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The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\ntotal 56\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:37 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:39 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6548 oct. 29 23:37 GDPC1.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1573 oct. 29 23:37 README.md\n-rw-r--r-- 1 simon-pierreboucher staff 14211 oct. 29 23:37 UNRATE.csv","is_error":true,"tool_use_id":"toolu_01AoAaWHvm17P8QevDkRLTfZ"}]},"uuid":"35be9cb3-1352-4af6-8dd3-dffea7b53a37","timestamp":"2026-10-30T05:06:34.883Z","toolUseResult":"Error: Exit code 1\n# F2-conf-04 — Okun's law in U.S. quarterly data with autocorrelation-robust uncertainty\n\n## Scientific question\nEstimate the Okun's-law relation between changes in the U.S. unemployment rate and real GDP growth, with uncertainty that is valid under serially correlated disturbances. The estimand is the slope β (the \"Okun coefficient\") in the linear projection Δu_t = α + β g_t + e_t, where, for quarter t: u_t is the quarterly average of the three monthly unemployment-rate observations of quarter t (in percentage points), Δu_t = u_t − u_{t−1}, and g_t = 100 · ln(GDPC1_t / GDPC1_{t−1}) is quarterly real GDP growth in percent (quarter-on-quarter, **not** annualised). The regression sample is the 240 quarters from 1960Q1 to 2019Q4 inclusive (the 1960Q1 observation uses the 1959Q4 levels for its differences). Estimate β by OLS, report a Newey–West (Bartlett-kernel HAC) standard error with a bandwidth you justify, and state whether the Okun coefficient is negative and significantly different from zero at the 5% level.\n\n## Inputs\n- `data/UNRATE.csv` — observed data: U.S. civilian unemployment rate, seasonally adjusted, percent, monthly, 1948-01 to 2026-09 (945 data rows). Columns: `observation_date` (YYYY-MM-DD), `UNRATE`. Source: U.S. Bureau of Labor Statistics via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/GDPC1.csv` — observed data: U.S. real GDP, seasonally adjusted annual rate, billions of chained 2017 dollars, quarterly, 1947Q1 to 2026Q3 (319 data rows). Columns: `observation_date` (first day of the quarter), `GDPC1`. Source: U.S. Bureau of Economic Analysis via FRED (`https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`), retrieved 2026-10-30 UTC; terms in `data/README.md`.\n- `data/README.md` — provenance, retrieval time, SHA-256 checksums and license.\n\n## Information-availability rules\n- Use only the files under `data/`. The files are the current vintage (not real-time data); treat them as given. Observations outside the 1960Q1–2019Q4 window are only used for the levels needed to form the first differences; do not extend the window.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | – | \"F2-conf-04\" |\n| `beta_hat` | number | pp of unemployment per percent of quarterly growth | OLS Okun coefficient |\n| `alpha_hat` | number | pp | OLS intercept |\n| `r_squared` | number | – | R² of the regression |\n| `se_iid` | number | – | conventional OLS standard error of `beta_hat` (independent, homoskedastic errors) — the naïve baseline |\n| `se_hac` | number | – | Newey–West (Bartlett) HAC standard error of `beta_hat` |\n| `hac_lags` | integer | quarters | bandwidth (number of lags) used for `se_hac` |\n| `beta_ci_low`, `beta_ci_high` | number | – | 95% confidence interval for β built from `se_hac` |\n| `okun_negative_5pct` | boolean | – | true if and only if β̂ < 0 and H0: β = 0 is rejected at the 5% level (two-sided) using `se_hac` |\n| `mean_growth` | number | percent | sample mean of g_t over the 240 regression quarters |\n| `first_quarter`, `last_quarter` | string | – | first and last quarter of the regression sample, formatted like \"1960Q1\" |\n| `n_analysed` | integer | – | quarters used in the regression |\n| `feasibility` | string | – | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | – | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | – | data problems detected and how handled (empty list if none) |\n| `n_input_rows` | object | – | {\"UNRATE.csv\": data rows read (excluding the header), \"GDPC1.csv\": data rows read} |\n| `random_seed` | integer or null | – | seed used, if any |\n\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n- `out/figure1.png` — scatter of Δu_t against g_t over the sample with the fitted line.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). 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They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n==> 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 320 data/GDPC1.csv\n 946 data/UNRATE.csv\n 1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv\nobservation_date object\nUNRATE float64\ndtype: object {'observation_date': 0, 'UNRATE': 1} UNRATE\ncount 944.000000\nmean 5.652966\nstd 1.703534\nmin 2.500000\n25% 4.300000\n50% 5.450000\n75% 6.700000\nmax 14.800000\n1 0\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64\nobservation_date object\nGDPC1 float64\ndtype: object {'observation_date': 0, 'GDPC1': 0} GDPC1\ncount 319.000000\nmean 10458.210204\nstd 6485.042448\nmin 2172.432000\n25% 4848.918000\n50% 8920.193000\n75% 16340.058000\nmax 24611.077000\n0 0\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64","is_error":false}]},"uuid":"59109df9-bd2b-4d84-ae02-3a9da03b797c","timestamp":"2026-10-30T05:06:39.272Z","toolUseResult":{"stdout":"# Data provenance — F2-conf-04\n\nBoth files are unmodified downloads from FRED (Federal Reserve Bank of St. Louis), retrieved on 2026-10-30 03:32:42 UTC with\n`curl -L \"https://fred.stlouisfed.org/graph/fredgraph.csv?id=<SERIES>\"`. They are the current vintage as of the retrieval time (not real-time vintages).\n\n| file | series | description | units | frequency | rows (data, excl. header) | SHA-256 |\n|---|---|---|---|---|---|---|\n| `UNRATE.csv` | UNRATE | Unemployment Rate, 16 years and over, seasonally adjusted. Source: U.S. Bureau of Labor Statistics, Current Population Survey. | percent | monthly, 1948-01 to 2026-09 | 945 | `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c` |\n| `GDPC1.csv` | GDPC1 | Real Gross Domestic Product, seasonally adjusted annual rate, chained 2017 dollars. Source: U.S. Bureau of Economic Analysis, NIPA Table 1.1.6. | billions of chained 2017 dollars | quarterly, 1947Q1 to 2026Q3 | 319 | `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343` |\n\nColumns: `observation_date` (YYYY-MM-DD; for GDPC1 the first day of the quarter), and the series value. Missing values, if any, appear as an empty field.\n\nLicense / terms: the underlying series are U.S. federal government statistical releases (public domain). Redistribution through FRED is subject to the FRED® Terms of Use (https://fred.stlouisfed.org/legal/), which permit non-commercial research use with attribution: \"Source: U.S. Bureau of Labor Statistics and U.S. Bureau of Economic Analysis, retrieved from FRED, Federal Reserve Bank of St. Louis\".\n==> 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 320 data/GDPC1.csv\n 946 data/UNRATE.csv\n 1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv\nobservation_date object\nUNRATE float64\ndtype: object {'observation_date': 0, 'UNRATE': 1} UNRATE\ncount 944.000000\nmean 5.652966\nstd 1.703534\nmin 2.500000\n25% 4.300000\n50% 5.450000\n75% 6.700000\nmax 14.800000\n1 0\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64\nobservation_date object\nGDPC1 float64\ndtype: object {'observation_date': 0, 'GDPC1': 0} GDPC1\ncount 319.000000\nmean 10458.210204\nstd 6485.042448\nmin 2172.432000\n25% 4848.918000\n50% 8920.193000\n75% 16340.058000\nmax 24611.077000\n0 0\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f1e968c4-747d-4cb9-b289-d44668c1ccfa","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228/data","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}18{"type":"api-request-blob","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","hash":"9270ad11e145c82b29b7ec3fbce300939a706a61703002ea1cc9061f63499733","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01345PJL3kJdEm2vK1Qgy5t1","name":"Bash","input":{"command":"cat data/README.md; 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cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4, OLS with Newey-West HAC SE. Run from workspace root.\"\"\"\nimport json, math\nimport numpy as np, pandas as pd, statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib; matplotlib.use(\"Agg\"); import matplotlib.pyplot as plt\n\nu = pd.read_csv(\"data/UNRATE.csv\"); g = pd.read_csv(\"data/GDPC1.csv\")\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nissues = []\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n issues.append({\"issue\": f\"UNRATE has {len(miss)} missing monthly value(s): \" + \", \".join(miss.observation_date),\n \"handling\": \"Outside the 1959Q4-2019Q4 levels window; not used. Quarterly averages inside the window are checked to have all 3 months.\"})\nu[\"q\"] = u.date.dt.to_period(\"Q\"); g[\"q\"] = g.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ngq = g.set_index(\"q\").GDPC1\nwin = pd.period_range(\"1959Q4\", \"2019Q4\", freq=\"Q\")\nassert (uq.loc[win, \"count\"] == 3).all(), \"incomplete quarter in window\"\nassert gq.loc[win].notna().all() and len(gq.loc[win]) == len(win)\ndf = pd.DataFrame({\"u\": uq.loc[win, \"mean\"], \"y\": gq.loc[win]})\ndf[\"du\"] = df.u.diff(); df[\"g\"] = 100 * np.log(df.y / df.y.shift(1))\ndf = df.loc[pd.Period(\"1960Q1\"):].dropna()\nT = len(df); assert T == 240\n\nX = sm.add_constant(df.g.values); y = df.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) plug-in rule of thumb floor(4*(T/100)^(2/9))\nL = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nb, a = ols.params[1], ols.params[0]\nse_hac = float(hac.bse[1]); z = stats.norm.ppf(0.975)\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = 2 * stats.norm.sf(abs(b / se_hac))\n\n# sensitivity (reported in report, not a required key)\nsens = {}\nfor lag in [0, 2, 4, 6, 8, 12]:\n for corr in [False, True]:\n r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"use_correction\": corr})\n sens[f\"L{lag}_{'dfcorr' if corr else 'nocorr'}\"] = float(r.bse[1])\nresid_ac = [float(pd.Series(ols.resid).autocorr(k)) for k in range(1, 5)]\n\nres = {\n \"task_id\": \"F2-conf-04\", \"beta_hat\": float(b), \"alpha_hat\": float(a), \"r_squared\": float(ols.rsquared),\n \"se_iid\": float(ols.bse[1]), \"se_hac\": se_hac, \"hac_lags\": L,\n \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n \"mean_growth\": float(df.g.mean()), \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n \"n_analysed\": int(T), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nwith open(\"out/diagnostics.json\", \"w\") as f:\n json.dump({\"p_hac\": float(p_hac), \"t_hac\": float(b / se_hac), \"se_sensitivity\": sens,\n \"resid_autocorr_1to4\": resid_ac, \"alpha_se_hac\": float(hac.bse[0])}, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(df.g, df.du, s=12, alpha=0.6)\nxs = np.linspace(df.g.min(), df.g.max(), 50); ax.plot(xs, a + b * xs, \"r-\",\n label=f\"OLS: Δu = {a:.3f} {b:+.3f}·g\")\nax.set_xlabel(\"Real GDP growth g_t (%, q/q, not annualised)\"); ax.set_ylabel(\"Δu_t (pp)\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (T=240)\"); ax.legend(); fig.tight_layout()\nfig.savefig(\"out/figure1.png\", dpi=150)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/diagnostics.json"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":153,"cache_read_input_tokens":5977,"output_tokens":2175,"output_tokens_details":{"thinking_tokens":192},"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":153},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqj5qBYdMFHqxmdfGa9","requestRef":"7fb72fe4-d78c-4e8c-a1c2-6d11ee405a9b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"19da1682-571e-4f4f-9f38-2057c7c327ed","timestamp":"2026-10-30T05:06:57.902Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228/data","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"19da1682-571e-4f4f-9f38-2057c7c327ed","isSidechain":false,"promptId":"8ee9f70f-d201-428c-974a-0f6abef477d7","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/RC0228/data/out/analysis.py\", line 7, in <module>\n u = pd.read_csv(\"data/UNRATE.csv\"); g = pd.read_csv(\"data/GDPC1.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","is_error":true,"tool_use_id":"toolu_01EZnPKKKTzKVbkYrCMFEoD3"}]},"uuid":"87fd8304-8bbd-46c5-bd99-46405673a9a7","timestamp":"2026-10-30T05:06:58.673Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"/Users/simon-pierreboucher/wp29/runs/RC0228/data/out/analysis.py\", line 7, in <module>\n u = pd.read_csv(\"data/UNRATE.csv\"); g = pd.read_csv(\"data/GDPC1.csv\")\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1026, in read_csv\n return _read(filepath_or_buffer, kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 620, in _read\n parser = TextFileReader(filepath_or_buffer, **kwds)\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1620, in __init__\n self._engine = self._make_engine(f, self.engine)\n ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/parsers/readers.py\", line 1880, in _make_engine\n self.handles = get_handle(\n ~~~~~~~~~~^\n f,\n ^^\n ...<6 lines>...\n storage_options=self.options.get(\"storage_options\", None),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/Users/simon-pierreboucher/wp29/venv/lib/python3.14/site-packages/pandas/io/common.py\", line 873, in get_handle\n handle = open(\n handle,\n ...<3 lines>...\n newline=\"\",\n )\nFileNotFoundError: [Errno 2] No such file or directory: 'data/UNRATE.csv'","sandboxed":false,"sourceToolAssistantUUID":"19da1682-571e-4f4f-9f38-2057c7c327ed","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228/data","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}29{"type":"api-request-blob","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","hash":"8d66ca25a5f49ca3ab2629116b783e5d2d9ee07399177b208a631ec7fc3e2167","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01EZnPKKKTzKVbkYrCMFEoD3","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4, OLS with Newey-West HAC SE. Run from workspace root.\"\"\"\nimport json, math\nimport numpy as np, pandas as pd, statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib; matplotlib.use(\"Agg\"); import matplotlib.pyplot as plt\n\nu = pd.read_csv(\"data/UNRATE.csv\"); g = pd.read_csv(\"data/GDPC1.csv\")\nn_rows = {\"UNRATE.csv\": int(len(u)), \"GDPC1.csv\": int(len(g))}\nissues = []\nu[\"date\"] = pd.to_datetime(u.observation_date); g[\"date\"] = pd.to_datetime(g.observation_date)\nmiss = u[u.UNRATE.isna()]\nif len(miss):\n issues.append({\"issue\": f\"UNRATE has {len(miss)} missing monthly value(s): \" + \", \".join(miss.observation_date),\n \"handling\": \"Outside the 1959Q4-2019Q4 levels window; not used. Quarterly averages inside the window are checked to have all 3 months.\"})\nu[\"q\"] = u.date.dt.to_period(\"Q\"); g[\"q\"] = g.date.dt.to_period(\"Q\")\nuq = u.groupby(\"q\").UNRATE.agg([\"mean\", \"count\"])\ngq = g.set_index(\"q\").GDPC1\nwin = pd.period_range(\"1959Q4\", \"2019Q4\", freq=\"Q\")\nassert (uq.loc[win, \"count\"] == 3).all(), \"incomplete quarter in window\"\nassert gq.loc[win].notna().all() and len(gq.loc[win]) == len(win)\ndf = pd.DataFrame({\"u\": uq.loc[win, \"mean\"], \"y\": gq.loc[win]})\ndf[\"du\"] = df.u.diff(); df[\"g\"] = 100 * np.log(df.y / df.y.shift(1))\ndf = df.loc[pd.Period(\"1960Q1\"):].dropna()\nT = len(df); assert T == 240\n\nX = sm.add_constant(df.g.values); y = df.du.values\nols = sm.OLS(y, X).fit()\n# Bandwidth: Newey-West (1994) plug-in rule of thumb floor(4*(T/100)^(2/9))\nL = int(math.floor(4 * (T / 100) ** (2 / 9)))\nhac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nb, a = ols.params[1], ols.params[0]\nse_hac = float(hac.bse[1]); z = stats.norm.ppf(0.975)\nlo, hi = b - z * se_hac, b + z * se_hac\np_hac = 2 * stats.norm.sf(abs(b / se_hac))\n\n# sensitivity (reported in report, not a required key)\nsens = {}\nfor lag in [0, 2, 4, 6, 8, 12]:\n for corr in [False, True]:\n r = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"use_correction\": corr})\n sens[f\"L{lag}_{'dfcorr' if corr else 'nocorr'}\"] = float(r.bse[1])\nresid_ac = [float(pd.Series(ols.resid).autocorr(k)) for k in range(1, 5)]\n\nres = {\n \"task_id\": \"F2-conf-04\", \"beta_hat\": float(b), \"alpha_hat\": float(a), \"r_squared\": float(ols.rsquared),\n \"se_iid\": float(ols.bse[1]), \"se_hac\": se_hac, \"hac_lags\": L,\n \"beta_ci_low\": float(lo), \"beta_ci_high\": float(hi),\n \"okun_negative_5pct\": bool(b < 0 and p_hac < 0.05),\n \"mean_growth\": float(df.g.mean()), \"first_quarter\": str(df.index[0]), \"last_quarter\": str(df.index[-1]),\n \"n_analysed\": int(T), \"feasibility\": \"complete\", \"feasibility_reason\": None,\n \"data_issues\": issues, \"n_input_rows\": n_rows, \"random_seed\": None,\n}\nwith open(\"out/results.json\", \"w\") as f: json.dump(res, f, indent=2)\nwith open(\"out/diagnostics.json\", \"w\") as f:\n json.dump({\"p_hac\": float(p_hac), \"t_hac\": float(b / se_hac), \"se_sensitivity\": sens,\n \"resid_autocorr_1to4\": resid_ac, \"alpha_se_hac\": float(hac.bse[0])}, f, indent=2)\n\nfig, ax = plt.subplots(figsize=(6, 4.5))\nax.scatter(df.g, df.du, s=12, alpha=0.6)\nxs = np.linspace(df.g.min(), df.g.max(), 50); ax.plot(xs, a + b * xs, \"r-\",\n label=f\"OLS: Δu = {a:.3f} {b:+.3f}·g\")\nax.set_xlabel(\"Real GDP growth g_t (%, q/q, not annualised)\"); ax.set_ylabel(\"Δu_t (pp)\")\nax.set_title(\"Okun's law, US 1960Q1–2019Q4 (T=240)\"); ax.legend(); fig.tight_layout()\nfig.savefig(\"out/figure1.png\", dpi=150)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/diagnostics.json"}}]}}30{"type":"api-request","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","id":"147484d9-4a24-40cc-87d9-ea5696b42262","timestamp":"2026-10-30T05:06:58.675Z","version":"2.1.314","querySource":"sdk","shapeHash":"b82d4b8b548f1c87b9c60de27ac5575fe32f27938af51f32cadc406ad355ecf3","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":"7fb72fe4-d78c-4e8c-a1c2-6d11ee405a9b","keep":8,"tail":["8d66ca25a5f49ca3ab2629116b783e5d2d9ee07399177b208a631ec7fc3e2167","73945f7a10e5c0ca12f5a5283cb9f4c7a1b0aa7b0c98e6052279b02da58a5518"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"3a80e9a2e08ddc0598fbf1b3f202aecfaf43e2dec89021212680fd922f58ebad"}31{"parentUuid":"87fd8304-8bbd-46c5-bd99-46405673a9a7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkQeYNtYBDoP1iM7QV","type":"message","role":"assistant","content":[{"type":"text","text":"Shell was left in data/; I accidentally created data/out. 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Quarterly averages inside the window are checked to have all 3 months.\"\n }\n ],\n \"n_input_rows\": {\n \"UNRATE.csv\": 945,\n \"GDPC1.csv\": 319\n },\n \"random_seed\": null\n}\n{\n \"p_hac\": 9.508157930953398e-13,\n \"t_hac\": -7.137445096377235,\n \"se_sensitivity\": {\n \"L0_nocorr\": 0.028836223207270188,\n \"L0_dfcorr\": 0.028957130333809866,\n \"L2_nocorr\": 0.034227320236154,\n \"L2_dfcorr\": 0.03437083164224769,\n \"L4_nocorr\": 0.03583463330296578,\n \"L4_dfcorr\": 0.03598488399675887,\n \"L6_nocorr\": 0.03687902855135959,\n \"L6_dfcorr\": 0.03703365828007509,\n \"L8_nocorr\": 0.03713550417540173,\n \"L8_dfcorr\": 0.03729120927832651,\n \"L12_nocorr\": 0.03640885110228042,\n \"L12_dfcorr\": 0.03656150942843313\n },\n \"resid_autocorr_1to4\": [\n 0.38935129370212984,\n 0.2910132943069726,\n 0.13988210991084532,\n 0.07090953047434041\n ],\n \"alpha_se_hac\": 0.04367102655621451\n}","is_error":false}]},"uuid":"343b1786-f36f-4658-b59b-249513e8665a","timestamp":"2026-10-30T05:07:01.847Z","toolUseResult":{"stdout":"GDPC1.csv\nREADME.md\nUNRATE.csv\n{\n \"task_id\": \"F2-conf-04\",\n \"beta_hat\": -0.25576772774872947,\n \"alpha_hat\": 0.18438835418210964,\n \"r_squared\": 0.40825082845620597,\n \"se_iid\": 0.019960092914910015,\n \"se_hac\": 0.03583463330296578,\n \"hac_lags\": 4,\n \"beta_ci_low\": -0.326002318421742,\n \"beta_ci_high\": -0.18553313707571695,\n \"okun_negative_5pct\": true,\n \"mean_growth\": 0.7535027550652363,\n \"first_quarter\": \"1960Q1\",\n \"last_quarter\": \"2019Q4\",\n \"n_analysed\": 240,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE has 1 missing monthly value(s): 2025-10-01\",\n \"handling\": \"Outside the 1959Q4-2019Q4 levels window; not used. 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Moving it out."},{"type":"tool_use","id":"toolu_01NkXHrkMsqfrEe2D7xDm2an","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RC0228 && mkdir -p out && mv data/out/analysis.py out/ && rmdir data/out && ls data && ~/wp29/venv/bin/python out/analysis.py && cat out/diagnostics.json"}}]}}35{"type":"api-request","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","id":"2e2cbb97-6e45-4b28-ae0f-d5070c8825c2","timestamp":"2026-10-30T05:07:01.849Z","version":"2.1.314","querySource":"sdk","shapeHash":"b82d4b8b548f1c87b9c60de27ac5575fe32f27938af51f32cadc406ad355ecf3","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":"147484d9-4a24-40cc-87d9-ea5696b42262","keep":10,"tail":["f9639b69100803ee19f546b0b4d37827c137c25858e02766a9d0375467c8f424","cb13fa47e4d37a0a39c5ac95a2e21e12b6e88234e1ec2a4adbce1c85cba412cf"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"28728e61b365d98f8d51fa44b06d89b903b7dfc77b0189e456016ff20f0cb5ee"}36{"parentUuid":"343b1786-f36f-4658-b59b-249513e8665a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkeD8BxzF3YAhrCRhD","type":"message","role":"assistant","content":[{"type":"text","text":"Now the report and claims."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1017,"cache_read_input_tokens":9042,"output_tokens":2380,"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":1017},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXqkdvFma8YaeSpai4ae","requestRef":"2e2cbb97-6e45-4b28-ae0f-d5070c8825c2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"038ce83e-f1fd-427f-a01b-b6a0f4121f6d","timestamp":"2026-10-30T05:07:02.926Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"038ce83e-f1fd-427f-a01b-b6a0f4121f6d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXqkeD8BxzF3YAhrCRhD","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_0197uGLuHBFHxrXxYF6tW4TD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4\n\n## Question\nEstimate β in Δu_t = α + β g_t + e_t, where u_t is the quarterly average of monthly UNRATE (pp), Δu_t = u_t − u_{t−1}, and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (quarter-on-quarter, not annualised). The sample is the 240 quarters 1960Q1–2019Q4. β is estimated by OLS, its standard error is Newey–West (Bartlett) HAC, and we test whether β is negative and significant at the 5% level.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows read; `data/GDPC1.csv`: 319 quarterly rows read. SHA-256 checksums match `data/README.md`.\n- Monthly unemployment was averaged to calendar quarters. The script checks that every quarter from 1959Q4 to 2019Q4 has all three months, and that GDPC1 has no gaps in that range. Only levels from 1959Q4 to 2019Q4 are used; 1959Q4 serves only to form the 1960Q1 differences.\n- Data issue: UNRATE is missing for 2025-10 (empty field). That month is outside the window, so it was not used and does not affect any result.\n- No other exclusions were made. n = 240.\n\n## Method\n- OLS of Δu_t on a constant and g_t (statsmodels).\n- Baseline: the conventional i.i.d./homoskedastic OLS standard error.\n- HAC: Newey–West with the Bartlett kernel and bandwidth L = floor(4·(T/100)^(2/9)) = floor(4·2.4^(2/9)) = **4 lags** for T = 240. This is the Newey–West (1994) rule of thumb, and 4 quarters equals one year of dependence. The OLS residuals support this choice: their autocorrelations at lags 1–4 are 0.39, 0.29, 0.14 and 0.07, so dependence dies out after about one year. No small-sample degrees-of-freedom correction was applied. The 95% CI is β̂ ± 1.96·se_hac, and the test uses the normal distribution, which is the standard asymptotic HAC inference.\n- The script `out/analysis.py` is deterministic and uses no randomness. It writes `out/results.json`, `out/figure1.png`, and `out/diagnostics.json` (sensitivity output, not a required deliverable).\n\n## Results\n| quantity | value |\n|---|---|\n| β̂ (Okun coefficient) | −0.2558 |\n| α̂ | 0.1844 pp |\n| R² | 0.408 |\n| mean g_t | 0.754 % per quarter |\n| n | 240 (1960Q1–2019Q4) |\n\nWhen quarterly real GDP growth is 1 percentage point higher, the quarterly change in the unemployment rate is about 0.26 pp lower on average. The fitted line implies unemployment is stable (Δu = 0) at g = α̂/(−β̂) ≈ 0.72% per quarter, roughly 2.9% annualised. That is close to the sample mean growth of 0.754% per quarter. Figure: `out/figure1.png`.\n\n## Uncertainty\n| SE type | se(β̂) |\n|---|---|\n| i.i.d. OLS | 0.0200 |\n| Newey–West, L = 4 (main) | 0.0358 |\n\n- 95% HAC CI: [−0.326, −0.186]. The HAC t-statistic is −7.14 (p ≈ 1e-12).\n- **The Okun coefficient is negative and significantly different from zero at the 5% level** (`okun_negative_5pct = true`).\n- The HAC standard error is about 1.8 times the naïve one, because the residuals are positively autocorrelated. The naïve standard error overstates precision.\n- Bandwidth sensitivity of the HAC SE:\n\n | lags | 0 (White) | 2 | 4 | 6 | 8 | 12 |\n |---|---|---|---|---|---|---|\n | se(β̂) | 0.0288 | 0.0342 | 0.0358 | 0.0369 | 0.0371 | 0.0364 |\n\n A degrees-of-freedom correction changes the SE by less than 0.5%. The conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- The data are the current vintage, not real-time, so GDP revisions are included.\n- The result is a linear projection over the full sample, not a structural or causal estimate. The relation may be unstable over time: we did not test for breaks (for example, post-1984 or the 2008 recession), and we did not test for asymmetry.\n- The specification is static. It omits lags of g_t, so part of the dynamic Okun relation ends up in the serially correlated residuals, which the HAC SE accounts for.\n- HAC inference uses the asymptotic normal distribution. Fixed-b or other small-sample refinements were not applied. Given t ≈ −7, they would not change the conclusion.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 (n=240), OLS gives an Okun coefficient of about -0.256: 1 pp higher quarterly real GDP growth goes with a 0.26 pp smaller quarterly change in unemployment.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"This is a linear projection on current-vintage data, not a causal estimate. Parameter stability over time was not tested.\"},\n {\"id\":\"C2\",\"text\":\"With a Newey-West Bartlett HAC SE (4 lags), the coefficient is negative and significant at 5%. The 95% CI is [-0.326, -0.186].\",\"results_keys\":[\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\"],\"status\":\"supported\",\"caveat\":\"Inference relies on the asymptotic normal distribution. The conclusion holds for bandwidths from 0 to 12 lags.\"},\n {\"id\":\"C3\",\"text\":\"The naive i.i.d. SE (0.020) understates the uncertainty: the HAC SE (0.036) is about 1.8 times larger, because the residuals are positively autocorrelated.\",\"results_keys\":[\"se_iid\",\"se_hac\"],\"status\":\"supported\",\"caveat\":\"The residual autocorrelations are in out/diagnostics.json, not in results.json.\"}\n]\nEOF\n~/wp29/venv/bin/python -c \"import json;json.load(open('out/claims.json'))\"; 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":1017,"cache_read_input_tokens":9042,"output_tokens":2380,"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":1017},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqkdvFma8YaeSpai4ae","requestRef":"2e2cbb97-6e45-4b28-ae0f-d5070c8825c2","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"eb27a85f-8a72-4be2-9bbe-d9d90d441a6a","timestamp":"2026-10-30T05:07:22.088Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}38{"parentUuid":"eb27a85f-8a72-4be2-9bbe-d9d90d441a6a","isSidechain":false,"promptId":"8ee9f70f-d201-428c-974a-0f6abef477d7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_0197uGLuHBFHxrXxYF6tW4TD","type":"tool_result","content":"analysis.py\nclaims.json\ndiagnostics.json\nfigure1.png\nreport.md\nresults.json","is_error":false}]},"uuid":"9948ca3c-2694-43b7-992d-f1508afb1dc2","timestamp":"2026-10-30T05:07:22.137Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\ndiagnostics.json\nfigure1.png\nreport.md\nresults.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"eb27a85f-8a72-4be2-9bbe-d9d90d441a6a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}39{"type":"api-request-blob","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","hash":"5d57b2c30a3ca058beea4c9cbeee7f3114cc35bcde624e46d85f8357db83ea3e","message":{"role":"assistant","content":[{"type":"text","text":"Now the report and claims."},{"type":"tool_use","id":"toolu_0197uGLuHBFHxrXxYF6tW4TD","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in U.S. quarterly data, 1960Q1–2019Q4\n\n## Question\nEstimate β in Δu_t = α + β g_t + e_t, where u_t is the quarterly average of monthly UNRATE (pp), Δu_t = u_t − u_{t−1}, and g_t = 100·ln(GDPC1_t/GDPC1_{t−1}) (quarter-on-quarter, not annualised). The sample is the 240 quarters 1960Q1–2019Q4. β is estimated by OLS, its standard error is Newey–West (Bartlett) HAC, and we test whether β is negative and significant at the 5% level.\n\n## Data and exclusions\n- `data/UNRATE.csv`: 945 monthly rows read; `data/GDPC1.csv`: 319 quarterly rows read. SHA-256 checksums match `data/README.md`.\n- Monthly unemployment was averaged to calendar quarters. The script checks that every quarter from 1959Q4 to 2019Q4 has all three months, and that GDPC1 has no gaps in that range. Only levels from 1959Q4 to 2019Q4 are used; 1959Q4 serves only to form the 1960Q1 differences.\n- Data issue: UNRATE is missing for 2025-10 (empty field). That month is outside the window, so it was not used and does not affect any result.\n- No other exclusions were made. n = 240.\n\n## Method\n- OLS of Δu_t on a constant and g_t (statsmodels).\n- Baseline: the conventional i.i.d./homoskedastic OLS standard error.\n- HAC: Newey–West with the Bartlett kernel and bandwidth L = floor(4·(T/100)^(2/9)) = floor(4·2.4^(2/9)) = **4 lags** for T = 240. This is the Newey–West (1994) rule of thumb, and 4 quarters equals one year of dependence. The OLS residuals support this choice: their autocorrelations at lags 1–4 are 0.39, 0.29, 0.14 and 0.07, so dependence dies out after about one year. No small-sample degrees-of-freedom correction was applied. The 95% CI is β̂ ± 1.96·se_hac, and the test uses the normal distribution, which is the standard asymptotic HAC inference.\n- The script `out/analysis.py` is deterministic and uses no randomness. It writes `out/results.json`, `out/figure1.png`, and `out/diagnostics.json` (sensitivity output, not a required deliverable).\n\n## Results\n| quantity | value |\n|---|---|\n| β̂ (Okun coefficient) | −0.2558 |\n| α̂ | 0.1844 pp |\n| R² | 0.408 |\n| mean g_t | 0.754 % per quarter |\n| n | 240 (1960Q1–2019Q4) |\n\nWhen quarterly real GDP growth is 1 percentage point higher, the quarterly change in the unemployment rate is about 0.26 pp lower on average. The fitted line implies unemployment is stable (Δu = 0) at g = α̂/(−β̂) ≈ 0.72% per quarter, roughly 2.9% annualised. That is close to the sample mean growth of 0.754% per quarter. Figure: `out/figure1.png`.\n\n## Uncertainty\n| SE type | se(β̂) |\n|---|---|\n| i.i.d. OLS | 0.0200 |\n| Newey–West, L = 4 (main) | 0.0358 |\n\n- 95% HAC CI: [−0.326, −0.186]. The HAC t-statistic is −7.14 (p ≈ 1e-12).\n- **The Okun coefficient is negative and significantly different from zero at the 5% level** (`okun_negative_5pct = true`).\n- The HAC standard error is about 1.8 times the naïve one, because the residuals are positively autocorrelated. The naïve standard error overstates precision.\n- Bandwidth sensitivity of the HAC SE:\n\n | lags | 0 (White) | 2 | 4 | 6 | 8 | 12 |\n |---|---|---|---|---|---|---|\n | se(β̂) | 0.0288 | 0.0342 | 0.0358 | 0.0369 | 0.0371 | 0.0364 |\n\n A degrees-of-freedom correction changes the SE by less than 0.5%. The conclusion does not depend on the bandwidth.\n\n## Limitations and open issues\n- The data are the current vintage, not real-time, so GDP revisions are included.\n- The result is a linear projection over the full sample, not a structural or causal estimate. The relation may be unstable over time: we did not test for breaks (for example, post-1984 or the 2008 recession), and we did not test for asymmetry.\n- The specification is static. It omits lags of g_t, so part of the dynamic Okun relation ends up in the serially correlated residuals, which the HAC SE accounts for.\n- HAC inference uses the asymptotic normal distribution. Fixed-b or other small-sample refinements were not applied. Given t ≈ −7, they would not change the conclusion.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 (n=240), OLS gives an Okun coefficient of about -0.256: 1 pp higher quarterly real GDP growth goes with a 0.26 pp smaller quarterly change in unemployment.\",\"results_keys\":[\"beta_hat\",\"alpha_hat\",\"r_squared\",\"n_analysed\",\"first_quarter\",\"last_quarter\"],\"status\":\"supported\",\"caveat\":\"This is a linear projection on current-vintage data, not a causal estimate. Parameter stability over time was not tested.\"},\n {\"id\":\"C2\",\"text\":\"With a Newey-West Bartlett HAC SE (4 lags), the coefficient is negative and significant at 5%. The 95% CI is [-0.326, -0.186].\",\"results_keys\":[\"se_hac\",\"hac_lags\",\"beta_ci_low\",\"beta_ci_high\",\"okun_negative_5pct\"],\"status\":\"supported\",\"caveat\":\"Inference relies on the asymptotic normal distribution. The conclusion holds for bandwidths from 0 to 12 lags.\"},\n {\"id\":\"C3\",\"text\":\"The naive i.i.d. 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A quarter with 1 percentage point higher real GDP growth goes with an unemployment change about 0.26 points smaller. All required files are written, and running `out/analysis.py` regenerates `out/results.json` from `data/`.\n\n| quantity | value |\n|---|---|\n| Okun coefficient β̂ | −0.2558 |\n| intercept α̂ | 0.184 percentage points |\n| R² | 0.408 |\n| conventional standard error | 0.0200 |\n| autocorrelation-robust (Newey–West) standard error, 4 lags | 0.0358 |\n| 95% confidence interval | [−0.326, −0.186] |\n| t-statistic | −7.1 |\n| mean quarterly growth | 0.754% |\n\n- **Bandwidth:** I used 4 lags, from the standard Newey–West rule of thumb (floor(4·(T/100)^(2/9)) for T = 240). Four quarters is one year, and the regression errors' correlation fades over about that span: 0.39, 0.29, 0.14 and 0.07 at lags 1–4.\n- **Why the robust error matters:** it is about 1.8 times the conventional one, so the conventional error overstates precision. The conclusion holds for any bandwidth from 0 to 12 lags.\n- **Data issue:** unemployment is missing for October 2025. That month is outside the sample window, so it has no effect.\n\n**Limitations:**\n- The data are the current revised figures, not what was known in real time.\n- This is a single linear fit over the whole period, not a causal estimate. I did not test whether the relation changed over time (for example after 1984 or around 2008) or behaves differently in booms and recessions.\n- The confidence interval and test rely on large-sample theory. Given a t-statistic near −7, small-sample refinements would not change the conclusion.\n\nWhile setting up, I accidentally created a stray `data/out/` folder and removed it straight away. `data/` is unchanged: both files still match the checksums in `data/README.md`.\n\nFiles are in `out/`:\n- `results.json`\n- `report.md`\n- `analysis.py`\n- `claims.json`\n- `figure1.png`\n- `diagnostics.json` (extra: bandwidth sensitivity and error correlations)"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":247,"cache_read_input_tokens":12447,"output_tokens":846,"output_tokens_details":{"thinking_tokens":37},"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":247},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXqnH4C1LXG8jcb3vJrn","requestRef":"fae88ed5-cb09-4d00-96c4-4227c09279d4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5c7dc6dd-515a-497b-a4ba-5f927b4cf36a","timestamp":"2026-10-30T05:07:32.411Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0228","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","version":"2.1.314","gitBranch":"HEAD"}50{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"5c7dc6dd-515a-497b-a4ba-5f927b4cf36a","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60"}51{"type":"cost-state","sessionId":"cb293359-bdbf-4db7-b643-ba035f2bff60","totalCostUSD":0.1885688,"totalAPIDuration":57325,"totalAPIDurationWithoutRetries":57321,"totalToolDuration":1984,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":59430,"startTime":1793336793005,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":6109,"thinkingTokens":276,"cacheReadInputTokens":51834,"cacheCreationInputTokens":11190,"cacheCreationEphemeral5mInputTokens":11190,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.1885688}},"hasUnknownModelCost":false}52