spb/wp29_uqo

1{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}2{"type":"queue-operation","operation":"enqueue","timestamp":"2026-10-30T04:49:43.737Z","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","content":"Begin"}3{"type":"queue-operation","operation":"dequeue","timestamp":"2026-10-30T04:49:43.743Z","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}4{"parentUuid":null,"isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":"Begin"},"uuid":"896bb54d-c68a-4413-9d48-b891da82e7e6","timestamp":"2026-10-30T04:49:43.748Z","permissionMode":"default","promptSource":"sdk","turnOrigin":"sdk","turnPosition":{"promptIndex":0,"turnIndex":1},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}5{"type":"api-request-shape","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","timestamp":"2026-10-30T04:49:43.762Z","version":"2.1.314","shape":{"system":[{"type":"text","text":"You are Claude Code, Anthropic's official CLI for Claude, running within the Claude Agent SDK."},{"type":"text","text":"CWD: /Users/simon-pierreboucher/wp29/runs/RC0175\nDate: 2026-10-30\n\nYou are a research assistant executing one bounded scientific task inside the current working directory, which is your workspace.\n\nEnvironment facts (identical for every task):\n- The task statement is the file `TASK.md` in the workspace. It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\nSEPARATE REVIEW (additional)\n- After the gate passes, a separate reviewer session that has not seen your work in progress will examine `TASK.md` and everything under `out/` and may return a list of issues (file `out/review/review.json`). You will then be resumed once to address them: fix what is justified, explain in `out/report.md` (Limitations and open issues) what you disagree with, re-run the gate, and update `out/claims.json` and `out/state/phases.json`. The reviewer has no reference answers; its role is to challenge unsupported claims, violated information rules, unverified computations, and inconsistencies between your report and your results.\n"}],"tools":[{"name":"Bash","description":"execute shell commands","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"command":{"description":"The command to execute","type":"string"},"timeout":{"description":"Optional timeout in milliseconds (max 600000 for a foreground command)","type":"number"},"description":{"description":"Clear, concise description of what this command does in active voice. Never use words like \"complex\" or \"risk\" in the description - just describe what it does.\n\nSay what the command does in plain words: do not echo the command's text, its flags, or file paths - the user reads this description, often without seeing the command.\n\nFor simple commands (git, npm, standard CLI tools), keep it brief (5-10 words):\n- ls → \"List files in current directory\"\n- git status → \"Show working tree status\"\n- npm install → \"Install package dependencies\"\n\nFor commands that are harder to parse at a glance (piped commands, obscure flags, etc.), add enough context to clarify what it does:\n- find . -name \"*.tmp\" -exec rm {} \\; → \"Find and delete all .tmp files recursively\"\n- git reset --hard origin/main → \"Discard all local changes and match remote main\"\n- curl -s url | jq '.data[]' → \"Fetch JSON from URL and extract data array elements\"","type":"string"},"dangerouslyDisableSandbox":{"description":"Set this to true to dangerously override sandbox mode and run commands without sandboxing.","type":"boolean"}},"required":["command"],"additionalProperties":false}},{"name":"Edit","description":"modify file contents in place","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to modify","type":"string"},"old_string":{"description":"The text to replace","type":"string"},"new_string":{"description":"The text to replace it with (must be different from old_string)","type":"string"},"replace_all":{"description":"Replace all occurrences of old_string (default false)","default":false,"type":"boolean"}},"required":["file_path","old_string","new_string"],"additionalProperties":false}},{"name":"Read","description":"read files, images, PDFs, notebooks","input_schema":{"$schema":"https://json-schema.org/draft/2020-12/schema","type":"object","properties":{"file_path":{"description":"The absolute path to the file to read","type":"string"},"offset":{"description":"The line number to start reading from. Only provide if the file is too large to read at once","type":"integer","minimum":0,"maximum":9007199254740991},"limit":{"description":"The number of lines to read. Only provide if the file is too large to read at once.","type":"integer","exclusiveMinimum":0,"maximum":9007199254740991},"pages":{"description":"Page range for PDF files (e.g., \"1-5\", \"3\", \"10-20\"). Only applicable to PDF files. Maximum 20 pages per request.","type":"string"},"allow_large":{"description":"Set to true to read a text file, or a line range of one, that is over the usual size limits, up to what still fits in your context. Only use this when you genuinely need all of it or the user asked for the whole file; otherwise read it in parts with offset and limit, or search it.","type":"boolean"}},"required":["file_path"],"additionalProperties":false}}]}}6{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9","message":{"role":"system","content":[],"output_config":{"effort":"medium"}}}7{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"bd487e17-f85b-4fa7-9590-c91fd8763093","timestamp":"2026-10-30T04:49:43.762Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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"}},"keep":0,"tail":["252097215a6af3e04abe3f3c5a1cce73f5f388825c554cef2907d381aaf4102e","d43ab0e0b8ec9db790a6459cb2f4bc4ffce02825ca05063162023be60912ced9"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",0,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"72c9f5db8f1d0bdc2a2adfe25141dd9f208b43fe9b6b05282f2be7b35616bc74"}8{"parentUuid":"896bb54d-c68a-4413-9d48-b891da82e7e6","isSidechain":false,"attachment":{"type":"session_context","context":{}},"type":"attachment","uuid":"43a54df4-30d4-48b8-bc44-8546b69a427a","timestamp":"2026-10-30T04:49:43.753Z","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}9{"parentUuid":"43a54df4-30d4-48b8-bc44-8546b69a427a","isSidechain":false,"attachment":{"type":"date","date":"2026-10-30"},"type":"attachment","uuid":"b5a320e7-2dcc-4d5d-858c-0cce073eed51","timestamp":"2026-10-30T04:49:43.753Z","rendered":[{"content":"<system-reminder>\nToday's date is 2026-10-30.\n</system-reminder>"}],"renderedRole":"system","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}10{"parentUuid":"b5a320e7-2dcc-4d5d-858c-0cce073eed51","isSidechain":false,"attachment":{"type":"remote_session_change","url":null,"commit":"Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>","pr":"🤖 Generated with [Claude Code](https://claude.com/claude-code)","sendUserFileHint":false,"managedCommit":false,"managedPr":false},"type":"attachment","uuid":"8ef715b9-f537-416f-abc5-6f2dd0513b14","timestamp":"2026-10-30T04:49:43.754Z","rendered":[{"content":"<system-reminder>\nAttribution for git commits and pull requests you create from here on (this replaces Claude Code's own earlier attribution guidance, such as a previous copy of this reminder; the user's own instructions about these lines, such as a CLAUDE.md or memory rule, take precedence over this reminder, but do not add attribution lines this reminder leaves out):\n- End git commit messages with:\nCo-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>\n- End pull request descriptions with:\n🤖 Generated with [Claude Code](https://claude.com/claude-code)\n</system-reminder>"}],"renderedRole":"user","renderedBesideToolResult":true,"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}11{"type":"tools-used","tools":["Bash"],"sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}12{"parentUuid":"8ef715b9-f537-416f-abc5-6f2dd0513b14","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSBcbsXq673vEjHDaK","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":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":142,"output_tokens_details":{"thinking_tokens":27},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":482,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpSBHWE5PLAY6N9S3zR","requestRef":"bd487e17-f85b-4fa7-9590-c91fd8763093","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4075ed37-685a-4aa2-ad48-c22fae497d5e","timestamp":"2026-10-30T04:49:45.828Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}13{"parentUuid":"4075ed37-685a-4aa2-ad48-c22fae497d5e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSBcbsXq673vEjHDaK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01R75ae4UAjcN2H1VwtFJqqN","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":4,"cache_creation_input_tokens":2811,"cache_read_input_tokens":1504,"output_tokens":142,"output_tokens_details":{"thinking_tokens":27},"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":2811},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpSBHWE5PLAY6N9S3zR","requestRef":"bd487e17-f85b-4fa7-9590-c91fd8763093","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b75e78b3-fd3d-452a-b807-668a4819f789","timestamp":"2026-10-30T04:49:46.482Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}14{"parentUuid":"b75e78b3-fd3d-452a-b807-668a4819f789","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"type":"tool_result","content":"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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nGDPC1.csv\nREADME.md\nUNRATE.csv","is_error":true,"tool_use_id":"toolu_01R75ae4UAjcN2H1VwtFJqqN"}]},"uuid":"8be63e89-dcdc-4246-800e-c00eaa61cf21","timestamp":"2026-10-30T04:49:46.538Z","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.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nGDPC1.csv\nREADME.md\nUNRATE.csv","sandboxed":false,"sourceToolAssistantUUID":"b75e78b3-fd3d-452a-b807-668a4819f789","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"0120d983cd82bc4f6a752dd80fdd227e9ffb1b02268dd6de8a78fdddbd665d6d","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01R75ae4UAjcN2H1VwtFJqqN","name":"Bash","input":{"command":"cat TASK.md; ls -R | head -50; ls out/state 2>/dev/null && cat out/state/log.md","description":"Read task and list workspace"}}]}}16{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"6121bff3-467d-420e-9bdb-ca2cba287779","timestamp":"2026-10-30T04:49:46.543Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"bd487e17-f85b-4fa7-9590-c91fd8763093","keep":2,"tail":["0120d983cd82bc4f6a752dd80fdd227e9ffb1b02268dd6de8a78fdddbd665d6d","29a94321c41450fb8ff4470282ba212cad9560b67c40a674c11458dfd681ca9e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",3,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6ce23067a7fb73581575ce8fc5b742a1976b09810b8fc13e6f9ec1f50647c111"}17{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"8be63e89-dcdc-4246-800e-c00eaa61cf21","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}18{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}19{"parentUuid":"8be63e89-dcdc-4246-800e-c00eaa61cf21","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSLm23JzpXAUUAKYUq","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01USf4X6Vu4Tb8UC696h6YtW","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; head -3 data/*.csv; tail -3 data/*.csv; wc -l data/*.csv; shasum -a 256 data/*.csv","description":"Inspect data and gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":2661,"cache_read_input_tokens":4315,"output_tokens":146,"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":2661},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpSLUu4PwZANVnUJs2v","requestRef":"6121bff3-467d-420e-9bdb-ca2cba287779","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"4b5351ed-9a68-498e-b7cc-a09b4a196885","timestamp":"2026-10-30T04:49:49.095Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}20{"parentUuid":"4b5351ed-9a68-498e-b7cc-a09b4a196885","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01USf4X6Vu4Tb8UC696h6YtW","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{\n  \"task_id\": \"F6-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"sample_start\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"sample_end\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_sub1\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_sub2\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"intercept\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"hac_lags\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv","is_error":false}]},"uuid":"32bc5039-1bea-4f1b-b424-31530a4995e7","timestamp":"2026-10-30T04:49:49.132Z","toolUseResult":{"stdout":"# Data provenance — F6-conf-03\n\nBoth files are raw FRED CSV downloads (`observation_date`, value), retrieved 2026-10-30T03:23:28Z, unaltered. Terms: FRED terms of use, https://fred.stlouisfed.org/legal/ ; the underlying series are produced by US government agencies and are in the public domain.\n\n## `GDPC1.csv` (319 rows)\n*Real Gross Domestic Product* (FRED `GDPC1`), quarterly, billions of chained 2017 dollars, seasonally adjusted annual rate. Source: U.S. Bureau of Economic Analysis. Observation dates are the first day of each quarter (e.g. `1947-01-01` = 1947Q1). Period 1947-01-01 to 2026-07-01 (current vintage as of retrieval).\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=GDPC1`\n- SHA-256: `1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343`\n\n## `UNRATE.csv` (945 rows)\n*Unemployment Rate* (FRED `UNRATE`), monthly, percent, seasonally adjusted, civilian labour force 16+. Source: U.S. Bureau of Labor Statistics. Observation dates are the first day of each month. Period 1948-01-01 to 2026-09-01.\n- URL: `https://fred.stlouisfed.org/graph/fredgraph.csv?id=UNRATE`\n- SHA-256: `621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c`\n{\n  \"task_id\": \"F6-conf-03\",\n  \"required_files\": [\n    \"out/results.json\",\n    \"out/report.md\",\n    \"out/analysis.py\",\n    \"out/claims.json\",\n    \"out/figure1.png\",\n    \"out/table1.csv\"\n  ],\n  \"results_keys\": {\n    \"task_id\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"feasibility_reason\": {\n      \"type\": \"string\",\n      \"nullable\": true\n    },\n    \"data_issues\": {\n      \"type\": \"array\",\n      \"nullable\": false\n    },\n    \"n_input_rows\": {\n      \"type\": \"object\",\n      \"nullable\": false\n    },\n    \"n_analysed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"random_seed\": {\n      \"type\": \"integer\",\n      \"nullable\": true\n    },\n    \"sample_start\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"sample_end\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"n_sub1\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"n_sub2\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"baseline_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"model_metric\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_estimate\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"intercept\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_se\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"hac_lags\": {\n      \"type\": \"integer\",\n      \"nullable\": false\n    },\n    \"effect_ci_low\": {\n==> data/GDPC1.csv <==\nobservation_date,GDPC1\n1947-01-01,2182.681\n1947-04-01,2176.892\n\n==> data/UNRATE.csv <==\nobservation_date,UNRATE\n1948-01-01,3.4\n1948-02-01,3.8\n==> data/GDPC1.csv <==\n2026-01-01,24274.383\n2026-04-01,24408.011\n2026-07-01,24611.077\n\n==> data/UNRATE.csv <==\n2026-07-01,4.1\n2026-08-01,4.1\n2026-09-01,4.2\n     320 data/GDPC1.csv\n     946 data/UNRATE.csv\n    1266 total\n1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343  data/GDPC1.csv\n621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c  data/UNRATE.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"4b5351ed-9a68-498e-b7cc-a09b4a196885","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"2a1e1bdbe7268efd4297a7c79f802a1ca36f96f507bb60f31ebd43c3688f3ce0","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01USf4X6Vu4Tb8UC696h6YtW","name":"Bash","input":{"command":"cat data/README.md; cat gate_spec.json | head -80; head -3 data/*.csv; tail -3 data/*.csv; wc -l data/*.csv; shasum -a 256 data/*.csv","description":"Inspect data and gate spec"}}]}}22{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"5a6f8321-4d00-4fb4-aff9-12af16dbed6c","timestamp":"2026-10-30T04:49:49.135Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"6121bff3-467d-420e-9bdb-ca2cba287779","keep":4,"tail":["2a1e1bdbe7268efd4297a7c79f802a1ca36f96f507bb60f31ebd43c3688f3ce0","908f3ecd53fd93ca6858eeeefa2df9a5ecc118ab3aeba000ff0373d6f6d39bd1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",5,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"2f4bb4a764fe98bd4a4a39558b182255bb27501f1951fc2559d13ee5c212d6ad"}23{"parentUuid":"32bc5039-1bea-4f1b-b424-31530a4995e7","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSXoYdNyL2L2f3f5Ft","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JdmKRFLCbTNJ6Xyjrj7PNx","name":"Bash","input":{"command":"sed -n 80,400p gate_spec.json; cat bundle.json | head -50","description":"Read rest of gate spec"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1679,"cache_read_input_tokens":6976,"output_tokens":103,"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":1679},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpSXYfEsNGMb9kQSgqi","requestRef":"5a6f8321-4d00-4fb4-aff9-12af16dbed6c","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"f5072da9-2098-4d4f-a78b-48ec41ff6ca9","timestamp":"2026-10-30T04:49:50.714Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}24{"parentUuid":"f5072da9-2098-4d4f-a78b-48ec41ff6ca9","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JdmKRFLCbTNJ6Xyjrj7PNx","type":"tool_result","content":"    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"beta_sub1\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"beta_sub2\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"chow_f_stat\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"chow_p_value\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"stability_conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\",\n    \"chronology\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"effect_se\",\n    \"chow_f_stat\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [\n    [\n      \"sample_start\",\n      \"sample_end\"\n    ]\n  ],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F6-conf-03\",\n  \"family\": \"F6\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.725294+00:00\",\n  \"files\": {\n    \"TASK.md\": \"64afb4744b89e1ab9b45cefc3fad325b1857216f04a3d3cc54ed3b1681db2a0e\",\n    \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n    \"data/README.md\": \"7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658\",\n    \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n    \"gate_spec.json\": \"bced13a911d728cc853d5a88f4412cf86338d82c325eb7891ba2f5fd86317f61\"\n  }\n}","is_error":false}]},"uuid":"ac1fdf9f-8346-4799-99db-f8cb2ae1466b","timestamp":"2026-10-30T04:49:50.730Z","toolUseResult":{"stdout":"    \"effect_ci_low\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"effect_ci_high\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"beta_sub1\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"beta_sub2\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"chow_f_stat\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"chow_p_value\": {\n      \"type\": \"number\",\n      \"nullable\": false\n    },\n    \"stability_conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    },\n    \"conclusion\": {\n      \"type\": \"string\",\n      \"nullable\": false\n    }\n  },\n  \"report_sections\": [\n    \"Question\",\n    \"Data and exclusions\",\n    \"Method\",\n    \"Results\",\n    \"Uncertainty\",\n    \"Limitations and open issues\"\n  ],\n  \"family_checks\": [\n    \"ci_contains_estimate\",\n    \"positive_se\",\n    \"claims_reference_existing_keys\",\n    \"phase_ledger_consistent\",\n    \"exclusion_ledger_sums\",\n    \"chronology\"\n  ],\n  \"ci_pairs\": [\n    [\n      \"effect_estimate\",\n      \"effect_ci_low\",\n      \"effect_ci_high\"\n    ]\n  ],\n  \"positive_keys\": [\n    \"effect_se\",\n    \"chow_f_stat\",\n    \"baseline_metric\",\n    \"model_metric\"\n  ],\n  \"chronology_pairs\": [\n    [\n      \"sample_start\",\n      \"sample_end\"\n    ]\n  ],\n  \"analysis_timeout_s\": 300,\n  \"reproducibility_tolerance_rel\": 1e-06\n}{\n  \"task_id\": \"F6-conf-03\",\n  \"family\": \"F6\",\n  \"split\": \"conf\",\n  \"version\": \"1.0\",\n  \"created_utc\": \"2026-10-30T03:42:41.725294+00:00\",\n  \"files\": {\n    \"TASK.md\": \"64afb4744b89e1ab9b45cefc3fad325b1857216f04a3d3cc54ed3b1681db2a0e\",\n    \"data/GDPC1.csv\": \"1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343\",\n    \"data/README.md\": \"7e61e05785988f6cf32be948415ecb9511994533c3a1ef76958fd13ffc4a8658\",\n    \"data/UNRATE.csv\": \"621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c\",\n    \"gate_spec.json\": \"bced13a911d728cc853d5a88f4412cf86338d82c325eb7891ba2f5fd86317f61\"\n  }\n}","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"f5072da9-2098-4d4f-a78b-48ec41ff6ca9","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}25{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"62e09e407798d8c244ad125a04d53d6427481eadf49cb43cf31adc855c2d7c32","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01JdmKRFLCbTNJ6Xyjrj7PNx","name":"Bash","input":{"command":"sed -n 80,400p gate_spec.json; cat bundle.json | head -50","description":"Read rest of gate spec"}}]}}26{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"6b60f1e3-c3b9-46bb-bde5-6b641e628c2b","timestamp":"2026-10-30T04:49:50.733Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"5a6f8321-4d00-4fb4-aff9-12af16dbed6c","keep":6,"tail":["62e09e407798d8c244ad125a04d53d6427481eadf49cb43cf31adc855c2d7c32","adaf2c3f553fc6fa59d06c3ed76d40a347e4413d6d2b9b784cd473a53c48d19b"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",7,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"91df34c95b592757d87c0465c9a7126464d3d3f774a25def4995fe25559e9dc0"}27{"parentUuid":"ac1fdf9f-8346-4799-99db-f8cb2ae1466b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSefYQerkgq85wBM3i","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":1047,"cache_read_input_tokens":8655,"output_tokens":472,"output_tokens_details":{"thinking_tokens":69},"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":1047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":405,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpSeNvPhCzeA3r6Z6bm","requestRef":"6b60f1e3-c3b9-46bb-bde5-6b641e628c2b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5ed96d6f-4593-4e0d-acbc-6ca2e4f777a9","timestamp":"2026-10-30T04:49:53.256Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}28{"parentUuid":"5ed96d6f-4593-4e0d-acbc-6ca2e4f777a9","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpSefYQerkgq85wBM3i","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01AjpDp3cqBs3i2nWEUn5n9Q","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['GDPC1','UNRATE']:\n    d=pd.read_csv(f'data/{f}.csv'); print(f,d.shape,d.dtypes.to_dict())\n    d['observation_date']=pd.to_datetime(d['observation_date'])\n    print(d.isna().sum().to_dict(), d.observation_date.duplicated().sum(), d.observation_date.min(), d.observation_date.max())\n    v=pd.to_numeric(d[f],errors='coerce'); print('nonnum',v.isna().sum(), v.describe().to_dict())\n    print(d[v.isna()])\n    fr='QS' if f=='GDPC1' else 'MS'\n    full=pd.date_range(d.observation_date.min(),d.observation_date.max(),freq=fr); print('missing dates',sorted(set(full)-set(d.observation_date)))\n\"","description":"Audit data files"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1047,"cache_read_input_tokens":8655,"output_tokens":472,"output_tokens_details":{"thinking_tokens":69},"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":1047},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpSeNvPhCzeA3r6Z6bm","requestRef":"6b60f1e3-c3b9-46bb-bde5-6b641e628c2b","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"c27b4a4c-8793-4ff2-9105-c143c96f4cbc","timestamp":"2026-10-30T04:49:55.897Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}29{"parentUuid":"c27b4a4c-8793-4ff2-9105-c143c96f4cbc","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01AjpDp3cqBs3i2nWEUn5n9Q","type":"tool_result","content":"GDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')}\n{'observation_date': 0, 'GDPC1': 0} 0 1947-01-01 00:00:00 2026-07-01 00:00:00\nnonnum 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}\nEmpty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: []\nmissing dates []\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n{'observation_date': 0, 'UNRATE': 1} 0 1948-01-01 00:00:00 2026-09-01 00:00:00\nnonnum 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}\n    observation_date  UNRATE\n933       2025-10-01     NaN\nmissing dates []","is_error":false}]},"uuid":"a4fa2a52-b6f7-49b3-983d-95a35480b9e1","timestamp":"2026-10-30T04:49:56.171Z","toolUseResult":{"stdout":"GDPC1 (319, 2) {'observation_date': dtype('O'), 'GDPC1': dtype('float64')}\n{'observation_date': 0, 'GDPC1': 0} 0 1947-01-01 00:00:00 2026-07-01 00:00:00\nnonnum 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}\nEmpty DataFrame\nColumns: [observation_date, GDPC1]\nIndex: []\nmissing dates []\nUNRATE (945, 2) {'observation_date': dtype('O'), 'UNRATE': dtype('float64')}\n{'observation_date': 0, 'UNRATE': 1} 0 1948-01-01 00:00:00 2026-09-01 00:00:00\nnonnum 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}\n    observation_date  UNRATE\n933       2025-10-01     NaN\nmissing dates []","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"c27b4a4c-8793-4ff2-9105-c143c96f4cbc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}30{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"229d9c6f31f9be763599bd267821728678ad5873509af9ff70306c7fd12b260a","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01AjpDp3cqBs3i2nWEUn5n9Q","name":"Bash","input":{"command":"mkdir -p out/state; ~/wp29/venv/bin/python -c \"\nimport pandas as pd\nfor f in ['GDPC1','UNRATE']:\n    d=pd.read_csv(f'data/{f}.csv'); print(f,d.shape,d.dtypes.to_dict())\n    d['observation_date']=pd.to_datetime(d['observation_date'])\n    print(d.isna().sum().to_dict(), d.observation_date.duplicated().sum(), d.observation_date.min(), d.observation_date.max())\n    v=pd.to_numeric(d[f],errors='coerce'); print('nonnum',v.isna().sum(), v.describe().to_dict())\n    print(d[v.isna()])\n    fr='QS' if f=='GDPC1' else 'MS'\n    full=pd.date_range(d.observation_date.min(),d.observation_date.max(),freq=fr); print('missing dates',sorted(set(full)-set(d.observation_date)))\n\"","description":"Audit data files"}}]}}31{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"3c558d2e-d04f-4637-913a-9723ef025a02","timestamp":"2026-10-30T04:49:56.175Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"6b60f1e3-c3b9-46bb-bde5-6b641e628c2b","keep":8,"tail":["229d9c6f31f9be763599bd267821728678ad5873509af9ff70306c7fd12b260a","ae30a39579ec466e27d99bfbc3ed08c8c7a25d95cc0eb7316e3561d85a730a1e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",9,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a5cf6c86502898af780e160466d006a42ed881f1e866f5641b5c40b2250ea19a"}32{"parentUuid":"a4fa2a52-b6f7-49b3-983d-95a35480b9e1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpT3uXgDtUzgkC5jUn3","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":913,"cache_read_input_tokens":9702,"output_tokens":1303,"output_tokens_details":{"thinking_tokens":280},"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":913},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2855,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpT3f9UvPjJyCJkqKYQ","requestRef":"3c558d2e-d04f-4637-913a-9723ef025a02","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"00317bb0-be7e-4d4e-b4ec-49096554cdf1","timestamp":"2026-10-30T04:50:00.283Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}33{"parentUuid":"00317bb0-be7e-4d4e-b4ec-49096554cdf1","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpT3uXgDtUzgkC5jUn3","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01VRNDXbGqrDFexU89BnPRyv","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-03 (Okun's law, US 1960–2019)\n**Question**: how strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is the response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n**Estimand**: β in Δu_t = α + β g_t + ε_t, Δu_t = change in quarterly-average UNRATE (pp), g_t = 100·Δln GDPC1 (% q/q, not annualised), t = 1960Q1..2019Q4 (240 obs; 1960Q1 uses 1959Q4 levels). β is a reduced-form statistical association (conditional mean slope), meaningful as such under linearity and stable parameters within the sample; not a causal effect.\nUncertainty: Newey–West (Bartlett) HAC SE for full-sample β, lag reported; 95% CI = β ± 1.96·SE (normal). Stability: separate OLS by subperiod; classical Chow SSR F-test, df (2, n−4).\n**Outputs**: out/results.json (keys: sample_start, sample_end, n_sub1, n_sub2, baseline_metric, model_metric, effect_estimate, intercept, effect_se, hac_lags, effect_ci_low, effect_ci_high, beta_sub1, beta_sub2, chow_f_stat, chow_p_value, stability_conclusion, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (period,n,intercept,slope,slope_se; 3 rows); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules**: quarterly u = mean of 3 months; no quarter after 2019Q4 in any estimate; only the given files.\n**Invalidity / impossibility**: missing data inside 1959Q4–2019Q4, incomplete quarters, or file hash mismatch would compromise; using post-2019 data would violate rules.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":913,"cache_read_input_tokens":9702,"output_tokens":1303,"output_tokens_details":{"thinking_tokens":280},"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":913},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpT3f9UvPjJyCJkqKYQ","requestRef":"3c558d2e-d04f-4637-913a-9723ef025a02","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3f2ffb69-f53c-4591-a4b5-174be7b3f551","timestamp":"2026-10-30T04:50:07.694Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}34{"parentUuid":"3f2ffb69-f53c-4591-a4b5-174be7b3f551","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01VRNDXbGqrDFexU89BnPRyv","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"7e59dccd-34d1-4434-8608-a9c41900d331","timestamp":"2026-10-30T04:50:07.732Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3f2ffb69-f53c-4591-a4b5-174be7b3f551","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}35{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"338bdbbc97f10eeb34613fc24e5afff7086653042d9e7a1940cfaa1683fb1339","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01VRNDXbGqrDFexU89BnPRyv","name":"Bash","input":{"command":"cat > out/state/contract.md <<'EOF'\n# Contract — F6-conf-03 (Okun's law, US 1960–2019)\n**Question**: how strongly does the quarterly change in the US unemployment rate respond to quarterly real GDP growth, and is the response stable across 1960Q1–1983Q4 vs 1984Q1–2019Q4?\n**Estimand**: β in Δu_t = α + β g_t + ε_t, Δu_t = change in quarterly-average UNRATE (pp), g_t = 100·Δln GDPC1 (% q/q, not annualised), t = 1960Q1..2019Q4 (240 obs; 1960Q1 uses 1959Q4 levels). β is a reduced-form statistical association (conditional mean slope), meaningful as such under linearity and stable parameters within the sample; not a causal effect.\nUncertainty: Newey–West (Bartlett) HAC SE for full-sample β, lag reported; 95% CI = β ± 1.96·SE (normal). Stability: separate OLS by subperiod; classical Chow SSR F-test, df (2, n−4).\n**Outputs**: out/results.json (keys: sample_start, sample_end, n_sub1, n_sub2, baseline_metric, model_metric, effect_estimate, intercept, effect_se, hac_lags, effect_ci_low, effect_ci_high, beta_sub1, beta_sub2, chow_f_stat, chow_p_value, stability_conclusion, conclusion, task_id, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/figure1.png; out/table1.csv (period,n,intercept,slope,slope_se; 3 rows); out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules**: quarterly u = mean of 3 months; no quarter after 2019Q4 in any estimate; only the given files.\n**Invalidity / impossibility**: missing data inside 1959Q4–2019Q4, incomplete quarters, or file hash mismatch would compromise; using post-2019 data would violate rules.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md read fully\",\"contract.md written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed: contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}36{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"8a28f274-993b-4f29-932b-cc16ae3d9cf4","timestamp":"2026-10-30T04:50:07.737Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"3c558d2e-d04f-4637-913a-9723ef025a02","keep":10,"tail":["338bdbbc97f10eeb34613fc24e5afff7086653042d9e7a1940cfaa1683fb1339","219824b5f2ff52071dae855878c392a63d74e5397208b2a581bad13891a211ba"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"868668003f92002b3cbe1536cc1d4b2264af5b4f542b36bd3d5b4d194032a837"}37{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"7e59dccd-34d1-4434-8608-a9c41900d331","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}38{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}39{"parentUuid":"7e59dccd-34d1-4434-8608-a9c41900d331","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpTuQaWndrk3TA3P3zM","type":"message","role":"assistant","content":[{"type":"text","text":"Now the analysis script."}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1318,"cache_read_input_tokens":10615,"output_tokens":3725,"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":1318},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpTu6j389QViGzvhmAv","requestRef":"8a28f274-993b-4f29-932b-cc16ae3d9cf4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5221b2db-1670-434d-be4d-fe0d25f7fb7b","timestamp":"2026-10-30T04:50:09.913Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}40{"parentUuid":"5221b2db-1670-434d-be4d-fe0d25f7fb7b","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpTuQaWndrk3TA3P3zM","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_019qEXZgKYZXKiYc73s3nbbp","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4 (task F6-conf-03). Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nun_raw = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly mean of 3 monthly values (complete quarters only)\nun = un_raw.copy()\nmiss = un[un[\"UNRATE\"].isna()]\nif len(miss):\n    data_issues.append({\"issue\": f\"UNRATE missing for month(s) {', '.join(miss.observation_date.dt.strftime('%Y-%m').tolist())}\",\n                        \"handling\": \"Outside the 1959Q4-2019Q4 window needed for estimation; quarter would be incomplete and is not used. No imputation.\"})\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[un.groupby(\"q\")[\"UNRATE\"].apply(lambda s: s.notna().sum()) < 3, \"mean\"] = np.nan\ngdp = gdp_raw.copy(); gdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.DataFrame({\"u\": uq[\"mean\"], \"gdp\": gq}).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"gdp\"]).diff()\n# require consecutive quarters for differences\nidx = df.index\nconsec = pd.Series([True] + [(idx[i] - idx[i-1]).n == 1 for i in range(1, len(idx))], index=idx)\ndf.loc[~consec, [\"du\", \"g\"]] = np.nan\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage in chained 2017 dollars, not real-time data; historical growth rates reflect later revisions.\",\n                    \"handling\": \"Used as provided (the task specifies this series); noted as a limitation.\"})\n\n# --- exclusion ledger (unit: quarterly observations of the merged panel)\nexcl = []\nn0 = len(df)\nq_start, q_end = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nd1 = df[df.index >= q_start]; excl.append((\"1\", \"drop quarters before 1960Q1\", n0, len(d1), \"outside question window (1959Q4 levels used only for differencing)\"))\nd2 = d1[d1.index <= q_end]; excl.append((\"2\", \"drop quarters after 2019Q4\", len(d1), len(d2), \"information rule: post-2019Q4 must not enter any estimate\"))\nd3 = d2.dropna(subset=[\"du\", \"g\"]); excl.append((\"3\", \"drop quarters with missing du or g\", len(d2), len(d3), \"incomplete data\"))\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b, n_removed=a-b, rationale=t) for s, r, a, b, t in excl]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nd = d3.copy()\nassert len(d) == 240 and d.index.min() == q_start and d.index.max() == q_end\n\n# --- baseline: mean-only\nbaseline_rmse = float(np.sqrt(np.mean((d.du - d.du.mean())**2)))\n\n# --- HAC lag: Newey-West (1994) rule of thumb floor(4*(T/100)^(2/9))\ndef nw_lag(T): return int(np.floor(4 * (T / 100) ** (2 / 9)))\nL = nw_lag(len(d))\n\ndef fit(sub):\n    X = sm.add_constant(sub[\"g\"].values); y = sub[\"du\"].values\n    ols = sm.OLS(y, X).fit()\n    hac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\n    return ols, hac\n\nfull_ols, full_hac = fit(d)\ns1 = d[d.index <= pd.Period(\"1983Q4\", \"Q\")]; s2 = d[d.index >= pd.Period(\"1984Q1\", \"Q\")]\no1, h1 = fit(s1); o2, h2 = fit(s2)\nbeta, alpha = float(full_ols.params[1]), float(full_ols.params[0])\nse = float(full_hac.bse[1])\nz = stats.norm.ppf(0.975)\nlo, hi = beta - z * se, beta + z * se\nmodel_rmse = float(np.sqrt(np.mean(full_ols.resid**2)))\n\n# --- Chow test (classical SSR F, df 2, n-4)\nk = 2; n = len(d)\nssr_p, ssr1, ssr2 = full_ols.ssr, o1.ssr, o2.ssr\nF = ((ssr_p - (ssr1 + ssr2)) / k) / ((ssr1 + ssr2) / (n - 2 * k))\np = float(stats.f.sf(F, k, n - 2 * k))\n\n# sensitivity of HAC SE to lag length (reported, not used for main CI)\nsens = {}\nfor lag in [0, 2, 4, 8, 12]:\n    r = sm.OLS(d.du.values, sm.add_constant(d.g.values)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    sens[str(lag)] = float(r.bse[1])\n\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nres = {\n    \"task_id\": \"F6-conf-03\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"n_analysed\": int(n), \"random_seed\": None,\n    \"sample_start\": str(d.index.min().start_time.date()), \"sample_end\": str(d.index.max().start_time.date()),\n    \"n_sub1\": int(len(s1)), \"n_sub2\": int(len(s2)),\n    \"baseline_metric\": baseline_rmse, \"model_metric\": model_rmse,\n    \"effect_estimate\": beta, \"intercept\": alpha, \"effect_se\": se, \"hac_lags\": L,\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\": conclusion,\n    # supplementary keys\n    \"ols_iid_se\": float(full_ols.bse[1]), \"r_squared\": float(full_ols.rsquared),\n    \"intercept_sub1\": float(o1.params[0]), \"intercept_sub2\": float(o2.params[0]),\n    \"se_hac_sub1\": float(h1.bse[1]), \"se_hac_sub2\": float(h2.bse[1]),\n    \"hac_se_by_lag\": sens, \"chow_df\": [k, n - 2 * k],\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\n\npd.DataFrame([\n    {\"period\": \"full 1960Q1-2019Q4\", \"n\": n, \"intercept\": alpha, \"slope\": beta, \"slope_se\": se},\n    {\"period\": \"sub1 1960Q1-1983Q4\", \"n\": len(s1), \"intercept\": float(o1.params[0]), \"slope\": float(o1.params[1]), \"slope_se\": float(h1.bse[1])},\n    {\"period\": \"sub2 1984Q1-2019Q4\", \"n\": len(s2), \"intercept\": float(o2.params[0]), \"slope\": float(o2.params[1]), \"slope_se\": float(h2.bse[1])},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nfor sub, o, c, lab in [(s1, o1, \"tab:blue\", \"1960Q1-1983Q4\"), (s2, o2, \"tab:red\", \"1984Q1-2019Q4\")]:\n    ax.scatter(sub.g, sub.du, s=14, alpha=0.6, color=c, label=f\"{lab} (n={len(sub)})\")\n    xs = np.linspace(sub.g.min(), sub.g.max(), 50)\n    ax.plot(xs, o.params[0] + o.params[1] * xs, color=c, lw=2, label=f\"fit {lab}: slope={o.params[1]:.3f}\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\"); ax.set_ylabel(\"Change in unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, US quarterly data\"); ax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se:.4f} (L={L}) CI=[{lo:.4f},{hi:.4f}] beta1={o1.params[1]:.4f} beta2={o2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1318,"cache_read_input_tokens":10615,"output_tokens":3725,"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":1318},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpTu6j389QViGzvhmAv","requestRef":"8a28f274-993b-4f29-932b-cc16ae3d9cf4","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"7050b5e4-07ca-438b-8f91-e36bbc38d21b","timestamp":"2026-10-30T04:50:37.288Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"7050b5e4-07ca-438b-8f91-e36bbc38d21b","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_019qEXZgKYZXKiYc73s3nbbp","type":"tool_result","content":"n=240 beta=-0.2558 HAC se=0.0358 (L=4) CI=[-0.3260,-0.1855] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n{\n  \"task_id\": \"F6-conf-03\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing for month(s) 2025-10\",\n      \"handling\": \"Outside the 1959Q4-2019Q4 window needed for estimation; quarter would be incomplete and is not used. No imputation.\"\n    },\n    {\n      \"issue\": \"GDPC1 is the current (2026) vintage in chained 2017 dollars, not real-time data; historical growth rates reflect later revisions.\",\n      \"handling\": \"Used as provided (the task specifies this series); noted as a limitation.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"GDPC1.csv\": 319,\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 240,\n  \"random_seed\": null,\n  \"sample_start\": \"1960-01-01\",\n  \"sample_end\": \"2019-10-01\",\n  \"n_sub1\": 96,\n  \"n_sub2\": 144,\n  \"baseline_metric\": 0.32465793964999184,\n  \"model_metric\": 0.2497438816636253,\n  \"effect_estimate\": -0.2557677277487258,\n  \"intercept\": 0.18438835418210714,\n  \"effect_se\": 0.03583463330296569,\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  \"ols_iid_se\": 0.01996009291490984,\n  \"r_squared\": 0.4082508284562034,\n  \"intercept_sub1\": 0.2542326539884382,\n  \"intercept_sub2\": 0.15168581347724602,\n  \"se_hac_sub1\": 0.03928680828959863,\n  \"se_hac_sub2\": 0.06831428793738023,\n  \"hac_se_by_lag\": {\n    \"0\": 0.028836223207269963,\n    \"2\": 0.03422732023615383,\n    \"4\": 0.03583463330296569,\n    \"8\": 0.03713550417540189,\n    \"12\": 0.0364088511022809\n  },\n  \"chow_df\": [\n    2,\n    236\n  ]\n}period,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\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop quarters before 1960Q1,319,267,52,outside question window (1959Q4 levels used only for differencing)\n2,drop quarters after 2019Q4,267,240,27,information rule: post-2019Q4 must not enter any estimate\n3,drop quarters with missing du or g,240,240,0,incomplete data","is_error":false}]},"uuid":"734ee051-ebf9-4938-8701-027b42c9326d","timestamp":"2026-10-30T04:50:38.532Z","toolUseResult":{"stdout":"n=240 beta=-0.2558 HAC se=0.0358 (L=4) CI=[-0.3260,-0.1855] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n{\n  \"task_id\": \"F6-conf-03\",\n  \"feasibility\": \"complete\",\n  \"feasibility_reason\": null,\n  \"data_issues\": [\n    {\n      \"issue\": \"UNRATE missing for month(s) 2025-10\",\n      \"handling\": \"Outside the 1959Q4-2019Q4 window needed for estimation; quarter would be incomplete and is not used. No imputation.\"\n    },\n    {\n      \"issue\": \"GDPC1 is the current (2026) vintage in chained 2017 dollars, not real-time data; historical growth rates reflect later revisions.\",\n      \"handling\": \"Used as provided (the task specifies this series); noted as a limitation.\"\n    }\n  ],\n  \"n_input_rows\": {\n    \"GDPC1.csv\": 319,\n    \"UNRATE.csv\": 945\n  },\n  \"n_analysed\": 240,\n  \"random_seed\": null,\n  \"sample_start\": \"1960-01-01\",\n  \"sample_end\": \"2019-10-01\",\n  \"n_sub1\": 96,\n  \"n_sub2\": 144,\n  \"baseline_metric\": 0.32465793964999184,\n  \"model_metric\": 0.2497438816636253,\n  \"effect_estimate\": -0.2557677277487258,\n  \"intercept\": 0.18438835418210714,\n  \"effect_se\": 0.03583463330296569,\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  \"ols_iid_se\": 0.01996009291490984,\n  \"r_squared\": 0.4082508284562034,\n  \"intercept_sub1\": 0.2542326539884382,\n  \"intercept_sub2\": 0.15168581347724602,\n  \"se_hac_sub1\": 0.03928680828959863,\n  \"se_hac_sub2\": 0.06831428793738023,\n  \"hac_se_by_lag\": {\n    \"0\": 0.028836223207269963,\n    \"2\": 0.03422732023615383,\n    \"4\": 0.03583463330296569,\n    \"8\": 0.03713550417540189,\n    \"12\": 0.0364088511022809\n  },\n  \"chow_df\": [\n    2,\n    236\n  ]\n}period,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\nstep,rule,n_before,n_after,n_removed,rationale\n1,drop quarters before 1960Q1,319,267,52,outside question window (1959Q4 levels used only for differencing)\n2,drop quarters after 2019Q4,267,240,27,information rule: post-2019Q4 must not enter any estimate\n3,drop quarters with missing du or g,240,240,0,incomplete data","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"7050b5e4-07ca-438b-8f91-e36bbc38d21b","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"b2a7cb77f0afcee09a28ceec6003c2e214720297910b25672f7467d5683c5f5f","message":{"role":"assistant","content":[{"type":"text","text":"Now the analysis script."},{"type":"tool_use","id":"toolu_019qEXZgKYZXKiYc73s3nbbp","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"Okun's law, US 1960Q1-2019Q4 (task F6-conf-03). Run from workspace root:\n   ~/wp29/venv/bin/python out/analysis.py\nReads only data/, writes out/results.json, out/table1.csv, out/figure1.png, out/exclusions.csv.\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\nfrom scipy import stats\nimport matplotlib\nmatplotlib.use(\"Agg\")\nimport matplotlib.pyplot as plt\n\nOUT = \"out\"; os.makedirs(OUT, exist_ok=True)\ngdp_raw = pd.read_csv(\"data/GDPC1.csv\", parse_dates=[\"observation_date\"])\nun_raw = pd.read_csv(\"data/UNRATE.csv\", parse_dates=[\"observation_date\"])\nn_input_rows = {\"GDPC1.csv\": int(len(gdp_raw)), \"UNRATE.csv\": int(len(un_raw))}\ndata_issues = []\n\n# --- unemployment: quarterly mean of 3 monthly values (complete quarters only)\nun = un_raw.copy()\nmiss = un[un[\"UNRATE\"].isna()]\nif len(miss):\n    data_issues.append({\"issue\": f\"UNRATE missing for month(s) {', '.join(miss.observation_date.dt.strftime('%Y-%m').tolist())}\",\n                        \"handling\": \"Outside the 1959Q4-2019Q4 window needed for estimation; quarter would be incomplete and is not used. No imputation.\"})\nun[\"q\"] = un[\"observation_date\"].dt.to_period(\"Q\")\nuq = un.groupby(\"q\")[\"UNRATE\"].agg([\"mean\", \"count\"])\nuq.loc[un.groupby(\"q\")[\"UNRATE\"].apply(lambda s: s.notna().sum()) < 3, \"mean\"] = np.nan\ngdp = gdp_raw.copy(); gdp[\"q\"] = gdp[\"observation_date\"].dt.to_period(\"Q\")\ngq = gdp.set_index(\"q\")[\"GDPC1\"]\ndf = pd.DataFrame({\"u\": uq[\"mean\"], \"gdp\": gq}).sort_index()\ndf[\"du\"] = df[\"u\"].diff()\ndf[\"g\"] = 100 * np.log(df[\"gdp\"]).diff()\n# require consecutive quarters for differences\nidx = df.index\nconsec = pd.Series([True] + [(idx[i] - idx[i-1]).n == 1 for i in range(1, len(idx))], index=idx)\ndf.loc[~consec, [\"du\", \"g\"]] = np.nan\n\ndata_issues.append({\"issue\": \"GDPC1 is the current (2026) vintage in chained 2017 dollars, not real-time data; historical growth rates reflect later revisions.\",\n                    \"handling\": \"Used as provided (the task specifies this series); noted as a limitation.\"})\n\n# --- exclusion ledger (unit: quarterly observations of the merged panel)\nexcl = []\nn0 = len(df)\nq_start, q_end = pd.Period(\"1960Q1\", \"Q\"), pd.Period(\"2019Q4\", \"Q\")\nd1 = df[df.index >= q_start]; excl.append((\"1\", \"drop quarters before 1960Q1\", n0, len(d1), \"outside question window (1959Q4 levels used only for differencing)\"))\nd2 = d1[d1.index <= q_end]; excl.append((\"2\", \"drop quarters after 2019Q4\", len(d1), len(d2), \"information rule: post-2019Q4 must not enter any estimate\"))\nd3 = d2.dropna(subset=[\"du\", \"g\"]); excl.append((\"3\", \"drop quarters with missing du or g\", len(d2), len(d3), \"incomplete data\"))\npd.DataFrame([dict(step=s, rule=r, n_before=a, n_after=b, n_removed=a-b, rationale=t) for s, r, a, b, t in excl]).to_csv(f\"{OUT}/exclusions.csv\", index=False)\nd = d3.copy()\nassert len(d) == 240 and d.index.min() == q_start and d.index.max() == q_end\n\n# --- baseline: mean-only\nbaseline_rmse = float(np.sqrt(np.mean((d.du - d.du.mean())**2)))\n\n# --- HAC lag: Newey-West (1994) rule of thumb floor(4*(T/100)^(2/9))\ndef nw_lag(T): return int(np.floor(4 * (T / 100) ** (2 / 9)))\nL = nw_lag(len(d))\n\ndef fit(sub):\n    X = sm.add_constant(sub[\"g\"].values); y = sub[\"du\"].values\n    ols = sm.OLS(y, X).fit()\n    hac = sm.OLS(y, X).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\n    return ols, hac\n\nfull_ols, full_hac = fit(d)\ns1 = d[d.index <= pd.Period(\"1983Q4\", \"Q\")]; s2 = d[d.index >= pd.Period(\"1984Q1\", \"Q\")]\no1, h1 = fit(s1); o2, h2 = fit(s2)\nbeta, alpha = float(full_ols.params[1]), float(full_ols.params[0])\nse = float(full_hac.bse[1])\nz = stats.norm.ppf(0.975)\nlo, hi = beta - z * se, beta + z * se\nmodel_rmse = float(np.sqrt(np.mean(full_ols.resid**2)))\n\n# --- Chow test (classical SSR F, df 2, n-4)\nk = 2; n = len(d)\nssr_p, ssr1, ssr2 = full_ols.ssr, o1.ssr, o2.ssr\nF = ((ssr_p - (ssr1 + ssr2)) / k) / ((ssr1 + ssr2) / (n - 2 * k))\np = float(stats.f.sf(F, k, n - 2 * k))\n\n# sensitivity of HAC SE to lag length (reported, not used for main CI)\nsens = {}\nfor lag in [0, 2, 4, 8, 12]:\n    r = sm.OLS(d.du.values, sm.add_constant(d.g.values)).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": lag, \"kernel\": \"bartlett\", \"use_correction\": False})\n    sens[str(lag)] = float(r.bse[1])\n\nconclusion = \"positive_effect\" if lo > 0 else (\"negative_effect\" if hi < 0 else \"no_detectable_effect\")\nres = {\n    \"task_id\": \"F6-conf-03\", \"feasibility\": \"complete\", \"feasibility_reason\": None,\n    \"data_issues\": data_issues, \"n_input_rows\": n_input_rows, \"n_analysed\": int(n), \"random_seed\": None,\n    \"sample_start\": str(d.index.min().start_time.date()), \"sample_end\": str(d.index.max().start_time.date()),\n    \"n_sub1\": int(len(s1)), \"n_sub2\": int(len(s2)),\n    \"baseline_metric\": baseline_rmse, \"model_metric\": model_rmse,\n    \"effect_estimate\": beta, \"intercept\": alpha, \"effect_se\": se, \"hac_lags\": L,\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\": conclusion,\n    # supplementary keys\n    \"ols_iid_se\": float(full_ols.bse[1]), \"r_squared\": float(full_ols.rsquared),\n    \"intercept_sub1\": float(o1.params[0]), \"intercept_sub2\": float(o2.params[0]),\n    \"se_hac_sub1\": float(h1.bse[1]), \"se_hac_sub2\": float(h2.bse[1]),\n    \"hac_se_by_lag\": sens, \"chow_df\": [k, n - 2 * k],\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f: json.dump(res, f, indent=2)\n\npd.DataFrame([\n    {\"period\": \"full 1960Q1-2019Q4\", \"n\": n, \"intercept\": alpha, \"slope\": beta, \"slope_se\": se},\n    {\"period\": \"sub1 1960Q1-1983Q4\", \"n\": len(s1), \"intercept\": float(o1.params[0]), \"slope\": float(o1.params[1]), \"slope_se\": float(h1.bse[1])},\n    {\"period\": \"sub2 1984Q1-2019Q4\", \"n\": len(s2), \"intercept\": float(o2.params[0]), \"slope\": float(o2.params[1]), \"slope_se\": float(h2.bse[1])},\n]).to_csv(f\"{OUT}/table1.csv\", index=False)\n\nfig, ax = plt.subplots(figsize=(7, 5))\nfor sub, o, c, lab in [(s1, o1, \"tab:blue\", \"1960Q1-1983Q4\"), (s2, o2, \"tab:red\", \"1984Q1-2019Q4\")]:\n    ax.scatter(sub.g, sub.du, s=14, alpha=0.6, color=c, label=f\"{lab} (n={len(sub)})\")\n    xs = np.linspace(sub.g.min(), sub.g.max(), 50)\n    ax.plot(xs, o.params[0] + o.params[1] * xs, color=c, lw=2, label=f\"fit {lab}: slope={o.params[1]:.3f}\")\nax.axhline(0, color=\"grey\", lw=0.5); ax.axvline(0, color=\"grey\", lw=0.5)\nax.set_xlabel(\"Real GDP growth g_t (% q/q, not annualised)\"); ax.set_ylabel(\"Change in unemployment rate Δu_t (pp)\")\nax.set_title(\"Okun's law, US quarterly data\"); ax.legend(fontsize=8)\nfig.tight_layout(); fig.savefig(f\"{OUT}/figure1.png\", dpi=120); plt.close(fig)\n\nprint(f\"n={n} beta={beta:.4f} HAC se={se:.4f} (L={L}) CI=[{lo:.4f},{hi:.4f}] beta1={o1.params[1]:.4f} beta2={o2.params[1]:.4f} Chow F={F:.3f} p={p:.4g}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/results.json out/table1.csv out/exclusions.csv","description":"Write and run analysis script"}}]}}43{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"58822a71-177b-4313-b320-9cd68b5e9077","timestamp":"2026-10-30T04:50:38.536Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"8a28f274-993b-4f29-932b-cc16ae3d9cf4","keep":12,"tail":["b2a7cb77f0afcee09a28ceec6003c2e214720297910b25672f7467d5683c5f5f","ff1fcfac1068474075b3f8ad7feab49663e30394237c9898ee55816bf990cefd"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"9014597a9972e61581e2c635c2de533d469ad0262960e155020b09e2a939ed81"}44{"parentUuid":"734ee051-ebf9-4938-8701-027b42c9326d","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpWB2VCjLQc4VP2HEy9","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":4938,"cache_read_input_tokens":11933,"output_tokens":997,"output_tokens_details":{"thinking_tokens":246},"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":4938},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":2247,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpWAkNaLznSuiq2QPBc","requestRef":"58822a71-177b-4313-b320-9cd68b5e9077","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"09ae633c-868b-4692-8d84-986b822c74da","timestamp":"2026-10-30T04:50:42.192Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"09ae633c-868b-4692-8d84-986b822c74da","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpWB2VCjLQc4VP2HEy9","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Ftq6ijMgQCPtdZQ1tfySC8","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\nimport re\ns=open('analysis.py').read()\ns=s.replace('''conclusion = \"positive_effect\"''','''# supplementary robustness of the stability test: HAC Wald with subperiod-2 dummies\nD = (d.index >= pd.Period(\"1984Q1\", \"Q\")).astype(float)\nXi = np.column_stack([np.ones(n), d.g.values, D, D * d.g.values])\nri = sm.OLS(d.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nR = np.array([[0, 0, 1, 0], [0, 0, 0, 1]])\nwt = ri.wald_test(R, use_f=False, scalar=True)\nhac_wald_chi2, hac_wald_p = float(wt.statistic), float(wt.pvalue)\np_int_diff_hac, p_slope_diff_hac = float(ri.pvalues[2]), float(ri.pvalues[3])\nresid_var_ratio = float((o1.ssr / (len(s1) - 2)) / (o2.ssr / (len(s2) - 2)))\n\nconclusion = \"positive_effect\"''')\ns=s.replace('''\"hac_se_by_lag\": sens,''','''\"hac_se_by_lag\": sens,\n    \"hac_wald_break_chi2\": hac_wald_chi2, \"hac_wald_break_p\": hac_wald_p,\n    \"hac_p_intercept_diff\": p_int_diff_hac, \"hac_p_slope_diff\": p_slope_diff_hac,\n    \"resid_var_ratio_sub1_sub2\": resid_var_ratio,''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py && grep -E \"wald|hac_p|ratio\" out/results.json","description":"Add HAC-robust break test and rerun"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4938,"cache_read_input_tokens":11933,"output_tokens":997,"output_tokens_details":{"thinking_tokens":246},"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":4938},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpWAkNaLznSuiq2QPBc","requestRef":"58822a71-177b-4313-b320-9cd68b5e9077","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5d4f9905-a6ab-4541-ae2e-dada9f7d14ee","timestamp":"2026-10-30T04:50:47.695Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}46{"parentUuid":"5d4f9905-a6ab-4541-ae2e-dada9f7d14ee","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01Ftq6ijMgQCPtdZQ1tfySC8","type":"tool_result","content":"n=240 beta=-0.2558 HAC se=0.0358 (L=4) CI=[-0.3260,-0.1855] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n  \"hac_wald_break_chi2\": 10.926210056989923,\n  \"hac_wald_break_p\": 0.004240368816511458,\n  \"hac_p_intercept_diff\": 0.24996972549597873,\n  \"hac_p_slope_diff\": 0.8536158012976183,\n  \"resid_var_ratio_sub1_sub2\": 1.6261064345399512,","is_error":false}]},"uuid":"e7933de2-df67-4978-b09e-31439c61e14e","timestamp":"2026-10-30T04:50:48.905Z","toolUseResult":{"stdout":"n=240 beta=-0.2558 HAC se=0.0358 (L=4) CI=[-0.3260,-0.1855] beta1=-0.2590 beta2=-0.2734 Chow F=6.090 p=0.002636\n  \"hac_wald_break_chi2\": 10.926210056989923,\n  \"hac_wald_break_p\": 0.004240368816511458,\n  \"hac_p_intercept_diff\": 0.24996972549597873,\n  \"hac_p_slope_diff\": 0.8536158012976183,\n  \"resid_var_ratio_sub1_sub2\": 1.6261064345399512,","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5d4f9905-a6ab-4541-ae2e-dada9f7d14ee","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}47{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"37208468c6cdebffe14527db1cc2df7ff334f25704d893349733b1be1a169933","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01Ftq6ijMgQCPtdZQ1tfySC8","name":"Bash","input":{"command":"cd out && ~/wp29/venv/bin/python - <<'EOF'\nimport re\ns=open('analysis.py').read()\ns=s.replace('''conclusion = \"positive_effect\"''','''# supplementary robustness of the stability test: HAC Wald with subperiod-2 dummies\nD = (d.index >= pd.Period(\"1984Q1\", \"Q\")).astype(float)\nXi = np.column_stack([np.ones(n), d.g.values, D, D * d.g.values])\nri = sm.OLS(d.du.values, Xi).fit(cov_type=\"HAC\", cov_kwds={\"maxlags\": L, \"kernel\": \"bartlett\", \"use_correction\": False})\nR = np.array([[0, 0, 1, 0], [0, 0, 0, 1]])\nwt = ri.wald_test(R, use_f=False, scalar=True)\nhac_wald_chi2, hac_wald_p = float(wt.statistic), float(wt.pvalue)\np_int_diff_hac, p_slope_diff_hac = float(ri.pvalues[2]), float(ri.pvalues[3])\nresid_var_ratio = float((o1.ssr / (len(s1) - 2)) / (o2.ssr / (len(s2) - 2)))\n\nconclusion = \"positive_effect\"''')\ns=s.replace('''\"hac_se_by_lag\": sens,''','''\"hac_se_by_lag\": sens,\n    \"hac_wald_break_chi2\": hac_wald_chi2, \"hac_wald_break_p\": hac_wald_p,\n    \"hac_p_intercept_diff\": p_int_diff_hac, \"hac_p_slope_diff\": p_slope_diff_hac,\n    \"resid_var_ratio_sub1_sub2\": resid_var_ratio,''')\nopen('analysis.py','w').write(s)\nEOF\ncd .. && ~/wp29/venv/bin/python out/analysis.py && grep -E \"wald|hac_p|ratio\" out/results.json","description":"Add HAC-robust break test and rerun"}}]}}48{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"8416aa4d-6127-45e3-bf22-81b31cdd5440","timestamp":"2026-10-30T04:50:48.910Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"58822a71-177b-4313-b320-9cd68b5e9077","keep":14,"tail":["37208468c6cdebffe14527db1cc2df7ff334f25704d893349733b1be1a169933","a8ee49dd78e13213cd0f22dd31916d7f595f5a800af103ccd43026423ad53c8c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",15,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"32cefdf0bde07000dcce1a2fbf68c09966add0b9f27bd2916f2d91a614a52f7d"}49{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"e7933de2-df67-4978-b09e-31439c61e14e","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}50{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}51{"parentUuid":"e7933de2-df67-4978-b09e-31439c61e14e","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpWwPV8LqKeDbXzKkin","type":"message","role":"assistant","content":[{"type":"thinking","thinking":"","signature":"CAQSrgcKEAgTGAI4AUIIdGhpbmtpbmcSDFN+kD0CAnnT2/UXaRoMhQj+0T3YX3espkzlIjAM0eDA9I2N6huy3stOAJWHehvwK9e5q65XubtY3jlCrB8JnI/Z/WManqJsoI2DolcqywZnxDeUc3Cshqte+Ga2oA/KMk4DgIryRDs+/3iNQ0WbwzIlFMld+jJVkVQyXv+6+oLPdMl1eSMpAy1oyBaqQPJE5a0DrWi8LPQ7jtOB1F51sKc2Zvo8Ia9Ks0+P9fiNcASJSQtVNmJNG0w5yDMspt0Te39D+/IBB8M2Rc/Gyl7AUZC0RWHaSUaEgYHR4QkD6qncduIGJtbGrZbBywUzJ5MM5l7CAoLIdqNPtH1w9Jh/e6iqO0aziDcXgaINUxs1krZz5nAC/gf5PXZjC6bsWQC89FzikFMfK2JjkWAs8Kk4pZy+B8wnlttINqM3SyyDlyGjdTaCLS3bTW/9YtF0pqxuHCpB5RCtWLEqcA2HmRdaDiRDuX9WPGSh5/MdDM2yuPQsQLKDEq1mazqdCYhrxAundUSpJJYGGNpow3brVAiA8d1pl0251F5+KT1GKqx1BBu3ObTjV57BQS9Gy+sYHWzOBz8Cta+fD6CISbQ4uNZu0h8637MU9rcojl36sNLu8NLCNKkqORbPBhvY/O3ljRRjOJ0ieDWZn2IzHbbfxfMAQSlYBm+Ce0yXi7HbDEtOcLUZXN1J8O55XHCCqJyGxzk6loKRsnPMSEwlNIoiCsWV/awZh4Vuob5ZRPbBQT3/N1z1T0e2shJ2BcN3NE39XaCSsAHNTJpO2Owp/8IVoRDnjrSi7AVteA8fVec8k80ytEMDiEoY0Z6kxwS6viV6YjyLC6MjLM0b5jFDTMVXZ9hpkpQIJ+J9aSQNMeodi3WGtifRtoaGyikT3h7y3PgcZ0ivPQ5MRg6lLLJgOVVhQ5qrdAj9pZc18Smu2s5c+DTebdbX91WVKORDQONXGYpCnM7caINb6KeYeDU10PMlyNobDkketqGS8T6/Jtl0hIQmomtcKy5c13nq+c59PljxQwH8CoRS1OXO29iw54EHYy9ehkA0i/TWoFwNgfExGbF1R345YdLW2kLRmW48audEe1UNxrWLHrps4/qxqjkU4JZGJ6DAmOQoWQFskSs19qRgq78Oqi6PGZQgif30QY4sXlTN8N1+qGf40zgSBNoO7nLyz36Gc1o/tDbJz7JJ7KUP9/NyfuZYuV6KsmQc4Q7kNXWwkcLjvGWEcmL111sYAQ=="}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1207,"cache_read_input_tokens":16871,"output_tokens":1290,"output_tokens_details":{"thinking_tokens":62},"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":1207},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":403,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpWw7Mg894A3wwJy28T","requestRef":"8416aa4d-6127-45e3-bf22-81b31cdd5440","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"69134bf2-5064-4210-844d-089923e70ce5","timestamp":"2026-10-30T04:50:50.620Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}52{"parentUuid":"69134bf2-5064-4210-844d-089923e70ce5","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpWwPV8LqKeDbXzKkin","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01H2NpjpR49fju6sRP7rRUSd","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README/bundle) | 319 | 2 (observation_date, GDPC1 float) | 1947-01-01 – 2026-07-01, quarterly, no gaps | 0 | 0 |\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches) | 945 | 2 (observation_date, UNRATE float) | 1948-01-01 – 2026-09-01, monthly, no date gaps | 1 (2025-10, empty value) | 0 |\n\nUnits: GDPC1 bn chained 2017 USD SAAR (min 2172.4, max 24611.1); UNRATE percent SA (min 2.5, max 14.8). No implausible values.\nAnomalies: UNRATE 2025-10 missing (outside the estimation window; that quarter is set to NaN, never used). GDPC1 is a current vintage (revised data, not real-time).\nInformation availability: both files extend past 2019Q4; post-2019Q4 quarters are dropped (exclusions.csv step 2) before any estimation. 1959Q4 levels used only to form 1960Q1 differences, as the task prescribes.\nExclusion ledger unit: quarters of the merged quarterly panel (319 GDP quarters, 1947Q1–2026Q3): 319 − 52 − 27 − 0 = 240 analysed.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of both inputs computed and matched README\",\"schema/missing/dup/date-gap audit run\",\"exclusion ledger 319-52-27-0=240\"],\"notes\":\"UNRATE 2025-10 NaN outside window\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) computed on same 240-quarter sample = 0.3247\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs; writes results.json, table1.csv, figure1.png, exclusions.csv\",\"no randomness (random_seed null)\"],\"notes\":\"HAC lag rule floor(4(T/100)^(2/9))=4\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1-P3 passed: audit, baseline 0.3247, OLS/HAC/Chow implemented. Decision: HAC L=4 (NW rule), normal CI; added HAC Wald break test as robustness since classical Chow assumes equal variances.\" >> out/state/log.md\ncp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo IDENTICAL; ls out","description":"Write audit, update ledger, reproducibility check"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1207,"cache_read_input_tokens":16871,"output_tokens":1290,"output_tokens_details":{"thinking_tokens":62},"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":1207},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpWw7Mg894A3wwJy28T","requestRef":"8416aa4d-6127-45e3-bf22-81b31cdd5440","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"abdc6e93-8c72-4ac5-92ca-6647dad00864","timestamp":"2026-10-30T04:51:00.932Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}53{"parentUuid":"abdc6e93-8c72-4ac5-92ca-6647dad00864","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01H2NpjpR49fju6sRP7rRUSd","type":"tool_result","content":"IDENTICAL\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate\ntable1.csv","is_error":false}]},"uuid":"25f1d872-c058-4bae-b739-99d023724311","timestamp":"2026-10-30T04:51:02.094Z","toolUseResult":{"stdout":"IDENTICAL\nanalysis.py\nexclusions.csv\nfigure1.png\nresults.json\nstate\ntable1.csv","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"abdc6e93-8c72-4ac5-92ca-6647dad00864","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}54{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"602e6d3b1d639bc51d650ebbde799afe579a6e628b55eb30393fd0087f8066c2","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01H2NpjpR49fju6sRP7rRUSd","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n| file | SHA-256 | rows (excl. header) | cols | range | missing | dup dates |\n|---|---|---|---|---|---|---|\n| GDPC1.csv | 1c6c6e9f59e383e20c849dd299175c7a307526c09abdc18f18da9754fc5b3343 (matches README/bundle) | 319 | 2 (observation_date, GDPC1 float) | 1947-01-01 – 2026-07-01, quarterly, no gaps | 0 | 0 |\n| UNRATE.csv | 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches) | 945 | 2 (observation_date, UNRATE float) | 1948-01-01 – 2026-09-01, monthly, no date gaps | 1 (2025-10, empty value) | 0 |\n\nUnits: GDPC1 bn chained 2017 USD SAAR (min 2172.4, max 24611.1); UNRATE percent SA (min 2.5, max 14.8). No implausible values.\nAnomalies: UNRATE 2025-10 missing (outside the estimation window; that quarter is set to NaN, never used). GDPC1 is a current vintage (revised data, not real-time).\nInformation availability: both files extend past 2019Q4; post-2019Q4 quarters are dropped (exclusions.csv step 2) before any estimation. 1959Q4 levels used only to form 1960Q1 differences, as the task prescribes.\nExclusion ledger unit: quarters of the merged quarterly panel (319 GDP quarters, 1947Q1–2026Q3): 319 − 52 − 27 − 0 = 240 analysed.\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P1']={\"status\":\"passed\",\"checks\":[\"SHA-256 of both inputs computed and matched README\",\"schema/missing/dup/date-gap audit run\",\"exclusion ledger 319-52-27-0=240\"],\"notes\":\"UNRATE 2025-10 NaN outside window\"}\np['P2']={\"status\":\"passed\",\"checks\":[\"baseline_metric (mean-only RMSE) computed on same 240-quarter sample = 0.3247\"],\"notes\":\"\"}\np['P3']={\"status\":\"passed\",\"checks\":[\"analysis.py runs; writes results.json, table1.csv, figure1.png, exclusions.csv\",\"no randomness (random_seed null)\"],\"notes\":\"HAC lag rule floor(4(T/100)^(2/9))=4\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P1-P3 passed: audit, baseline 0.3247, OLS/HAC/Chow implemented. Decision: HAC L=4 (NW rule), normal CI; added HAC Wald break test as robustness since classical Chow assumes equal variances.\" >> out/state/log.md\ncp out/results.json /tmp/r1.json; rm out/results.json out/table1.csv out/figure1.png; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp out/results.json /tmp/r1.json && echo IDENTICAL; ls out","description":"Write audit, update ledger, reproducibility check"}}]}}55{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"de98db2f-66c8-42d6-980a-1b645307a360","timestamp":"2026-10-30T04:51:02.097Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"8416aa4d-6127-45e3-bf22-81b31cdd5440","keep":16,"tail":["602e6d3b1d639bc51d650ebbde799afe579a6e628b55eb30393fd0087f8066c2","9b31da3cf40ce14fad78ad870187e4aadfcc92b93096889a01e762b8d8b1a5b1"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",17,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"a49c26ecb3f0b6e04584fccd5454bf02a364d7e334b8055b6938b9d2136f53a1"}56{"parentUuid":"25f1d872-c058-4bae-b739-99d023724311","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpXun24JUv2ekAhEcnE","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01RvVF6VKh3KQjSkYmAd6oMD","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"n_sub1+n_sub2==n_analysed==240\", r['n_sub1']+r['n_sub2']==r['n_analysed']==240))\nc.append((\"exclusions sum: 319-sum(removed)==240\", 319-e.n_removed.sum()==240 and (e.n_before-e.n_after==e.n_removed).all()))\nc.append((\"effect_se>0\", r['effect_se']>0))\nc.append((\"CI contains estimate\", r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']))\nc.append((\"CI symmetric 1.96*se\", abs((r['effect_ci_high']-r['effect_ci_low'])/2-1.959964*r['effect_se'])<1e-5))\nc.append((\"model_metric<baseline_metric\", r['model_metric']<r['baseline_metric']))\nc.append((\"R2 identity 1-(model/baseline)^2\", abs(1-(r['model_metric']/r['baseline_metric'])**2-r['r_squared'])<1e-10))\nc.append((\"sample_end<=2019-10-01 and start<end\", r['sample_start']<r['sample_end']=='2019-10-01'))\nc.append((\"chow p consistent with stability_conclusion\", (r['chow_p_value']<0.05)==(r['stability_conclusion']=='unstable')))\nc.append((\"conclusion consistent with CI\", r['conclusion']=='negative_effect' and r['effect_ci_high']<0))\nc.append((\"table1 matches results\", np.isclose(t.slope[0],r['effect_estimate']) and np.isclose(t.slope[1],r['beta_sub1']) and np.isclose(t.slope[2],r['beta_sub2']) and len(t)==3))\nc.append((\"magnitude plausible (Okun beta ~ -0.2..-0.5 in literature-scale)\", -0.6<r['effect_estimate']<-0.1))\nc.append((\"chow df == (2,236)\", r['chow_df']==[2,236]))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of analysis.py: results.json byte-identical (cmp) -> PASS\\n\")\n    for k,v in c: f.write(f\"- {k} -> {'PASS' if v else 'FAIL'}\\n\"); print(k,v)\nEOF","description":"Run internal consistency checks"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1334,"cache_read_input_tokens":18078,"output_tokens":985,"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":1334},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpXuVQA3y5BggPzjnsV","requestRef":"de98db2f-66c8-42d6-980a-1b645307a360","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"9513be56-d601-4785-a980-d9b4623f04df","timestamp":"2026-10-30T04:51:10.962Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}57{"parentUuid":"9513be56-d601-4785-a980-d9b4623f04df","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01RvVF6VKh3KQjSkYmAd6oMD","type":"tool_result","content":"n_sub1+n_sub2==n_analysed==240 True\nexclusions sum: 319-sum(removed)==240 True\neffect_se>0 True\nCI contains estimate True\nCI symmetric 1.96*se True\nmodel_metric<baseline_metric True\nR2 identity 1-(model/baseline)^2 True\nsample_end<=2019-10-01 and start<end True\nchow p consistent with stability_conclusion True\nconclusion consistent with CI True\ntable1 matches results True\nmagnitude plausible (Okun beta ~ -0.2..-0.5 in literature-scale) True\nchow df == (2,236) True","is_error":false}]},"uuid":"ac5a298c-f550-4d7f-98f3-cb86b297c798","timestamp":"2026-10-30T04:51:11.212Z","toolUseResult":{"stdout":"n_sub1+n_sub2==n_analysed==240 True\nexclusions sum: 319-sum(removed)==240 True\neffect_se>0 True\nCI contains estimate True\nCI symmetric 1.96*se True\nmodel_metric<baseline_metric True\nR2 identity 1-(model/baseline)^2 True\nsample_end<=2019-10-01 and start<end True\nchow p consistent with stability_conclusion True\nconclusion consistent with CI True\ntable1 matches results True\nmagnitude plausible (Okun beta ~ -0.2..-0.5 in literature-scale) True\nchow df == (2,236) True","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"9513be56-d601-4785-a980-d9b4623f04df","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}58{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"412fdae32adb796af6fb397f74cd0d8afebc75ce7454b90efcba4851ca68ed3a","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01RvVF6VKh3KQjSkYmAd6oMD","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json,pandas as pd,numpy as np\nr=json.load(open('out/results.json')); t=pd.read_csv('out/table1.csv'); e=pd.read_csv('out/exclusions.csv')\nc=[]\nc.append((\"n_sub1+n_sub2==n_analysed==240\", r['n_sub1']+r['n_sub2']==r['n_analysed']==240))\nc.append((\"exclusions sum: 319-sum(removed)==240\", 319-e.n_removed.sum()==240 and (e.n_before-e.n_after==e.n_removed).all()))\nc.append((\"effect_se>0\", r['effect_se']>0))\nc.append((\"CI contains estimate\", r['effect_ci_low']<r['effect_estimate']<r['effect_ci_high']))\nc.append((\"CI symmetric 1.96*se\", abs((r['effect_ci_high']-r['effect_ci_low'])/2-1.959964*r['effect_se'])<1e-5))\nc.append((\"model_metric<baseline_metric\", r['model_metric']<r['baseline_metric']))\nc.append((\"R2 identity 1-(model/baseline)^2\", abs(1-(r['model_metric']/r['baseline_metric'])**2-r['r_squared'])<1e-10))\nc.append((\"sample_end<=2019-10-01 and start<end\", r['sample_start']<r['sample_end']=='2019-10-01'))\nc.append((\"chow p consistent with stability_conclusion\", (r['chow_p_value']<0.05)==(r['stability_conclusion']=='unstable')))\nc.append((\"conclusion consistent with CI\", r['conclusion']=='negative_effect' and r['effect_ci_high']<0))\nc.append((\"table1 matches results\", np.isclose(t.slope[0],r['effect_estimate']) and np.isclose(t.slope[1],r['beta_sub1']) and np.isclose(t.slope[2],r['beta_sub2']) and len(t)==3))\nc.append((\"magnitude plausible (Okun beta ~ -0.2..-0.5 in literature-scale)\", -0.6<r['effect_estimate']<-0.1))\nc.append((\"chow df == (2,236)\", r['chow_df']==[2,236]))\nwith open('out/state/checks.md','w') as f:\n    f.write(\"# Verification checks (P4)\\n\\n- Clean re-run of analysis.py: results.json byte-identical (cmp) -> PASS\\n\")\n    for k,v in c: f.write(f\"- {k} -> {'PASS' if v else 'FAIL'}\\n\"); print(k,v)\nEOF","description":"Run internal consistency checks"}}]}}59{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"9e38d0d1-719d-4657-b68f-6b96a66cb9ab","timestamp":"2026-10-30T04:51:11.216Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"de98db2f-66c8-42d6-980a-1b645307a360","keep":18,"tail":["412fdae32adb796af6fb397f74cd0d8afebc75ce7454b90efcba4851ca68ed3a","b636fc33411619c57f2d63d2e587dda166514218c6415fdd6211b073eaa5df1c"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"4b31a3edf74af88e5d6ecebef748e971bc04e713dbf600af41e6a92d7bcf9389"}60{"parentUuid":"ac5a298c-f550-4d7f-98f3-cb86b297c798","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpYamYbk9BHfa926D2J","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":1198,"cache_read_input_tokens":19412,"output_tokens":238,"output_tokens_details":{"thinking_tokens":106},"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":1198},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":587,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpYaVgLAyWgE5yxpryt","requestRef":"9e38d0d1-719d-4657-b68f-6b96a66cb9ab","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"ee958990-c089-4f6e-86f3-e4805fa5e4a0","timestamp":"2026-10-30T04:51:13.238Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}61{"parentUuid":"ee958990-c089-4f6e-86f3-e4805fa5e4a0","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpYamYbk9BHfa926D2J","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01LgRnWuS4j4xLrvtYqSXn9M","name":"Bash","input":{"command":"~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30; ls out/state/gate_receipts","description":"Run gate at end of P4"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1198,"cache_read_input_tokens":19412,"output_tokens":238,"output_tokens_details":{"thinking_tokens":106},"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":1198},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpYaVgLAyWgE5yxpryt","requestRef":"9e38d0d1-719d-4657-b68f-6b96a66cb9ab","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"43d152ca-6df1-4fdb-9345-a380afa9f24a","timestamp":"2026-10-30T04:51:14.026Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}62{"parentUuid":"43d152ca-6df1-4fdb-9345-a380afa9f24a","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01LgRnWuS4j4xLrvtYqSXn9M","type":"tool_result","content":"  [ok] key:model_metric: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:intercept: ok\n  [ok] key:effect_se: ok\n  [ok] key:hac_lags: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:beta_sub1: ok\n  [ok] key:beta_sub2: ok\n  [ok] key:chow_f_stat: ok\n  [ok] key:chow_p_value: ok\n  [ok] key:stability_conclusion: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] ci:effect_estimate: -0.32600231842173816 <= -0.2557677277487258 <= -0.18553313707571345\n  [ok] positive:effect_se: 0.03583463330296569\n  [ok] positive:chow_f_stat: 6.0903330062337675\n  [ok] positive:baseline_metric: 0.32465793964999184\n  [ok] positive:model_metric: 0.2497438816636253\n  [ok] chronology:sample_start<=sample_end: 1960-01-01 vs 2019-10-01\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045115.194808Z.json\n20261030T045115.194808Z.json","is_error":false}]},"uuid":"92e22168-a8f3-4de9-b8d5-74dd23ce5151","timestamp":"2026-10-30T04:51:15.204Z","toolUseResult":{"stdout":"  [ok] key:model_metric: ok\n  [ok] key:effect_estimate: ok\n  [ok] key:intercept: ok\n  [ok] key:effect_se: ok\n  [ok] key:hac_lags: ok\n  [ok] key:effect_ci_low: ok\n  [ok] key:effect_ci_high: ok\n  [ok] key:beta_sub1: ok\n  [ok] key:beta_sub2: ok\n  [ok] key:chow_f_stat: ok\n  [ok] key:chow_p_value: ok\n  [ok] key:stability_conclusion: ok\n  [ok] key:conclusion: ok\n  [FAIL] section:Question: heading not found\n  [FAIL] section:Data and exclusions: heading not found\n  [FAIL] section:Method: heading not found\n  [FAIL] section:Results: heading not found\n  [FAIL] section:Uncertainty: heading not found\n  [FAIL] section:Limitations and open issues: heading not found\n  [FAIL] claims_json: out/claims.json missing\n  [FAIL] phase_ledger: phases not terminal: ['P4', 'P5']\n  [ok] exclusion_ledger: 3 steps consistent\n  [ok] ci:effect_estimate: -0.32600231842173816 <= -0.2557677277487258 <= -0.18553313707571345\n  [ok] positive:effect_se: 0.03583463330296569\n  [ok] positive:chow_f_stat: 6.0903330062337675\n  [ok] positive:baseline_metric: 0.32465793964999184\n  [ok] positive:model_metric: 0.2497438816636253\n  [ok] chronology:sample_start<=sample_end: 1960-01-01 vs 2019-10-01\n  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045115.194808Z.json\n20261030T045115.194808Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"43d152ca-6df1-4fdb-9345-a380afa9f24a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}63{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"26c7cc5719c105f9401e1e81fc161c7bdaaca5f0f5511f862c55b66d4feb19d5","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_01LgRnWuS4j4xLrvtYqSXn9M","name":"Bash","input":{"command":"~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -30; ls out/state/gate_receipts","description":"Run gate at end of P4"}}]}}64{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"4261972f-2558-444f-bbbc-ad02ae69582e","timestamp":"2026-10-30T04:51:15.207Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"9e38d0d1-719d-4657-b68f-6b96a66cb9ab","keep":20,"tail":["26c7cc5719c105f9401e1e81fc161c7bdaaca5f0f5511f862c55b66d4feb19d5","2b1277ba46e57aff85f1cb164b9541f0bc2beb5a40f4b945b49f029ba4f4c97e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",21,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"6fce688da794ea20f017b59dc897f1431803c4102ffc887d5783c7827cf11abd"}65{"parentUuid":"92e22168-a8f3-4de9-b8d5-74dd23ce5151","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpYspyLUGxfk7CX7qQN","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":933,"cache_read_input_tokens":20610,"output_tokens":455,"output_tokens_details":{"thinking_tokens":57},"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":933},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":414,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpYsYrgVf4EmnwLkzdC","requestRef":"4261972f-2558-444f-bbbc-ad02ae69582e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"b028d18c-c04a-492d-a76a-677a7191870a","timestamp":"2026-10-30T04:51:16.889Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}66{"parentUuid":"b028d18c-c04a-492d-a76a-677a7191870a","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpYspyLUGxfk7CX7qQN","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014NxpJzeA8zAPUN37axuj5U","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical\",\"13 internal consistency checks PASS (out/state/checks.md)\",\"gate run 20261030T045115.194808Z: all P4-scope checks ok incl. reproducibility; remaining FAILs are P5 deliverables (report sections, claims.json, phase ledger)\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T045115.194808Z.json\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed: reproducible, consistency checks pass, gate receipt 20261030T045115 (P5 items pending).\" >> out/state/log.md","description":"Update phase ledger for P4"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":933,"cache_read_input_tokens":20610,"output_tokens":455,"output_tokens_details":{"thinking_tokens":57},"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":933},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpYsYrgVf4EmnwLkzdC","requestRef":"4261972f-2558-444f-bbbc-ad02ae69582e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"454921ef-4dbf-41c4-a472-5869947234a8","timestamp":"2026-10-30T04:51:19.788Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}67{"parentUuid":"454921ef-4dbf-41c4-a472-5869947234a8","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014NxpJzeA8zAPUN37axuj5U","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"80060590-e013-4463-bbcf-010602136d7c","timestamp":"2026-10-30T04:51:19.821Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"454921ef-4dbf-41c4-a472-5869947234a8","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}68{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"7dc2fccc55ba8f68a6a0a73830dbbdcb7a738e112ae0878ecdc3267d45fce366","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_014NxpJzeA8zAPUN37axuj5U","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P4']={\"status\":\"passed\",\"checks\":[\"clean re-run: results.json byte-identical\",\"13 internal consistency checks PASS (out/state/checks.md)\",\"gate run 20261030T045115.194808Z: all P4-scope checks ok incl. reproducibility; remaining FAILs are P5 deliverables (report sections, claims.json, phase ledger)\"],\"notes\":\"gate receipt out/state/gate_receipts/20261030T045115.194808Z.json\"}\np['P5']={\"status\":\"running\",\"checks\":[],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P4 passed: reproducible, consistency checks pass, gate receipt 20261030T045115 (P5 items pending).\" >> out/state/log.md","description":"Update phase ledger for P4"}}]}}69{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"db1129db-eed6-4f2e-8678-1298c6e00f9d","timestamp":"2026-10-30T04:51:19.825Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"4261972f-2558-444f-bbbc-ad02ae69582e","keep":22,"tail":["7dc2fccc55ba8f68a6a0a73830dbbdcb7a738e112ae0878ecdc3267d45fce366","679225accb5312e922e2f5c5a7d55dc1bc1126da5d24566f89d0188844ea89af"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",23,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"5f7de18301cb40b21140d6b890cff968dc594741b2b2dbb37a43edbed721764e"}70{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"80060590-e013-4463-bbcf-010602136d7c","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}71{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}72{"parentUuid":"80060590-e013-4463-bbcf-010602136d7c","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpZDZsS9QyFEHqfsEhy","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_017uFnptSSAxKwSwAizDhseR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960–2019 (F6-conf-03)\n\n## Question\nHow strongly does the quarterly change in the US unemployment rate (Δu_t, percentage points) respond to quarterly real GDP growth (g_t = 100·Δln GDPC1, percent, not annualised), in Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4? And is (α, β) the same in 1960Q1–1983Q4 and 1984Q1–2019Q4? β is a reduced-form association (a conditional-mean slope), not a structural causal effect.\n\n## Data and exclusions\nInputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09) (`n_input_rows`). The SHA-256 hashes match `data/README.md`. Quarterly u_t is the simple mean of the three monthly UNRATE values; a quarter with any missing month is set to missing. Data issues (`data_issues`): (i) UNRATE for 2025-10 is empty. It falls outside the window, so it is never used and is not imputed. (ii) GDPC1 is the current vintage, not real-time data.\nExclusion ledger (`out/exclusions.csv`, unit = quarters of the merged panel): 319 quarters → drop pre-1960Q1 (−52; 1959Q4 levels are used only to form the 1960Q1 differences) → drop post-2019Q4 (−27, information rule) → drop missing Δu or g (−0) → 240 analysed (`n_analysed`). The sample runs from `sample_start` = 1960-01-01 to `sample_end` = 2019-10-01. Subperiods: `n_sub1` = 96 quarters and `n_sub2` = 144 quarters.\n\n## Method\n- **Baseline:** mean-only prediction of Δu, scored by RMSE (`baseline_metric`).\n- **Main model:** OLS of Δu_t on a constant and g_t over the full sample (`effect_estimate`, `intercept`), scored by in-sample residual RMSE (`model_metric`).\n- **Standard errors:** Newey–West HAC, Bartlett kernel, no small-sample correction. The lag length `hac_lags` = 4 comes from the Newey–West (1994) rule floor(4·(T/100)^{2/9}) with T = 240.\n- **95% confidence interval:** β ± 1.96·SE, using the normal approximation (`effect_ci_low`, `effect_ci_high`).\n- **Subperiods:** separate OLS fits (`beta_sub1`, `beta_sub2`; HAC SEs with the same lag in `out/table1.csv`).\n- **Stability:** classical Chow test, F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)] with df (2, 236) (`chow_f_stat`, `chow_p_value`, `chow_df`).\n- **Robustness (supplementary):** the classical Chow test assumes homoskedastic, serially uncorrelated errors with equal variance in both subperiods. As a check, I also ran a HAC Wald test of the subperiod-2 intercept and slope dummies in a pooled interaction regression (`hac_wald_break_chi2`, `hac_wald_break_p`). I also reported the individual dummy p-values (`hac_p_intercept_diff`, `hac_p_slope_diff`) and the ratio of residual variances (`resid_var_ratio_sub1_sub2`).\n- No randomness is involved (`random_seed` = null). Outputs: `out/figure1.png` (scatter with the two subperiod fits) and `out/table1.csv`.\n\n## Results\n- **Full-sample slope:** β = −0.256 (`effect_estimate`) and α = 0.184 pp (`intercept`). A 1 percentage point higher quarterly growth rate goes with a quarterly unemployment change that is about 0.26 pp lower. The growth rate that keeps unemployment unchanged, −α/β, is about 0.72% per quarter (derived from `intercept` and `effect_estimate`).\n- **Fit:** residual RMSE is 0.250 pp (`model_metric`), against 0.325 pp for the mean-only baseline (`baseline_metric`). R² = 0.408 (`r_squared`).\n- **Subperiod slopes:** β₁ = −0.259 (`beta_sub1`) and β₂ = −0.273 (`beta_sub2`). The slopes are nearly identical. The intercepts differ: 0.254 (`intercept_sub1`) vs 0.152 (`intercept_sub2`).\n- **Chow test:** F = 6.09 (`chow_f_stat`), p = 0.0026 (`chow_p_value`). Joint equality of (α, β) is rejected at the 5% level, so `stability_conclusion` = **unstable**.\n- **Conclusion code:** `conclusion` = **negative_effect**, because the 95% interval for β lies entirely below zero.\n\n| period | n | intercept | slope | HAC SE (L=4) |\n|---|---|---|---|---|\n| full | 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(Source: `out/table1.csv`, generated by `out/analysis.py`.)\n\n## Uncertainty\n- **Main interval:** HAC SE of β = 0.0358 (`effect_se`). 95% CI [−0.326, −0.186] (`effect_ci_low`, `effect_ci_high`).\n- **Comparison with the iid SE:** the iid OLS SE is 0.0200 (`ols_iid_se`). Correcting for heteroskedasticity and autocorrelation therefore widens the interval by about 1.8 times.\n- **Sensitivity to lag length:** the HAC SE is 0.029, 0.034, 0.036, 0.037 and 0.036 for lags 0, 2, 4, 8 and 12 (`hac_se_by_lag`). The sign conclusion does not change with any of these lags.\n- **Stability test without the equal-variance assumption:** the HAC Wald break test also rejects joint equality, χ²(2) = 10.9, p = 0.0042 (`hac_wald_break_chi2`, `hac_wald_break_p`).\n- **Individual shifts:** neither the intercept shift (p = 0.25, `hac_p_intercept_diff`) nor the slope shift (p = 0.85, `hac_p_slope_diff`) is significant on its own. The joint rejection therefore comes from the intercept and slope shifts together, which are correlated because mean growth differs between the subperiods. It should not be read as evidence that the Okun slope itself changed.\n\n## Limitations and open issues\n- **How to read \"unstable\":** the Chow rejection concerns the pair (α, β). The point estimates suggest that the instability is mainly a shift in the intercept, and with it in the growth rate that keeps unemployment unchanged (lower trend growth after 1984). It does not look like a change in the slope; I found no evidence that the slope changed (`hac_p_slope_diff`). A claim that \"Okun's coefficient changed\" would not be supported.\n- **Classical Chow assumptions:** the classical test assumes equal error variance across subperiods. The residual variance in subperiod 1 is about 1.63 times that in subperiod 2 (`resid_var_ratio_sub1_sub2`), and the errors are serially correlated, so the classical F p-value is not exact. The HAC Wald check reaches the same verdict, but it is asymptotic and relies on a short HAC lag in samples of 96 and 144 quarters.\n- **Fixed break date:** the break date 1984Q1 is imposed by the task, not estimated. I did not test other break dates, and a sup-F test with an estimated break date would have different critical values.\n- **Data vintage:** GDPC1 is a 2026 vintage. Revised GDP growth differs from what was observed at the time, which matters for the real-time use of Okun's law.\n- **Model specification:**\n  - The equation is a static OLS model with no lags of growth and no allowance for measurement error in g_t; measurement error in g_t would bias β towards zero.\n  - I did not check for nonlinearity or asymmetry between recessions and expansions.\n  - Endogeneity is not addressed: output and unemployment are determined jointly, so β is not causal.\n- **Other choices not tested:**\n  - Different HAC kernels or bandwidth rules beyond the lag grid above.\n  - Fixed-b inference.\n  - The 1.96 normal critical value instead of a t critical value.\n- **What would invalidate the result:**\n  - An error in the quarterly aggregation (checked: 240 complete quarters, with no missing months in the window).\n  - Post-2019 data leaking into the sample (excluded at step 2 of the ledger, and `sample_end` = 2019-10-01).\n  - Strong serial correlation beyond 12 lags.\n  \n  None of these was detected, but the robustness checks above are limited to what is listed.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 the OLS Okun coefficient is about -0.256 pp per 1% quarterly real GDP growth, with a HAC (NW, 4 lags) 95% CI of about [-0.326, -0.186], so the effect is negative.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association on revised (current-vintage) data; normal-approximation CI.\"},\n {\"id\":\"C2\",\"text\":\"Growth explains a substantial part of quarterly unemployment changes: residual RMSE 0.250 pp vs 0.325 pp for the mean-only baseline.\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) between 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (F=6.09, p=0.0026), so the relation is classified unstable.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"hac_wald_break_p\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes equal variances (residual variance ratio ~1.63); a HAC Wald test gives the same verdict.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the slope difference is not individually significant, and the instability appears to come mainly from the intercept.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"intercept_sub1\",\"intercept_sub2\",\"hac_p_slope_diff\",\"hac_p_intercept_diff\"],\"status\":\"supported\",\"caveat\":\"The intercept shift is also not individually significant (p=0.25); only the joint test rejects. The attribution to the intercept rests on point estimates.\"},\n {\"id\":\"C5\",\"text\":\"The sign conclusion is robust to the HAC lag length (0 to 12 lags).\",\"results_keys\":[\"hac_se_by_lag\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel lags 0,2,4,8,12 were checked.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, every number cites a results key\",\"claims.json written with 5 claims referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | grep -vE \"^\\s+\\[ok\\]\" | tail -15","description":"Write report, claims, run final gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":470,"cache_read_input_tokens":21543,"output_tokens":4294,"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":470},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpZDHWMhjRsMctv9dHH","requestRef":"db1129db-eed6-4f2e-8678-1298c6e00f9d","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"86adb3f4-1271-4827-a4e1-0221f8fa7e4c","timestamp":"2026-10-30T04:51:55.340Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}73{"parentUuid":"86adb3f4-1271-4827-a4e1-0221f8fa7e4c","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_017uFnptSSAxKwSwAizDhseR","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045156.615880Z.json","is_error":false}]},"uuid":"60b71ff3-9326-4164-a14c-cfda12809012","timestamp":"2026-10-30T04:51:56.625Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — PASSED (0 of 48 checks failed)\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045156.615880Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"86adb3f4-1271-4827-a4e1-0221f8fa7e4c","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}74{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"4e105039be4d9d7a82e39edcd06791ce8a737c7eb923384b0d791cef71dcf5df","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_017uFnptSSAxKwSwAizDhseR","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# Okun's law in the United States, 1960–2019 (F6-conf-03)\n\n## Question\nHow strongly does the quarterly change in the US unemployment rate (Δu_t, percentage points) respond to quarterly real GDP growth (g_t = 100·Δln GDPC1, percent, not annualised), in Δu_t = α + β·g_t + ε_t over 1960Q1–2019Q4? And is (α, β) the same in 1960Q1–1983Q4 and 1984Q1–2019Q4? β is a reduced-form association (a conditional-mean slope), not a structural causal effect.\n\n## Data and exclusions\nInputs: `data/GDPC1.csv` (319 quarterly rows, 1947Q1–2026Q3) and `data/UNRATE.csv` (945 monthly rows, 1948-01 to 2026-09) (`n_input_rows`). The SHA-256 hashes match `data/README.md`. Quarterly u_t is the simple mean of the three monthly UNRATE values; a quarter with any missing month is set to missing. Data issues (`data_issues`): (i) UNRATE for 2025-10 is empty. It falls outside the window, so it is never used and is not imputed. (ii) GDPC1 is the current vintage, not real-time data.\nExclusion ledger (`out/exclusions.csv`, unit = quarters of the merged panel): 319 quarters → drop pre-1960Q1 (−52; 1959Q4 levels are used only to form the 1960Q1 differences) → drop post-2019Q4 (−27, information rule) → drop missing Δu or g (−0) → 240 analysed (`n_analysed`). The sample runs from `sample_start` = 1960-01-01 to `sample_end` = 2019-10-01. Subperiods: `n_sub1` = 96 quarters and `n_sub2` = 144 quarters.\n\n## Method\n- **Baseline:** mean-only prediction of Δu, scored by RMSE (`baseline_metric`).\n- **Main model:** OLS of Δu_t on a constant and g_t over the full sample (`effect_estimate`, `intercept`), scored by in-sample residual RMSE (`model_metric`).\n- **Standard errors:** Newey–West HAC, Bartlett kernel, no small-sample correction. The lag length `hac_lags` = 4 comes from the Newey–West (1994) rule floor(4·(T/100)^{2/9}) with T = 240.\n- **95% confidence interval:** β ± 1.96·SE, using the normal approximation (`effect_ci_low`, `effect_ci_high`).\n- **Subperiods:** separate OLS fits (`beta_sub1`, `beta_sub2`; HAC SEs with the same lag in `out/table1.csv`).\n- **Stability:** classical Chow test, F = [(SSR_pooled − SSR_1 − SSR_2)/2] / [(SSR_1 + SSR_2)/(n − 4)] with df (2, 236) (`chow_f_stat`, `chow_p_value`, `chow_df`).\n- **Robustness (supplementary):** the classical Chow test assumes homoskedastic, serially uncorrelated errors with equal variance in both subperiods. As a check, I also ran a HAC Wald test of the subperiod-2 intercept and slope dummies in a pooled interaction regression (`hac_wald_break_chi2`, `hac_wald_break_p`). I also reported the individual dummy p-values (`hac_p_intercept_diff`, `hac_p_slope_diff`) and the ratio of residual variances (`resid_var_ratio_sub1_sub2`).\n- No randomness is involved (`random_seed` = null). Outputs: `out/figure1.png` (scatter with the two subperiod fits) and `out/table1.csv`.\n\n## Results\n- **Full-sample slope:** β = −0.256 (`effect_estimate`) and α = 0.184 pp (`intercept`). A 1 percentage point higher quarterly growth rate goes with a quarterly unemployment change that is about 0.26 pp lower. The growth rate that keeps unemployment unchanged, −α/β, is about 0.72% per quarter (derived from `intercept` and `effect_estimate`).\n- **Fit:** residual RMSE is 0.250 pp (`model_metric`), against 0.325 pp for the mean-only baseline (`baseline_metric`). R² = 0.408 (`r_squared`).\n- **Subperiod slopes:** β₁ = −0.259 (`beta_sub1`) and β₂ = −0.273 (`beta_sub2`). The slopes are nearly identical. The intercepts differ: 0.254 (`intercept_sub1`) vs 0.152 (`intercept_sub2`).\n- **Chow test:** F = 6.09 (`chow_f_stat`), p = 0.0026 (`chow_p_value`). Joint equality of (α, β) is rejected at the 5% level, so `stability_conclusion` = **unstable**.\n- **Conclusion code:** `conclusion` = **negative_effect**, because the 95% interval for β lies entirely below zero.\n\n| period | n | intercept | slope | HAC SE (L=4) |\n|---|---|---|---|---|\n| full | 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(Source: `out/table1.csv`, generated by `out/analysis.py`.)\n\n## Uncertainty\n- **Main interval:** HAC SE of β = 0.0358 (`effect_se`). 95% CI [−0.326, −0.186] (`effect_ci_low`, `effect_ci_high`).\n- **Comparison with the iid SE:** the iid OLS SE is 0.0200 (`ols_iid_se`). Correcting for heteroskedasticity and autocorrelation therefore widens the interval by about 1.8 times.\n- **Sensitivity to lag length:** the HAC SE is 0.029, 0.034, 0.036, 0.037 and 0.036 for lags 0, 2, 4, 8 and 12 (`hac_se_by_lag`). The sign conclusion does not change with any of these lags.\n- **Stability test without the equal-variance assumption:** the HAC Wald break test also rejects joint equality, χ²(2) = 10.9, p = 0.0042 (`hac_wald_break_chi2`, `hac_wald_break_p`).\n- **Individual shifts:** neither the intercept shift (p = 0.25, `hac_p_intercept_diff`) nor the slope shift (p = 0.85, `hac_p_slope_diff`) is significant on its own. The joint rejection therefore comes from the intercept and slope shifts together, which are correlated because mean growth differs between the subperiods. It should not be read as evidence that the Okun slope itself changed.\n\n## Limitations and open issues\n- **How to read \"unstable\":** the Chow rejection concerns the pair (α, β). The point estimates suggest that the instability is mainly a shift in the intercept, and with it in the growth rate that keeps unemployment unchanged (lower trend growth after 1984). It does not look like a change in the slope; I found no evidence that the slope changed (`hac_p_slope_diff`). A claim that \"Okun's coefficient changed\" would not be supported.\n- **Classical Chow assumptions:** the classical test assumes equal error variance across subperiods. The residual variance in subperiod 1 is about 1.63 times that in subperiod 2 (`resid_var_ratio_sub1_sub2`), and the errors are serially correlated, so the classical F p-value is not exact. The HAC Wald check reaches the same verdict, but it is asymptotic and relies on a short HAC lag in samples of 96 and 144 quarters.\n- **Fixed break date:** the break date 1984Q1 is imposed by the task, not estimated. I did not test other break dates, and a sup-F test with an estimated break date would have different critical values.\n- **Data vintage:** GDPC1 is a 2026 vintage. Revised GDP growth differs from what was observed at the time, which matters for the real-time use of Okun's law.\n- **Model specification:**\n  - The equation is a static OLS model with no lags of growth and no allowance for measurement error in g_t; measurement error in g_t would bias β towards zero.\n  - I did not check for nonlinearity or asymmetry between recessions and expansions.\n  - Endogeneity is not addressed: output and unemployment are determined jointly, so β is not causal.\n- **Other choices not tested:**\n  - Different HAC kernels or bandwidth rules beyond the lag grid above.\n  - Fixed-b inference.\n  - The 1.96 normal critical value instead of a t critical value.\n- **What would invalidate the result:**\n  - An error in the quarterly aggregation (checked: 240 complete quarters, with no missing months in the window).\n  - Post-2019 data leaking into the sample (excluded at step 2 of the ledger, and `sample_end` = 2019-10-01).\n  - Strong serial correlation beyond 12 lags.\n  \n  None of these was detected, but the robustness checks above are limited to what is listed.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Over 1960Q1-2019Q4 the OLS Okun coefficient is about -0.256 pp per 1% quarterly real GDP growth, with a HAC (NW, 4 lags) 95% CI of about [-0.326, -0.186], so the effect is negative.\",\"results_keys\":[\"effect_estimate\",\"effect_se\",\"hac_lags\",\"effect_ci_low\",\"effect_ci_high\",\"conclusion\"],\"status\":\"supported\",\"caveat\":\"Reduced-form association on revised (current-vintage) data; normal-approximation CI.\"},\n {\"id\":\"C2\",\"text\":\"Growth explains a substantial part of quarterly unemployment changes: residual RMSE 0.250 pp vs 0.325 pp for the mean-only baseline.\",\"results_keys\":[\"model_metric\",\"baseline_metric\",\"r_squared\"],\"status\":\"supported\",\"caveat\":\"In-sample fit only.\"},\n {\"id\":\"C3\",\"text\":\"The classical Chow test rejects joint equality of (alpha, beta) between 1960Q1-1983Q4 and 1984Q1-2019Q4 at 5% (F=6.09, p=0.0026), so the relation is classified unstable.\",\"results_keys\":[\"chow_f_stat\",\"chow_p_value\",\"stability_conclusion\",\"hac_wald_break_p\"],\"status\":\"supported\",\"caveat\":\"Classical test assumes equal variances (residual variance ratio ~1.63); a HAC Wald test gives the same verdict.\"},\n {\"id\":\"C4\",\"text\":\"The subperiod slopes are nearly identical (-0.259 vs -0.273); the slope difference is not individually significant, and the instability appears to come mainly from the intercept.\",\"results_keys\":[\"beta_sub1\",\"beta_sub2\",\"intercept_sub1\",\"intercept_sub2\",\"hac_p_slope_diff\",\"hac_p_intercept_diff\"],\"status\":\"supported\",\"caveat\":\"The intercept shift is also not individually significant (p=0.25); only the joint test rejects. The attribution to the intercept rests on point estimates.\"},\n {\"id\":\"C5\",\"text\":\"The sign conclusion is robust to the HAC lag length (0 to 12 lags).\",\"results_keys\":[\"hac_se_by_lag\"],\"status\":\"supported\",\"caveat\":\"Only Bartlett kernel lags 0,2,4,8,12 were checked.\"}\n]\nEOF\n~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']={\"status\":\"passed\",\"checks\":[\"report.md with 6 sections, every number cites a results key\",\"claims.json written with 5 claims referencing existing keys\",\"final gate run (see notes)\"],\"notes\":\"\"}\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | grep -vE \"^\\s+\\[ok\\]\" | tail -15","description":"Write report, claims, run final gate"}}]}}75{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"7f090358-0a36-495b-b385-951a4db113a6","timestamp":"2026-10-30T04:51:56.629Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"db1129db-eed6-4f2e-8678-1298c6e00f9d","keep":24,"tail":["4e105039be4d9d7a82e39edcd06791ce8a737c7eb923384b0d791cef71dcf5df","36bec9ed83fed9b58f3f8239f8c0d2d8483c81854f9b7928903c737344e5a1ed"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",25,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"127a47432d5240d9990fd820fa81b878e0c4530efa492fece01d5744e0cf3270"}76{"parentUuid":"60b71ff3-9326-4164-a14c-cfda12809012","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpbvxKz1P6RsufGhebK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01JTfT5huT5ozj5Jr5YcMvwa","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['notes']=\"final gate receipt out/state/gate_receipts/20261030T045156.615880Z.json: PASSED 0/48 failed\"\np['P5']['checks'][2]=\"final gate 20261030T045156.615880Z PASSED (0 of 48 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed: report, claims written; gate PASSED (receipt 20261030T045156.615880Z).\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -2","description":"Record gate receipt and confirm pass"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":4389,"cache_read_input_tokens":22013,"output_tokens":353,"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":4389},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpbvhgk2WUqLUUGyhsF","requestRef":"7f090358-0a36-495b-b385-951a4db113a6","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6a00a052-e4ef-4862-a69d-fc91e925406a","timestamp":"2026-10-30T04:52:00.146Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}77{"parentUuid":"6a00a052-e4ef-4862-a69d-fc91e925406a","isSidechain":false,"promptId":"d13ad6fb-f512-4235-87cb-2965ec5690a7","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01JTfT5huT5ozj5Jr5YcMvwa","type":"tool_result","content":"  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045201.345099Z.json","is_error":false}]},"uuid":"db00d41b-028c-45ba-a1c2-ea5ebf7c8303","timestamp":"2026-10-30T04:52:01.352Z","toolUseResult":{"stdout":"  [ok] reproducibility: regenerated in 1.1s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0175/out/state/gate_receipts/20261030T045201.345099Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6a00a052-e4ef-4862-a69d-fc91e925406a","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}78{"type":"api-request-blob","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","hash":"5581c9b992cd0ebde42d6605f5ec4def9b28857742e7658169423e6617d5e2d3","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01JTfT5huT5ozj5Jr5YcMvwa","name":"Bash","input":{"command":"~/wp29/venv/bin/python - <<'EOF'\nimport json\np=json.load(open('out/state/phases.json'))\np['P5']['notes']=\"final gate receipt out/state/gate_receipts/20261030T045156.615880Z.json: PASSED 0/48 failed\"\np['P5']['checks'][2]=\"final gate 20261030T045156.615880Z PASSED (0 of 48 failed)\"\njson.dump(p,open('out/state/phases.json','w'),indent=1)\nEOF\necho \"- 2026-10-30 P5 passed: report, claims written; gate PASSED (receipt 20261030T045156.615880Z).\" >> out/state/log.md\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . 2>&1 | tail -2","description":"Record gate receipt and confirm pass"}}]}}79{"type":"api-request","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","id":"a3252cb9-90ab-477d-a90f-55ce64a9a7c1","timestamp":"2026-10-30T04:52:01.357Z","version":"2.1.314","querySource":"sdk","shapeHash":"223d3fbcaa7f0eb1eb50b51f7867e85359822ec8ba7c5aa5bc71744de42df5df","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":"7f090358-0a36-495b-b385-951a4db113a6","keep":26,"tail":["5581c9b992cd0ebde42d6605f5ec4def9b28857742e7658169423e6617d5e2d3","e4116c885cacebd35c789317d2df26092ffc8ae5ca6a2546aa64edcc63e50195"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",27,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"b0d6544f85927111a1797733e969e65bf19fd12c50f970f5a4cf28e2513511d2"}80{"parentUuid":"db00d41b-028c-45ba-a1c2-ea5ebf7c8303","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpcHA1ePZQgTdoQGVwv","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":442,"cache_read_input_tokens":26402,"output_tokens":1000,"output_tokens_details":{"thinking_tokens":49},"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":442},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":6553,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpcGsPnx9kiJmim1nMC","requestRef":"a3252cb9-90ab-477d-a90f-55ce64a9a7c1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"1ecdb6d2-04e1-4228-bc20-377342d48094","timestamp":"2026-10-30T04:52:09.209Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}81{"parentUuid":"1ecdb6d2-04e1-4228-bc20-377342d48094","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpcHA1ePZQgTdoQGVwv","type":"message","role":"assistant","content":[{"type":"text","text":"I've finished the analysis. Over 1960Q1–2019Q4, quarterly unemployment falls by about 0.26 percentage points for each extra 1% of quarterly real GDP growth. The Chow test says the relationship is not stable across the two subperiods, but that rejection doesn't look like a change in the slope itself. The final gate run passed with 0 of 48 checks failed, and re-running `out/analysis.py` from a clean state gives identical results.\n\n**Full sample (240 quarters):**\n- **Okun coefficient:** −0.256, intercept 0.184 percentage points.\n- **Uncertainty:** the Newey–West standard error is 0.036, using 4 lags from the standard rule of thumb. The 95% interval is [−0.326, −0.186], entirely below zero, so the conclusion is `negative_effect`.\n- **Fit:** the model's error (RMSE) is 0.250 percentage points, against 0.325 for the simplest baseline (always predicting the average change). R² is 0.41.\n\n**Stability:**\n- **Subperiod slopes:** nearly identical, −0.259 for 1960–83 (96 quarters) and −0.273 for 1984–2019 (144 quarters).\n- **Chow test:** F = 6.09, p = 0.0026, so `stability_conclusion` is `unstable`.\n- **What drives the rejection:** I added a robustness test that allows for unequal error variance and serial correlation. It also rejects (p = 0.004), but neither the slope change (p = 0.85) nor the intercept change (p = 0.25) is significant on its own. The joint rejection comes mostly from a lower intercept after 1984 (0.254 vs 0.152), not a different slope. The report says plainly that \"Okun's coefficient changed\" would not be supported.\n\n**Limitations:**\n- **Revised data:** the GDP series is the 2026 revision, not what was known at the time.\n- **Assumptions behind the Chow test:** it assumes equal error variance in both periods, but the variance is about 1.6 times higher in the earlier period and the errors are serially correlated. The robustness test is meant to cover this.\n- **Not checked:**\n  - break dates other than the 1984Q1 split the task imposes;\n  - lagged growth terms;\n  - asymmetry between recessions and expansions;\n  - two-way causation between output and unemployment, so the coefficient is an association, not a causal effect.\n- **Missing data point:** the October 2025 unemployment value is blank in the input file. That is outside the study period, so it was never used.\n\nEverything is in `out/`. The main files are `results.json`, `report.md`, `analysis.py`, `figure1.png` (scatter with the two subperiod fitted lines), `table1.csv` and `claims.json`. The exclusion ledger and working notes are in `exclusions.csv` and `state/`."}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":442,"cache_read_input_tokens":26402,"output_tokens":1000,"output_tokens_details":{"thinking_tokens":49},"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":442},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpcGsPnx9kiJmim1nMC","requestRef":"a3252cb9-90ab-477d-a90f-55ce64a9a7c1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"fe5288f4-ca79-4eda-834c-1354b0668b02","timestamp":"2026-10-30T04:52:10.840Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0175","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","version":"2.1.314","gitBranch":"HEAD"}82{"type":"last-prompt","lastPrompt":"Begin","leafUuid":"fe5288f4-ca79-4eda-834c-1354b0668b02","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}83{"type":"atis-latch","atis":"","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240"}84{"type":"cost-state","sessionId":"fe75ff9c-8556-4e80-b9ed-a7aff7e35240","totalCostUSD":0.4766057999999999,"totalAPIDuration":139167,"totalAPIDurationWithoutRetries":139160,"totalToolDuration":7891,"totalLinesAdded":0,"totalLinesRemoved":0,"totalDuration":147247,"startTime":1793335783602,"modelUsage":{"claude-opus-5-5":{"inputTokens":30,"outputTokens":15503,"thinkingTokens":896,"cacheReadInputTokens":198629,"cacheCreationInputTokens":25340,"cacheCreationEphemeral5mInputTokens":25340,"cacheCreationEphemeral1hInputTokens":0,"webSearchRequests":0,"costUSD":0.4766057999999999}},"hasUnknownModelCost":false}85