ci[1]),\n \"beta_sub1\": float(fits[\"sub1\"][0].params[1]),\n \"beta_sub2\": float(fits[\"sub2\"][0].params[1]),\n \"chow_f_stat\": chow_f,\n \"chow_p_value\": chow_p,\n \"stability_conclusion\": \"stable\" if chow_p >= 0.05 else \"unstable\",\n \"conclusion\": conclusion,\n # supplementary (not required)\n \"ols_classical_se_full\": float(ols.bse[1]),\n \"r_squared_full\": float(ols.rsquared),\n \"intercept_sub1\": float(fits[\"sub1\"][0].params[0]),\n \"intercept_sub2\": float(fits[\"sub2\"][0].params[0]),\n \"beta_sub1_hac_se\": float(fits[\"sub1\"][1].bse[1]),\n \"beta_sub2_hac_se\": float(fits[\"sub2\"][1].bse[1]),\n \"implied_zero_du_growth_full\": float(-alpha / beta),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh_:\n json.dump(results, fh_, indent=2)\n\nrows = [(\"full_1960Q1_2019Q4\", n, alpha, beta, se)]\nfor lab, key in [(\"sub1_1960Q1_1983Q4\", \"sub1\"), (\"sub2_1984Q1_2019Q4\", \"sub2\")]:\n f, fh, nn = fits[key]\n rows.append((lab, nn, float(f.params[0]), float(f.params[1]), float(fh.bse[1])))\npd.DataFrame(rows, columns=[\"period\", \"n\", \"intercept\", \"slope\", \"slope_se\"]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ng = reg[\"g\"].values\nax.scatter(g[s1], y[s1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\", color=\"tab:blue\")\nax.scatter(g[s2], y[s2], s=14, alpha=0.6, label=\"1984Q1-2019Q4\", color=\"tab:orange\")\nxx = np.linspace(g.min(), g.max(), 50)\nfor key, c in [(\"sub1\", \"tab:blue\"), (\"sub2\", \"tab:orange\")]:\n a, b = fits[key][0].params\n ax.plot(xx, a + b * xx, color=c, lw=2, label=f\"fit {key}: du = {a:.2f} {b:+.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()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: results[k] for k in [\"effect_estimate\", \"effect_se\", \"effect_ci_low\", \"effect_ci_high\",\n \"beta_sub1\", \"beta_sub2\", \"chow_f_stat\", \"chow_p_value\", \"baseline_metric\", \"model_metric\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv","description":"Write and run analysis script"}}]}}44{"type":"api-request","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","id":"7f3f5374-668c-4359-9147-cbc55b6bd9cf","timestamp":"2026-10-30T04:49:19.743Z","version":"2.1.314","querySource":"sdk","shapeHash":"c3c94937d96da46f69f6d4c09f4ff501be59660c9cdf3cce35bc2dad0b838ad3","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":"0eb3b63c-7dd6-4bc6-8984-50d44cd97c8d","keep":12,"tail":["5c8f17645ef435c4b9d19e76769b455f297280a8620c34d04bb1f61374921e3b","a38dcd7f61d90f9686482270cd26c750d1984959e15b9935c16e1043c10d9c29"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a0604b5157b6fc2d5f73d98023cfb522735de7e68f9f658db7de5943625c478d"}45{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"80a30751-c143-47e9-b8c5-0cb445e6cc77","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290"}46{"type":"atis-latch","atis":"","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290"}47{"parentUuid":"80a30751-c143-47e9-b8c5-0cb445e6cc77","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpQMHiiFyJeqGHnwcRr","type":"message","role":"assistant","content":[{"type":"text","text":"The previous `cd` persisted; 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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, 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)\ngdp = pd.read_csv(\"data/GDPC1.csv\", na_values=[\".\"])\nun = pd.read_csv(\"data/UNRATE.csv\", na_values=[\".\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(un))}\ngdp[\"observation_date\"] = pd.to_datetime(gdp[\"observation_date\"])\nun[\"observation_date\"] = pd.to_datetime(un[\"observation_date\"])\n\ndata_issues = []\nmiss = un[un[\"UNRATE\"].isna()][\"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nif miss:\n data_issues.append({\"issue\": f\"UNRATE missing value(s) at {miss} (blank in raw FRED CSV)\",\n \"handling\": \"outside the 1959Q4-2019Q4 window; dropped with all post-2019Q4 observations, no effect on estimates\"})\ndata_issues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are the 2026 vintage (revised, not real-time)\",\n \"handling\": \"all observations after 2019Q4 excluded before computation; vintage noted as limitation\"})\n\n# Quarterly unemployment = mean of 3 months; require all 3 months present\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ndf = gdp.set_index(\"q\")[[\"GDPC1\"]].join(uq, how=\"left\")\n\nexcl = []\ndef log(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nn0 = len(df)\nwin = df.loc[pd.Period(\"1959Q4\"):pd.Period(\"2019Q4\")].copy()\nlog(1, \"keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit)\", n0, len(win),\n \"task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\")\nassert (win[\"count\"] == 3).all() and win[\"GDPC1\"].notna().all(), \"incomplete data inside window\"\nwin[\"u\"] = win[\"mean\"]\nwin[\"du\"] = win[\"u\"].diff()\nwin[\"g\"] = 100 * np.log(win[\"GDPC1\"]).diff()\nreg = win.dropna(subset=[\"du\", \"g\"]).copy()\nlog(2, \"drop 1959Q4 (no lagged level, differences undefined)\", len(win), len(reg),\n \"first-difference construction\")\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert len(reg) == 240 and str(reg.index[0]) == \"1960Q1\" and str(reg.index[-1]) == \"2019Q4\"\n\ny, X = reg[\"du\"].values, sm.add_constant(reg[\"g\"].values)\nn = len(y)\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n# Full sample OLS with Newey-West HAC (Bartlett) SE\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nols = sm.OLS(y, X).fit()\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se, beta + z * se)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n\n# Subperiods\ns1 = reg.index <= pd.Period(\"1983Q4\")\ns2 = ~s1\nfits = {}\nfor name, m in [(\"sub1\", s1), (\"sub2\", s2)]:\n Xs = sm.add_constant(reg[\"g\"].values[m])\n f = sm.OLS(y[m], Xs).fit()\n fh = sm.OLS(y[m], Xs).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n fits[name] = (f, fh, int(m.sum()))\nssr_p = ols.ssr\nssr_u = fits[\"sub1\"][0].ssr + fits[\"sub2\"][0].ssr\nk = 2\nchow_f = float(((ssr_p - ssr_u) / k) / (ssr_u / (n - 2 * k)))\nchow_p = float(stats.f.sf(chow_f, k, n - 2 * k))\n\nconclusion = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\nresults = {\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\": reg.index[0].start_time.strftime(\"%Y-%m-%d\"),\n \"sample_end\": reg.index[-1].start_time.strftime(\"%Y-%m-%d\"),\n \"n_sub1\": fits[\"sub1\"][2],\n \"n_sub2\": fits[\"sub2\"][2],\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(ci[0]),\n \"effect_ci_high\": float(ci[1]),\n \"beta_sub1\": float(fits[\"sub1\"][0].params[1]),\n \"beta_sub2\": float(fits[\"sub2\"][0].params[1]),\n \"chow_f_stat\": chow_f,\n \"chow_p_value\": chow_p,\n \"stability_conclusion\": \"stable\" if chow_p >= 0.05 else \"unstable\",\n \"conclusion\": conclusion,\n # supplementary (not required)\n \"ols_classical_se_full\": float(ols.bse[1]),\n \"r_squared_full\": float(ols.rsquared),\n \"intercept_sub1\": float(fits[\"sub1\"][0].params[0]),\n \"intercept_sub2\": float(fits[\"sub2\"][0].params[0]),\n \"beta_sub1_hac_se\": float(fits[\"sub1\"][1].bse[1]),\n \"beta_sub2_hac_se\": float(fits[\"sub2\"][1].bse[1]),\n \"implied_zero_du_growth_full\": float(-alpha / beta),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh_:\n json.dump(results, fh_, indent=2)\n\nrows = [(\"full_1960Q1_2019Q4\", n, alpha, beta, se)]\nfor lab, key in [(\"sub1_1960Q1_1983Q4\", \"sub1\"), (\"sub2_1984Q1_2019Q4\", \"sub2\")]:\n f, fh, nn = fits[key]\n rows.append((lab, nn, float(f.params[0]), float(f.params[1]), float(fh.bse[1])))\npd.DataFrame(rows, columns=[\"period\", \"n\", \"intercept\", \"slope\", \"slope_se\"]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ng = reg[\"g\"].values\nax.scatter(g[s1], y[s1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\", color=\"tab:blue\")\nax.scatter(g[s2], y[s2], s=14, alpha=0.6, label=\"1984Q1-2019Q4\", color=\"tab:orange\")\nxx = np.linspace(g.min(), g.max(), 50)\nfor key, c in [(\"sub1\", \"tab:blue\"), (\"sub2\", \"tab:orange\")]:\n a, b = fits[key][0].params\n ax.plot(xx, a + b * xx, color=c, lw=2, label=f\"fit {key}: du = {a:.2f} {b:+.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()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: results[k] for k in [\"effect_estimate\", \"effect_se\", \"effect_ci_low\", \"effect_ci_high\",\n \"beta_sub1\", \"beta_sub2\", \"chow_f_stat\", \"chow_p_value\", \"baseline_metric\", \"model_metric\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat 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":192,"cache_read_input_tokens":16636,"output_tokens":3588,"output_tokens_details":{"thinking_tokens":24},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":192},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpQXjG6L5sfMbtj87nq","requestRef":"a1902df1-54a4-4d7b-9426-9a9e83d818b7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e1e04fba-f8d7-4ac7-8834-c6d18d66e3b5","timestamp":"2026-10-30T04:49:45.636Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"e1e04fba-f8d7-4ac7-8834-c6d18d66e3b5","isSidechain":false,"promptId":"f1abe550-c839-453e-86a3-50749a1a8dd0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LK3wmqSwrtoSzFCYFce9S4","type":"tool_result","content":"{\n \"effect_estimate\": -0.2557677277487258,\n \"effect_se\": 0.03598488399675878,\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 \"baseline_metric\": 0.32465793964999184,\n \"model_metric\": 0.2497438816636253\n}\nperiod,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,keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit),319,241,78,task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\n2,\"drop 1959Q4 (no lagged level, differences undefined)\",241,240,1,first-difference construction","is_error":false}]},"uuid":"a713c7e9-b125-46ee-9085-37268d2284f2","timestamp":"2026-10-30T04:49:46.838Z","toolUseResult":{"stdout":"{\n \"effect_estimate\": -0.2557677277487258,\n \"effect_se\": 0.03598488399675878,\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 \"baseline_metric\": 0.32465793964999184,\n \"model_metric\": 0.2497438816636253\n}\nperiod,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,keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit),319,241,78,task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\n2,\"drop 1959Q4 (no lagged level, differences undefined)\",241,240,1,first-difference construction","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"e1e04fba-f8d7-4ac7-8834-c6d18d66e3b5","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}55{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"f6a372556bc6c0af11a9310fe08db0baeda08103c4c43a4a89c876cb2c1a6926","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01LK3wmqSwrtoSzFCYFce9S4","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. 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, 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)\ngdp = pd.read_csv(\"data/GDPC1.csv\", na_values=[\".\"])\nun = pd.read_csv(\"data/UNRATE.csv\", na_values=[\".\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(un))}\ngdp[\"observation_date\"] = pd.to_datetime(gdp[\"observation_date\"])\nun[\"observation_date\"] = pd.to_datetime(un[\"observation_date\"])\n\ndata_issues = []\nmiss = un[un[\"UNRATE\"].isna()][\"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\nif miss:\n data_issues.append({\"issue\": f\"UNRATE missing value(s) at {miss} (blank in raw FRED CSV)\",\n \"handling\": \"outside the 1959Q4-2019Q4 window; dropped with all post-2019Q4 observations, no effect on estimates\"})\ndata_issues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are the 2026 vintage (revised, not real-time)\",\n \"handling\": \"all observations after 2019Q4 excluded before computation; vintage noted as limitation\"})\n\n# Quarterly unemployment = mean of 3 months; require all 3 months present\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ndf = gdp.set_index(\"q\")[[\"GDPC1\"]].join(uq, how=\"left\")\n\nexcl = []\ndef log(step, rule, before, after, why):\n excl.append(dict(step=step, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\nn0 = len(df)\nwin = df.loc[pd.Period(\"1959Q4\"):pd.Period(\"2019Q4\")].copy()\nlog(1, \"keep quarters 1959Q4-2019Q4 (GDPC1 quarters as unit)\", n0, len(win),\n \"task window; 1959Q4 needed only for lagged levels; post-2019Q4 forbidden\")\nassert (win[\"count\"] == 3).all() and win[\"GDPC1\"].notna().all(), \"incomplete data inside window\"\nwin[\"u\"] = win[\"mean\"]\nwin[\"du\"] = win[\"u\"].diff()\nwin[\"g\"] = 100 * np.log(win[\"GDPC1\"]).diff()\nreg = win.dropna(subset=[\"du\", \"g\"]).copy()\nlog(2, \"drop 1959Q4 (no lagged level, differences undefined)\", len(win), len(reg),\n \"first-difference construction\")\npd.DataFrame(excl).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nassert len(reg) == 240 and str(reg.index[0]) == \"1960Q1\" and str(reg.index[-1]) == \"2019Q4\"\n\ny, X = reg[\"du\"].values, sm.add_constant(reg[\"g\"].values)\nn = len(y)\n# Baseline: mean-only prediction\nbaseline_rmse = float(np.sqrt(np.mean((y - y.mean()) ** 2)))\n# Full sample OLS with Newey-West HAC (Bartlett) SE\nHAC_LAGS = int(np.floor(4 * (n / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nols = sm.OLS(y, X).fit()\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\nalpha, beta = float(ols.params[0]), float(ols.params[1])\nse = float(hac.bse[1])\nz = stats.norm.ppf(0.975)\nci = (beta - z * se, beta + z * se)\nmodel_rmse = float(np.sqrt(np.mean(ols.resid ** 2)))\n\n# Subperiods\ns1 = reg.index <= pd.Period(\"1983Q4\")\ns2 = ~s1\nfits = {}\nfor name, m in [(\"sub1\", s1), (\"sub2\", s2)]:\n Xs = sm.add_constant(reg[\"g\"].values[m])\n f = sm.OLS(y[m], Xs).fit()\n fh = sm.OLS(y[m], Xs).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS, \"use_correction\": True})\n fits[name] = (f, fh, int(m.sum()))\nssr_p = ols.ssr\nssr_u = fits[\"sub1\"][0].ssr + fits[\"sub2\"][0].ssr\nk = 2\nchow_f = float(((ssr_p - ssr_u) / k) / (ssr_u / (n - 2 * k)))\nchow_p = float(stats.f.sf(chow_f, k, n - 2 * k))\n\nconclusion = \"positive_effect\" if ci[0] > 0 else (\"negative_effect\" if ci[1] < 0 else \"no_detectable_effect\")\nresults = {\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\": reg.index[0].start_time.strftime(\"%Y-%m-%d\"),\n \"sample_end\": reg.index[-1].start_time.strftime(\"%Y-%m-%d\"),\n \"n_sub1\": fits[\"sub1\"][2],\n \"n_sub2\": fits[\"sub2\"][2],\n \"baseline_metric\": baseline_rmse,\n \"model_metric\": model_rmse,\n \"effect_estimate\": beta,\n \"intercept\": alpha,\n \"effect_se\": se,\n \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(ci[0]),\n \"effect_ci_high\": float(ci[1]),\n \"beta_sub1\": float(fits[\"sub1\"][0].params[1]),\n \"beta_sub2\": float(fits[\"sub2\"][0].params[1]),\n \"chow_f_stat\": chow_f,\n \"chow_p_value\": chow_p,\n \"stability_conclusion\": \"stable\" if chow_p >= 0.05 else \"unstable\",\n \"conclusion\": conclusion,\n # supplementary (not required)\n \"ols_classical_se_full\": float(ols.bse[1]),\n \"r_squared_full\": float(ols.rsquared),\n \"intercept_sub1\": float(fits[\"sub1\"][0].params[0]),\n \"intercept_sub2\": float(fits[\"sub2\"][0].params[0]),\n \"beta_sub1_hac_se\": float(fits[\"sub1\"][1].bse[1]),\n \"beta_sub2_hac_se\": float(fits[\"sub2\"][1].bse[1]),\n \"implied_zero_du_growth_full\": float(-alpha / beta),\n}\nwith open(f\"{OUT}/results.json\", \"w\") as fh_:\n json.dump(results, fh_, indent=2)\n\nrows = [(\"full_1960Q1_2019Q4\", n, alpha, beta, se)]\nfor lab, key in [(\"sub1_1960Q1_1983Q4\", \"sub1\"), (\"sub2_1984Q1_2019Q4\", \"sub2\")]:\n f, fh, nn = fits[key]\n rows.append((lab, nn, float(f.params[0]), float(f.params[1]), float(fh.bse[1])))\npd.DataFrame(rows, columns=[\"period\", \"n\", \"intercept\", \"slope\", \"slope_se\"]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\ng = reg[\"g\"].values\nax.scatter(g[s1], y[s1], s=14, alpha=0.6, label=\"1960Q1-1983Q4\", color=\"tab:blue\")\nax.scatter(g[s2], y[s2], s=14, alpha=0.6, label=\"1984Q1-2019Q4\", color=\"tab:orange\")\nxx = np.linspace(g.min(), g.max(), 50)\nfor key, c in [(\"sub1\", \"tab:blue\"), (\"sub2\", \"tab:orange\")]:\n a, b = fits[key][0].params\n ax.plot(xx, a + b * xx, color=c, lw=2, label=f\"fit {key}: du = {a:.2f} {b:+.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()\nfig.savefig(f\"{OUT}/figure1.png\", dpi=120)\nprint(json.dumps({k: results[k] for k in [\"effect_estimate\", \"effect_se\", \"effect_ci_low\", \"effect_ci_high\",\n \"beta_sub1\", \"beta_sub2\", \"chow_f_stat\", \"chow_p_value\", \"baseline_metric\", \"model_metric\"]}, indent=1))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/table1.csv out/exclusions.csv","description":"Write and run analysis 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\"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 \"ols_classical_se_full\": 0.01996009291490984,\n \"r_squared_full\": 0.4082508284562034,\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 \"implied_zero_du_growth_full\": 0.7209211099660549,\n \"hac_slope_diff_p_value\": 0.8548271295127585,\n \"hac_intercept_diff_p_value\": 0.25395491785679547,\n \"hac_se_lag_sensitivity\": {\n \"0\": 0.028957130333809637,\n \"2\": 0.034370831642247524,\n \"8\": 0.03729120927832667,\n \"12\": 0.03656150942843361\n },\n \"resid_variance_sub1\": 0.07850312611325812,\n \"resid_variance_sub2\": 0.04827674526450526\n}","is_error":false}]},"uuid":"3b165d2d-f649-4276-ab8e-4d481f203723","timestamp":"2026-10-30T04:49:56.341Z","toolUseResult":{"stdout":" \"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 \"ols_classical_se_full\": 0.01996009291490984,\n \"r_squared_full\": 0.4082508284562034,\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 \"implied_zero_du_growth_full\": 0.7209211099660549,\n \"hac_slope_diff_p_value\": 0.8548271295127585,\n \"hac_intercept_diff_p_value\": 0.25395491785679547,\n \"hac_se_lag_sensitivity\": {\n \"0\": 0.028957130333809637,\n \"2\": 0.034370831642247524,\n \"8\": 0.03729120927832667,\n \"12\": 0.03656150942843361\n },\n \"resid_variance_sub1\": 0.07850312611325812,\n \"resid_variance_sub2\": 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","media_type":"image/png"}}]}]},"uuid":"69672ad1-e99e-4793-889d-3d8e07e61758","timestamp":"2026-10-30T04:50:05.044Z","toolUseResult":{"type":"image","file":{"base64":"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out/results.json /tmp/r1.json; rm 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\nr=json.load(open('out/results.json'))\ne=pd.read_csv('out/exclusions.csv')\nprint('excl sum', 319-e.n_removed.sum()==r['n_analysed'])\nprint('n split', r['n_sub1']+r['n_sub2']==r['n_analysed'], r['n_sub1'],r['n_sub2'])\nprint('ci', r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high'], 'se>0', r['effect_se']>0)\nprint('model<baseline', r['model_metric']<r['baseline_metric'])\nprint('R2 identity', abs(1-(r['model_metric']/r['baseline_metric'])**2 - r['r_squared_full'])<1e-12)\nprint('dates', r['sample_start'],r['sample_end'])\nprint('beta within sub range-ish', r['beta_sub1'],r['beta_sub2'],r['effect_estimate'])\nEOF","description":"Clean rerun and consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":657,"cache_read_input_tokens":22327,"output_tokens":669,"output_tokens_details":{"thinking_tokens":56},"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":657},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":2,"requestId":"req_011CgXpTY9uNit6DjWRNq2Ab","requestRef":"9aa17f23-7aee-4175-acce-abb90aedde1f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"97b2de81-bd82-4dc8-a89e-571cb63cae2f","timestamp":"2026-10-30T04:50:08.779Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}74{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"97b2de81-bd82-4dc8-a89e-571cb63cae2f","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290"}75{"type":"atis-latch","atis":"","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290"}76{"parentUuid":"97b2de81-bd82-4dc8-a89e-571cb63cae2f","isSidechain":false,"promptId":"f1abe550-c839-453e-86a3-50749a1a8dd0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01XVDp95NSHP61o836ngHtPS","type":"tool_result","content":"IDENTICAL\nexcl sum True\nn split True 96 144\nci True se>0 True\nmodel<baseline True\nR2 identity True\ndates 1960-01-01 2019-10-01\nbeta within sub range-ish -0.25902105345680587 -0.27339263306757905 -0.2557677277487258","is_error":false}]},"uuid":"008af13e-d136-4afd-a6ec-9b4429e2e79d","timestamp":"2026-10-30T04:50:10.188Z","toolUseResult":{"stdout":"IDENTICAL\nexcl sum True\nn split True 96 144\nci True se>0 True\nmodel<baseline True\nR2 identity True\ndates 1960-01-01 2019-10-01\nbeta within sub range-ish -0.25902105345680587 -0.27339263306757905 -0.2557677277487258","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"97b2de81-bd82-4dc8-a89e-571cb63cae2f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}77{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"2926c79c73c9b635c0edb4cb5f44dab0b06d7559799cc431adae15c269eabbf4","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01PJj7ta9YeRQc2vdWTf22UE","name":"Read","input":{"file_path":"/Users/simon-pierreboucher/wp29/runs/RC0169/out/figure1.png"}},{"type":"tool_use","id":"toolu_01XVDp95NSHP61o836ngHtPS","name":"Bash","input":{"command":"cp out/results.json /tmp/r1.json; rm 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\nr=json.load(open('out/results.json'))\ne=pd.read_csv('out/exclusions.csv')\nprint('excl sum', 319-e.n_removed.sum()==r['n_analysed'])\nprint('n split', r['n_sub1']+r['n_sub2']==r['n_analysed'], r['n_sub1'],r['n_sub2'])\nprint('ci', r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high'], 'se>0', r['effect_se']>0)\nprint('model<baseline', r['model_metric']<r['baseline_metric'])\nprint('R2 identity', abs(1-(r['model_metric']/r['baseline_metric'])**2 - r['r_squared_full'])<1e-12)\nprint('dates', r['sample_start'],r['sample_end'])\nprint('beta within sub range-ish', r['beta_sub1'],r['beta_sub2'],r['effect_estimate'])\nEOF","description":"Clean rerun and consistency checks"}}]}}78{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"0a25b576ba9561f1a80afa803d75d2beb698cff8ff90937bc59876903e721ac6","message":{"role":"user","content":[{"tool_use_id":"toolu_01PJj7ta9YeRQc2vdWTf22UE","type":"tool_result","content":[{"type":"image","source":{"type":"base64","data":"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sum True\nn split True 96 144\nci True se>0 True\nmodel<baseline True\nR2 identity True\ndates 1960-01-01 2019-10-01\nbeta within sub range-ish -0.25902105345680587 -0.27339263306757905 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json, table1.csv, figure1.png, exclusions.csv; re-ran analysis.py); results.json byte-identical | PASS |\n| Exclusion ledger: 319 GDPC1 quarters − 78 − 1 = 240 = n_analysed | PASS |\n| n_sub1 + n_sub2 = 96 + 144 = 240 = n_analysed | PASS |\n| sample_start 1960-01-01 < sample_end 2019-10-01; no quarter after 2019Q4 used (asserted in script) | PASS |\n| All 3 monthly UNRATE values present for every quarter 1959Q4–2019Q4; no missing GDPC1 (asserted) | PASS |\n| effect_se > 0; CI contains estimate; CI symmetric with z=1.96 | PASS |\n| model_metric (0.250) < baseline_metric (0.325); 1 − (model/baseline)^2 = R² (0.408) to 1e-12 | PASS |\n| Magnitude: β ≈ −0.26 pp per 1% q/q growth, in the conventional Okun range (≈ −0.3 to −0.5 per annualised-equivalent... i.e. ≈ −0.25 to −0.5 here) | PASS (plausible) |\n| Chow df = (2, 236); F > 0 | PASS |\n| Full-sample β (−0.256) lies 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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: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 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/RC0169/out/state/gate_receipts/20261030T045019.492534Z.json\n20261030T045019.492534Z.json","is_error":false}]},"uuid":"0a242e76-4b0c-403e-810e-e7413db13f16","timestamp":"2026-10-30T04:50:19.502Z","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: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 2 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: 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n| check | outcome |\n|---|---|\n| Clean re-run (deleted results.json, table1.csv, figure1.png, exclusions.csv; re-ran analysis.py); results.json byte-identical | PASS |\n| Exclusion ledger: 319 GDPC1 quarters − 78 − 1 = 240 = n_analysed | PASS |\n| n_sub1 + n_sub2 = 96 + 144 = 240 = n_analysed | PASS |\n| sample_start 1960-01-01 < sample_end 2019-10-01; no quarter after 2019Q4 used (asserted in script) | PASS |\n| All 3 monthly UNRATE values present for every quarter 1959Q4–2019Q4; no missing GDPC1 (asserted) | PASS |\n| effect_se > 0; CI contains estimate; CI symmetric with z=1.96 | PASS |\n| model_metric (0.250) < baseline_metric (0.325); 1 − (model/baseline)^2 = R² (0.408) to 1e-12 | PASS |\n| Magnitude: β ≈ −0.26 pp per 1% q/q growth, in the conventional Okun range (≈ −0.3 to −0.5 per annualised-equivalent... i.e. ≈ −0.25 to −0.5 here) | PASS (plausible) |\n| Chow df = (2, 236); F > 0 | PASS |\n| Full-sample β (−0.256) lies outside [β_sub2, β_sub1] = [−0.273, −0.259]: explained by the intercept shift between subperiods (pooled fit with a common intercept tilts the slope); not an error | NOTED |\n| HAC SE sensitivity to lag length (0,2,4,8,12): 0.029–0.037, conclusion unchanged | PASS |\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate (P4)"}}]}}86{"type":"api-request","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","id":"f16806b3-483a-441c-be28-9aed2f6591a7","timestamp":"2026-10-30T04:50:19.506Z","version":"2.1.314","querySource":"sdk","shapeHash":"c3c94937d96da46f69f6d4c09f4ff501be59660c9cdf3cce35bc2dad0b838ad3","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":"b33fb11e-4e6c-4c2d-905f-03252807f89e","keep":24,"tail":["31608d7cc82df728a4f3c0608e2bf0b7f65b79eee8049c3dc2ec77e2519ee6c2","b6859660703aa05faccc1160e963f0df016d58bf937ef56381be79f4b6277dc8"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7ec479471cc641af34c26bb47a34c2dc49a1f232ed58f167250f3809b05a42d8"}87{"parentUuid":"0a242e76-4b0c-403e-810e-e7413db13f16","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpUksATd6eRZi5uaBbw","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":1908,"cache_read_input_tokens":24474,"output_tokens":474,"output_tokens_details":{"thinking_tokens":44},"server_tool_use":{"web_search_requests":0,"web_fetch_requests":0},"service_tier":"standard","cache_creation":{"ephemeral_1h_input_tokens":0,"ephemeral_5m_input_tokens":1908},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":403,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpUkbHFgtJw6RdDuhQs","requestRef":"f16806b3-483a-441c-be28-9aed2f6591a7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c6f94a91-c4c0-48ad-8245-80d212f56c75","timestamp":"2026-10-30T04:50:21.218Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}88{"parentUuid":"c6f94a91-c4c0-48ad-8245-80d212f56c75","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpUksATd6eRZi5uaBbw","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XaLmDct5Axnsiz7hosGn1t","name":"Bash","input":{"command":"sed 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Estimand: β in Δu_t = α + β·g_t + ε_t, 1960Q1–2019Q4, with u_t the quarterly mean of monthly UNRATE (percent) and g_t = 100·Δln GDPC1 (percent, not annualised). Stability: separate OLS on 1960Q1–1983Q4 and 1984Q1–2019Q4 plus a classical Chow F-test of joint equality of (α, β).\n\n## Data and exclusions\nInputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01–2026-09) (`n_input_rows`). SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Data issues (`data_issues`): one blank UNRATE value (2025-10), outside the window; both series extend past 2019Q4 and are the 2026 revised vintage. Exclusions (`out/exclusions.csv`): 319 GDPC1 quarters → 241 in 1959Q4–2019Q4 (78 removed, incl. everything after 2019Q4) → 240 after losing 1959Q4 to differencing. All three months were present in every quarter of the window. Regression sample: `sample_start` = 1960-01-01 to `sample_end` = 2019-10-01, `n_analysed` = 240; `n_sub1` = 96, `n_sub2` = 144.\n\n## Method\n- Baseline: mean-only prediction of Δu; RMSE `baseline_metric`.\n- Main: full-sample OLS of Δu on a constant and g; point estimates `effect_estimate`, `intercept`; in-sample residual RMSE `model_metric`.\n- Uncertainty: Newey–West HAC standard error (Bartlett kernel, small-sample correction) with `hac_lags` = 4 quarters, chosen by the Newey–West (1994) rule floor(4(T/100)^{2/9}) with T = 240; 95% CI = β ± 1.96·SE (`effect_ci_low`, `effect_ci_high`). `conclusion` is set from whether this interval excludes zero.\n- Stability: OLS by subperiod (`beta_sub1`, `beta_sub2`); classical Chow F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)], df (2, 236) (`chow_f_stat`, `chow_p_value`, `stability_conclusion`). Supplementary (not required): HAC Wald tests in an interaction model with a post-1984 dummy, and HAC SE sensitivity to the lag length.\n- No randomness used (`random_seed` = null). Everything is produced by `out/analysis.py` (also `out/table1.csv`, `out/figure1.png`).\n\n## Results\n- Okun coefficient: `effect_estimate` = −0.256 pp of unemployment per 1% quarterly real GDP growth; `intercept` = 0.184 pp. So unemployment is flat when quarterly growth is about 0.72% (`implied_zero_du_growth_full`), roughly 2.9% annualised.\n- Fit: `model_metric` = 0.250 pp, compared with `baseline_metric` = 0.325 pp for the mean-only baseline (`r_squared_full` = 0.408).\n- Subperiods (Table 1, `out/table1.csv`): `beta_sub1` = −0.259 (`intercept_sub1` = 0.254) and `beta_sub2` = −0.273 (`intercept_sub2` = 0.152). The slopes are almost the same. What differs is the intercept: after 1984, unemployment stops rising at a lower growth rate, which fits slower trend growth.\n- Chow test: `chow_f_stat` = 6.09, `chow_p_value` = 0.0026 → `stability_conclusion` = `unstable`. The joint (α, β) relation is not stable. The difference appears to come from the intercept, not the slope. Testing the slope difference alone with HAC gives p = 0.85 (`hac_slope_diff_p_value`). The intercept difference alone gives p = 0.25 (`hac_intercept_diff_p_value`). The joint HAC Wald test still rejects (`hac_joint_break_f_stat` = 5.37, `hac_joint_break_p_value` = 0.0052), because the two estimates are strongly correlated.\n- Figure 1 (`out/figure1.png`): scatter of Δu against g with the two subperiod fitted lines.\n- Conclusion code: `conclusion` = `negative_effect`.\n\n## Uncertainty\nHAC SE `effect_se` = 0.036 (classical OLS SE `ols_classical_se_full` = 0.020). The HAC SE is about 1.8 times the classical one, so the residuals are serially correlated and/or heteroskedastic. 95% CI [`effect_ci_low`, `effect_ci_high`] = [−0.326, −0.185], well below zero. Changing the lag length (0, 2, 8, 12) gives SEs of 0.029–0.037 (`hac_se_lag_sensitivity`). Those choices do not change the conclusion. The subperiod HAC SEs are `beta_sub1_hac_se` = 0.040 and `beta_sub2_hac_se` = 0.069, so the two slopes cannot be told apart statistically.\n\n## Limitations and open issues\n- **The Chow test assumes things the data do not meet.** The classical Chow F assumes homoskedastic, serially uncorrelated errors, and it was required in that form. Residual variance differs between the subperiods (`resid_variance_sub1` = 0.079 vs `resid_variance_sub2` = 0.048, i.e. the Great Moderation). The large HAC/OLS SE ratio shows the errors are not i.i.d. So the classical p-value is not exactly valid. The HAC joint Wald test (p = 0.0052) points the same way. Even so, a Wald test that allows different variances in each subperiod, or a sup-Wald test with an unknown break date, was not run. The 1984 break date is set by the task rather than estimated, which makes conventional inference a fair test of a pre-specified hypothesis. It does not show the break actually happened in 1984.\n- **What \"unstable\" means here.** The rejection applies to (α, β) jointly. The question asks whether the *response* (β) is stable, and our data give no evidence that the slope changed. Reading `stability_conclusion` = unstable as \"Okun's coefficient changed\" would overstate the result.\n- **Specification threats.** The static difference form leaves out lagged growth. Unemployment usually lags output, and this is part of why the residuals are autocorrelated. A dynamic model could give a different long-run coefficient. The relation is also treated as linear and symmetric; asymmetry between recessions and expansions was not checked. OLS treats g as exogenous. β is a reduced-form association, not a causal effect, and measurement error in GDP would bias |β| towards zero.\n- **Data vintage.** GDPC1 is the 2026 revised vintage. Results based on real-time data, or on a different benchmark revision, could differ somewhat. Not checked.\n- **Outliers and influence.** No influence diagnostics were run. Large recession quarters (e.g. 1980, 2008–09) visibly drive the fit in Figure 1.\n- **Not checked:** other kernels or bandwidth selectors (beyond the lag sensitivity reported), t versus normal critical values (with T = 240 the difference is negligible), and the robustness of the result to the break date.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4, a 1% higher quarterly real GDP growth rate is associated with a 0.256 pp smaller rise in the unemployment rate; the HAC 95% interval lies entirely below zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"hac_lags\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association from a static difference model; not causal.\"},\n {\"id\":\"C2\",\"text\":\"The Okun regression lowers the in-sample RMSE of the quarterly change in unemployment from 0.325 pp (mean-only baseline) to 0.250 pp.\",\"results_keys\":[\"baseline_metric\",\"model_metric\",\"r_squared_full\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only; no out-of-sample evaluation.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of intercept and slope across 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (F = 6.09, p = 0.0026), so the relation is classified as unstable.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"hac_joint_break_p_value\"],\"status\":\"supported\",\"caveat\":\"Classical Chow assumes i.i.d. errors, which the data contradict; a HAC joint Wald test also rejects.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273) and their difference is not statistically significant; the instability is in the intercept/joint relation rather than in the Okun coefficient itself.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"beta_sub1_hac_se\",\"beta_sub2_hac_se\",\"hac_slope_diff_p_value\",\"intercept_sub1\",\"intercept_sub2\",\"hac_intercept_diff_p_value\"],\"status\":\"supported\",\"caveat\":\"The individual intercept difference is not significant either (HAC p = 0.25); the attribution to the intercept is based on point estimates and the joint test.\"},\n {\"id\":\"C5\",\"text\":\"HAC inference on the full-sample beta is robust to the choice of Newey-West lag length between 0 and 12.\",\"results_keys\":[\"hac_se_lag_sensitivity\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel with fixed lags examined.\"}\n]\nEOF","description":"Write report and claims ledger"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":575,"cache_read_input_tokens":26382,"output_tokens":3654,"output_tokens_details":{"thinking_tokens":42},"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":575},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpV7uCJMQkT7Pzd8s2R","requestRef":"a878c0b5-4b46-4ac7-af15-f5bfb85a331d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"57666f16-7acd-48cb-9723-b2fab4a6c566","timestamp":"2026-10-30T04:50:55.573Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}94{"parentUuid":"57666f16-7acd-48cb-9723-b2fab4a6c566","isSidechain":false,"promptId":"f1abe550-c839-453e-86a3-50749a1a8dd0","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MB6wnboRxKDPjz1h3B31dk","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"ebfb1c99-341a-42d2-9363-dfe5a92c5186","timestamp":"2026-10-30T04:50:55.615Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"57666f16-7acd-48cb-9723-b2fab4a6c566","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}95{"type":"api-request-blob","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","hash":"3d04d32f1514ad9c3b59c38df80a30ed4c7bd02bcd0044e6ef6bf88efa398a72","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MB6wnboRxKDPjz1h3B31dk","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960–2019 (F6-conf-03)\n\n## Question\nHow strongly 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, 1960Q1–2019Q4, with u_t the quarterly mean of monthly UNRATE (percent) and g_t = 100·Δln GDPC1 (percent, not annualised). Stability: separate OLS on 1960Q1–1983Q4 and 1984Q1–2019Q4 plus a classical Chow F-test of joint equality of (α, β).\n\n## Data and exclusions\nInputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01–2026-09) (`n_input_rows`). SHA-256 hashes match `data/README.md` (see `out/state/data_audit.md`). Data issues (`data_issues`): one blank UNRATE value (2025-10), outside the window; both series extend past 2019Q4 and are the 2026 revised vintage. Exclusions (`out/exclusions.csv`): 319 GDPC1 quarters → 241 in 1959Q4–2019Q4 (78 removed, incl. everything after 2019Q4) → 240 after losing 1959Q4 to differencing. All three months were present in every quarter of the window. Regression sample: `sample_start` = 1960-01-01 to `sample_end` = 2019-10-01, `n_analysed` = 240; `n_sub1` = 96, `n_sub2` = 144.\n\n## Method\n- Baseline: mean-only prediction of Δu; RMSE `baseline_metric`.\n- Main: full-sample OLS of Δu on a constant and g; point estimates `effect_estimate`, `intercept`; in-sample residual RMSE `model_metric`.\n- Uncertainty: Newey–West HAC standard error (Bartlett kernel, small-sample correction) with `hac_lags` = 4 quarters, chosen by the Newey–West (1994) rule floor(4(T/100)^{2/9}) with T = 240; 95% CI = β ± 1.96·SE (`effect_ci_low`, `effect_ci_high`). `conclusion` is set from whether this interval excludes zero.\n- Stability: OLS by subperiod (`beta_sub1`, `beta_sub2`); classical Chow F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)], df (2, 236) (`chow_f_stat`, `chow_p_value`, `stability_conclusion`). Supplementary (not required): HAC Wald tests in an interaction model with a post-1984 dummy, and HAC SE sensitivity to the lag length.\n- No randomness used (`random_seed` = null). Everything is produced by `out/analysis.py` (also `out/table1.csv`, `out/figure1.png`).\n\n## Results\n- Okun coefficient: `effect_estimate` = −0.256 pp of unemployment per 1% quarterly real GDP growth; `intercept` = 0.184 pp. So unemployment is flat when quarterly growth is about 0.72% (`implied_zero_du_growth_full`), roughly 2.9% annualised.\n- Fit: `model_metric` = 0.250 pp, compared with `baseline_metric` = 0.325 pp for the mean-only baseline (`r_squared_full` = 0.408).\n- Subperiods (Table 1, `out/table1.csv`): `beta_sub1` = −0.259 (`intercept_sub1` = 0.254) and `beta_sub2` = −0.273 (`intercept_sub2` = 0.152). The slopes are almost the same. What differs is the intercept: after 1984, unemployment stops rising at a lower growth rate, which fits slower trend growth.\n- Chow test: `chow_f_stat` = 6.09, `chow_p_value` = 0.0026 → `stability_conclusion` = `unstable`. The joint (α, β) relation is not stable. The difference appears to come from the intercept, not the slope. Testing the slope difference alone with HAC gives p = 0.85 (`hac_slope_diff_p_value`). The intercept difference alone gives p = 0.25 (`hac_intercept_diff_p_value`). The joint HAC Wald test still rejects (`hac_joint_break_f_stat` = 5.37, `hac_joint_break_p_value` = 0.0052), because the two estimates are strongly correlated.\n- Figure 1 (`out/figure1.png`): scatter of Δu against g with the two subperiod fitted lines.\n- Conclusion code: `conclusion` = `negative_effect`.\n\n## Uncertainty\nHAC SE `effect_se` = 0.036 (classical OLS SE `ols_classical_se_full` = 0.020). The HAC SE is about 1.8 times the classical one, so the residuals are serially correlated and/or heteroskedastic. 95% CI [`effect_ci_low`, `effect_ci_high`] = [−0.326, −0.185], well below zero. Changing the lag length (0, 2, 8, 12) gives SEs of 0.029–0.037 (`hac_se_lag_sensitivity`). Those choices do not change the conclusion. The subperiod HAC SEs are `beta_sub1_hac_se` = 0.040 and `beta_sub2_hac_se` = 0.069, so the two slopes cannot be told apart statistically.\n\n## Limitations and open issues\n- **The Chow test assumes things the data do not meet.** The classical Chow F assumes homoskedastic, serially uncorrelated errors, and it was required in that form. Residual variance differs between the subperiods (`resid_variance_sub1` = 0.079 vs `resid_variance_sub2` = 0.048, i.e. the Great Moderation). The large HAC/OLS SE ratio shows the errors are not i.i.d. So the classical p-value is not exactly valid. The HAC joint Wald test (p = 0.0052) points the same way. Even so, a Wald test that allows different variances in each subperiod, or a sup-Wald test with an unknown break date, was not run. The 1984 break date is set by the task rather than estimated, which makes conventional inference a fair test of a pre-specified hypothesis. It does not show the break actually happened in 1984.\n- **What \"unstable\" means here.** The rejection applies to (α, β) jointly. The question asks whether the *response* (β) is stable, and our data give no evidence that the slope changed. Reading `stability_conclusion` = unstable as \"Okun's coefficient changed\" would overstate the result.\n- **Specification threats.** The static difference form leaves out lagged growth. Unemployment usually lags output, and this is part of why the residuals are autocorrelated. A dynamic model could give a different long-run coefficient. The relation is also treated as linear and symmetric; asymmetry between recessions and expansions was not checked. OLS treats g as exogenous. β is a reduced-form association, not a causal effect, and measurement error in GDP would bias |β| towards zero.\n- **Data vintage.** GDPC1 is the 2026 revised vintage. Results based on real-time data, or on a different benchmark revision, could differ somewhat. Not checked.\n- **Outliers and influence.** No influence diagnostics were run. Large recession quarters (e.g. 1980, 2008–09) visibly drive the fit in Figure 1.\n- **Not checked:** other kernels or bandwidth selectors (beyond the lag sensitivity reported), t versus normal critical values (with T = 240 the difference is negligible), and the robustness of the result to the break date.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4, a 1% higher quarterly real GDP growth rate is associated with a 0.256 pp smaller rise in the unemployment rate; the HAC 95% interval lies entirely below zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"effect_ci_low\",\"effect_ci_high\",\"hac_lags\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association from a static difference model; not causal.\"},\n {\"id\":\"C2\",\"text\":\"The Okun regression lowers the in-sample RMSE of the quarterly change in unemployment from 0.325 pp (mean-only baseline) to 0.250 pp.\",\"results_keys\":[\"baseline_metric\",\"model_metric\",\"r_squared_full\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only; no out-of-sample evaluation.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of intercept and slope across 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (F = 6.09, p = 0.0026), so the relation is classified as unstable.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"hac_joint_break_p_value\"],\"status\":\"supported\",\"caveat\":\"Classical Chow assumes i.i.d. errors, which the data contradict; a HAC joint Wald test also rejects.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273) and their difference is not statistically significant; the instability is in the intercept/joint relation rather than in the Okun coefficient itself.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"beta_sub1_hac_se\",\"beta_sub2_hac_se\",\"hac_slope_diff_p_value\",\"intercept_sub1\",\"intercept_sub2\",\"hac_intercept_diff_p_value\"],\"status\":\"supported\",\"caveat\":\"The individual intercept difference is not significant either (HAC p = 0.25); the attribution to the intercept is based on point estimates and the joint test.\"},\n {\"id\":\"C5\",\"text\":\"HAC inference on the full-sample beta is robust to the choice of Newey-West lag length between 0 and 12.\",\"results_keys\":[\"hac_se_lag_sensitivity\",\"effect_se\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel with fixed lags examined.\"}\n]\nEOF","description":"Write report and claims 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Over 1960Q1–2019Q4, faster GDP growth is clearly tied to smaller rises in unemployment. The Chow test rejects stability, but the slope itself looks unchanged between the two periods; what moved is the intercept. The final gate run passed all 48 checks, and re-running `out/analysis.py` from a clean state gives an identical `out/results.json`.\n\n**Main estimate (240 quarters, 1960Q1–2019Q4):**\n- **Okun coefficient:** β = −0.256 percentage points of unemployment per 1% quarterly (not annualised) real GDP growth.\n- **Uncertainty:** the Newey–West standard error, using 4 lags, is 0.036, giving a 95% interval of [−0.326, −0.185]. The interval lies entirely below zero, so the conclusion code is `negative_effect`.\n- **Lag choice:** using 0 to 12 lags instead gives standard errors between 0.029 and 0.037, which doesn't change the conclusion.\n- **Fit:** the regression's error (RMSE) is 0.250 pp, against 0.325 pp for simply predicting the average change (R² = 0.41).\n- **Zero-change point:** unemployment holds steady at about 0.72% quarterly growth, roughly 2.9% annualised.\n\n**Stability:**\n- **Slopes by period:** −0.259 for 1960Q1–1983Q4 (96 quarters) and −0.273 for 1984Q1–2019Q4 (144 quarters).\n- **Chow test:** F = 6.09, p = 0.0026, so the result is classified as `unstable`.\n- **What drives the rejection:** the intercept falls from 0.254 to 0.152, while the slopes barely differ. Testing the slope difference alone gives p = 0.85. A joint test that allows for serial correlation also rejects (p = 0.005).\n- **Reading the \"unstable\" label:** the response of unemployment to growth itself shows no sign of change. What shifted after 1984 is the growth rate at which unemployment stops rising.\n\n**Limitations (discussed in the report):**\n- The classical Chow test assumes errors that are uncorrelated over time with constant variance. That assumption fails here: residual variance is 0.079 before 1984 and 0.048 after, and the robust standard error is 1.8 times the classical one.\n- The 1984 break date is set by the task, not estimated.\n- The model has no lagged growth terms and doesn't allow recessions to behave differently from expansions.\n- The GDP figures are the revised 2026 data, not what was known at the time.\n- I ran no checks on whether a few large recession quarters drive the fit.\n- One unemployment value is missing (October 2025), but it falls after 2019 and has no effect on any estimate.\n\nI added a few extra results beyond what the task asked for: the robust break tests, the lag-length comparison and the variance of residuals in each period. Everything was computed from the provided data with no random elements.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `table1.csv`\n- `figure1.png`\n- `claims.json`\n- `exclusions.csv`\n- `state/` (records of each step, plus the gate receipts)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":353,"cache_read_input_tokens":31582,"output_tokens":1105,"output_tokens_details":{"thinking_tokens":19},"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":353},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpYRwjNL6vXmaVGayzr","requestRef":"e5c2e5df-bef7-4108-9f8a-d427ed5ff9e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fa64e551-04e9-4797-b44c-e6b397d73995","timestamp":"2026-10-30T04:51:20.437Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0169","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","version":"2.1.314","gitBranch":"HEAD"}110{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"fa64e551-04e9-4797-b44c-e6b397d73995","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290"}111{"type":"cost-state","sessionId":"c68c16b6-da25-49c0-ba21-831cddab1290","totalCostUSD":0.6040034000000001,"totalAPIDuration":166718,"totalAPIDurationWithoutRetries":166709,"totalToolDuration":9663,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":176551,"startTime":1793335703893,"modelUsage":{"claude-opus-5-5":{"inputTokens":36,"outputTokens":19620,"thinkingTokens":1017,"cacheReadInputTokens":296522,"cacheCreationInputTokens":30431,"cacheCreationEphemeral5mInputTokens":30431,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.6040034000000001}},"hasUnknownModelCost":false}112