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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md`. Below is some general advice about doing careful empirical research; it adds nothing specific to this task and you are free to ignore it.\n\nCareful empirical work usually begins by reading the task statement slowly and more than once, because the most common errors in applied research come not from difficult mathematics but from a misunderstanding of what exactly is being asked, which quantity is of interest, and which inputs are legitimately available. It is worth pausing to think about what the quantity of interest actually means, under what conditions it is well defined, and what would make an estimate of it misleading. Many published results have been overturned not because the estimator was wrong but because the sample was not what the authors believed it to be, or because information that should not have been available at the time of a decision had quietly entered the analysis.\n\nData deserve suspicion. Files assembled from several sources often carry inconsistencies: duplicated records, mixed units, shifted dates, columns whose definitions changed mid-sample, or values that were revised after the fact. Good practice is to look at the data before modelling them: distributions, ranges, missing values, identifiers, and whether counts add up. When something looks odd, it is better to document it and decide explicitly how to handle it than to let a default silently decide. The same applies to transformations: scaling, differencing, winsorising and imputing all change what is being estimated, and each deserves a sentence of justification.\n\nSimple baselines are underrated. A naïve or unconditional estimate computed with exactly the same evaluation rule as the main method tells the reader how much the method actually adds and protects against the illusion of progress created by a complicated pipeline. If the elaborate method does not beat the simple one, that is a finding, not a failure.\n\nUncertainty is part of the result. Standard errors must respect the dependence structure of the data; a confidence interval that ignores clustering, serial correlation or overlapping targets is not conservative, it is wrong. Where the sampling distribution is unknown, resampling or simulation can help, but the assumptions behind them should be stated. A result that is not statistically distinguishable from zero should be reported as such, in plain words.\n\nReproducibility is a courtesy to the reader and a safeguard for the author. Keeping the analysis in a script that reads the raw inputs and regenerates every reported number makes it possible to check the work, to find mistakes, and to rerun the analysis when the inputs change. Fixing random seeds, recording software versions and keeping intermediate files out of the final results all reduce the chance that a number in the report cannot be traced back to a computation.\n\nFinally, honesty about limitations is what distinguishes research from advocacy. Every analysis has weaknesses: assumptions that may not hold, data that may not be representative, methods that trade one bias for another. Stating them clearly, together with what would be needed to resolve them, is more valuable than a confident conclusion that the evidence does not support. 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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.\ntotal 56\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:27 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6548 oct. 29 23:27 GDPC1.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1185 oct. 29 23:27 README.md\n-rw-r--r-- 1 simon-pierreboucher staff 14211 oct. 29 23:27 UNRATE.csv","is_error":true,"tool_use_id":"toolu_011yCk1VQbkXTiRkfNk9Zwug"}]},"uuid":"fe4d3b76-f9ff-472d-ba08-10abfe2d5156","timestamp":"2026-10-30T05:31:10.871Z","toolUseResult":"Error: Exit code 1\n# F6-conf-03 — Okun's law in the United States, 1960–2019: coefficient, uncertainty and stability\n\n## Scientific question\nHow strongly do quarterly changes in the US unemployment rate respond to real output growth, and has that response been stable? The estimand is the Okun coefficient β in the difference form Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4, where Δu_t is the change in the quarterly-average civilian unemployment rate (percentage points) and g_t = 100·(ln GDPC1_t − ln GDPC1_{t−1}) is quarterly real GDP growth in percent (not annualised); uncertainty must be robust to heteroskedasticity and serial correlation. Stability is assessed by estimating β separately on 1960Q1–1983Q4 and 1984Q1–2019Q4 and by a classical Chow test of the joint equality of (α, β) across the two subperiods. The chain required is: question → data audit → baseline → justified method → results → uncertainty → failure discussion → one figure and one table → short report.\n\n## Inputs\n- `data/GDPC1.csv` — observed data, FRED GDPC1 (US real GDP, quarterly, billions of chained 2017 dollars, SAAR; BEA), full history, raw FRED CSV (`observation_date` = first day of the quarter, `GDPC1`). Provenance in `data/README.md`.\n- `data/UNRATE.csv` — observed data, FRED UNRATE (US civilian unemployment rate, monthly, percent, SA; BLS), full history, raw FRED CSV (`observation_date` = first day of the month, `UNRATE`). Provenance in `data/README.md`.\n- `data/README.md` — provenance, retrieval dates, SHA-256.\n\n## Information-availability rules\n- Quarterly unemployment u_t is the simple average of the three monthly UNRATE values of quarter t. Δu_t = u_t − u_{t−1}.\n- The regression sample is the 240 quarters 1960Q1–2019Q4 (the 1960Q1 differences use 1959Q4 levels). Quarters after 2019Q4 are outside the question and must not enter any estimate. Subperiod 1 = 1960Q1–1983Q4, subperiod 2 = 1984Q1–2019Q4.\n- Standard errors for the full-sample β must be HAC (Newey–West with Bartlett kernel); choose and report the lag length. The Chow test is the classical sum-of-squared-residuals F-test with 2 and n − 4 degrees of freedom.\n- Use only the files listed above.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `sample_start`, `sample_end` | string | ISO date | first day of the first and last quarter in the regression sample (`1960-01-01`, `2019-10-01`) |\n| `n_sub1`, `n_sub2` | integer | quarters | observations in each subperiod |\n| `baseline_metric` | number | percentage points | RMSE of the mean-only prediction of Δu over the full sample, sqrt(mean((Δu − mean Δu)²)) |\n| `model_metric` | number | percentage points | RMSE of the full-sample OLS residuals, sqrt(mean(e²)) |\n| `effect_estimate` | number | pp per percent growth | full-sample OLS Okun coefficient β |\n| `intercept` | number | percentage points | full-sample OLS intercept α |\n| `effect_se` | number | pp per percent growth | HAC (Newey–West) standard error of β |\n| `hac_lags` | integer | quarters | Newey–West lag length used |\n| `effect_ci_low`, `effect_ci_high` | number | pp per percent growth | 95% confidence interval for β based on `effect_se` |\n| `beta_sub1`, `beta_sub2` | number | pp per percent growth | OLS slope in subperiod 1 and 2 |\n| `chow_f_stat`, `chow_p_value` | number | — | Chow F statistic and p-value |\n| `stability_conclusion` | string | — | `stable` if the Chow test does not reject at 5%, else `unstable` |\n| `conclusion` | string | — | one of the codes below (sign of β from its 95% interval) |\n| `task_id` | string | copied from this file |\n| `feasibility` | string | `complete` / `partial` / `infeasible` |\n| `feasibility_reason` | string or null | why, if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | every data problem detected and how it was handled (empty list allowed) |\n| `n_input_rows` | object {file: integer} | rows read per input file (file name as key, header row excluded) |\n| `n_analysed` | integer or null | observations actually used in the main estimate |\n| `random_seed` | integer or null | seed used, if any |\n\n(`n_analysed` = quarters in the full-sample regression.)\n\nThe `conclusion` code must be one of: `positive_effect` (the 95% interval for the effect estimand lies above zero), `negative_effect` (lies below zero), `no_detectable_effect` (the interval / test does not reject zero at the 5% level), `premise_invalid` (the data provided do not allow the stated estimand to be computed — in that case set `feasibility` to `infeasible`, explain in `feasibility_reason`, and set every inestimable key to `null`; never substitute an invented or proxy estimate).\n\n- `out/figure1.png` — scatter of Δu_t against g_t with the two subperiod fitted lines, produced by `out/analysis.py` with matplotlib.\n- `out/table1.csv` — one row per sample (full, subperiod 1, subperiod 2) with columns `period`, `n`, `intercept`, `slope`, `slope_se` (header + three data rows), produced by `out/analysis.py`.\n- `out/report.md` with exactly these six sections, in this order: *Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues*. The last section must contain a genuine failure discussion: what would invalidate the result, what was not checked, and what the main threats are.\n- `out/analysis.py` — replication entry point: `~/wp29/venv/bin/python out/analysis.py`, run from the workspace root, must regenerate `out/results.json`, `out/figure1.png` and `out/table1.csv` from `data/` only, deterministically (fix any seed; use the matplotlib `Agg` backend).\n- `out/claims.json` — list of the central claims of the report: `[{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}]`. Every claim must reference existing keys of `out/results.json`; a claim must only be marked `supported` if the reported numbers actually support it.\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib available). Do not modify `data/`.\n- Write all deliverables under `out/`. This task is designed to be completed in well under 20 minutes of work; prefer a short, correct chain over an elaborate one.\ntotal 56\ndrwxr-xr-x 5 simon-pierreboucher staff 160 oct. 29 23:27 .\ndrwxr-xr-x 6 simon-pierreboucher staff 192 oct. 29 23:42 ..\n-rw-r--r-- 1 simon-pierreboucher staff 6548 oct. 29 23:27 GDPC1.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1185 oct. 29 23:27 README.md\n-rw-r--r-- 1 simon-pierreboucher staff 14211 oct. 29 23:27 UNRATE.csv","sandboxed":false,"sourceToolAssistantUUID":"4c82a1ef-576f-4579-a3c9-1cdc9a46bdb3","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}14{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"847659b536cb0ab48a7f6007bca2d0a7f3af8935975d99ef34e75e5843390199","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_011yCk1VQbkXTiRkfNk9Zwug","name":"Bash","input":{"command":"cat TASK.md; ls -la data; ls out 2>/dev/null"}}]}}15{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"f665fb66-f8f7-4f45-8a6b-5a4be90eb3f7","timestamp":"2026-10-30T05:31:10.875Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"69a76a93-b3fc-4084-9ecd-84b1536467a8","keep":2,"tail":["847659b536cb0ab48a7f6007bca2d0a7f3af8935975d99ef34e75e5843390199","c3e1dcbdddcfce9a2793543bb5331e1baf7fa036ad050d9123ae2d489a0b31b6"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b7b4aa5a646130ba8f84ea7303ec010a8a786990cd39f4a873f3dda9b60c413d"}16{"parentUuid":"fe4d3b76-f9ff-472d-ba08-10abfe2d5156","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbUCqoijP2peMCNjvX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01CDfJvpuPD8ay4Kvr1XwqFH","name":"Bash","input":{"command":"cat data/README.md; head -3 data/*.csv; tail -3 data/*.csv; wc -l data/*.csv; cd data; shasum -a 256 *.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2758,"cache_read_input_tokens":3186,"output_tokens":328,"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":2758},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsbTtzbMacTyyxUQEwb","requestRef":"f665fb66-f8f7-4f45-8a6b-5a4be90eb3f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3e774831-88e8-4935-ac5f-85236adb014b","timestamp":"2026-10-30T05:31:14.484Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}17{"parentUuid":"3e774831-88e8-4935-ac5f-85236adb014b","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01CDfJvpuPD8ay4Kvr1XwqFH","type":"tool_result","content":"# 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==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n 320 data/GDPC1.csv\n 946 data/UNRATE.csv\n 1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv","is_error":false}]},"uuid":"536b4246-7336-41cd-bc2e-9480a2049371","timestamp":"2026-10-30T05:31:14.550Z","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==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n 320 data/GDPC1.csv\n 946 data/UNRATE.csv\n 1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3e774831-88e8-4935-ac5f-85236adb014b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}18{"parentUuid":"536b4246-7336-41cd-bc2e-9480a2049371","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbUCqoijP2peMCNjvX","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01A2KcXSVC8ALRcmnRTFKnLa","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['GDPC1','UNRATE']:\n d=pd.read_csv(f+'.csv'); print(f, d.shape, d.dtypes.to_dict())\n d['observation_date']=pd.to_datetime(d.observation_date)\n print(d.observation_date.duplicated().sum(), d[f].isna().sum(), d[f].describe().to_dict())\n print(d.observation_date.diff().value_counts().head())\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2758,"cache_read_input_tokens":3186,"output_tokens":328,"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":2758},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbTtzbMacTyyxUQEwb","requestRef":"f665fb66-f8f7-4f45-8a6b-5a4be90eb3f7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"40745240-106e-4f2d-8211-e8801a0a30c7","timestamp":"2026-10-30T05:31:15.933Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}19{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"40745240-106e-4f2d-8211-e8801a0a30c7","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}20{"type":"atis-latch","atis":"","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}21{"parentUuid":"40745240-106e-4f2d-8211-e8801a0a30c7","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01A2KcXSVC8ALRcmnRTFKnLa","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\nGDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')}\n0 0 {'count': 319.0, 'mean': 10458.210203761757, 'std': 6485.0424476807175, 'min': 2172.432, '25%': 4848.918, '50%': 8920.193, '75%': 16340.058, 'max': 24611.077}\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n0 1 {'count': 944.0, 'mean': 5.652966101694915, 'std': 1.703533753130343, 'min': 2.5, '25%': 4.3, '50%': 5.45, '75%': 6.7, 'max': 14.8}\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64","is_error":false}]},"uuid":"1fb20e29-cc31-4ff4-ac02-3ac0c81f0e6d","timestamp":"2026-10-30T05:31:16.159Z","toolUseResult":{"stdout":"(eval):cd:1: no such file or directory: data\nGDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')}\n0 0 {'count': 319.0, 'mean': 10458.210203761757, 'std': 6485.0424476807175, 'min': 2172.432, '25%': 4848.918, '50%': 8920.193, '75%': 16340.058, 'max': 24611.077}\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n0 1 {'count': 944.0, 'mean': 5.652966101694915, 'std': 1.703533753130343, 'min': 2.5, '25%': 4.3, '50%': 5.45, '75%': 6.7, 'max': 14.8}\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"40745240-106e-4f2d-8211-e8801a0a30c7","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}22{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"dcdec2b02c4488a0d2a6bd81419f2e19ae10fdef3ce96bc2644231464badfb23","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01CDfJvpuPD8ay4Kvr1XwqFH","name":"Bash","input":{"command":"cat data/README.md; head -3 data/*.csv; tail -3 data/*.csv; wc -l data/*.csv; cd data; shasum -a 256 *.csv"}},{"type":"tool_use","id":"toolu_01A2KcXSVC8ALRcmnRTFKnLa","name":"Bash","input":{"command":"cd data; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['GDPC1','UNRATE']:\n d=pd.read_csv(f+'.csv'); print(f, d.shape, d.dtypes.to_dict())\n d['observation_date']=pd.to_datetime(d.observation_date)\n print(d.observation_date.duplicated().sum(), d[f].isna().sum(), d[f].describe().to_dict())\n print(d.observation_date.diff().value_counts().head())\n\""}}]}}23{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"e2b822b2c70f0c4abd8dcbab6b20dab653324a03cda44ebbef6f0ac74a452447","message":{"role":"user","content":[{"tool_use_id":"toolu_01CDfJvpuPD8ay4Kvr1XwqFH","type":"tool_result","content":"# 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==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n 320 data/GDPC1.csv\n 946 data/UNRATE.csv\n 1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c UNRATE.csv","is_error":false},{"tool_use_id":"toolu_01A2KcXSVC8ALRcmnRTFKnLa","type":"tool_result","content":"(eval):cd:1: no such file or directory: data\nGDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')}\n0 0 {'count': 319.0, 'mean': 10458.210203761757, 'std': 6485.0424476807175, 'min': 2172.432, '25%': 4848.918, '50%': 8920.193, '75%': 16340.058, 'max': 24611.077}\nobservation_date\n92 days 158\n91 days 100\n90 days 60\nName: count, dtype: int64\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n0 1 {'count': 944.0, 'mean': 5.652966101694915, 'std': 1.703533753130343, 'min': 2.5, '25%': 4.3, '50%': 5.45, '75%': 6.7, 'max': 14.8}\nobservation_date\n31 days 551\n30 days 314\n28 days 59\n29 days 20\nName: count, dtype: int64","is_error":false}]}}24{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"b9939a30-a24f-4711-8690-74bc569054a9","timestamp":"2026-10-30T05:31:16.162Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"f665fb66-f8f7-4f45-8a6b-5a4be90eb3f7","keep":4,"tail":["dcdec2b02c4488a0d2a6bd81419f2e19ae10fdef3ce96bc2644231464badfb23","e2b822b2c70f0c4abd8dcbab6b20dab653324a03cda44ebbef6f0ac74a452447"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",1],"value":{"type":"ephemeral"}}],"requestDigest":"e9bc266df522ef02a67ca31decc59ffe1278ef080b304727ded709de57e3bd65"}25{"parentUuid":"1fb20e29-cc31-4ff4-ac02-3ac0c81f0e6d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbrivQ7x8Ro1PThdpe","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSmQYKEAgTGAI4AUIIdGhpbmtpbmcSDDULQW393Me6WN7h2RoMBRpW3m8FBN3/mcDuIjDakSXDa2ulQGbCutqECA3ahgQKFAT976zUKwlfDAcwcWlfLN1OLjDmqqv3nIUeT7QqtgWxPMKTSbM4vt7BLm6YH+ZtSSSs2Vg9tVpwqU7z0hHgu7b1a7r6ZrJyecyJgCMSBESqgkylmduhOPWPWv8sUoiVGkYUMXSdF+S3cwMq9PJDzA4nYpsxmoEtTJNFTvgEsz0w9QA9COAVe7GPJoN5UdHFJk/KAT3FoMAAqdPL9pdnnQ+Wtn8SMMJ8v8HJ9+bgaJvTAEYwgsSiOwydY+qn6c7fVgtqDnBlKhAbgevnagUyDi+qMwCVJlPG2YHT8YQ67e0vnUL/b/Azx/KnBzaebeM0+nCIVHNE/dp+L2bPTuKLX6JArz8GMzZqQ5Hrq4yOku+XWvhatbYE5ClEmcoqO+vOcaVWwBE0aI+JuXJ0dtsBuNkAYyisX7Hp6/6sXGNPvt7W0Vr183bDoHDvWX7TREbha5O5R1uMOgCyL7+pOG3Ui6WOhRW2/sIu6qpSkaa7Drdow/hskrlcdpcAFNOtUG2UejTlwaJWyO2tsExhSbJNxwyrPvQR+/Pksy5q/5jk9VirRKMxm+Dr3DAyI5U3eSaVblHLuy2oygrx4txITVg+wSJ0bIWQybf0u1aBGBJ9hGHQ8QWM2r5F2jdeprhy4da26yIADI8m2Vv8yT/GOX4n7/YrZb9Y1eh07oS0JxUagulEA2YK981lKnJsj507s+mNu5UOkCkhuTsHIAQHnKAR988JC3B1XLmox5A9g/KFyNz5LHRA+mJz6BiUeZYxi7WabHjMD1FywX0kYKZRqAko7PJRWbnSxDOygw345YSJ1WeWv+Zhn7g/KJOStg85lWlmzNFIpCL1QjMBjyYR0htmtpaDDwKGyI3SGfr8Bpg0r4BOLTf3zrN+zThGC7ImjKzgAxks2kY9SKISh4zac7MFT1GMKL/456hObujT0o+MBrPDwPnsHTa2Tqfqx0JjHT9FKqDag63lGAE="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1659,"cache_read_input_tokens":5944,"output_tokens":269,"output_tokens_details":{"thinking_tokens":66},"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":1659},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":410,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsbrU3WjQaMSrMKv2JW","requestRef":"b9939a30-a24f-4711-8690-74bc569054a9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"bb7cdaa1-5071-47e6-b745-5a1af935772b","timestamp":"2026-10-30T05:31:17.775Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}26{"parentUuid":"bb7cdaa1-5071-47e6-b745-5a1af935772b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsbrivQ7x8Ro1PThdpe","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01SG6Hswy1J2HvD7ov8MhZd2","name":"Bash","input":{"command":"~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv'); print(d[d.UNRATE.isna()]); print(d[d.UNRATE.diff().abs()>1])\ng=pd.read_csv('data/GDPC1.csv'); import numpy as np; r=100*np.log(g.GDPC1).diff(); print(g[r.abs()>3].assign(r=r))\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1659,"cache_read_input_tokens":5944,"output_tokens":269,"output_tokens_details":{"thinking_tokens":66},"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":1659},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsbrU3WjQaMSrMKv2JW","requestRef":"b9939a30-a24f-4711-8690-74bc569054a9","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"8a533019-c39a-449d-b80b-0df6ce645d44","timestamp":"2026-10-30T05:31:18.991Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}27{"parentUuid":"8a533019-c39a-449d-b80b-0df6ce645d44","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n d=pd.read_csv('data/UNRATE.csv'); print(d[d.UNRATE.isna()]); print(d[d.UNRATE.diff().abs()>1])\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_01SG6Hswy1J2HvD7ov8MhZd2"}]},"uuid":"8a371770-5a88-496d-8547-723cdc4164b8","timestamp":"2026-10-30T05:31:19.216Z","toolUseResult":"Error: Exit code 1\nTraceback (most recent call last):\n File \"<string>\", line 3, in <module>\n d=pd.read_csv('data/UNRATE.csv'); print(d[d.UNRATE.isna()]); print(d[d.UNRATE.diff().abs()>1])\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":"8a533019-c39a-449d-b80b-0df6ce645d44","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}28{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"000433ff296f7d9e40d8f82bcfa8aa1b12060dcb1c7d2f3bc52fd7a370714b7b","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01SG6Hswy1J2HvD7ov8MhZd2","name":"Bash","input":{"command":"~/wp29/venv/bin/python 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print(g[r.abs()>3].assign(r=r))\n\""}}]}}29{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"f547a96d-f986-431e-a603-79721b9b2c25","timestamp":"2026-10-30T05:31:19.219Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"b9939a30-a24f-4711-8690-74bc569054a9","keep":6,"tail":["000433ff296f7d9e40d8f82bcfa8aa1b12060dcb1c7d2f3bc52fd7a370714b7b","6f10c96feb5fafad3bf510233afe34625bb2fd31e1ee047a34b4133e48d6181f"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"7f0cab5aaf2522d8ce9d599c5abe2f7d9851a63755c519235389789679712989"}30{"parentUuid":"8a371770-5a88-496d-8547-723cdc4164b8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsc5oEsipDkwuWkJjc5","type":"message","role":"assistant","content":[{"type":"text","text":"The earlier `cd data` persisted; returning to workspace root."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":991,"cache_read_input_tokens":7603,"output_tokens":253,"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":991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsc5XcSZEyXtowM3nCv","requestRef":"f547a96d-f986-431e-a603-79721b9b2c25","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"da54f181-7aad-4332-98b7-4cef711f4d3b","timestamp":"2026-10-30T05:31:20.518Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}31{"parentUuid":"da54f181-7aad-4332-98b7-4cef711f4d3b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsc5oEsipDkwuWkJjc5","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01GMKdWrWWPPXa11P9KZiZRF","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv'); print(d[d.UNRATE.isna()]); print(d[d.UNRATE.diff().abs()>1])\ng=pd.read_csv('data/GDPC1.csv'); import numpy as np; r=100*np.log(g.GDPC1).diff(); print(g[r.abs()>3].assign(r=r))\n\""},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":991,"cache_read_input_tokens":7603,"output_tokens":253,"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":991},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsc5XcSZEyXtowM3nCv","requestRef":"f547a96d-f986-431e-a603-79721b9b2c25","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fb73f694-e0de-47c0-9e89-93e221d2e2ef","timestamp":"2026-10-30T05:31:21.860Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003/data","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}32{"parentUuid":"fb73f694-e0de-47c0-9e89-93e221d2e2ef","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01GMKdWrWWPPXa11P9KZiZRF","type":"tool_result","content":" observation_date UNRATE\n933 2025-10-01 NaN\n observation_date UNRATE\n21 1949-10-01 7.9\n22 1949-11-01 6.4\n867 2020-04-01 14.8\n868 2020-05-01 13.2\n869 2020-06-01 11.0\n871 2020-08-01 8.4\n observation_date GDPC1 r\n12 1950-01-01 2346.104 3.856364\n13 1950-04-01 2417.682 3.005315\n14 1950-07-01 2511.127 3.792243\n23 1952-10-01 2843.941 3.231176\n125 1978-04-01 7058.920 3.791960\n293 2020-04-01 19077.992 -8.204321\n294 2020-07-01 20558.879 7.475750","is_error":false}]},"uuid":"7d9dafe7-4136-4718-b064-555f5b4c06f8","timestamp":"2026-10-30T05:31:22.087Z","toolUseResult":{"stdout":" observation_date UNRATE\n933 2025-10-01 NaN\n observation_date UNRATE\n21 1949-10-01 7.9\n22 1949-11-01 6.4\n867 2020-04-01 14.8\n868 2020-05-01 13.2\n869 2020-06-01 11.0\n871 2020-08-01 8.4\n observation_date GDPC1 r\n12 1950-01-01 2346.104 3.856364\n13 1950-04-01 2417.682 3.005315\n14 1950-07-01 2511.127 3.792243\n23 1952-10-01 2843.941 3.231176\n125 1978-04-01 7058.920 3.791960\n293 2020-04-01 19077.992 -8.204321\n294 2020-07-01 20558.879 7.475750","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"fb73f694-e0de-47c0-9e89-93e221d2e2ef","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}33{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"79b6d7fc97727b0ae121b244a935319c3a221358f60735ed3cbb7c0b5937cec7","message":{"role":"assistant","content":[{"type":"text","text":"The earlier `cd data` persisted; returning to workspace root."},{"type":"tool_use","id":"toolu_01GMKdWrWWPPXa11P9KZiZRF","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nd=pd.read_csv('data/UNRATE.csv'); print(d[d.UNRATE.isna()]); print(d[d.UNRATE.diff().abs()>1])\ng=pd.read_csv('data/GDPC1.csv'); import numpy as np; r=100*np.log(g.GDPC1).diff(); print(g[r.abs()>3].assign(r=r))\n\""}}]}}34{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"96282920-ea9a-4c6f-a974-50b1ef5f1ad5","timestamp":"2026-10-30T05:31:22.090Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"f547a96d-f986-431e-a603-79721b9b2c25","keep":8,"tail":["79b6d7fc97727b0ae121b244a935319c3a221358f60735ed3cbb7c0b5937cec7","840d1002fce55e211ea144969972d70ca0fc0904807dfc25a390eb9402944bd5"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"32221479417763724eee7229aebf42f9d7cbf9cb1cccbafca82684f73502edb0"}35{"parentUuid":"7d9dafe7-4136-4718-b064-555f5b4c06f8","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXscJ5foLjSapi93ERdB","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":520,"cache_read_input_tokens":8594,"output_tokens":3918,"output_tokens_details":{"thinking_tokens":233},"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":520},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2380,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXscHo3mxkNNazNEyBRV","requestRef":"96282920-ea9a-4c6f-a974-50b1ef5f1ad5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"d879a221-b5df-400d-8b26-af48f5c6047e","timestamp":"2026-10-30T05:31:25.710Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}36{"parentUuid":"d879a221-b5df-400d-8b26-af48f5c6047e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXscJ5foLjSapi93ERdB","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01KT6E9QRegWgTnzft5jJA6t","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 0 # no randomness used; fixed for completeness\nnp.random.seed(SEED)\nOUT = \"out\"\nissues = []\n\ngdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nur = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(ur))}\n\n# ---- audit ----\nfor name, d, col in [(\"GDPC1.csv\", gdp, \"GDPC1\"), (\"UNRATE.csv\", ur, \"UNRATE\")]:\n if d[\"observation_date\"].duplicated().any():\n raise SystemExit(f\"duplicate dates in {name}\")\n miss = d.loc[d[col].isna(), \"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\n if miss:\n inside = [m for m in miss if \"1959-10-01\" <= m <= \"2019-12-01\"]\n issues.append({\"issue\": f\"{name}: missing value(s) at {miss}\",\n \"handling\": \"outside the 1959Q4-2019Q4 window needed; not used\" if not inside\n else \"inside window -- quarter dropped\"})\n# monthly -> quarterly average, require 3 non-missing months\nur[\"q\"] = ur[\"observation_date\"].dt.to_period(\"Q\")\nuq = ur.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq = uq[uq[\"count\"] == 3][\"mean\"].rename(\"u\")\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.concat([uq, gq], axis=1).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"GDPC1\"]).diff()\n# ensure consecutive quarters for differencing\ndf = df.loc[pd.Period(\"1960Q1\"):pd.Period(\"2019Q4\")]\nassert len(df) == 240 and df[[\"du\", \"g\"]].notna().all().all(), \"sample incomplete\"\nassert (np.diff(df.index.asi8) == 1).all()\nissues.append({\"issue\": \"Post-2019 observations (incl. COVID-19 2020 outliers) present in files\",\n \"handling\": \"excluded per task; no estimate uses quarters after 2019Q4\"})\nissues.append({\"issue\": \"GDPC1 is the current (2026) vintage, revised long after the fact; not real-time data\",\n \"handling\": \"used as is (task asks for a historical relationship); noted as limitation\"})\n\nT = len(df)\nHAC_LAGS = int(math.floor(4 * (T / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb\n\ndef fit(d, lags=HAC_LAGS):\n X = sm.add_constant(d[\"g\"].values)\n ols = sm.OLS(d[\"du\"].values, X).fit()\n hac = sm.OLS(d[\"du\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lags, \"kernel\": \"bartlett\"})\n return ols, hac\n\nols, hac = fit(df)\ns1 = df.loc[:pd.Period(\"1983Q4\")]; s2 = df.loc[pd.Period(\"1984Q1\"):]\no1, h1 = fit(s1); o2, h2 = fit(s2)\n\nbeta, alpha = ols.params[1], ols.params[0]\nse = hac.bse[1]\nz = stats.norm.ppf(0.975)\nssr_p, ssr_1, ssr_2 = ols.ssr, o1.ssr, o2.ssr\nk = 2\nF = ((ssr_p - (ssr_1 + ssr_2)) / k) / ((ssr_1 + ssr_2) / (T - 2 * k))\np = float(stats.f.sf(F, k, T - 2 * k))\nlo, hi = beta - z * se, beta + 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(ols.resid ** 2))),\n \"effect_estimate\": float(beta), \"intercept\": float(alpha),\n \"effect_se\": float(se), \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"beta_sub1\": float(o1.params[1]), \"beta_sub2\": float(o2.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\": SEED,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\n\n# table: slope_se is HAC (Bartlett, same lag length) for every row\npd.DataFrame([\n {\"period\": \"full_1960Q1_2019Q4\", \"n\": T, \"intercept\": alpha, \"slope\": beta, \"slope_se\": se},\n {\"period\": \"sub1_1960Q1_1983Q4\", \"n\": len(s1), \"intercept\": o1.params[0], \"slope\": o1.params[1], \"slope_se\": h1.bse[1]},\n {\"period\": \"sub2_1984Q1_2019Q4\", \"n\": len(s2), \"intercept\": o2.params[0], \"slope\": o2.params[1], \"slope_se\": h2.bse[1]},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\n# supplementary diagnostics (not in results.json)\nrows = []\nfor L in [0, 2, 4, 8, 12]:\n rows.append({\"hac_lags\": L, \"beta_se\": fit(df, L)[1].bse[1]})\npd.DataFrame(rows).to_csv(f\"{OUT}/hac_lag_sensitivity.csv\", index=False)\n# Wald test of beta equality with HAC (interaction model), robust to heteroskedasticity\nD = (df.index >= pd.Period(\"1984Q1\")).astype(float)\nXi = np.column_stack([np.ones(T), df.g, D, D * df.g])\nwi = sm.OLS(df.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS})\nwt_joint = wi.wald_test(np.array([[0, 0, 1, 0], [0, 0, 0, 1]]), scalar=True)\nwt_slope = wi.wald_test(np.array([[0, 0, 0, 1]]), scalar=True)\nlb = sm.stats.acorr_ljungbox(ols.resid, lags=[4, 8])\nbp = sm.stats.het_breuschpagan(ols.resid, sm.add_constant(df.g.values))\nwith open(f\"{OUT}/diagnostics.json\", \"w\") as f:\n json.dump({\"hac_wald_joint_stat\": float(wt_joint.statistic), \"hac_wald_joint_p\": float(wt_joint.pvalue),\n \"hac_wald_slope_only_stat\": float(wt_slope.statistic), \"hac_wald_slope_only_p\": float(wt_slope.pvalue),\n \"ljungbox_p_lag4\": float(lb.lb_pvalue.iloc[0]), \"ljungbox_p_lag8\": float(lb.lb_pvalue.iloc[1]),\n \"breusch_pagan_p\": float(bp[1]),\n \"r2_full\": float(ols.rsquared), \"alpha_sub1\": float(o1.params[0]), \"alpha_sub2\": float(o2.params[0]),\n \"implied_zero_du_growth_full\": float(-alpha / beta)}, f, indent=2)\n\n# figure\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(s1.g, s1.du, s=14, alpha=.6, label=\"1960Q1–1983Q4\", color=\"C0\")\nax.scatter(s2.g, s2.du, s=14, alpha=.6, label=\"1984Q1–2019Q4\", color=\"C1\", marker=\"^\")\nxs = np.linspace(df.g.min(), df.g.max(), 50)\nax.plot(xs, o1.params[0] + o1.params[1] * xs, color=\"C0\", lw=2, label=f\"fit 1: β={o1.params[1]:.3f}\")\nax.plot(xs, o2.params[0] + o2.params[1] * xs, color=\"C1\", lw=2, label=f\"fit 2: β={o2.params[1]:.3f}\")\nax.axhline(0, color=\"grey\", lw=.5); ax.axvline(0, color=\"grey\", lw=.5)\nax.set_xlabel(\"Real GDP growth g_t (%, q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate (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 out/hac_lag_sensitivity.csv 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":520,"cache_read_input_tokens":8594,"output_tokens":3918,"output_tokens_details":{"thinking_tokens":233},"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":520},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXscHo3mxkNNazNEyBRV","requestRef":"96282920-ea9a-4c6f-a974-50b1ef5f1ad5","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1b590ee6-2d0e-462e-8f2e-0a05c411be04","timestamp":"2026-10-30T05:31:52.848Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"1b590ee6-2d0e-462e-8f2e-0a05c411be04","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}38{"type":"atis-latch","atis":"","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}39{"parentUuid":"1b590ee6-2d0e-462e-8f2e-0a05c411be04","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01KT6E9QRegWgTnzft5jJA6t","type":"tool_result","content":"{\n \"task_id\": \"F6-conf-03\",\n \"sample_start\": \"1960-01-01\",\n \"sample_end\": \"2019-10-01\",\n \"n_sub1\": 96,\n \"n_sub2\": 144,\n \"baseline_metric\": 0.32465793964999184,\n \"model_metric\": 0.2497438816636253,\n \"effect_estimate\": -0.2557677277487258,\n \"intercept\": 0.18438835418210714,\n \"effect_se\": 0.03583463330296569,\n \"hac_lags\": 4,\n \"effect_ci_low\": -0.32600231842173816,\n \"effect_ci_high\": -0.18553313707571345,\n \"beta_sub1\": -0.25902105345680587,\n \"beta_sub2\": -0.27339263306757905,\n \"chow_f_stat\": 6.0903330062337675,\n \"chow_p_value\": 0.002636323851733417,\n \"stability_conclusion\": \"unstable\",\n \"conclusion\": \"negative_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE.csv: missing value(s) at ['2025-10-01']\",\n \"handling\": \"outside the 1959Q4-2019Q4 window needed; not used\"\n },\n {\n \"issue\": \"Post-2019 observations (incl. COVID-19 2020 outliers) present in files\",\n \"handling\": \"excluded per task; no estimate uses quarters after 2019Q4\"\n },\n {\n \"issue\": \"GDPC1 is the current (2026) vintage, revised long after the fact; not real-time data\",\n \"handling\": \"used as is (task asks for a historical relationship); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"GDPC1.csv\": 319,\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 240,\n \"random_seed\": 0\n}\nperiod,n,intercept,slope,slope_se\nfull_1960Q1_2019Q4,240,0.18438835418210714,-0.2557677277487258,0.03583463330296569\nsub1_1960Q1_1983Q4,96,0.2542326539884382,-0.25902105345680587,0.03928680828959863\nsub2_1984Q1_2019Q4,144,0.15168581347724602,-0.27339263306757905,0.06831428793738023\nhac_lags,beta_se\n0,0.028836223207269963\n2,0.03422732023615383\n4,0.03583463330296569\n8,0.03713550417540189\n12,0.0364088511022809\n{\n \"hac_wald_joint_stat\": 10.926210056989923,\n \"hac_wald_joint_p\": 0.004240368816511458,\n \"hac_wald_slope_only_stat\": 0.03404280243683726,\n \"hac_wald_slope_only_p\": 0.8536158012976183,\n \"ljungbox_p_lag4\": 5.687122707043189e-13,\n \"ljungbox_p_lag8\": 2.136204501530993e-11,\n \"breusch_pagan_p\": 0.0003948637133711691,\n \"r2_full\": 0.4082508284562034,\n \"alpha_sub1\": 0.2542326539884382,\n \"alpha_sub2\": 0.15168581347724602,\n \"implied_zero_du_growth_full\": 0.7209211099660549\n}","is_error":false}]},"uuid":"4c6c3d77-f13f-43ba-9692-dbaefdf1e2df","timestamp":"2026-10-30T05:31:53.728Z","toolUseResult":{"stdout":"{\n \"task_id\": \"F6-conf-03\",\n \"sample_start\": \"1960-01-01\",\n \"sample_end\": \"2019-10-01\",\n \"n_sub1\": 96,\n \"n_sub2\": 144,\n \"baseline_metric\": 0.32465793964999184,\n \"model_metric\": 0.2497438816636253,\n \"effect_estimate\": -0.2557677277487258,\n \"intercept\": 0.18438835418210714,\n \"effect_se\": 0.03583463330296569,\n \"hac_lags\": 4,\n \"effect_ci_low\": -0.32600231842173816,\n \"effect_ci_high\": -0.18553313707571345,\n \"beta_sub1\": -0.25902105345680587,\n \"beta_sub2\": -0.27339263306757905,\n \"chow_f_stat\": 6.0903330062337675,\n \"chow_p_value\": 0.002636323851733417,\n \"stability_conclusion\": \"unstable\",\n \"conclusion\": \"negative_effect\",\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"UNRATE.csv: missing value(s) at ['2025-10-01']\",\n \"handling\": \"outside the 1959Q4-2019Q4 window needed; not used\"\n },\n {\n \"issue\": \"Post-2019 observations (incl. COVID-19 2020 outliers) present in files\",\n \"handling\": \"excluded per task; no estimate uses quarters after 2019Q4\"\n },\n {\n \"issue\": \"GDPC1 is the current (2026) vintage, revised long after the fact; not real-time data\",\n \"handling\": \"used as is (task asks for a historical relationship); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"GDPC1.csv\": 319,\n \"UNRATE.csv\": 945\n },\n \"n_analysed\": 240,\n \"random_seed\": 0\n}\nperiod,n,intercept,slope,slope_se\nfull_1960Q1_2019Q4,240,0.18438835418210714,-0.2557677277487258,0.03583463330296569\nsub1_1960Q1_1983Q4,96,0.2542326539884382,-0.25902105345680587,0.03928680828959863\nsub2_1984Q1_2019Q4,144,0.15168581347724602,-0.27339263306757905,0.06831428793738023\nhac_lags,beta_se\n0,0.028836223207269963\n2,0.03422732023615383\n4,0.03583463330296569\n8,0.03713550417540189\n12,0.0364088511022809\n{\n \"hac_wald_joint_stat\": 10.926210056989923,\n \"hac_wald_joint_p\": 0.004240368816511458,\n \"hac_wald_slope_only_stat\": 0.03404280243683726,\n \"hac_wald_slope_only_p\": 0.8536158012976183,\n \"ljungbox_p_lag4\": 5.687122707043189e-13,\n \"ljungbox_p_lag8\": 2.136204501530993e-11,\n \"breusch_pagan_p\": 0.0003948637133711691,\n \"r2_full\": 0.4082508284562034,\n \"alpha_sub1\": 0.2542326539884382,\n \"alpha_sub2\": 0.15168581347724602,\n \"implied_zero_du_growth_full\": 0.7209211099660549\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"1b590ee6-2d0e-462e-8f2e-0a05c411be04","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}40{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"3226f2503e4b661e823a8940fa29c4d75ac2a7421697a78b0fd0b908431a7073","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQS/AkKEAgTGAI4AUIIdGhpbmtpbmcSDLbxZN/OtvDPW9SMtRoMNwGRjq6q5j7DXrChIjCcXdx32RknqIMzPVE1UobaKjo2vbIicUfuAmeHUQL0dKvXWxJfSrDSGgKrqlZUoEQqmQkLIySgs1qU5WKu/Cyk4nNpitdrgNK/gjXRiHPYVZK5zAjKkq9rLvNpB/JyxXbuypMd+6y+qlvPMS/p2hkrXu1JbczOexu+M9dnScw+x8Iv5kaTbgcbafuVA9trVM1qO57PlwxOLoxprxxl/XsLx3Uv4pC4Cy1bm6gZ5IL2pGtrlSt6a939Dcp3rLcaTq3drW7Y1U572JbnqXIM5yKMvPFUwmTSM30RHmpKZa1yszxSKKByN+KQGC+MzEKNNiD5nBvSGGEQiPwi7vtAjtUhVL3OSh5IlAiH8owqVuuFuKX50Fjla2kO8+GvDgcawsD7p3Mf9KVlqVoNKafwc/uSWaGsxG6tIsMmCRXgwtzNx5YQwUkDQUouKkDjRfpXl89nHQk0221+6fXwA2AtXbEcX96FX4oOPRXl6kt/eTQNFpLin0YpIwatleuU/eQBue3MzRakXq/2UVAstl3aE2lADBNAwfd4/gP8ws1KkzDl+lZH2IQFogpsr3EJrdsVxQMi4OITIEEr8lvXiZDMMhcJqYl5XTcIARLu1b2s74BSY5vTqublH91cpMAJSUXo9e3CMlUZTsgei8OEqt48VOX3HR4SwrvaWEG+qg5SB5B+RUhgNCu+OF7YepjHn08DW2PbZe1aXJKwCX+E2t5Mv7Te2Nmft+/lQFfYmU4h/+liu7/jyjyPqG+nwjValXHJJqk9jpfwv+swjva54d8byiLiFBJz8m7YwHyJZ8VjJ8avPzlVIJuHQBbxVNuO3FBZo2+R7tOjiK1WB53Bndw8ld2Fjl1xxTMo1HEcVgHpyhGWe9AKsvx25Ng3iq7z7y0cM9Pf65/Jhjf4veME9v0MgSYG8XlNhvfrpKCNQ/UxgOYR1E2f5dsSAjwCmOHA5sk5Mx/cqZEABB8q9mdYRHBVDJrVzuKP2J73VNUcWMQVSkqxGUZgAgRVgzAHjmXHzWTS+4CfvvPofZCheY+pyBPfAK1BG/YsIIjvdvljaJ1oXyfJUW5CyGPD+UFUZK71CO6ym+QSKOOf6IA0sSe8onxTFNdHgkmlSJaHikC1COcd5bqUkNx8qxprQ/rmrkIMB+ZOtHXqydtaxDDsKR1Tqkg4GTXXeKLZsC1TRGFFtKmilGPReLlsHfNCr6WYdU/cTmv17mGzke1F1qjpGFyiWIbvPgKTgjH2DsgdUokYQMDDAMiXXHjW4hsEipjQnQg7xRLeRGuma2/6oWdKTedQJ5UW8cLIhjW1eeuJvM7NWbrAJ7JHXih8hhtoqO8xCfbzqwjHM5q9zQf/B02nGWujNRndKAfNS0v1GgLDz2F9AOXcnjrd5GWvrSHwhMJE7RxxEVvxAbMXMqN4pGbLnGTFw2JvjCvBv/HbErIBIpT2esjK9hOtx+mVH+iKpzH38llczWKCo+tmofl6fNEjDLGf3Rg7OnnxCPcL0oifFUrTDd5v0PgXNuusTQzv+URxNjIw30G1vxveGl3zzrsCnDYpwDgKd9PBbmuCT+9uobnzdFf6Nm3su90EWtQDkVfWV0py6UvyGZoB3INTVMh+X886bJx7MVC/Wmnh+EX3+EKRaXqeGAE="},{"type":"tool_use","id":"toolu_01KT6E9QRegWgTnzft5jJA6t","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law (difference form), US 1960Q1-2019Q4. Run from workspace root:\n ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, math\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nSEED = 0 # no randomness used; fixed for completeness\nnp.random.seed(SEED)\nOUT = \"out\"\nissues = []\n\ngdp = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nur = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_rows = {\"GDPC1.csv\": int(len(gdp)), \"UNRATE.csv\": int(len(ur))}\n\n# ---- audit ----\nfor name, d, col in [(\"GDPC1.csv\", gdp, \"GDPC1\"), (\"UNRATE.csv\", ur, \"UNRATE\")]:\n if d[\"observation_date\"].duplicated().any():\n raise SystemExit(f\"duplicate dates in {name}\")\n miss = d.loc[d[col].isna(), \"observation_date\"].dt.strftime(\"%Y-%m-%d\").tolist()\n if miss:\n inside = [m for m in miss if \"1959-10-01\" <= m <= \"2019-12-01\"]\n issues.append({\"issue\": f\"{name}: missing value(s) at {miss}\",\n \"handling\": \"outside the 1959Q4-2019Q4 window needed; not used\" if not inside\n else \"inside window -- quarter dropped\"})\n# monthly -> quarterly average, require 3 non-missing months\nur[\"q\"] = ur[\"observation_date\"].dt.to_period(\"Q\")\nuq = ur.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq = uq[uq[\"count\"] == 3][\"mean\"].rename(\"u\")\ngdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.concat([uq, gq], axis=1).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"GDPC1\"]).diff()\n# ensure consecutive quarters for differencing\ndf = df.loc[pd.Period(\"1960Q1\"):pd.Period(\"2019Q4\")]\nassert len(df) == 240 and df[[\"du\", \"g\"]].notna().all().all(), \"sample incomplete\"\nassert (np.diff(df.index.asi8) == 1).all()\nissues.append({\"issue\": \"Post-2019 observations (incl. COVID-19 2020 outliers) present in files\",\n \"handling\": \"excluded per task; no estimate uses quarters after 2019Q4\"})\nissues.append({\"issue\": \"GDPC1 is the current (2026) vintage, revised long after the fact; not real-time data\",\n \"handling\": \"used as is (task asks for a historical relationship); noted as limitation\"})\n\nT = len(df)\nHAC_LAGS = int(math.floor(4 * (T / 100) ** (2 / 9))) # Newey-West (1994) rule of thumb\n\ndef fit(d, lags=HAC_LAGS):\n X = sm.add_constant(d[\"g\"].values)\n ols = sm.OLS(d[\"du\"].values, X).fit()\n hac = sm.OLS(d[\"du\"].values, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lags, \"kernel\": \"bartlett\"})\n return ols, hac\n\nols, hac = fit(df)\ns1 = df.loc[:pd.Period(\"1983Q4\")]; s2 = df.loc[pd.Period(\"1984Q1\"):]\no1, h1 = fit(s1); o2, h2 = fit(s2)\n\nbeta, alpha = ols.params[1], ols.params[0]\nse = hac.bse[1]\nz = stats.norm.ppf(0.975)\nssr_p, ssr_1, ssr_2 = ols.ssr, o1.ssr, o2.ssr\nk = 2\nF = ((ssr_p - (ssr_1 + ssr_2)) / k) / ((ssr_1 + ssr_2) / (T - 2 * k))\np = float(stats.f.sf(F, k, T - 2 * k))\nlo, hi = beta - z * se, beta + 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(ols.resid ** 2))),\n \"effect_estimate\": float(beta), \"intercept\": float(alpha),\n \"effect_se\": float(se), \"hac_lags\": HAC_LAGS,\n \"effect_ci_low\": float(lo), \"effect_ci_high\": float(hi),\n \"beta_sub1\": float(o1.params[1]), \"beta_sub2\": float(o2.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\": SEED,\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\n\n# table: slope_se is HAC (Bartlett, same lag length) for every row\npd.DataFrame([\n {\"period\": \"full_1960Q1_2019Q4\", \"n\": T, \"intercept\": alpha, \"slope\": beta, \"slope_se\": se},\n {\"period\": \"sub1_1960Q1_1983Q4\", \"n\": len(s1), \"intercept\": o1.params[0], \"slope\": o1.params[1], \"slope_se\": h1.bse[1]},\n {\"period\": \"sub2_1984Q1_2019Q4\", \"n\": len(s2), \"intercept\": o2.params[0], \"slope\": o2.params[1], \"slope_se\": h2.bse[1]},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\n# supplementary diagnostics (not in results.json)\nrows = []\nfor L in [0, 2, 4, 8, 12]:\n rows.append({\"hac_lags\": L, \"beta_se\": fit(df, L)[1].bse[1]})\npd.DataFrame(rows).to_csv(f\"{OUT}/hac_lag_sensitivity.csv\", index=False)\n# Wald test of beta equality with HAC (interaction model), robust to heteroskedasticity\nD = (df.index >= pd.Period(\"1984Q1\")).astype(float)\nXi = np.column_stack([np.ones(T), df.g, D, D * df.g])\nwi = sm.OLS(df.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": HAC_LAGS})\nwt_joint = wi.wald_test(np.array([[0, 0, 1, 0], [0, 0, 0, 1]]), scalar=True)\nwt_slope = wi.wald_test(np.array([[0, 0, 0, 1]]), scalar=True)\nlb = sm.stats.acorr_ljungbox(ols.resid, lags=[4, 8])\nbp = sm.stats.het_breuschpagan(ols.resid, sm.add_constant(df.g.values))\nwith open(f\"{OUT}/diagnostics.json\", \"w\") as f:\n json.dump({\"hac_wald_joint_stat\": float(wt_joint.statistic), \"hac_wald_joint_p\": float(wt_joint.pvalue),\n \"hac_wald_slope_only_stat\": float(wt_slope.statistic), \"hac_wald_slope_only_p\": float(wt_slope.pvalue),\n \"ljungbox_p_lag4\": float(lb.lb_pvalue.iloc[0]), \"ljungbox_p_lag8\": float(lb.lb_pvalue.iloc[1]),\n \"breusch_pagan_p\": float(bp[1]),\n \"r2_full\": float(ols.rsquared), \"alpha_sub1\": float(o1.params[0]), \"alpha_sub2\": float(o2.params[0]),\n \"implied_zero_du_growth_full\": float(-alpha / beta)}, f, indent=2)\n\n# figure\nfig, ax = plt.subplots(figsize=(7, 5))\nax.scatter(s1.g, s1.du, s=14, alpha=.6, label=\"1960Q1–1983Q4\", color=\"C0\")\nax.scatter(s2.g, s2.du, s=14, alpha=.6, label=\"1984Q1–2019Q4\", color=\"C1\", marker=\"^\")\nxs = np.linspace(df.g.min(), df.g.max(), 50)\nax.plot(xs, o1.params[0] + o1.params[1] * xs, color=\"C0\", lw=2, label=f\"fit 1: β={o1.params[1]:.3f}\")\nax.plot(xs, o2.params[0] + o2.params[1] * xs, color=\"C1\", lw=2, label=f\"fit 2: β={o2.params[1]:.3f}\")\nax.axhline(0, color=\"grey\", lw=.5); ax.axvline(0, color=\"grey\", lw=.5)\nax.set_xlabel(\"Real GDP growth g_t (%, q/q, not annualised)\")\nax.set_ylabel(\"Δ unemployment rate (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 out/hac_lag_sensitivity.csv out/diagnostics.json"}}]}}41{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"ddad9d72-a125-463a-b662-83546ba98d2f","timestamp":"2026-10-30T05:31:53.731Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"96282920-ea9a-4c6f-a974-50b1ef5f1ad5","keep":10,"tail":["3226f2503e4b661e823a8940fa29c4d75ac2a7421697a78b0fd0b908431a7073","cb5ed1a3a52012ba562a479d7ff41274cefffa94dcbbc92ad0b0f0b38cfebaac"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"11967896d8f2c91f54189b4457d01b7a72a23da7366c3e053329083256d0473f"}42{"parentUuid":"4c6c3d77-f13f-43ba-9692-dbaefdf1e2df","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsedNL7rQo1qL6Q7ERZ","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":5060,"cache_read_input_tokens":9114,"output_tokens":409,"output_tokens_details":{"thinking_tokens":154},"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":5060},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":1071,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsed5DpPncxL8wUScxw","requestRef":"ddad9d72-a125-463a-b662-83546ba98d2f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"0cbb1b93-38a7-4b17-bdd8-b226af543a4e","timestamp":"2026-10-30T05:31:56.630Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}43{"parentUuid":"0cbb1b93-38a7-4b17-bdd8-b226af543a4e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsedNL7rQo1qL6Q7ERZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01XXnJfwXv6th6mCDjYMGNsh","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; 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cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960Q1–2019Q4 (task F6-conf-03)\n\n## Question\nHow much does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is that response stable? The estimand is β in Δu_t = α + β·g_t + ε_t, where Δu_t is the change in the quarterly-average UNRATE (pp) and g_t = 100·Δln GDPC1_t (percent, not annualised). The sample is the 240 quarters 1960Q1–2019Q4. Stability is checked by splitting the sample at 1984Q1 and running a classical Chow test.\n\n## Data and exclusions\n- `GDPC1.csv`: 319 rows (1947Q1–2026Q3), no missing values, no duplicate dates. `UNRATE.csv`: 945 rows (1948-01 to 2026-09), no duplicate dates. One value is missing (2025-10-01), which is outside the window and has no effect on any estimate.\n- Quarterly u_t is the simple mean of the three monthly values. The code only builds a quarter when all three months are present. 1959Q4 levels are used only to form the 1960Q1 differences. The code checks that the 240 quarters are consecutive and complete.\n- Everything after 2019Q4 is excluded, including the 2020 COVID outliers (g = −8.2 % and +7.5 %, u up to 14.8 %).\n- The data are the current (2026) vintage of GDP, not real-time data. This is acceptable for a historical relationship but is listed below as a limitation.\n- Subperiod counts: n₁ = 96 (1960Q1–1983Q4) and n₂ = 144 (1984Q1–2019Q4).\n\n## Method\n- **Baseline:** the mean-only prediction of Δu. Its RMSE is 0.325 pp.\n- **Main model:** OLS of Δu_t on a constant and g_t over the full sample.\n- **Standard errors:** Newey–West HAC with a Bartlett kernel. The lag length is 4, from the rule floor(4·(T/100)^{2/9}) with T = 240. HAC is needed because the residuals are strongly autocorrelated (Ljung–Box p ≈ 6e−13 at lag 4) and heteroskedastic (Breusch–Pagan p ≈ 4e−4).\n- **Confidence interval:** 95% normal interval, β̂ ± 1.96·SE.\n- **Subperiods:** separate OLS fits. Their table SEs are HAC with the same 4 lags.\n- **Chow test:** the classical SSR F-test of joint equality of (α, β), F = [(SSR_p − SSR₁ − SSR₂)/2] / [(SSR₁ + SSR₂)/(T − 4)], with (2, 236) df.\n- **Supplementary checks** (in `out/diagnostics.json` and `out/hac_lag_sensitivity.csv`): a HAC-robust Wald test of the break using an interaction model, and HAC SEs for lags 0–12.\n\n## Results\n| sample | n | α | β | HAC SE(β) |\n|---|---|---|---|---|\n| Full 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(The table is also saved as `out/table1.csv`. The figure is `out/figure1.png`.)\n\n- **Fit:** the regression lowers RMSE from 0.325 pp (mean-only baseline) to 0.250 pp, with R² = 0.41.\n- **Effect size:** one extra percentage point of quarterly real growth goes with about a 0.26 pp smaller rise in the unemployment rate in that quarter.\n- **Growth that keeps unemployment flat:** about 0.72 % per quarter over the full sample.\n- **Stability:** the Chow statistic is F = 6.09 (p = 0.0026), so equality of (α, β) is rejected at 5%. **stability_conclusion = unstable.**\n- **What drives the break:** the intercept, not the slope. The two slopes are almost identical (−0.259 vs −0.273). A HAC Wald test on the slope difference alone gives p = 0.85, while the HAC joint test still rejects (p = 0.004). The growth rate that keeps unemployment flat falls from about 0.98 % to about 0.55 % per quarter, which fits slower trend growth after 1984. So the coefficient that responds to growth looks stable; the relationship as a whole does not.\n\n## Uncertainty\n- HAC SE(β) = 0.0358 with 4 lags. The 95% CI is [−0.326, −0.186], entirely below zero, so **conclusion = negative_effect**.\n- The SE is not sensitive to the lag choice: 0.029 at 0 lags (plain OLS SE, no HAC correction), then 0.034, 0.036, 0.037 and 0.036 at 2, 4, 8 and 12 lags. The sign conclusion holds for every lag length.\n- The subperiod-2 slope is estimated less precisely (SE 0.068).\n\n## Limitations and open issues\n- **The classical Chow test assumes errors that are homoskedastic and serially uncorrelated.** Both assumptions fail here (see the Ljung–Box and Breusch–Pagan results above), so its nominal p-value is not exact. The HAC Wald test reaches the same rejection, which is reassuring, but it is asymptotic.\n- **The break date is fixed in advance at 1984 (the Great Moderation).** It is not estimated. A sup-F (Andrews/Quandt) test or a rolling estimate was not run and might show a different or a gradual break.\n- **β is not a causal parameter.** Output and unemployment are determined jointly, and OLS of Δu on g is a reduced-form association. Reversing the regression (g on Δu) would not give 1/β. Measurement error in quarterly GDP growth biases |β| toward zero.\n- **Static specification.** No lags of g are included. Lagged effects (labour hoarding) would show up in the serially correlated residuals and could make the contemporaneous β understate the total response. Dynamic and gap-form (output gap) versions were not checked.\n- **Data vintage.** Later revisions to GDPC1 change g_t, so a real-time analysis could give a different β. The chained-dollar base year does not affect log growth, but benchmark revisions do.\n- **Things that would overturn the stability reading:** evidence that the \"intercept shift\" is really a slowly drifting trend, or that the slope changed within subperiods, for example during the jobless recoveries after 1991, 2001 and 2009. Neither was tested.\n- **Unemployment is averaged within quarters.** This smooths the series, and the timing of the averaging relative to GDP may produce some of the serial correlation.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 the Okun coefficient is about -0.26 pp per 1% quarterly real growth, with HAC 95% CI entirely below zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not causal; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The regression on growth reduces RMSE of quarterly unemployment changes from 0.325 pp (mean-only baseline) to 0.250 pp.\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only; no out-of-sample evaluation.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) between 1960-1983 and 1984-2019 at 5%.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\"],\"status\":\"supported\",\"caveat\":\"Classical Chow test assumes iid errors, which fail here; a HAC Wald test (diagnostics.json) agrees.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the instability is driven by the intercept rather than the Okun slope.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"intercept\"],\"status\":\"supported\",\"caveat\":\"Slope-only HAC Wald test (p=0.85) and subperiod intercepts are in out/diagnostics.json and out/table1.csv, not in results.json; break date imposed a priori.\"}\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":494,"cache_read_input_tokens":14174,"output_tokens":3136,"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":494},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXsf274ikaDPkGvzx3cb","requestRef":"d36e1845-4f03-4d37-aa9f-f4b6f206b04e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2ebe24a3-0b35-42c9-a908-a65e2000334a","timestamp":"2026-10-30T05:32:27.322Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}48{"parentUuid":"2ebe24a3-0b35-42c9-a908-a65e2000334a","isSidechain":false,"promptId":"7840726f-133b-4b51-86e2-4cdd796b917f","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Kf2hhhGDzB32JecKdjw8qT","type":"tool_result","content":"analysis.py\nclaims.json\ndiagnostics.json\nfigure1.png\nhac_lag_sensitivity.csv\nreport.md\nresults.json\ntable1.csv","is_error":false}]},"uuid":"218104c4-582e-461a-9340-819643ff9a7a","timestamp":"2026-10-30T05:32:27.359Z","toolUseResult":{"stdout":"analysis.py\nclaims.json\ndiagnostics.json\nfigure1.png\nhac_lag_sensitivity.csv\nreport.md\nresults.json\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"2ebe24a3-0b35-42c9-a908-a65e2000334a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}49{"type":"api-request-blob","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","hash":"454462db6686a5cd54705779619617bbdb66b1dac7cba2929398c54d0f4da8ee","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01Kf2hhhGDzB32JecKdjw8qT","name":"Bash","input":{"command":"cd /Users/simon-pierreboucher/wp29/runs/RA0003; cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960Q1–2019Q4 (task F6-conf-03)\n\n## Question\nHow much does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is that response stable? The estimand is β in Δu_t = α + β·g_t + ε_t, where Δu_t is the change in the quarterly-average UNRATE (pp) and g_t = 100·Δln GDPC1_t (percent, not annualised). The sample is the 240 quarters 1960Q1–2019Q4. Stability is checked by splitting the sample at 1984Q1 and running a classical Chow test.\n\n## Data and exclusions\n- `GDPC1.csv`: 319 rows (1947Q1–2026Q3), no missing values, no duplicate dates. `UNRATE.csv`: 945 rows (1948-01 to 2026-09), no duplicate dates. One value is missing (2025-10-01), which is outside the window and has no effect on any estimate.\n- Quarterly u_t is the simple mean of the three monthly values. The code only builds a quarter when all three months are present. 1959Q4 levels are used only to form the 1960Q1 differences. The code checks that the 240 quarters are consecutive and complete.\n- Everything after 2019Q4 is excluded, including the 2020 COVID outliers (g = −8.2 % and +7.5 %, u up to 14.8 %).\n- The data are the current (2026) vintage of GDP, not real-time data. This is acceptable for a historical relationship but is listed below as a limitation.\n- Subperiod counts: n₁ = 96 (1960Q1–1983Q4) and n₂ = 144 (1984Q1–2019Q4).\n\n## Method\n- **Baseline:** the mean-only prediction of Δu. Its RMSE is 0.325 pp.\n- **Main model:** OLS of Δu_t on a constant and g_t over the full sample.\n- **Standard errors:** Newey–West HAC with a Bartlett kernel. The lag length is 4, from the rule floor(4·(T/100)^{2/9}) with T = 240. HAC is needed because the residuals are strongly autocorrelated (Ljung–Box p ≈ 6e−13 at lag 4) and heteroskedastic (Breusch–Pagan p ≈ 4e−4).\n- **Confidence interval:** 95% normal interval, β̂ ± 1.96·SE.\n- **Subperiods:** separate OLS fits. Their table SEs are HAC with the same 4 lags.\n- **Chow test:** the classical SSR F-test of joint equality of (α, β), F = [(SSR_p − SSR₁ − SSR₂)/2] / [(SSR₁ + SSR₂)/(T − 4)], with (2, 236) df.\n- **Supplementary checks** (in `out/diagnostics.json` and `out/hac_lag_sensitivity.csv`): a HAC-robust Wald test of the break using an interaction model, and HAC SEs for lags 0–12.\n\n## Results\n| sample | n | α | β | HAC SE(β) |\n|---|---|---|---|---|\n| Full 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(The table is also saved as `out/table1.csv`. The figure is `out/figure1.png`.)\n\n- **Fit:** the regression lowers RMSE from 0.325 pp (mean-only baseline) to 0.250 pp, with R² = 0.41.\n- **Effect size:** one extra percentage point of quarterly real growth goes with about a 0.26 pp smaller rise in the unemployment rate in that quarter.\n- **Growth that keeps unemployment flat:** about 0.72 % per quarter over the full sample.\n- **Stability:** the Chow statistic is F = 6.09 (p = 0.0026), so equality of (α, β) is rejected at 5%. **stability_conclusion = unstable.**\n- **What drives the break:** the intercept, not the slope. The two slopes are almost identical (−0.259 vs −0.273). A HAC Wald test on the slope difference alone gives p = 0.85, while the HAC joint test still rejects (p = 0.004). The growth rate that keeps unemployment flat falls from about 0.98 % to about 0.55 % per quarter, which fits slower trend growth after 1984. So the coefficient that responds to growth looks stable; the relationship as a whole does not.\n\n## Uncertainty\n- HAC SE(β) = 0.0358 with 4 lags. The 95% CI is [−0.326, −0.186], entirely below zero, so **conclusion = negative_effect**.\n- The SE is not sensitive to the lag choice: 0.029 at 0 lags (plain OLS SE, no HAC correction), then 0.034, 0.036, 0.037 and 0.036 at 2, 4, 8 and 12 lags. The sign conclusion holds for every lag length.\n- The subperiod-2 slope is estimated less precisely (SE 0.068).\n\n## Limitations and open issues\n- **The classical Chow test assumes errors that are homoskedastic and serially uncorrelated.** Both assumptions fail here (see the Ljung–Box and Breusch–Pagan results above), so its nominal p-value is not exact. The HAC Wald test reaches the same rejection, which is reassuring, but it is asymptotic.\n- **The break date is fixed in advance at 1984 (the Great Moderation).** It is not estimated. A sup-F (Andrews/Quandt) test or a rolling estimate was not run and might show a different or a gradual break.\n- **β is not a causal parameter.** Output and unemployment are determined jointly, and OLS of Δu on g is a reduced-form association. Reversing the regression (g on Δu) would not give 1/β. Measurement error in quarterly GDP growth biases |β| toward zero.\n- **Static specification.** No lags of g are included. Lagged effects (labour hoarding) would show up in the serially correlated residuals and could make the contemporaneous β understate the total response. Dynamic and gap-form (output gap) versions were not checked.\n- **Data vintage.** Later revisions to GDPC1 change g_t, so a real-time analysis could give a different β. The chained-dollar base year does not affect log growth, but benchmark revisions do.\n- **Things that would overturn the stability reading:** evidence that the \"intercept shift\" is really a slowly drifting trend, or that the slope changed within subperiods, for example during the jobless recoveries after 1991, 2001 and 2009. Neither was tested.\n- **Unemployment is averaged within quarters.** This smooths the series, and the timing of the averaging relative to GDP may produce some of the serial correlation.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 the Okun coefficient is about -0.26 pp per 1% quarterly real growth, with HAC 95% CI entirely below zero.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association, not causal; current-vintage data.\"},\n {\"id\":\"C2\",\"text\":\"The regression on growth reduces RMSE of quarterly unemployment changes from 0.325 pp (mean-only baseline) to 0.250 pp.\",\"results_keys\":[\"baseline_metric\",\"model_metric\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only; no out-of-sample evaluation.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) between 1960-1983 and 1984-2019 at 5%.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\"],\"status\":\"supported\",\"caveat\":\"Classical Chow test assumes iid errors, which fail here; a HAC Wald test (diagnostics.json) agrees.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the instability is driven by the intercept rather than the Okun slope.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"intercept\"],\"status\":\"supported\",\"caveat\":\"Slope-only HAC Wald test (p=0.85) and subperiod intercepts are in out/diagnostics.json and out/table1.csv, not in results.json; break date imposed a priori.\"}\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"}}]}}50{"type":"api-request","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","id":"a76f1e3f-ffa8-470f-9f87-2e7d2aaacf62","timestamp":"2026-10-30T05:32:27.362Z","version":"2.1.314","querySource":"sdk","shapeHash":"3696e19b41c30649588af0b413116cb3e0dff7d3764eb200bcb6e52e5e178135","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":"d36e1845-4f03-4d37-aa9f-f4b6f206b04e","keep":14,"tail":["454462db6686a5cd54705779619617bbdb66b1dac7cba2929398c54d0f4da8ee","dda426dd135fe1a5b0c0237db40fa336ad255379df3e48db1452605fd183c378"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5051e0d6dd59780d60fd9ac36ad342e066def6c9f83048a3ca63cf9e6ada346d"}51{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"218104c4-582e-461a-9340-819643ff9a7a","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}52{"type":"atis-latch","atis":"","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}53{"parentUuid":"218104c4-582e-461a-9340-819643ff9a7a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsh79jSRb8NqR1eULrC","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":3194,"cache_read_input_tokens":14668,"output_tokens":930,"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":3194},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":5913,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXsh6s7jxeQSNT6bvqM6","requestRef":"a76f1e3f-ffa8-470f-9f87-2e7d2aaacf62","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"393feef4-d0a6-40f2-bf2b-a59a13e5c1eb","timestamp":"2026-10-30T05:32:34.434Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}54{"parentUuid":"393feef4-d0a6-40f2-bf2b-a59a13e5c1eb","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXsh79jSRb8NqR1eULrC","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the Okun's law analysis for 1960Q1–2019Q4. All the required files are in `out/`, and running `out/analysis.py` from the workspace root regenerates the results, table and figure.\n\n**Results**\n- **Okun coefficient:** −0.256 percentage points of unemployment change per 1% of quarterly real growth, with intercept 0.184 and 240 quarters used.\n- **Uncertainty:** the Newey–West standard error is 0.036 with 4 lags, chosen by the standard rule of thumb for 240 quarters. The 95% interval is [−0.326, −0.186], entirely below zero, so the conclusion is `negative_effect`. Changing the lag length from 0 to 12 moves the standard error only between 0.029 and 0.037.\n- **Fit versus baseline:** the regression lowers the error from 0.325 points (predicting the average change every quarter) to 0.250 points, with R² of 0.41.\n- **Subperiod slopes:** −0.259 for 1960–83 and −0.273 for 1984–2019.\n- **Stability:** the Chow test gives F = 6.09 (p = 0.0026), so the result is `unstable`. The break comes from the intercept, not the slope. A robust test of the slope difference alone gives p = 0.85. The quarterly growth needed to keep unemployment flat falls from about 0.98% to about 0.55%.\n\n**Data checks**\n- The only missing value is the October 2025 unemployment rate, outside the sample, so it has no effect.\n- Quarters after 2019Q4, including the 2020 COVID outliers, are excluded.\n- GDP is the current 2026 vintage rather than data as first published; I've recorded this as a limitation.\n\n**Limitations** (discussed in the report)\n- The classical Chow test assumes errors that are uncorrelated and of constant variance, and both assumptions fail here. A test that allows for this, which I added, gives the same rejection (p = 0.004).\n- The 1984 break date is fixed in advance rather than estimated.\n- The coefficient is an association, not a causal effect.\n- The model has no lagged growth terms, and no output-gap or real-time-data versions were tried.\n\nBesides the required files, I wrote two supplementary ones: `diagnostics.json` and `hac_lag_sensitivity.csv`. Claim C4 relies on the slope-only test and subperiod intercepts, which are in `diagnostics.json` and `table1.csv` rather than `results.json`; the claim's caveat says so.\n\nFiles are in `out/`:\n- results.json\n- report.md\n- analysis.py\n- table1.csv\n- figure1.png\n- claims.json\n- diagnostics.json\n- hac_lag_sensitivity.csv"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3194,"cache_read_input_tokens":14668,"output_tokens":930,"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":3194},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXsh6s7jxeQSNT6bvqM6","requestRef":"a76f1e3f-ffa8-470f-9f87-2e7d2aaacf62","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6db712f7-fced-43b1-84e0-acca8d44b92d","timestamp":"2026-10-30T05:32:35.751Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RA0003","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","version":"2.1.314","gitBranch":"HEAD"}55{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"6db712f7-fced-43b1-84e0-acca8d44b92d","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15"}56{"type":"cost-state","sessionId":"1519f70e-160c-49c2-8574-77afdf7bae15","totalCostUSD":0.2810994,"totalAPIDuration":84709,"totalAPIDurationWithoutRetries":84704,"totalToolDuration":2463,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":90310,"startTime":1793338265457,"modelUsage":{"claude-opus-5-5":{"inputTokens":18,"outputTokens":9314,"thinkingTokens":470,"cacheReadInputTokens":64787,"cacheCreationInputTokens":16358,"cacheCreationEphemeral5mInputTokens":16358,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.2810994}},"hasUnknownModelCost":false}57