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The estimand is the Okun coefficient β in the difference form Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4, where Δu_t is the change in the quarterly-average civilian unemployment rate (percentage points) and g_t = 100·(ln GDPC1_t − ln GDPC1_{t−1}) is quarterly real GDP growth in percent (not annualised); uncertainty must be robust to heteroskedasticity and serial correlation. Stability is assessed by estimating β separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and by a classical Chow test of the joint equality of (α, β) across the two subperiods. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/GDPC1.csv` — observed data, FRED GDPC1 (US real GDP, quarterly, billions of chained 2017 dollars, SAAR; BEA), full history, raw FRED CSV (`observation_date` = first day of the quarter, `GDPC1`). Provenance in `data/README.md`.\n- `data/UNRATE.csv` — observed data, FRED UNRATE (US civilian unemployment rate, monthly, percent, SA; BLS), full history, raw FRED CSV (`observation_date` = first day of the month, `UNRATE`). Provenance in `data/README.md`.\n- `data/README.md` — provenance, retrieval dates, SHA-256.\n\n## Information-availability rules\n- Quarterly unemployment u_t is the simple average of the three monthly UNRATE values of quarter t. Δu_t = u_t − u_{t−1}.\n- The regression sample is the 240 quarters 1960Q1–2019Q4 (the 1960Q1 differences use 1959Q4 levels). Quarters after 2019Q4 are outside the question and must not enter any estimate. Subperiod 1 = 1960Q1–1983Q4, subperiod 2 = 1984Q1–2019Q4.\n- Standard errors for the full-sample β must be HAC (Newey–West with Bartlett kernel); choose and report the lag length. The Chow test is the classical sum-of-squared-residuals F-test with 2 and n − 4 degrees of freedom.\n- Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first day of the first and last quarter in the regression sample (`1960-01-01`, `2019-10-01`) |\n| `n_sub1`, `n_sub2` | integer | quarters | observations in each subperiod |\n| `baseline_metric` | number | percentage points | RMSE of the mean-only prediction of Δu over the full sample, sqrt(mean((Δu − mean Δu)²)) |\n| `model_metric` | number | percentage points | RMSE of the full-sample OLS residuals, sqrt(mean(e²)) |\n| `effect_estimate` | number | pp per percent growth | full-sample OLS Okun coefficient β |\n| `intercept` | number | percentage points | full-sample OLS intercept α |\n| `effect_se` | number | pp per percent growth | HAC (Newey–West) standard error of β |\n| `hac_lags` | integer | quarters | Newey–West lag length used |\n| `effect_ci_low`, `effect_ci_high` | number | pp per percent growth | 95% confidence interval for β based on `effect_se` |\n| `beta_sub1`, `beta_sub2` | number | pp per percent growth | OLS slope in subperiod 1 and 2 |\n| `chow_f_stat`, `chow_p_value` | number | — | Chow F statistic and p-value |\n| `stability_conclusion` | string | — | `stable` if the Chow test does not reject at 5%, else `unstable` |\n| `conclusion` | string | — | one of the codes below (sign of β from its 95% interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = quarters in the full-sample regression.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of Δu_t against g_t with the two subperiod fitted lines, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per sample (full, subperiod 1, subperiod 2) with columns `period`, `n`, `intercept`, `slope`, `slope_se` (header + three data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":false}]},"uuid":"b801422b-b1f8-487f-aa87-3c88978f9d5c","timestamp":"2026-10-30T04:50:52.835Z","toolUseResult":{"stdout":"# F6-conf-03 — Okun's law in the United States, 1960–2019: coefficient, uncertainty and stability\n\n## Scientific question\nHow strongly do quarterly changes in the US unemployment rate respond to real output growth, and has that response been stable? The estimand is the Okun coefficient β in the difference form Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4, where Δu_t is the change in the quarterly-average civilian unemployment rate (percentage points) and g_t = 100·(ln GDPC1_t − ln GDPC1_{t−1}) is quarterly real GDP growth in percent (not annualised); uncertainty must be robust to heteroskedasticity and serial correlation. Stability is assessed by estimating β separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and by a classical Chow test of the joint equality of (α, β) across the two subperiods. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/GDPC1.csv` — observed data, FRED GDPC1 (US real GDP, quarterly, billions of chained 2017 dollars, SAAR; BEA), full history, raw FRED CSV (`observation_date` = first day of the quarter, `GDPC1`). Provenance in `data/README.md`.\n- `data/UNRATE.csv` — observed data, FRED UNRATE (US civilian unemployment rate, monthly, percent, SA; BLS), full history, raw FRED CSV (`observation_date` = first day of the month, `UNRATE`). Provenance in `data/README.md`.\n- `data/README.md` — provenance, retrieval dates, SHA-256.\n\n## Information-availability rules\n- Quarterly unemployment u_t is the simple average of the three monthly UNRATE values of quarter t. Δu_t = u_t − u_{t−1}.\n- The regression sample is the 240 quarters 1960Q1–2019Q4 (the 1960Q1 differences use 1959Q4 levels). Quarters after 2019Q4 are outside the question and must not enter any estimate. Subperiod 1 = 1960Q1–1983Q4, subperiod 2 = 1984Q1–2019Q4.\n- Standard errors for the full-sample β must be HAC (Newey–West with Bartlett kernel); choose and report the lag length. The Chow test is the classical sum-of-squared-residuals F-test with 2 and n − 4 degrees of freedom.\n- Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first day of the first and last quarter in the regression sample (`1960-01-01`, `2019-10-01`) |\n| `n_sub1`, `n_sub2` | integer | quarters | observations in each subperiod |\n| `baseline_metric` | number | percentage points | RMSE of the mean-only prediction of Δu over the full sample, sqrt(mean((Δu − mean Δu)²)) |\n| `model_metric` | number | percentage points | RMSE of the full-sample OLS residuals, sqrt(mean(e²)) |\n| `effect_estimate` | number | pp per percent growth | full-sample OLS Okun coefficient β |\n| `intercept` | number | percentage points | full-sample OLS intercept α |\n| `effect_se` | number | pp per percent growth | HAC (Newey–West) standard error of β |\n| `hac_lags` | integer | quarters | Newey–West lag length used |\n| `effect_ci_low`, `effect_ci_high` | number | pp per percent growth | 95% confidence interval for β based on `effect_se` |\n| `beta_sub1`, `beta_sub2` | number | pp per percent growth | OLS slope in subperiod 1 and 2 |\n| `chow_f_stat`, `chow_p_value` | number | — | Chow F statistic and p-value |\n| `stability_conclusion` | string | — | `stable` if the Chow test does not reject at 5%, else `unstable` |\n| `conclusion` | string | — | one of the codes below (sign of β from its 95% interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = quarters in the full-sample regression.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of Δu_t against g_t with the two subperiod fitted lines, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per sample (full, subperiod 1, subperiod 2) with columns `period`, `n`, `intercept`, `slope`, `slope_se` (header + three data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. 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Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying series are produced by US government agencies and are in the public domain.\n\n## `GDPC1.csv` (319 rows)\n*Real Gross Domestic Product* (FRED `GDPC1`), quarterly, billions of chained 2017 dollars, seasonally adjusted annual rate. Source: U.S. Bureau of Economic Analysis. Observation dates are the first day of each quarter (e.g. `1947-01-01` = 1947Q1). Period 1947-01-01 to 2026-07-01 (current vintage as of retrieval).\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`\n- SHA-256: `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343`\n\n## `UNRATE.csv` (945 rows)\n*Unemployment Rate* (FRED `UNRATE`), monthly, percent, seasonally adjusted, civilian labour force 16+. Source: U.S. Bureau of Labor Statistics. Observation dates are the first day of each month. Period 1948-01-01 to 2026-09-01.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`\n- SHA-256: `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c`\n==> GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n 320 GDPC1.csv\n 946 UNRATE.csv\n 1266 total\nGDPC1.csv:0\nUNRATE.csv:1\nGDPC1.csv:observation_date,GDPC1\nUNRATE.csv:observation_date,UNRATE","is_error":false}]},"uuid":"720a2819-6981-4fad-b0fd-d2e53a595065","timestamp":"2026-10-30T04:50:54.701Z","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==> GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n 320 GDPC1.csv\n 946 UNRATE.csv\n 1266 total\nGDPC1.csv:0\nUNRATE.csv:1\nGDPC1.csv:observation_date,GDPC1\nUNRATE.csv:observation_date,UNRATE","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"d1e86b74-c618-4a23-873c-78af9135158e","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178/data","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","hash":"3437aa9d1889e51d002a3b027ec007e9dff292e121ce167a06cc64c9ac2f9ded","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01E9WmPMET8uDenFSWHyE2ZM","name":"Bash","input":{"command":"cd data; cat README.md; head -3 *.csv; tail -3 *.csv; wc -l *.csv; grep -c ',$' *.csv; grep -v '^[0-9-]*,[0-9.]*$' GDPC1.csv UNRATE.csv | head"}}]}}19{"type":"api-request","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","id":"74c0fd2c-9cfa-4fa3-9160-9322f97d3f5c","timestamp":"2026-10-30T04:50:54.704Z","version":"2.1.314","querySource":"sdk","shapeHash":"fe8bbe8cdffd1230fd702b0975e36bd7c483125f098914512f2784fda88a2da7","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":"464008fd-8c84-4192-a9d0-1072977251cb","keep":4,"tail":["3437aa9d1889e51d002a3b027ec007e9dff292e121ce167a06cc64c9ac2f9ded","bbea9f35bcdd850b5fd571d268ff75c4202bec8085624294ab5170a34cad6290"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3d7bd256b3ce700621bd5e6fb2104c234ca64f24fdc2c4b182e9ffab3f8e17c0"}20{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"720a2819-6981-4fad-b0fd-d2e53a595065","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835"}21{"type":"atis-latch","atis":"","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835"}22{"parentUuid":"720a2819-6981-4fad-b0fd-d2e53a595065","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpXMt8CYf6tJnrrTrPm","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":1008,"cache_read_input_tokens":4777,"output_tokens":115,"output_tokens_details":{"thinking_tokens":17},"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":1008},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":276,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpXMbWaQ37FYiw268f4","requestRef":"74c0fd2c-9cfa-4fa3-9160-9322f97d3f5c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"e8519c72-8c40-4176-8510-a388fca52864","timestamp":"2026-10-30T04:50:59.267Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178/data","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}23{"parentUuid":"e8519c72-8c40-4176-8510-a388fca52864","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpXMt8CYf6tJnrrTrPm","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SYbJfHyrnTFKpM3NDj86b1","name":"Bash","input":{"command":"cd data; 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cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. Run from workspace root.\"\"\"\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\nnp.random.seed(0)\nOUT = \"out\"\ngdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nun = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_rows = {\"GDPC1.csv\": len(gdp), \"UNRATE.csv\": len(un)}\nissues = []\n\n# ---- audit\nfor name, df, col, freq in [(\"GDPC1.csv\", gdp, \"GDPC1\", \"QS\"), (\"UNRATE.csv\", un, \"UNRATE\", \"MS\")]:\n d = df[\"observation_date\"]\n if d.duplicated().any():\n issues.append({\"issue\": f\"{name}: duplicated dates\", \"handling\": \"would abort\"}); raise SystemExit\n full = pd.date_range(d.min(), d.max(), freq=freq)\n if len(full) != len(d):\n issues.append({\"issue\": f\"{name}: {len(full)-len(d)} missing dates\", \"handling\": \"checked against sample window\"})\n miss = df[df[col].isna()]\n for dt in miss[\"observation_date\"]:\n inside = pd.Timestamp(\"1959-10-01\") <= dt <= pd.Timestamp(\"2019-12-31\")\n issues.append({\"issue\": f\"{name}: missing value at {dt.date()}\",\n \"handling\": (\"inside sample window -- quarter dropped\" if inside else\n \"outside the 1959Q4-2019Q4 window used; no effect on estimates\")})\n if (df[col].dropna() <= 0).any():\n issues.append({\"issue\": f\"{name}: non-positive values\", \"handling\": \"inspected\"})\nissues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are current vintages (revised data, GDP in chained 2017 dollars), not real-time\",\n \"handling\": \"observations after 2019Q4 excluded before any computation; revision/vintage effects acknowledged as limitation\"})\n\n# ---- build quarterly data (only through 2019Q4; 1959Q4 needed for first difference)\nun = un[(un.observation_date >= \"1959-10-01\") & (un.observation_date <= \"2019-12-01\")].copy()\nun[\"q\"] = un.observation_date.dt.to_period(\"Q\")\ncnt = un.groupby(\"q\")[\"UNRATE\"].count()\nassert (cnt == 3).all(), \"incomplete quarter in window\"\nuq = un.groupby(\"q\")[\"UNRATE\"].mean()\ngdp = gdp[(gdp.observation_date >= \"1959-10-01\") & (gdp.observation_date <= \"2019-10-01\")].copy()\ngdp[\"q\"] = gdp.observation_date.dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.DataFrame({\"u\": uq, \"y\": gq})\ndf[\"du\"] = df.u.diff()\ndf[\"g\"] = 100 * np.log(df.y).diff()\ndf = df.loc[pd.Period(\"1960Q1\"):pd.Period(\"2019Q4\")].dropna()\nassert len(df) == 240\n\ndef ols(d, hac=None):\n X = sm.add_constant(d[\"g\"].values)\n m = sm.OLS(d[\"du\"].values, X)\n return m.fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac, \"kernel\": \"bartlett\"}) if hac is not None else m.fit()\n\nT = len(df)\nL = int(np.floor(4 * (T / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nfull = ols(df, L)\ns1 = df.loc[:pd.Period(\"1983Q4\")]; s2 = df.loc[pd.Period(\"1984Q1\"):]\nr1, r2 = ols(s1, L), ols(s2, L)\n# HAC lag sensitivity\nsens = {lag: float(ols(df, lag).bse[1]) for lag in [0, 2, 4, 8, 12]}\nprint(\"HAC SE sensitivity by lag:\", sens)\nprint(\"OLS (iid) SE:\", float(ols(df).bse[1]))\n\n# Chow\nssr_p = float(ols(df).ssr); ssr_u = float(ols(s1).ssr + ols(s2).ssr); k = 2\nF = ((ssr_p - ssr_u) / k) / (ssr_u / (T - 2 * k))\np = float(stats.f.sf(F, k, T - 2 * k))\n\nb, se = float(full.params[1]), float(full.bse[1])\nz = stats.norm.ppf(0.975)\nlo, hi = b - z * se, b + z * se\nconcl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\nres = {\n \"task_id\": \"F6-conf-03\",\n \"sample_start\": \"1960-01-01\", \"sample_end\": \"2019-10-01\",\n \"n_sub1\": int(len(s1)), \"n_sub2\": int(len(s2)),\n \"baseline_metric\": float(np.sqrt(np.mean((df.du - df.du.mean()) ** 2))),\n \"model_metric\": float(np.sqrt(np.mean(full.resid ** 2))),\n \"effect_estimate\": b, \"intercept\": float(full.params[0]),\n \"effect_se\": se, \"hac_lags\": L,\n \"effect_ci_low\": lo, \"effect_ci_high\": hi,\n \"beta_sub1\": float(r1.params[1]), \"beta_sub2\": float(r2.params[1]),\n \"chow_f_stat\": float(F), \"chow_p_value\": p,\n \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows,\n \"n_analysed\": int(T), \"random_seed\": 0,\n}\nos.makedirs(OUT, exist_ok=True)\njson.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\n\npd.DataFrame([\n {\"period\": \"full_1960Q1-2019Q4\", \"n\": T, \"intercept\": full.params[0], \"slope\": full.params[1], \"slope_se\": full.bse[1]},\n {\"period\": \"sub1_1960Q1-1983Q4\", \"n\": len(s1), \"intercept\": r1.params[0], \"slope\": r1.params[1], \"slope_se\": r1.bse[1]},\n {\"period\": \"sub2_1984Q1-2019Q4\", \"n\": len(s2), \"intercept\": r2.params[0], \"slope\": r2.params[1], \"slope_se\": r2.bse[1]},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(s1.g, s1.du, s=12, alpha=.6, label=\"1960Q1–1983Q4\")\nax.scatter(s2.g, s2.du, s=12, alpha=.6, label=\"1984Q1–2019Q4\")\nxs = np.linspace(df.g.min(), df.g.max(), 50)\nax.plot(xs, r1.params[0] + r1.params[1] * xs, label=f\"fit 1: β={r1.params[1]:.3f}\")\nax.plot(xs, r2.params[0] + r2.params[1] * xs, label=f\"fit 2: β={r2.params[1]:.3f}\")\nax.axhline(0, c=\"grey\", lw=.5); ax.axvline(0, c=\"grey\", lw=.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(f\"Okun's law, US 1960–2019 (Chow F={F:.2f}, p={p:.3f})\")\nax.legend(fontsize=8); fig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":252,"cache_read_input_tokens":5785,"output_tokens":3092,"output_tokens_details":{"thinking_tokens":95},"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":252},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpXj1cdn6bWK72zBY8u","requestRef":"984abf8b-cb89-4d10-bcc6-247c117a9e09","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"78483e31-bd00-4686-8e18-2dacb263af1b","timestamp":"2026-10-30T04:51:23.250Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178/data","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"78483e31-bd00-4686-8e18-2dacb263af1b","isSidechain":false,"promptId":"2381ced1-d8c5-424c-8658-044aa8b7d1c3","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0178/data/out/analysis.py\", line 12, in <module>\n gdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/GDPC1.csv'\ncat: out/table1.csv: No such file or directory","is_error":true,"tool_use_id":"toolu_01QN1xk56LS5FBp2ma29JdEG"}]},"uuid":"0fa3059b-1178-4e11-a7b4-8a938649e244","timestamp":"2026-10-30T04:51:24.344Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0178/data/out/analysis.py\", line 12, in <module>\n gdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/GDPC1.csv'\ncat: out/table1.csv: No such file or directory","sandboxed":false,"sourceToolAssistantUUID":"78483e31-bd00-4686-8e18-2dacb263af1b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178/data","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","hash":"88f2ec13857f8aaa9d29d417d55e90f24c976971a0973376c606db8938d6d069","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01QN1xk56LS5FBp2ma29JdEG","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. Run from workspace root.\"\"\"\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\nnp.random.seed(0)\nOUT = \"out\"\ngdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nun = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_rows = {\"GDPC1.csv\": len(gdp), \"UNRATE.csv\": len(un)}\nissues = []\n\n# ---- audit\nfor name, df, col, freq in [(\"GDPC1.csv\", gdp, \"GDPC1\", \"QS\"), (\"UNRATE.csv\", un, \"UNRATE\", \"MS\")]:\n d = df[\"observation_date\"]\n if d.duplicated().any():\n issues.append({\"issue\": f\"{name}: duplicated dates\", \"handling\": \"would abort\"}); raise SystemExit\n full = pd.date_range(d.min(), d.max(), freq=freq)\n if len(full) != len(d):\n issues.append({\"issue\": f\"{name}: {len(full)-len(d)} missing dates\", \"handling\": \"checked against sample window\"})\n miss = df[df[col].isna()]\n for dt in miss[\"observation_date\"]:\n inside = pd.Timestamp(\"1959-10-01\") <= dt <= pd.Timestamp(\"2019-12-31\")\n issues.append({\"issue\": f\"{name}: missing value at {dt.date()}\",\n \"handling\": (\"inside sample window -- quarter dropped\" if inside else\n \"outside the 1959Q4-2019Q4 window used; no effect on estimates\")})\n if (df[col].dropna() <= 0).any():\n issues.append({\"issue\": f\"{name}: non-positive values\", \"handling\": \"inspected\"})\nissues.append({\"issue\": \"Both series extend beyond 2019Q4 (to 2026) and are current vintages (revised data, GDP in chained 2017 dollars), not real-time\",\n \"handling\": \"observations after 2019Q4 excluded before any computation; revision/vintage effects acknowledged as limitation\"})\n\n# ---- build quarterly data (only through 2019Q4; 1959Q4 needed for first difference)\nun = un[(un.observation_date >= \"1959-10-01\") & (un.observation_date <= \"2019-12-01\")].copy()\nun[\"q\"] = un.observation_date.dt.to_period(\"Q\")\ncnt = un.groupby(\"q\")[\"UNRATE\"].count()\nassert (cnt == 3).all(), \"incomplete quarter in window\"\nuq = un.groupby(\"q\")[\"UNRATE\"].mean()\ngdp = gdp[(gdp.observation_date >= \"1959-10-01\") & (gdp.observation_date <= \"2019-10-01\")].copy()\ngdp[\"q\"] = gdp.observation_date.dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.DataFrame({\"u\": uq, \"y\": gq})\ndf[\"du\"] = df.u.diff()\ndf[\"g\"] = 100 * np.log(df.y).diff()\ndf = df.loc[pd.Period(\"1960Q1\"):pd.Period(\"2019Q4\")].dropna()\nassert len(df) == 240\n\ndef ols(d, hac=None):\n X = sm.add_constant(d[\"g\"].values)\n m = sm.OLS(d[\"du\"].values, X)\n return m.fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": hac, \"kernel\": \"bartlett\"}) if hac is not None else m.fit()\n\nT = len(df)\nL = int(np.floor(4 * (T / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb -> 4\nfull = ols(df, L)\ns1 = df.loc[:pd.Period(\"1983Q4\")]; s2 = df.loc[pd.Period(\"1984Q1\"):]\nr1, r2 = ols(s1, L), ols(s2, L)\n# HAC lag sensitivity\nsens = {lag: float(ols(df, lag).bse[1]) for lag in [0, 2, 4, 8, 12]}\nprint(\"HAC SE sensitivity by lag:\", sens)\nprint(\"OLS (iid) SE:\", float(ols(df).bse[1]))\n\n# Chow\nssr_p = float(ols(df).ssr); ssr_u = float(ols(s1).ssr + ols(s2).ssr); k = 2\nF = ((ssr_p - ssr_u) / k) / (ssr_u / (T - 2 * k))\np = float(stats.f.sf(F, k, T - 2 * k))\n\nb, se = float(full.params[1]), float(full.bse[1])\nz = stats.norm.ppf(0.975)\nlo, hi = b - z * se, b + z * se\nconcl = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\n\nres = {\n \"task_id\": \"F6-conf-03\",\n \"sample_start\": \"1960-01-01\", \"sample_end\": \"2019-10-01\",\n \"n_sub1\": int(len(s1)), \"n_sub2\": int(len(s2)),\n \"baseline_metric\": float(np.sqrt(np.mean((df.du - df.du.mean()) ** 2))),\n \"model_metric\": float(np.sqrt(np.mean(full.resid ** 2))),\n \"effect_estimate\": b, \"intercept\": float(full.params[0]),\n \"effect_se\": se, \"hac_lags\": L,\n \"effect_ci_low\": lo, \"effect_ci_high\": hi,\n \"beta_sub1\": float(r1.params[1]), \"beta_sub2\": float(r2.params[1]),\n \"chow_f_stat\": float(F), \"chow_p_value\": p,\n \"stability_conclusion\": \"stable\" if p >= 0.05 else \"unstable\",\n \"conclusion\": concl,\n \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows,\n \"n_analysed\": int(T), \"random_seed\": 0,\n}\nos.makedirs(OUT, exist_ok=True)\njson.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\n\npd.DataFrame([\n {\"period\": \"full_1960Q1-2019Q4\", \"n\": T, \"intercept\": full.params[0], \"slope\": full.params[1], \"slope_se\": full.bse[1]},\n {\"period\": \"sub1_1960Q1-1983Q4\", \"n\": len(s1), \"intercept\": r1.params[0], \"slope\": r1.params[1], \"slope_se\": r1.bse[1]},\n {\"period\": \"sub2_1984Q1-2019Q4\", \"n\": len(s2), \"intercept\": r2.params[0], \"slope\": r2.params[1], \"slope_se\": r2.bse[1]},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(s1.g, s1.du, s=12, alpha=.6, label=\"1960Q1–1983Q4\")\nax.scatter(s2.g, s2.du, s=12, alpha=.6, label=\"1984Q1–2019Q4\")\nxs = np.linspace(df.g.min(), df.g.max(), 50)\nax.plot(xs, r1.params[0] + r1.params[1] * xs, label=f\"fit 1: β={r1.params[1]:.3f}\")\nax.plot(xs, r2.params[0] + r2.params[1] * xs, label=f\"fit 2: β={r2.params[1]:.3f}\")\nax.axhline(0, c=\"grey\", lw=.5); ax.axvline(0, c=\"grey\", lw=.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate Δu_t (pp)\")\nax.set_title(f\"Okun's law, US 1960–2019 (Chow F={F:.2f}, p={p:.3f})\")\nax.legend(fontsize=8); fig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=150)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/table1.csv"}}]}}31{"type":"api-request","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","id":"523c885b-d4fe-4744-9fef-41f8abc6783c","timestamp":"2026-10-30T04:51:24.348Z","version":"2.1.314","querySource":"sdk","shapeHash":"fe8bbe8cdffd1230fd702b0975e36bd7c483125f098914512f2784fda88a2da7","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":"984abf8b-cb89-4d10-bcc6-247c117a9e09","keep":8,"tail":["88f2ec13857f8aaa9d29d417d55e90f24c976971a0973376c606db8938d6d069","c50414b88cb9791fe1c9056dae492176d47a9b7295d96cf47c551ee21700396b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"d5dd119b62f0c51e85f458005e73a2f5bf18c53d0b04022e83fa9723d205bb0d"}32{"parentUuid":"0fa3059b-1178-4e11-a7b4-8a938649e244","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpZYe6FijtG5gaDMJtH","type":"message","role":"assistant","content":[{"type":"text","text":"Oops, the shell was inside data/. 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- <<'EOF'\nimport re\ns=open(\"out/analysis.py\").read()\ns=s.replace('''b, se = float''','''# Robustness: HAC Wald test of break in (alpha,beta) jointly and in beta only (dummy-interaction regression)\nD = (df.index >= pd.Period(\"1984Q1\")).astype(float)\nXi = np.column_stack([np.ones(T), df.g.values, D, D * df.g.values])\nmi = sm.OLS(df.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\"})\nw_joint = mi.wald_test(np.array([[0, 0, 1, 0], [0, 0, 0, 1]]), use_f=False, scalar=True)\nw_slope = mi.wald_test(np.array([[0, 0, 0, 1]]), use_f=False, scalar=True)\nw_int = mi.wald_test(np.array([[0, 0, 1, 0]]), use_f=False, scalar=True)\nrob = {\"hac_se_by_lag\": {str(k_): v for k_, v in sens.items()}, \"ols_iid_se_beta\": float(ols(df).bse[1]),\n \"hac_wald_joint_break_chi2\": float(w_joint.statistic), \"hac_wald_joint_break_p\": float(w_joint.pvalue),\n \"hac_wald_slope_break_chi2\": float(w_slope.statistic), \"hac_wald_slope_break_p\": float(w_slope.pvalue),\n \"hac_wald_intercept_break_chi2\": float(w_int.statistic), \"hac_wald_intercept_break_p\": float(w_int.pvalue),\n \"slope_diff_sub2_minus_sub1\": float(mi.params[3]), \"slope_diff_hac_se\": float(mi.bse[3]),\n \"intercept_diff_sub2_minus_sub1\": float(mi.params[2]), \"intercept_diff_hac_se\": float(mi.bse[2]),\n \"durbin_watson_full_resid\": float(sm.stats.stattools.durbin_watson(ols(df).resid))}\nprint(\"Robustness:\", json.dumps(rob, indent=2))\n\nb, se = float''')\ns=s.replace('''json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)''','''json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\njson.dump(rob, open(f\"{OUT}/robustness.json\", \"w\"), indent=2)''')\nopen(\"out/analysis.py\",\"w\").write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py | sed -n '/Robustness/,/^}/p'"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1061,"cache_read_input_tokens":9877,"output_tokens":1200,"output_tokens_details":{"thinking_tokens":182},"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":1061},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpZq35HMGk6x7pt6HUk","requestRef":"afad0971-90f4-4a7d-865b-4e8d6a1da78e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"61088090-0c36-4bcb-b6d2-c4a629158e53","timestamp":"2026-10-30T04:51:38.677Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"61088090-0c36-4bcb-b6d2-c4a629158e53","isSidechain":false,"promptId":"2381ced1-d8c5-424c-8658-044aa8b7d1c3","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01MLmKy418Nv1s5D4XPCPXLA","type":"tool_result","content":"Robustness: {\n \"hac_se_by_lag\": {\n \"0\": 0.028836223207269963,\n \"2\": 0.03422732023615383,\n \"4\": 0.03583463330296569,\n \"8\": 0.03713550417540189,\n \"12\": 0.0364088511022809\n },\n \"ols_iid_se_beta\": 0.01996009291490984,\n \"hac_wald_joint_break_chi2\": 10.926210056989923,\n \"hac_wald_joint_break_p\": 0.004240368816511458,\n \"hac_wald_slope_break_chi2\": 0.03404280243683726,\n \"hac_wald_slope_break_p\": 0.8536158012976183,\n \"hac_wald_intercept_break_chi2\": 1.3234728892848857,\n \"hac_wald_intercept_break_p\": 0.2499697254959754,\n \"slope_diff_sub2_minus_sub1\": -0.014371579610773073,\n \"slope_diff_hac_se\": 0.07789182021505127,\n \"intercept_diff_sub2_minus_sub1\": -0.10254684051119209,\n \"intercept_diff_hac_se\": 0.08913838462748305,\n \"durbin_watson_full_resid\": 1.2210168267740218\n}","is_error":false}]},"uuid":"27d8ec8c-93fe-447e-8e46-b3ed870e50c3","timestamp":"2026-10-30T04:51:39.959Z","toolUseResult":{"stdout":"Robustness: {\n \"hac_se_by_lag\": {\n \"0\": 0.028836223207269963,\n \"2\": 0.03422732023615383,\n \"4\": 0.03583463330296569,\n \"8\": 0.03713550417540189,\n \"12\": 0.0364088511022809\n },\n \"ols_iid_se_beta\": 0.01996009291490984,\n \"hac_wald_joint_break_chi2\": 10.926210056989923,\n \"hac_wald_joint_break_p\": 0.004240368816511458,\n \"hac_wald_slope_break_chi2\": 0.03404280243683726,\n \"hac_wald_slope_break_p\": 0.8536158012976183,\n \"hac_wald_intercept_break_chi2\": 1.3234728892848857,\n \"hac_wald_intercept_break_p\": 0.2499697254959754,\n \"slope_diff_sub2_minus_sub1\": -0.014371579610773073,\n \"slope_diff_hac_se\": 0.07789182021505127,\n \"intercept_diff_sub2_minus_sub1\": -0.10254684051119209,\n \"intercept_diff_hac_se\": 0.08913838462748305,\n \"durbin_watson_full_resid\": 1.2210168267740218\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"61088090-0c36-4bcb-b6d2-c4a629158e53","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","hash":"c118d1d79faa9c39a5fc327e6441c49273e9c841ba74fa11f9a2c7bd6bf6dff1","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01MLmKy418Nv1s5D4XPCPXLA","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport re\ns=open(\"out/analysis.py\").read()\ns=s.replace('''b, se = float''','''# Robustness: HAC Wald test of break in (alpha,beta) jointly and in beta only (dummy-interaction regression)\nD = (df.index >= pd.Period(\"1984Q1\")).astype(float)\nXi = np.column_stack([np.ones(T), df.g.values, D, D * df.g.values])\nmi = sm.OLS(df.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\"})\nw_joint = mi.wald_test(np.array([[0, 0, 1, 0], [0, 0, 0, 1]]), use_f=False, scalar=True)\nw_slope = mi.wald_test(np.array([[0, 0, 0, 1]]), use_f=False, scalar=True)\nw_int = mi.wald_test(np.array([[0, 0, 1, 0]]), use_f=False, scalar=True)\nrob = {\"hac_se_by_lag\": {str(k_): v for k_, v in sens.items()}, \"ols_iid_se_beta\": float(ols(df).bse[1]),\n \"hac_wald_joint_break_chi2\": float(w_joint.statistic), \"hac_wald_joint_break_p\": float(w_joint.pvalue),\n \"hac_wald_slope_break_chi2\": float(w_slope.statistic), \"hac_wald_slope_break_p\": float(w_slope.pvalue),\n \"hac_wald_intercept_break_chi2\": float(w_int.statistic), \"hac_wald_intercept_break_p\": float(w_int.pvalue),\n \"slope_diff_sub2_minus_sub1\": float(mi.params[3]), \"slope_diff_hac_se\": float(mi.bse[3]),\n \"intercept_diff_sub2_minus_sub1\": float(mi.params[2]), \"intercept_diff_hac_se\": float(mi.bse[2]),\n \"durbin_watson_full_resid\": float(sm.stats.stattools.durbin_watson(ols(df).resid))}\nprint(\"Robustness:\", json.dumps(rob, indent=2))\n\nb, se = float''')\ns=s.replace('''json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)''','''json.dump(res, open(f\"{OUT}/results.json\", \"w\"), indent=2)\njson.dump(rob, open(f\"{OUT}/robustness.json\", \"w\"), indent=2)''')\nopen(\"out/analysis.py\",\"w\").write(s)\nEOF\n~/wp29/venv/bin/python out/analysis.py | sed -n 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> out/report.md <<'EOF'\n# Okun's law in the US, 1960Q1–2019Q4 (task F6-conf-03)\n\n## Question\nHow much does the quarterly change in the US unemployment rate (Δu_t, pp) respond to quarterly real GDP growth (g_t = 100·Δln GDPC1, %, not annualised), in Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4? And is (α, β) the same in 1960Q1–1983Q4 as in 1984Q1–2019Q4?\n\n## Data and exclusions\n- `GDPC1.csv` (319 rows, 1947Q1–2026Q3) and `UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09). SHA-256 checksums match `data/README.md`. Neither file has duplicate or missing dates.\n- Data problem found: UNRATE has an empty value for 2025-10-01. This falls outside the 1959Q4–2019Q4 window, so it does not affect any estimate.\n- I dropped all observations after 2019Q4 before doing any computation. Quarterly u_t is the mean of the three monthly values; the script checks that every quarter in the window has all 3 months. 1959Q4 levels are used only to form the 1960Q1 differences. The result is 240 regression quarters: 96 in subperiod 1 and 144 in subperiod 2.\n- Both series are current vintages, i.e. revised data with GDP in chained 2017 dollars, not real-time data. This is noted as a limitation below.\n\n## Method\n- Baseline: predict Δu by its full-sample mean. RMSE = 0.325 pp.\n- Model: OLS of Δu_t on a constant and g_t. Standard errors are Newey–West HAC with a Bartlett kernel. The lag length is 4, from the rule L = floor(4·(T/100)^(2/9)) with T = 240. The 95% CI uses ±1.96·SE (normal approximation).\n- Subperiod OLS fits. Classical Chow test: F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)], compared with F(2, 236).\n- Robustness checks, written to `out/robustness.json` by the same script: HAC standard errors at lags 0/2/4/8/12; a HAC Wald version of the break test using a regression with subperiod dummies and interactions; the break-even growth rate −α/β in each subperiod.\n- Everything is reproduced by `~/wp29/venv/bin/python out/analysis.py`, which is deterministic.\n\n## Results\n| sample | n | α | β | HAC SE(β) |\n|---|---|---|---|---|\n| 1960Q1–2019Q4 | 240 | 0.184 | −0.256 | 0.036 |\n| 1960Q1–1983Q4 | 96 | 0.254 | −0.259 | 0.039 |\n| 1984Q1–2019Q4 | 144 | 0.152 | −0.273 | 0.068 |\n\n(`out/table1.csv`; figure: `out/figure1.png`.)\n\n- Full sample: β = −0.256 pp of unemployment per 1% quarterly GDP growth, with 95% CI [−0.326, −0.186]. The interval lies entirely below zero, so the conclusion code is `negative_effect`. Model RMSE is 0.250 pp, against 0.325 pp for the mean-only baseline.\n- The Chow test gives F = 6.09 with p = 0.0026. Under the classical test, the joint equality of (α, β) across the two subperiods is rejected at 5%, so the stability conclusion is `unstable`.\n- The break is not in the slope. The two subperiod slopes are almost the same (−0.259 vs −0.273). Their difference is −0.014 with HAC SE 0.078, and a HAC Wald test of the slope alone gives p = 0.85. What changes is the intercept, together with the slope: the break-even growth rate −α/β falls from about 0.98% per quarter to about 0.55% per quarter. This is consistent with slower trend growth after 1984. The joint HAC Wald test also rejects (χ²(2) = 10.9, p = 0.004). The intercept difference on its own is not significant (p = 0.25), because the intercept and slope estimates are strongly correlated. In short, the data reject a stable Okun relation as a whole, but the β coefficient itself looks stable.\n\n## Uncertainty\n- The HAC standard error of β is 0.036, against 0.020 under iid assumptions. The Durbin–Watson statistic of the residuals is 1.22, which indicates positive autocorrelation, so the naive standard error is too small.\n- The HAC standard error moves little with the lag length: 0.029 at lag 0 (heteroskedasticity-robust only), 0.034 at lag 2, 0.036 at lag 4, 0.037 at lag 8 and 0.036 at lag 12. The CI stays well away from zero at every lag.\n- The classical Chow test assumes iid homoskedastic errors, which these residuals violate. Its p-value of 0.0026 is therefore probably too small. The HAC Wald version (p = 0.004) reaches the same conclusion, which makes the rejection more credible.\n- The subperiod table reports HAC standard errors (lag 4) for the subperiod slopes too.\n\n## Limitations and open issues\n- **What would invalidate the result:** Okun's law is a reduced-form co-movement, not a causal effect of output on unemployment. Both variables are driven by common shocks, and growth is measured with error. Measurement error biases β toward zero. If the true relation is dynamic (lags of g matter), the contemporaneous β understates the cumulative response. I did not estimate a distributed-lag specification.\n- **Break date:** I used the 1984Q1 break date imposed by the task, not a date estimated from the data. Searching for the break (e.g. Quandt–Andrews or Bai–Perron) could find a different date, and with a searched date the classical Chow critical values would no longer apply. A rejection at 1984 also does not show that the relation is stable within each subperiod. I did not check for rolling or recursive instability, such as the \"jobless recoveries\" after 1991, 2001 and 2009, or the 2008–09 episode.\n- **Data vintage:** the estimates use today's revised GDP and benchmark-revised unemployment, not the data available in real time. Coefficients estimated on real-time data can differ.\n- **Not checked:** the gap-form Okun law, possible asymmetry between expansions and recessions, the influence of outliers (e.g. 1980–82, 2008Q4–2009Q1; the largest Δu values are visible in the figure), and the small-sample performance of the HAC standard errors with a rule-of-thumb lag. The CI uses a normal approximation; a t(238) distribution would make no material difference.\n- **Main threat to the stability conclusion:** whether the model is \"unstable\" depends on testing α and β jointly. A reader who cares only about β would conclude it is stable (slope-only p = 0.85). I report the stability label as the task defines it (joint test, `unstable`) and flag this distinction.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 a 1% higher quarterly real GDP growth is associated with a 0.256 pp lower change in the unemployment rate; the 95% HAC CI [-0.326,-0.186] excludes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not causal; revised-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Okun regression improves on the mean-only baseline (RMSE 0.250 vs 0.325 pp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) across 1960-83 and 1984-2019 at 5% (F=6.09, p=0.0026).\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes iid errors (residuals autocorrelated); HAC Wald version (robustness.json) also rejects, p=0.004.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the instability reflects the intercept/break-even growth rate rather than beta.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"n_sub1\",\"n_sub2\"],\"status\":\"supported\",\"caveat\":\"Slope-difference test (HAC p=0.85) and break-even growth are in out/robustness.json, not results.json; failing to reject is not proof of equality.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));[print(k) for c in json.load(open('out/claims.json')) for k in c['results_keys'] if k not in r]\"; ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1125,"cache_read_input_tokens":13159,"output_tokens":3188,"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":1125},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpbNziQRGKGwQZepjVW","requestRef":"94e4c8e2-07ae-41f0-8130-c64166b3243f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6ae9137a-5b29-42de-883b-dbfeea4ed8b4","timestamp":"2026-10-30T04:52:17.827Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}58{"parentUuid":"6ae9137a-5b29-42de-883b-dbfeea4ed8b4","isSidechain":false,"promptId":"2381ced1-d8c5-424c-8658-044aa8b7d1c3","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Q2HzcA2dusnYDwykX4vwiA","type":"tool_result","content":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\nrobustness.json\ntable1.csv","is_error":false}]},"uuid":"b81c6b17-03fc-40db-b046-4640c78f498a","timestamp":"2026-10-30T04:52:17.882Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\nfigure1.png\nreport.md\nresults.json\nrobustness.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6ae9137a-5b29-42de-883b-dbfeea4ed8b4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}59{"type":"api-request-blob","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","hash":"24dcb3fe261baf959bd4efada75df6b30e2ce4a610ac1d0f5a1c33945cb1fd2a","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_01Q2HzcA2dusnYDwykX4vwiA","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in the US, 1960Q1–2019Q4 (task F6-conf-03)\n\n## Question\nHow much does the quarterly change in the US unemployment rate (Δu_t, pp) respond to quarterly real GDP growth (g_t = 100·Δln GDPC1, %, not annualised), in Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4? And is (α, β) the same in 1960Q1–1983Q4 as in 1984Q1–2019Q4?\n\n## Data and exclusions\n- `GDPC1.csv` (319 rows, 1947Q1–2026Q3) and `UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09). SHA-256 checksums match `data/README.md`. Neither file has duplicate or missing dates.\n- Data problem found: UNRATE has an empty value for 2025-10-01. This falls outside the 1959Q4–2019Q4 window, so it does not affect any estimate.\n- I dropped all observations after 2019Q4 before doing any computation. Quarterly u_t is the mean of the three monthly values; the script checks that every quarter in the window has all 3 months. 1959Q4 levels are used only to form the 1960Q1 differences. The result is 240 regression quarters: 96 in subperiod 1 and 144 in subperiod 2.\n- Both series are current vintages, i.e. revised data with GDP in chained 2017 dollars, not real-time data. This is noted as a limitation below.\n\n## Method\n- Baseline: predict Δu by its full-sample mean. RMSE = 0.325 pp.\n- Model: OLS of Δu_t on a constant and g_t. Standard errors are Newey–West HAC with a Bartlett kernel. The lag length is 4, from the rule L = floor(4·(T/100)^(2/9)) with T = 240. The 95% CI uses ±1.96·SE (normal approximation).\n- Subperiod OLS fits. Classical Chow test: F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)], compared with F(2, 236).\n- Robustness checks, written to `out/robustness.json` by the same script: HAC standard errors at lags 0/2/4/8/12; a HAC Wald version of the break test using a regression with subperiod dummies and interactions; the break-even growth rate −α/β in each subperiod.\n- Everything is reproduced by `~/wp29/venv/bin/python out/analysis.py`, which is deterministic.\n\n## Results\n| sample | n | α | β | HAC SE(β) |\n|---|---|---|---|---|\n| 1960Q1–2019Q4 | 240 | 0.184 | −0.256 | 0.036 |\n| 1960Q1–1983Q4 | 96 | 0.254 | −0.259 | 0.039 |\n| 1984Q1–2019Q4 | 144 | 0.152 | −0.273 | 0.068 |\n\n(`out/table1.csv`; figure: `out/figure1.png`.)\n\n- Full sample: β = −0.256 pp of unemployment per 1% quarterly GDP growth, with 95% CI [−0.326, −0.186]. The interval lies entirely below zero, so the conclusion code is `negative_effect`. Model RMSE is 0.250 pp, against 0.325 pp for the mean-only baseline.\n- The Chow test gives F = 6.09 with p = 0.0026. Under the classical test, the joint equality of (α, β) across the two subperiods is rejected at 5%, so the stability conclusion is `unstable`.\n- The break is not in the slope. The two subperiod slopes are almost the same (−0.259 vs −0.273). Their difference is −0.014 with HAC SE 0.078, and a HAC Wald test of the slope alone gives p = 0.85. What changes is the intercept, together with the slope: the break-even growth rate −α/β falls from about 0.98% per quarter to about 0.55% per quarter. This is consistent with slower trend growth after 1984. The joint HAC Wald test also rejects (χ²(2) = 10.9, p = 0.004). The intercept difference on its own is not significant (p = 0.25), because the intercept and slope estimates are strongly correlated. In short, the data reject a stable Okun relation as a whole, but the β coefficient itself looks stable.\n\n## Uncertainty\n- The HAC standard error of β is 0.036, against 0.020 under iid assumptions. The Durbin–Watson statistic of the residuals is 1.22, which indicates positive autocorrelation, so the naive standard error is too small.\n- The HAC standard error moves little with the lag length: 0.029 at lag 0 (heteroskedasticity-robust only), 0.034 at lag 2, 0.036 at lag 4, 0.037 at lag 8 and 0.036 at lag 12. The CI stays well away from zero at every lag.\n- The classical Chow test assumes iid homoskedastic errors, which these residuals violate. Its p-value of 0.0026 is therefore probably too small. The HAC Wald version (p = 0.004) reaches the same conclusion, which makes the rejection more credible.\n- The subperiod table reports HAC standard errors (lag 4) for the subperiod slopes too.\n\n## Limitations and open issues\n- **What would invalidate the result:** Okun's law is a reduced-form co-movement, not a causal effect of output on unemployment. Both variables are driven by common shocks, and growth is measured with error. Measurement error biases β toward zero. If the true relation is dynamic (lags of g matter), the contemporaneous β understates the cumulative response. I did not estimate a distributed-lag specification.\n- **Break date:** I used the 1984Q1 break date imposed by the task, not a date estimated from the data. Searching for the break (e.g. Quandt–Andrews or Bai–Perron) could find a different date, and with a searched date the classical Chow critical values would no longer apply. A rejection at 1984 also does not show that the relation is stable within each subperiod. I did not check for rolling or recursive instability, such as the \"jobless recoveries\" after 1991, 2001 and 2009, or the 2008–09 episode.\n- **Data vintage:** the estimates use today's revised GDP and benchmark-revised unemployment, not the data available in real time. Coefficients estimated on real-time data can differ.\n- **Not checked:** the gap-form Okun law, possible asymmetry between expansions and recessions, the influence of outliers (e.g. 1980–82, 2008Q4–2009Q1; the largest Δu values are visible in the figure), and the small-sample performance of the HAC standard errors with a rule-of-thumb lag. The CI uses a normal approximation; a t(238) distribution would make no material difference.\n- **Main threat to the stability conclusion:** whether the model is \"unstable\" depends on testing α and β jointly. A reader who cares only about β would conclude it is stable (slope-only p = 0.85). I report the stability label as the task defines it (joint test, `unstable`) and flag this distinction.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 a 1% higher quarterly real GDP growth is associated with a 0.256 pp lower change in the unemployment rate; the 95% HAC CI [-0.326,-0.186] excludes zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not causal; revised-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The Okun regression improves on the mean-only baseline (RMSE 0.250 vs 0.325 pp).\",\"results_keys\":[\"model_metric\",\"baseline_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) across 1960-83 and 1984-2019 at 5% (F=6.09, p=0.0026).\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes iid errors (residuals autocorrelated); HAC Wald version (robustness.json) also rejects, p=0.004.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the instability reflects the intercept/break-even growth rate rather than beta.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"n_sub1\",\"n_sub2\"],\"status\":\"supported\",\"caveat\":\"Slope-difference test (HAC p=0.85) and break-even growth are in out/robustness.json, not results.json; failing to reject is not proof of equality.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;r=json.load(open('out/results.json'));[print(k) for c in json.load(open('out/claims.json')) for k in c['results_keys'] if k not in r]\"; ls out"}}]}}60{"type":"api-request","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","id":"29f0fab2-aac2-418d-8235-0fe3c183b5ea","timestamp":"2026-10-30T04:52:17.886Z","version":"2.1.314","querySource":"sdk","shapeHash":"fe8bbe8cdffd1230fd702b0975e36bd7c483125f098914512f2784fda88a2da7","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":"94e4c8e2-07ae-41f0-8130-c64166b3243f","keep":18,"tail":["24dcb3fe261baf959bd4efada75df6b30e2ce4a610ac1d0f5a1c33945cb1fd2a","2f0fe955d06e2fb5a129b7af4444e98c9f836a1c69b7b13bc9439743cf545c6a"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"f34b6b83c1c163b964451571a1f9841cbf9f8e990671d90b1393ab60ec501313"}61{"parentUuid":"b81c6b17-03fc-40db-b046-4640c78f498a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpdV1RQXwFupWevbnvA","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3229,"cache_read_input_tokens":14284,"output_tokens":937,"output_tokens_details":{"thinking_tokens":34},"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":3229},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6516,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpdUn2tMimJdZpnHAmJ","requestRef":"29f0fab2-aac2-418d-8235-0fe3c183b5ea","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1dc9c501-27ab-4f4a-83ea-8037f27ead27","timestamp":"2026-10-30T04:52:26.283Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"1dc9c501-27ab-4f4a-83ea-8037f27ead27","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpdV1RQXwFupWevbnvA","type":"message","role":"assistant","content":[{"type":"text","text":"The analysis is finished. Over 1960Q1–2019Q4, 1% higher quarterly real GDP growth goes with a 0.256 percentage point (pp) smaller change in the unemployment rate. The classical Chow test says the relationship was not stable across the two subperiods, but the slope itself barely moved; the change is in the intercept. `out/analysis.py` runs cleanly from the workspace root and regenerates the results, figure and table.\n\n**Main results** (240 quarters: 96 in 1960–83, 144 in 1984–2019):\n- **Okun coefficient:** β = −0.256, Newey–West standard error 0.036 with 4 lags. I picked the lag count with the standard rule of thumb for 240 quarters. The 95% interval is [−0.326, −0.186], entirely below zero, so the conclusion code is `negative_effect`.\n- **Fit:** the regression's error is 0.250 pp, against 0.325 pp for simply predicting the average change.\n- **Stability:** the Chow test gives F = 6.09, p = 0.0026, so the result is `unstable`.\n- **What changed:** the two subperiod slopes are almost identical (−0.259 vs −0.273). A robust test of the slope alone finds no difference (p = 0.85). What changed is the GDP growth rate at which unemployment holds steady, which fell from about 0.98% to 0.55% per quarter. That fits with slower trend growth after 1984.\n- **Robustness:** the residuals are autocorrelated, so the classical Chow test is probably overconfident. A version robust to that also rejects stability (p = 0.004). The standard error for β stays between 0.029 and 0.037 for lag lengths from 0 to 12.\n\n**Data issues:** the unemployment file has one empty value (October 2025), which is outside the sample and has no effect. Both files are today's revised data, not what was available in real time.\n\n**Limitations** (discussed in the report):\n- This is a co-movement between output and unemployment, not a causal effect.\n- Only same-quarter growth is used, so delayed responses aren't captured.\n- The 1984 break date was set by the task, not found from the data, and I didn't look for instability within each subperiod.\n- I didn't test whether the relationship differs between recessions and expansions, or how much a few extreme quarters drive it.\n\nI added an extra file of robustness checks (`out/robustness.json`) that the task didn't ask for. Early on I accidentally created a folder inside `data/`; I moved the script out and deleted the folder, and the original data files were not touched.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- figure1.png\n- table1.csv\n- claims.json\n- robustness.json"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3229,"cache_read_input_tokens":14284,"output_tokens":937,"output_tokens_details":{"thinking_tokens":34},"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":3229},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpdUn2tMimJdZpnHAmJ","requestRef":"29f0fab2-aac2-418d-8235-0fe3c183b5ea","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"84d1b262-7091-443f-89e9-9eaa6601c940","timestamp":"2026-10-30T04:52:27.861Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0178","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","version":"2.1.314","gitBranch":"HEAD"}63{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"84d1b262-7091-443f-89e9-9eaa6601c940","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835"}64{"type":"cost-state","sessionId":"f678a194-cd83-4cd0-ad31-9bc07881f835","totalCostUSD":0.2858786,"totalAPIDuration":91804,"totalAPIDurationWithoutRetries":91797,"totalToolDuration":4937,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":97044,"startTime":1793335850825,"modelUsage":{"claude-opus-5-5":{"inputTokens":22,"outputTokens":9476,"thinkingTokens":489,"cacheReadInputTokens":81128,"cacheCreationInputTokens":16009,"cacheCreationEphemeral5mInputTokens":16009,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2858786}},"hasUnknownModelCost":false}65