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It is the complete and only authoritative description of what is required: the scientific question, the inputs under `data/`, the required output files and their exact keys, and the information-availability rules.\n- There is no network access. Use the Python interpreter `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels, matplotlib are installed). Standard shell tools are available. Do not try to install packages.\n- Write every deliverable under `./out/`. The required deliverables are listed in `TASK.md`; at minimum they always include `out/results.json`, `out/report.md`, and a replication entry point `out/analysis.py` that regenerates `out/results.json` from `data/` when run with `~/wp29/venv/bin/python out/analysis.py` from the workspace root.\n- You are working alone and unattended; nobody will answer questions. Make reasonable, documented decisions.\n\nIntegrity rules (identical for every task):\n- Never fabricate numbers, data, results, or references. Every number in `out/results.json` and `out/report.md` must be produced by code you ran on the provided inputs.\n- Respect the information-availability rules in `TASK.md` exactly (for example, never use information that would not have been available at the stated decision time).\n- If part of the task cannot be completed as specified, say so explicitly in `out/report.md`, explain why, and still write every required file (use `null` for values you could not legitimately produce). An honest statement of a limitation is always preferred to a fabricated or guessed value.\n- Do not modify anything under `data/`.\n- When you are done, end with a brief plain-text summary of what you produced and any limitations.\n\nComplete the task described in `TASK.md` by following this research protocol. The protocol adds no scientific information to the task; it organizes how you work and what evidence you leave behind.\n\nPHASES (execute in order; a phase is eligible only when its dependencies are satisfied):\n\nP0 Scope and contract (depends on: nothing)\n- Read `TASK.md` completely. In `out/state/contract.md`, restate in your own words: the scientific question; the quantity or quantities to be estimated (the estimands) and the assumptions under which they are meaningful; every required output file and key; the information-availability rules; what would make the task impossible or the result invalid.\n- Write the initial phase ledger `out/state/phases.json`: a JSON object mapping each phase id (P0 … P5) to {\"status\": \"pending\"|\"running\"|\"passed\"|\"failed\"|\"blocked\"|\"stale\"|\"skipped_optional\", \"checks\": [], \"notes\": \"\"}. Keep it current as you work. A phase may only be set to \"passed\" when the checks listed for it in this protocol have actually been executed and their outcome recorded in \"checks\".\n\nP1 Data audit and provenance (depends on: P0)\n- Inspect every input file: compute and record its SHA-256, row and column counts, schema, units, date ranges, missing values, duplicates and anomalies in `out/state/data_audit.md`.\n- Keep an exclusion ledger in `out/exclusions.csv` (columns: step, rule, n_before, n_after, n_removed, rationale) for every filtering step you apply, so that n_raw − total removed = n_analysed.\n- Identify any input that violates the information-availability rules and decide, in writing, how you will handle it.\n\nP2 Baseline (depends on: P1)\n- Before any elaborate method, compute the simplest valid baseline that answers the question (for example a naïve or unconditional estimate), with the same evaluation rule used later. Record it in `out/results.json` under the keys the task requires, or under `baseline_*` keys when the task does not name them.\n\nP3 Method and computation (depends on: P2)\n- Implement the analysis in `out/analysis.py` so that it reads only `data/`, writes `out/results.json` deterministically (fix every random seed you use and record it), and prints nothing that contradicts the written results. Any figure or table must be generated by this script.\n\nP4 Verification (depends on: P3)\n- Re-run `out/analysis.py` from a clean state and confirm it regenerates `out/results.json` with identical values. Record the check in `out/state/phases.json` under P4.\n- Check internal consistency: identities that must hold (for example counts that must add up, standard errors that must be positive, confidence intervals that must contain their estimates, date orderings that must respect the information rules), units, and magnitudes against the baseline. Record every check and its outcome in `out/state/checks.md`. A failed check must be fixed or honestly reported, never silently ignored.\n\nP5 Reporting (depends on: P4)\n- Write `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues. Every quantitative statement must cite the key of `out/results.json` it comes from.\n- Write `out/claims.json`: a list of the central claims of your report, each as {\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}. Do not state a claim in the report that is not in this ledger.\n- Set the final statuses in `out/state/phases.json`.\n\nSTOPPING RULES\n- Stop and declare the task complete only when every phase P0–P5 is \"passed\" with recorded checks. If a phase is \"blocked\" (missing information, impossible requirement, violated availability rule), stop, set the status, explain in `out/report.md` (section Limitations and open issues) and still write every required file with `null` for values you could not legitimately produce.\n- Do not spend effort on extensions that the task does not require.\n\nRECOVERY RULES\n- Append a short dated entry to `out/state/log.md` after each phase transition and after any important decision, so that work can be resumed by reading `out/state/`.\n- If you discover that an upstream input or decision changed after a downstream phase passed, mark the downstream phases \"stale\" and redo them; do not reuse stale results.\n- If you are interrupted and later resumed, first read `out/state/` and continue from the last consistent state; never re-run expensive steps that already have recorded, unchanged outputs.\n\nEXECUTABLE GATES (additional, mandatory)\n- A read-only gate program is provided at `~/wp29/gates/pgar_gate.py`. Run it from the workspace root as `~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py .` It checks the output contract of `TASK.md` (required files, keys, types, finite values, report sections, the claims ledger, the phase ledger, the exclusion ledger) and reproducibility (it re-runs `out/analysis.py` in a temporary copy and compares the regenerated `out/results.json` with yours). It never checks scientific correctness and it contains no reference answers.\n- You must run the gate at the end of P4 and again at the end of P5, and record each run's receipt (the gate writes `out/state/gate_receipts/<timestamp>.json`) in `out/state/phases.json`. You may not declare the task complete while the latest gate receipt reports `\"passed\": false`. Fix the reported problems and re-run the gate.\n- The controller will run the same gate after your session ends. If it fails, you will be resumed with the gate output and asked to fix the problems; each such cycle consumes your remaining budget.\n\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. 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The estimand is the coefficient on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.\n\n## Inputs\n- `data/firm_panel.csv` — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns `firm_id`, `year`, `leverage`, `cash_ratio`, `log_sales`, `rating_2025`, `default_next`; definitions, units and the date at which each field becomes known are in `data/README.md` (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.\n\n## Information-availability rules\n- A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in `variables_excluded`.\n- Firm-years whose outcome `default_next` is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.\n- Use only `data/firm_panel.csv`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-03\" |\n| `n_analysed` | integer | firm-years | observations in the estimation sample |\n| `n_firms` | integer | firms | distinct firms in the estimation sample |\n| `predictors_used` | array of strings | — | column names entered as regressors (excluding the intercept) |\n| `variables_excluded` | array of {\"variable\",\"reason\"} | — | columns of the file deliberately not used as predictors and why |\n| `beta_leverage` | number | probability points | OLS coefficient on `leverage` |\n| `se_leverage_cluster` | number | — | standard error of `beta_leverage` clustered by `firm_id` |\n| `r2` | number | — | in-sample R² of the model |\n| `default_rate` | number | — | baseline: share of firm-years with default_next = 1 in the estimation sample |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"firm_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the estimation sample: one row per firm-year used, columns `firm_id`, `year`, the predictors used and `default_next`; exactly `n_analysed` rows and no column that violates the information rule.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`). Column removals may be listed with `n_removed = 0`.\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). Do not modify `data/`.\n- This task is designed to be completed in well under 20 minutes of work.\nbundle.json\ndata\ngate_spec.json\nTASK.md\n\n./data:\nfirm_panel.csv\nREADME.md","is_error":true,"tool_use_id":"toolu_01CYrspJ9nP6sivbfZvnLRxy"}]},"uuid":"850c9998-288e-444f-9f72-2e5dc26f1b36","timestamp":"2026-10-30T04:46:06.883Z","toolUseResult":"Error: Exit code 1\n# F1-conf-03 — Information-respecting analytic dataset for next-year default\n\n## Scientific question\nEstimate the association between a firm's leverage at the end of year t and its default in year t+1, using only information that was available at the end of year t. The estimand is the coefficient on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e estimated by OLS on the firm-years whose outcome is observed, with standard errors clustered by firm. The analytic dataset must contain only predictors whose values were known at the origin year.\n\n## Inputs\n- `data/firm_panel.csv` — simulated data (synthetic firm-year panel; not observed data), 300 firms, years 2016–2022, one intended row per firm-year. Columns `firm_id`, `year`, `leverage`, `cash_ratio`, `log_sales`, `rating_2025`, `default_next`; definitions, units and the date at which each field becomes known are in `data/README.md` (codebook). Empty cells mean the value is not observed. The extract may contain assembly errors.\n\n## Information-availability rules\n- A variable may enter the model for origin year t only if its value would have been known at the end of year t (see the codebook's last column). Any variable that does not satisfy this rule must be excluded from the analytic dataset and listed in `variables_excluded`.\n- Firm-years whose outcome `default_next` is not observed cannot be used for estimation and must be listed in the exclusion ledger; do not impute outcomes.\n- Use only `data/firm_panel.csv`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-03\" |\n| `n_analysed` | integer | firm-years | observations in the estimation sample |\n| `n_firms` | integer | firms | distinct firms in the estimation sample |\n| `predictors_used` | array of strings | — | column names entered as regressors (excluding the intercept) |\n| `variables_excluded` | array of {\"variable\",\"reason\"} | — | columns of the file deliberately not used as predictors and why |\n| `beta_leverage` | number | probability points | OLS coefficient on `leverage` |\n| `se_leverage_cluster` | number | — | standard error of `beta_leverage` clustered by `firm_id` |\n| `r2` | number | — | in-sample R² of the model |\n| `default_rate` | number | — | baseline: share of firm-years with default_next = 1 in the estimation sample |\n| `feasibility` | string | — | \"complete\", \"partial\" or \"infeasible\" |\n| `feasibility_reason` | string or null | — | explanation if not complete |\n| `data_issues` | array of {\"issue\",\"handling\"} | — | every data problem detected and how it was handled (empty list if none) |\n| `n_input_rows` | object | rows | {\"firm_panel.csv\": number of data rows read (excluding the header)} |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — the estimation sample: one row per firm-year used, columns `firm_id`, `year`, the predictors used and `default_next`; exactly `n_analysed` rows and no column that violates the information rule.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the number of rows read to `n_analysed` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`). Column removals may be listed with `n_removed = 0`.\n- `out/report.md` with the sections: Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues.\n- `out/analysis.py` — running `~/wp29/venv/bin/python out/analysis.py` from the workspace root must regenerate `out/results.json`, `out/analytic.csv` and `out/exclusions.csv` from `data/` only, deterministically.\n- `out/claims.json` — a list of the central claims of the report: [{\"id\": \"C1\", \"text\": \"...\", \"results_keys\": [\"...\"], \"status\": \"supported\"|\"unsupported\"|\"not_evaluated\", \"caveat\": \"...\"}].\n\n## Constraints\n- No network access. Python: `~/wp29/venv/bin/python` (numpy, pandas, scipy, statsmodels available). 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it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\n{\n \"task_id\": \"F1-conf-03\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/analytic.csv\",\n \"out/exclusions.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_firms\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"predictors_used\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"variables_excluded\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"beta_leverage\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"se_leverage_cluster\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"r2\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"default_rate\": {\n \"type\": \"number\",\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 \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\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 \"exclusion_ledger_sums\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 data/README.md\n 2141 data/firm_panel.csv\nfirm_id,year,leverage,cash_ratio,log_sales,rating_2025,default_next\nF042,2022,0.5244,0.0709,6.0889,D,\nF188,2016,0.568,0.102,6.8038,D,0\nF186,2017,0.3798,0.1008,5.7041,D,1\nF105,2020,0.229,0.1368,4.5621,A,0","is_error":false}]},"uuid":"35be2c04-f58c-45b5-95c6-4dcb1c8820fa","timestamp":"2026-10-30T04:46:09.482Z","toolUseResult":{"stdout":"# Codebook — F1-conf-03 (`firm_panel.csv`)\n\nSimulated data (a synthetic firm-year panel generated for this exercise; it is not observed data). One intended row per firm and year, years 2016–2022, 300 firms. The extract was assembled from the firm master file and the annual accounts and may contain assembly errors.\n\n| column | type | definition | when the value becomes known |\n|---|---|---|---|\n| `firm_id` | string | firm identifier | at origin |\n| `year` | integer | fiscal year t (the origin year) | at origin |\n| `leverage` | number | total debt / total assets at the end of year t | end of year t |\n| `cash_ratio` | number | cash and equivalents / total assets at the end of year t | end of year t |\n| `log_sales` | number | natural log of sales in year t (thousands USD) | end of year t |\n| `rating_2025` | string (A/B/C/D) | credit rating assigned to the firm in the 2025 portfolio review; the review used the firm's complete realised default history through 2022 and its average leverage over the sample; one value per firm, constant across years (final-vintage field) | 2025 |\n| `default_next` | integer 0/1 | 1 if the firm defaulted in year t+1, 0 otherwise; empty when the outcome year had not yet been observed at the data cut (end of 2022) | end of year t+1 |\n{\n \"task_id\": \"F1-conf-03\",\n \"required_files\": [\n \"out/results.json\",\n \"out/report.md\",\n \"out/analysis.py\",\n \"out/claims.json\",\n \"out/analytic.csv\",\n \"out/exclusions.csv\"\n ],\n \"results_keys\": {\n \"task_id\": {\n \"type\": \"string\",\n \"nullable\": false\n },\n \"n_analysed\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"n_firms\": {\n \"type\": \"integer\",\n \"nullable\": false\n },\n \"predictors_used\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"variables_excluded\": {\n \"type\": \"array\",\n \"nullable\": false\n },\n \"beta_leverage\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"se_leverage_cluster\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"r2\": {\n \"type\": \"number\",\n \"nullable\": false\n },\n \"default_rate\": {\n \"type\": \"number\",\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 \"random_seed\": {\n \"type\": \"integer\",\n \"nullable\": true\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 \"exclusion_ledger_sums\",\n \"claims_reference_existing_keys\",\n \"phase_ledger_consistent\",\na1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f data/firm_panel.csv\n26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 data/README.md\n 2141 data/firm_panel.csv\nfirm_id,year,leverage,cash_ratio,log_sales,rating_2025,default_next\nF042,2022,0.5244,0.0709,6.0889,D,\nF188,2016,0.568,0.102,6.8038,D,0\nF186,2017,0.3798,0.1008,5.7041,D,1\nF105,2020,0.229,0.1368,4.5621,A,0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"6b5641ea-cadc-4d2b-aeb3-a4dd5d34c743","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}21{"type":"api-request-blob","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","hash":"25ceff662621980d8a8e5db9b4af00ff9ccb5d615431b250b77bbadf0e9066d0","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01QsTzWP9nnRi7bUXaMhZNWe","name":"Bash","input":{"command":"cat data/README.md; 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NaN NaN NaN NaN\nyear 2140.0 NaN NaN NaN ... 2017.0 2019.0 2021.0 2022.0\nleverage 2140.0 NaN NaN NaN ... 0.235 0.3829 0.5458 0.95\ncash_ratio 2140.0 NaN NaN NaN ... 0.104575 0.17855 0.265875 0.7406\nlog_sales 2140.0 NaN NaN NaN ... 4.331375 4.9998 5.652925 8.8786\nrating_2025 2140 4 D 1146 ... NaN NaN NaN NaN\ndefault_next 1833.0 NaN NaN NaN ... 0.0 0.0 0.0 1.0\n\n[7 rows x 11 columns]\nfull dup 40\nkey dup rows 80\n firm_id year leverage cash_ratio log_sales rating_2025 default_next\n812 F015 2016 0.4195 0.0748 3.1490 D 1.0\n1439 F015 2016 0.4195 0.0748 3.1490 D 1.0\n804 F017 2019 0.5908 0.3866 6.5654 C 0.0\n978 F017 2019 0.5908 0.3866 6.5654 C 0.0\n336 F024 2022 0.4509 0.2859 4.3565 D NaN\n1058 F024 2022 0.4509 0.2859 4.3565 D NaN\n102 F025 2018 0.2454 0.2691 4.3037 D 0.0\n1682 F025 2018 0.2454 0.2691 4.3037 D 0.0\n82 F040 2018 0.6918 0.2139 4.8758 D 1.0\n237 F040 2018 0.6918 0.2139 4.8758 D 1.0\n0 F042 2022 0.5244 0.0709 6.0889 D NaN\n15 F042 2022 0.5244 0.0709 6.0889 D NaN\n4 F062 2020 0.2496 0.2919 6.3207 B 0.0\n1176 F062 2020 0.2496 0.2919 6.3207 B 0.0\n752 F064 2018 0.4409 0.2196 5.6493 D 0.0\n1384 F064 2018 0.4409 0.2196 5.6493 D 0.0\n127 F065 2018 0.8433 0.6880 5.8157 D 0.0\n2072 F065 2018 0.8433 0.6880 5.8157 D 0.0\n50 F066 2021 0.4655 0.1123 3.2088 D 1.0\n1560 F066 2021 0.4655 0.1123 3.2088 D 1.0\nyear\n2016 305\n2017 301\n2018 309\n2019 305\n2020 306\n2021 307\n2022 307\nName: count, dtype: int64\n300\ndefault_next False True \nyear \n2016 305 0\n2017 301 0\n2018 309 0\n2019 305 0\n2020 306 0\n2021 307 0\n2022 0 307\ndefault_next\n0.0 1609\nNaN 307\n1.0 224\nName: count, dtype: int64\nrating_2025\n1 300\nName: count, dtype: int64\nTrue\nleverage 0 0\ncash_ratio 0 0\nlog_sales 0 2140","is_error":false}]},"uuid":"17018bf2-4e31-4b89-a6ec-ea4101964332","timestamp":"2026-10-30T04:46:13.928Z","toolUseResult":{"stdout":"(2140, 7)\nfirm_id object\nyear int64\nleverage float64\ncash_ratio float64\nlog_sales float64\nrating_2025 object\ndefault_next float64\ndtype: object\nfirm_id 0\nyear 0\nleverage 0\ncash_ratio 0\nlog_sales 0\nrating_2025 0\ndefault_next 307\ndtype: int64\n count unique top freq ... 25% 50% 75% max\nfirm_id 2140 300 F042 8 ... NaN NaN NaN NaN\nyear 2140.0 NaN NaN NaN ... 2017.0 2019.0 2021.0 2022.0\nleverage 2140.0 NaN NaN NaN ... 0.235 0.3829 0.5458 0.95\ncash_ratio 2140.0 NaN NaN NaN ... 0.104575 0.17855 0.265875 0.7406\nlog_sales 2140.0 NaN NaN NaN ... 4.331375 4.9998 5.652925 8.8786\nrating_2025 2140 4 D 1146 ... NaN NaN NaN NaN\ndefault_next 1833.0 NaN NaN NaN ... 0.0 0.0 0.0 1.0\n\n[7 rows x 11 columns]\nfull dup 40\nkey dup rows 80\n firm_id year leverage cash_ratio log_sales rating_2025 default_next\n812 F015 2016 0.4195 0.0748 3.1490 D 1.0\n1439 F015 2016 0.4195 0.0748 3.1490 D 1.0\n804 F017 2019 0.5908 0.3866 6.5654 C 0.0\n978 F017 2019 0.5908 0.3866 6.5654 C 0.0\n336 F024 2022 0.4509 0.2859 4.3565 D NaN\n1058 F024 2022 0.4509 0.2859 4.3565 D NaN\n102 F025 2018 0.2454 0.2691 4.3037 D 0.0\n1682 F025 2018 0.2454 0.2691 4.3037 D 0.0\n82 F040 2018 0.6918 0.2139 4.8758 D 1.0\n237 F040 2018 0.6918 0.2139 4.8758 D 1.0\n0 F042 2022 0.5244 0.0709 6.0889 D NaN\n15 F042 2022 0.5244 0.0709 6.0889 D NaN\n4 F062 2020 0.2496 0.2919 6.3207 B 0.0\n1176 F062 2020 0.2496 0.2919 6.3207 B 0.0\n752 F064 2018 0.4409 0.2196 5.6493 D 0.0\n1384 F064 2018 0.4409 0.2196 5.6493 D 0.0\n127 F065 2018 0.8433 0.6880 5.8157 D 0.0\n2072 F065 2018 0.8433 0.6880 5.8157 D 0.0\n50 F066 2021 0.4655 0.1123 3.2088 D 1.0\n1560 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Meaningful as a conditional association (not causal); LPM assumes linear conditional mean; cluster SEs assume independence across firms and enough clusters (~300).\n**Outputs:** out/results.json (task_id, n_analysed, n_firms, predictors_used, variables_excluded, beta_leverage, se_leverage_cluster, r2, default_rate, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** only predictors known at end of t (leverage, cash_ratio, log_sales). rating_2025 is known in 2025 and built from realised defaults -> leakage, must be excluded and listed. Rows with missing default_next excluded and logged, never imputed. Only data/firm_panel.csv.\n**Invalidating:** using rating_2025; imputing outcomes; duplicate rows double counting; no observed outcomes.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md and README read; contract written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"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":654,"cache_read_input_tokens":9275,"output_tokens":853,"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":654},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpAxzuicksgMmhDTRPs","requestRef":"e1065b1c-6457-44ef-b141-7f3fd31127e7","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"75424e05-6da2-4f5b-af3a-dc47d700f2b4","timestamp":"2026-10-30T04:46:26.076Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}37{"parentUuid":"75424e05-6da2-4f5b-af3a-dc47d700f2b4","isSidechain":false,"promptId":"b00a89ff-be83-4cfc-b9dd-ba9d011937c4","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_015AXLcUiv8dQem11g4ZAKbR","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"79bbb0fc-b1b5-42b8-9ec4-4f95ffb21554","timestamp":"2026-10-30T04:46:26.125Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"75424e05-6da2-4f5b-af3a-dc47d700f2b4","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}38{"type":"api-request-blob","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","hash":"de9ad0d046f6fa6029e9aecc9a89135126c61d2e2901461a75de89a73890e9ec","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_015AXLcUiv8dQem11g4ZAKbR","name":"Bash","input":{"command":"mkdir -p out/state && cat > out/state/contract.md <<'EOF'\n# Contract — F1-conf-03\n**Question:** association between end-of-year-t leverage and default in t+1, using only info known at end of t.\n**Estimand:** b1 (coef on leverage) in OLS LPM default_next ~ 1 + leverage + cash_ratio + log_sales, on firm-years with observed default_next; SEs clustered by firm_id. Meaningful as a conditional association (not causal); LPM assumes linear conditional mean; cluster SEs assume independence across firms and enough clusters (~300).\n**Outputs:** out/results.json (task_id, n_analysed, n_firms, predictors_used, variables_excluded, beta_leverage, se_leverage_cluster, r2, default_rate, feasibility, feasibility_reason, data_issues, n_input_rows, random_seed); out/analytic.csv; out/exclusions.csv; out/report.md (6 sections); out/analysis.py; out/claims.json.\n**Information rules:** only predictors known at end of t (leverage, cash_ratio, log_sales). rating_2025 is known in 2025 and built from realised defaults -> leakage, must be excluded and listed. Rows with missing default_next excluded and logged, never imputed. Only data/firm_panel.csv.\n**Invalidating:** using rating_2025; imputing outcomes; duplicate rows double counting; no observed outcomes.\nEOF\ncat > out/state/phases.json <<'EOF'\n{\"P0\":{\"status\":\"passed\",\"checks\":[\"TASK.md and README read; contract written\"],\"notes\":\"\"},\n\"P1\":{\"status\":\"running\",\"checks\":[],\"notes\":\"\"},\"P2\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\n\"P3\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P4\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"},\"P5\":{\"status\":\"pending\",\"checks\":[],\"notes\":\"\"}}\nEOF\necho \"- 2026-10-30 P0 passed; contract written.\" > out/state/log.md","description":"Write contract, phase ledger, log"}}]}}39{"type":"api-request","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","id":"4c35e494-206b-4a70-9552-2a02ec242f88","timestamp":"2026-10-30T04:46:26.129Z","version":"2.1.314","querySource":"sdk","shapeHash":"da6115fd787775b0904fc3311407834e54e877c29e199ba0d9f9f497fa30ef0f","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":"e1065b1c-6457-44ef-b141-7f3fd31127e7","keep":10,"tail":["de9ad0d046f6fa6029e9aecc9a89135126c61d2e2901461a75de89a73890e9ec","2c9793403d60b37c887f611b3fee9ce722132023182f1dfe311f484dc64fb57e"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",11,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"523c07c59af997e605796a2e36c064b958dad82a50beb10b724427e43ee37b03"}40{"parentUuid":"79bbb0fc-b1b5-42b8-9ec4-4f95ffb21554","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBYxpzUESYDSekErP7","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01TTJcj7Dm8Q6wwqqhGokMs6","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n- `data/firm_panel.csv` SHA-256 a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f; 2140 data rows, 7 columns.\n- `data/README.md` SHA-256 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 (codebook).\n- Schema: firm_id str (300 distinct, all match F###), year int 2016–2022, leverage [0.02,0.95], cash_ratio [0.0046,0.7406], log_sales [1.29,8.88] (log thousand USD), rating_2025 A/B/C/D (constant within firm), default_next 0/1 with 307 missing.\n- Duplicates: 40 rows are exact duplicates of another row (80 rows involved in 40 duplicated firm_id-year keys; all duplicate pairs identical on every column). After dropping: 2100 rows = 300 firms x 7 years, balanced.\n- Missing outcome: all 2022 rows (300 after dedup; 307 before) have empty default_next (outcome year 2023 beyond data cut). No other missing values.\n- Leakage: rating_2025 is assigned in 2025 using realised default history through 2022 -> violates info rule. Crosstab confirms: ratings A/B/C have zero defaults; all defaults are rated D. Excluded from predictors.\n- Anomalies noted: leverage bounded exactly at 0.02 and 0.95 (possible truncation/winsorisation in the generator; kept as is). Some firms have multiple default_next=1 years (up to 4); codebook does not define default as absorbing, so kept.\nEOF\necho \"- 2026-10-30 P1 audit done: 40 exact dup rows, 2022 outcomes missing, rating_2025 leaks.\" >> out/state/log.md","description":"Write data audit"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":868,"cache_read_input_tokens":9929,"output_tokens":734,"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":868},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpBYkvd1zxm1Mcf8sf4","requestRef":"4c35e494-206b-4a70-9552-2a02ec242f88","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"560b312c-25e7-4cb8-8162-48ef5cda6879","timestamp":"2026-10-30T04:46:33.586Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}41{"parentUuid":"560b312c-25e7-4cb8-8162-48ef5cda6879","isSidechain":false,"promptId":"b00a89ff-be83-4cfc-b9dd-ba9d011937c4","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01TTJcj7Dm8Q6wwqqhGokMs6","type":"tool_result","content":"(Bash completed with no output)","is_error":false}]},"uuid":"34a47171-dccf-4c0b-9964-c60976736e95","timestamp":"2026-10-30T04:46:33.633Z","toolUseResult":{"stdout":"","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"560b312c-25e7-4cb8-8162-48ef5cda6879","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}42{"type":"api-request-blob","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","hash":"00a8551b0883337df4e17cc2e75629923576aabee80d25c3b6f93debbfdf7f4b","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_01TTJcj7Dm8Q6wwqqhGokMs6","name":"Bash","input":{"command":"cat > out/state/data_audit.md <<'EOF'\n# Data audit\n- `data/firm_panel.csv` SHA-256 a1f1af88b1ab05900155991f2468cce09695af978923e5bc3ef9c28d45683b6f; 2140 data rows, 7 columns.\n- `data/README.md` SHA-256 26573e982ab6ca9e7f90d2b89bd2878aa6a670f90da21496bff787b59216b9a2 (codebook).\n- Schema: firm_id str (300 distinct, all match F###), year int 2016–2022, leverage [0.02,0.95], cash_ratio [0.0046,0.7406], log_sales [1.29,8.88] (log thousand USD), rating_2025 A/B/C/D (constant within firm), default_next 0/1 with 307 missing.\n- Duplicates: 40 rows are exact duplicates of another row (80 rows involved in 40 duplicated firm_id-year keys; all duplicate pairs identical on every column). After dropping: 2100 rows = 300 firms x 7 years, balanced.\n- Missing outcome: all 2022 rows (300 after dedup; 307 before) have empty default_next (outcome year 2023 beyond data cut). No other missing values.\n- Leakage: rating_2025 is assigned in 2025 using realised default history through 2022 -> violates info rule. Crosstab confirms: ratings A/B/C have zero defaults; all defaults are rated D. Excluded from predictors.\n- Anomalies noted: leverage bounded exactly at 0.02 and 0.95 (possible truncation/winsorisation in the generator; kept as is). Some firms have multiple default_next=1 years (up to 4); codebook does not define default as absorbing, so kept.\nEOF\necho \"- 2026-10-30 P1 audit done: 40 exact dup rows, 2022 outcomes missing, rating_2025 leaks.\" >> out/state/log.md","description":"Write data audit"}}]}}43{"type":"api-request","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","id":"9282886f-58ea-4add-8ac0-0779028a2e1a","timestamp":"2026-10-30T04:46:33.636Z","version":"2.1.314","querySource":"sdk","shapeHash":"da6115fd787775b0904fc3311407834e54e877c29e199ba0d9f9f497fa30ef0f","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":"4c35e494-206b-4a70-9552-2a02ec242f88","keep":12,"tail":["00a8551b0883337df4e17cc2e75629923576aabee80d25c3b6f93debbfdf7f4b","1e026d62a890859e701f5d12458474319c73f5c881c60c1b133ed5bff04612f4"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",13,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"988b2c4a21d4ed65dbba78004e530e87f7b59a2f04edbf21aea971452d35dbba"}44{"parentUuid":"34a47171-dccf-4c0b-9964-c60976736e95","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpC7AK5ZX6Fdi7CGkEz","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_014nBYU8PUiAXE53s4WuB42F","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting LPM of next-year default on leverage.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nIN = \"data/firm_panel.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(IN, dtype={\"firm_id\": str})\nn_raw = len(raw)\nled = []\ndef step(name, rule, before, after, why):\n led.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\n# Step 1: drop leaking column (no rows removed)\ndf = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"remove column rating_2025 (known only in 2025)\", n_raw, len(df),\n \"Final-vintage rating built from realised defaults through 2022; not known at end of t (look-ahead leakage).\")\n# Step 2: exact duplicate rows\nb = len(df)\ndup_full = int(raw.duplicated().sum())\ndf = df.loc[~raw.duplicated()].copy()\nassert not df.duplicated([\"firm_id\", \"year\"]).any()\nstep(\"2_drop_exact_duplicates\", \"drop rows identical on all columns to an earlier row\", b, len(df),\n \"Assembly error: duplicated firm-year records; keep one copy per firm-year.\")\n# Step 3: unobserved outcome\nb = len(df)\nn_missing_y = int(df[\"default_next\"].isna().sum())\nyears_missing = sorted(df.loc[df[\"default_next\"].isna(), \"year\"].unique().tolist())\ndf = df.dropna(subset=[\"default_next\"]).copy()\nstep(\"3_drop_unobserved_outcome\", \"drop rows with empty default_next\", b, len(df),\n \"Outcome year beyond data cut (end 2022); outcomes are not imputed.\")\n# Step 4: missing predictors (none expected)\npreds = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nb = len(df)\ndf = df.dropna(subset=preds).copy()\nstep(\"4_drop_missing_predictors\", \"drop rows with any missing predictor\", b, len(df), \"Complete-case for regressors.\")\n\ndf[\"default_next\"] = df[\"default_next\"].astype(int)\ndf = df.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\ndf[[\"firm_id\", \"year\"] + preds + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(led).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nX = sm.add_constant(df[preds]); y = df[\"default_next\"]\ngroups = pd.factorize(df[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nfit_hc = sm.OLS(y, X).fit(cov_type=\"HC1\")\n\nres = {\n \"task_id\": \"F1-conf-03\",\n \"n_analysed\": int(len(df)),\n \"n_firms\": int(df[\"firm_id\"].nunique()),\n \"predictors_used\": preds,\n \"variables_excluded\": [\n {\"variable\": \"rating_2025\", \"reason\": \"Known only in 2025 and constructed from realised default history through 2022 and sample-average leverage; violates end-of-year-t availability rule (outcome leakage).\"},\n {\"variable\": \"default_next\", \"reason\": \"Outcome variable (known end of t+1); dependent variable, not a predictor.\"},\n {\"variable\": \"firm_id\", \"reason\": \"Identifier; used only for clustering standard errors, not as a regressor.\"},\n {\"variable\": \"year\", \"reason\": \"Origin-year index; not part of the specified model, used only to identify firm-years.\"},\n ],\n \"beta_leverage\": float(fit.params[\"leverage\"]),\n \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n \"r2\": float(fit.rsquared),\n \"default_rate\": float(y.mean()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{dup_full} rows are exact duplicates of another row (duplicated firm_id-year keys, identical on all columns).\",\n \"handling\": \"Dropped duplicate copies, keeping one row per firm-year (exclusion step 2).\"},\n {\"issue\": f\"default_next empty for {n_missing_y} de-duplicated rows, all in origin year(s) {years_missing} (outcome beyond the end-2022 data cut).\",\n \"handling\": \"Excluded from estimation; not imputed (exclusion step 3).\"},\n {\"issue\": \"rating_2025 is a final-vintage field built from realised defaults (all defaults carry rating D) - look-ahead leakage.\",\n \"handling\": \"Column removed from analytic dataset and listed in variables_excluded (exclusion step 1).\"},\n {\"issue\": \"Some firms have default_next = 1 in several years (default not absorbing in the panel).\",\n \"handling\": \"Kept as recorded; codebook does not define default as absorbing. Noted as a limitation.\"},\n {\"issue\": \"leverage bounded exactly at 0.02 and 0.95 (possible truncation in the source).\",\n \"handling\": \"Kept as recorded; noted as a limitation.\"},\n ],\n \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"supplementary\": {\n \"intercept\": float(fit.params[\"const\"]),\n \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n \"se_cash_ratio_cluster\": float(fit.bse[\"cash_ratio\"]),\n \"se_log_sales_cluster\": float(fit.bse[\"log_sales\"]),\n \"ci95_leverage_cluster\": [float(v) for v in fit.conf_int().loc[\"leverage\"]],\n \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n \"se_leverage_hc1\": float(fit_hc.bse[\"leverage\"]),\n \"n_clusters\": int(len(np.unique(groups))),\n \"cluster_se_note\": \"statsmodels cluster-robust covariance with default small-sample correction (G/(G-1))*((N-1)/(N-K)); CI uses normal critical values.\",\n },\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(f\"n={res['n_analysed']} firms={res['n_firms']} beta_lev={res['beta_leverage']:.4f} se={res['se_leverage_cluster']:.4f} r2={res['r2']:.4f} rate={res['default_rate']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -80 && head -3 out/analytic.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":749,"cache_read_input_tokens":10797,"output_tokens":2535,"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":749},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpC6qUn9XwdW4iT4GEM","requestRef":"9282886f-58ea-4add-8ac0-0779028a2e1a","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"316404ea-0342-4ef5-8fc6-234fe1ae8f14","timestamp":"2026-10-30T04:46:53.553Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}45{"parentUuid":"316404ea-0342-4ef5-8fc6-234fe1ae8f14","isSidechain":false,"promptId":"b00a89ff-be83-4cfc-b9dd-ba9d011937c4","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_014nBYU8PUiAXE53s4WuB42F","type":"tool_result","content":"n=1800 firms=300 beta_lev=0.2638 se=0.0408 r2=0.0349 rate=0.1211\nstep,rule,n_before,n_after,n_removed,rationale\n1_drop_rating_2025,remove column rating_2025 (known only in 2025),2140,2140,0,Final-vintage rating built from realised defaults through 2022; not known at end of t (look-ahead leakage).\n2_drop_exact_duplicates,drop rows identical on all columns to an earlier row,2140,2100,40,Assembly error: duplicated firm-year records; keep one copy per firm-year.\n3_drop_unobserved_outcome,drop rows with empty default_next,2100,1800,300,Outcome year beyond data cut (end 2022); outcomes are not imputed.\n4_drop_missing_predictors,drop rows with any missing predictor,1800,1800,0,Complete-case for regressors.\n{\n \"task_id\": \"F1-conf-03\",\n \"n_analysed\": 1800,\n \"n_firms\": 300,\n \"predictors_used\": [\n \"leverage\",\n \"cash_ratio\",\n \"log_sales\"\n ],\n \"variables_excluded\": [\n {\n \"variable\": \"rating_2025\",\n \"reason\": \"Known only in 2025 and constructed from realised default history through 2022 and sample-average leverage; violates end-of-year-t availability rule (outcome leakage).\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"Outcome variable (known end of t+1); dependent variable, not a predictor.\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"Identifier; used only for clustering standard errors, not as a regressor.\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"Origin-year index; not part of the specified model, used only to identify firm-years.\"\n }\n ],\n \"beta_leverage\": 0.2637836102938267,\n \"se_leverage_cluster\": 0.0407569280434615,\n \"r2\": 0.03490564212216729,\n \"default_rate\": 0.12111111111111111,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"40 rows are exact duplicates of another row (duplicated firm_id-year keys, identical on all columns).\",\n \"handling\": \"Dropped duplicate copies, keeping one row per firm-year (exclusion step 2).\"\n },\n {\n \"issue\": \"default_next empty for 300 de-duplicated rows, all in origin year(s) [2022] (outcome beyond the end-2022 data cut).\",\n \"handling\": \"Excluded from estimation; not imputed (exclusion step 3).\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage field built from realised defaults (all defaults carry rating D) - look-ahead leakage.\",\n \"handling\": \"Column removed from analytic dataset and listed in variables_excluded (exclusion step 1).\"\n },\n {\n \"issue\": \"Some firms have default_next = 1 in several years (default not absorbing in the panel).\",\n \"handling\": \"Kept as recorded; codebook does not define default as absorbing. Noted as a limitation.\"\n },\n {\n \"issue\": \"leverage bounded exactly at 0.02 and 0.95 (possible truncation in the source).\",\n \"handling\": \"Kept as recorded; noted as a limitation.\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"supplementary\": {\n \"intercept\": 0.08997926409935522,\n \"beta_cash_ratio\": -0.18594837465723182,\n \"beta_log_sales\": -0.007342181807581363,\n \"se_cash_ratio_cluster\": 0.0588860837664576,\n \"se_log_sales_cluster\": 0.007626106514531905,\n \"ci95_leverage_cluster\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage_cluster\": 9.663918872288981e-11,\n \"se_leverage_hc1\": 0.03744708748506851,\n \"n_clusters\": 300,\n \"cluster_se_note\": \"statsmodels cluster-robust covariance with default small-sample correction (G/(G-1))*((N-1)/(N-K)); CI uses normal critical values.\"\n }\n}firm_id,year,leverage,cash_ratio,log_sales,default_next\nF001,2016,0.3269,0.1074,4.9304,0\nF001,2017,0.5682,0.3311,4.16,0","is_error":false}]},"uuid":"38a64d36-d8b1-4d36-996c-a047face78b5","timestamp":"2026-10-30T04:46:54.523Z","toolUseResult":{"stdout":"n=1800 firms=300 beta_lev=0.2638 se=0.0408 r2=0.0349 rate=0.1211\nstep,rule,n_before,n_after,n_removed,rationale\n1_drop_rating_2025,remove column rating_2025 (known only in 2025),2140,2140,0,Final-vintage rating built from realised defaults through 2022; not known at end of t (look-ahead leakage).\n2_drop_exact_duplicates,drop rows identical on all columns to an earlier row,2140,2100,40,Assembly error: duplicated firm-year records; keep one copy per firm-year.\n3_drop_unobserved_outcome,drop rows with empty default_next,2100,1800,300,Outcome year beyond data cut (end 2022); outcomes are not imputed.\n4_drop_missing_predictors,drop rows with any missing predictor,1800,1800,0,Complete-case for regressors.\n{\n \"task_id\": \"F1-conf-03\",\n \"n_analysed\": 1800,\n \"n_firms\": 300,\n \"predictors_used\": [\n \"leverage\",\n \"cash_ratio\",\n \"log_sales\"\n ],\n \"variables_excluded\": [\n {\n \"variable\": \"rating_2025\",\n \"reason\": \"Known only in 2025 and constructed from realised default history through 2022 and sample-average leverage; violates end-of-year-t availability rule (outcome leakage).\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"Outcome variable (known end of t+1); dependent variable, not a predictor.\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"Identifier; used only for clustering standard errors, not as a regressor.\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"Origin-year index; not part of the specified model, used only to identify firm-years.\"\n }\n ],\n \"beta_leverage\": 0.2637836102938267,\n \"se_leverage_cluster\": 0.0407569280434615,\n \"r2\": 0.03490564212216729,\n \"default_rate\": 0.12111111111111111,\n \"feasibility\": \"complete\",\n \"feasibility_reason\": null,\n \"data_issues\": [\n {\n \"issue\": \"40 rows are exact duplicates of another row (duplicated firm_id-year keys, identical on all columns).\",\n \"handling\": \"Dropped duplicate copies, keeping one row per firm-year (exclusion step 2).\"\n },\n {\n \"issue\": \"default_next empty for 300 de-duplicated rows, all in origin year(s) [2022] (outcome beyond the end-2022 data cut).\",\n \"handling\": \"Excluded from estimation; not imputed (exclusion step 3).\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage field built from realised defaults (all defaults carry rating D) - look-ahead leakage.\",\n \"handling\": \"Column removed from analytic dataset and listed in variables_excluded (exclusion step 1).\"\n },\n {\n \"issue\": \"Some firms have default_next = 1 in several years (default not absorbing in the panel).\",\n \"handling\": \"Kept as recorded; codebook does not define default as absorbing. Noted as a limitation.\"\n },\n {\n \"issue\": \"leverage bounded exactly at 0.02 and 0.95 (possible truncation in the source).\",\n \"handling\": \"Kept as recorded; noted as a limitation.\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"supplementary\": {\n \"intercept\": 0.08997926409935522,\n \"beta_cash_ratio\": -0.18594837465723182,\n \"beta_log_sales\": -0.007342181807581363,\n \"se_cash_ratio_cluster\": 0.0588860837664576,\n \"se_log_sales_cluster\": 0.007626106514531905,\n \"ci95_leverage_cluster\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage_cluster\": 9.663918872288981e-11,\n \"se_leverage_hc1\": 0.03744708748506851,\n \"n_clusters\": 300,\n \"cluster_se_note\": \"statsmodels cluster-robust covariance with default small-sample correction (G/(G-1))*((N-1)/(N-K)); CI uses normal critical values.\"\n }\n}firm_id,year,leverage,cash_ratio,log_sales,default_next\nF001,2016,0.3269,0.1074,4.9304,0\nF001,2017,0.5682,0.3311,4.16,0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"316404ea-0342-4ef5-8fc6-234fe1ae8f14","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}46{"type":"api-request-blob","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","hash":"1212b746364c74b218ef6f72b2ae31e82dbbeae7551fcb17d0ebae002eb747d8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_014nBYU8PUiAXE53s4WuB42F","name":"Bash","input":{"command":"cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting LPM of next-year default on leverage.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\"\"\"\nimport json, os\nimport numpy as np, pandas as pd\nimport statsmodels.api as sm\n\nIN = \"data/firm_panel.csv\"; OUT = \"out\"\nos.makedirs(OUT, exist_ok=True)\nraw = pd.read_csv(IN, dtype={\"firm_id\": str})\nn_raw = len(raw)\nled = []\ndef step(name, rule, before, after, why):\n led.append(dict(step=name, rule=rule, n_before=before, n_after=after, n_removed=before - after, rationale=why))\n\n# Step 1: drop leaking column (no rows removed)\ndf = raw.drop(columns=[\"rating_2025\"])\nstep(\"1_drop_rating_2025\", \"remove column rating_2025 (known only in 2025)\", n_raw, len(df),\n \"Final-vintage rating built from realised defaults through 2022; not known at end of t (look-ahead leakage).\")\n# Step 2: exact duplicate rows\nb = len(df)\ndup_full = int(raw.duplicated().sum())\ndf = df.loc[~raw.duplicated()].copy()\nassert not df.duplicated([\"firm_id\", \"year\"]).any()\nstep(\"2_drop_exact_duplicates\", \"drop rows identical on all columns to an earlier row\", b, len(df),\n \"Assembly error: duplicated firm-year records; keep one copy per firm-year.\")\n# Step 3: unobserved outcome\nb = len(df)\nn_missing_y = int(df[\"default_next\"].isna().sum())\nyears_missing = sorted(df.loc[df[\"default_next\"].isna(), \"year\"].unique().tolist())\ndf = df.dropna(subset=[\"default_next\"]).copy()\nstep(\"3_drop_unobserved_outcome\", \"drop rows with empty default_next\", b, len(df),\n \"Outcome year beyond data cut (end 2022); outcomes are not imputed.\")\n# Step 4: missing predictors (none expected)\npreds = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nb = len(df)\ndf = df.dropna(subset=preds).copy()\nstep(\"4_drop_missing_predictors\", \"drop rows with any missing predictor\", b, len(df), \"Complete-case for regressors.\")\n\ndf[\"default_next\"] = df[\"default_next\"].astype(int)\ndf = df.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\ndf[[\"firm_id\", \"year\"] + preds + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(led).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nX = sm.add_constant(df[preds]); y = df[\"default_next\"]\ngroups = pd.factorize(df[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nfit_hc = sm.OLS(y, X).fit(cov_type=\"HC1\")\n\nres = {\n \"task_id\": \"F1-conf-03\",\n \"n_analysed\": int(len(df)),\n \"n_firms\": int(df[\"firm_id\"].nunique()),\n \"predictors_used\": preds,\n \"variables_excluded\": [\n {\"variable\": \"rating_2025\", \"reason\": \"Known only in 2025 and constructed from realised default history through 2022 and sample-average leverage; violates end-of-year-t availability rule (outcome leakage).\"},\n {\"variable\": \"default_next\", \"reason\": \"Outcome variable (known end of t+1); dependent variable, not a predictor.\"},\n {\"variable\": \"firm_id\", \"reason\": \"Identifier; used only for clustering standard errors, not as a regressor.\"},\n {\"variable\": \"year\", \"reason\": \"Origin-year index; not part of the specified model, used only to identify firm-years.\"},\n ],\n \"beta_leverage\": float(fit.params[\"leverage\"]),\n \"se_leverage_cluster\": float(fit.bse[\"leverage\"]),\n \"r2\": float(fit.rsquared),\n \"default_rate\": float(y.mean()),\n \"feasibility\": \"complete\",\n \"feasibility_reason\": None,\n \"data_issues\": [\n {\"issue\": f\"{dup_full} rows are exact duplicates of another row (duplicated firm_id-year keys, identical on all columns).\",\n \"handling\": \"Dropped duplicate copies, keeping one row per firm-year (exclusion step 2).\"},\n {\"issue\": f\"default_next empty for {n_missing_y} de-duplicated rows, all in origin year(s) {years_missing} (outcome beyond the end-2022 data cut).\",\n \"handling\": \"Excluded from estimation; not imputed (exclusion step 3).\"},\n {\"issue\": \"rating_2025 is a final-vintage field built from realised defaults (all defaults carry rating D) - look-ahead leakage.\",\n \"handling\": \"Column removed from analytic dataset and listed in variables_excluded (exclusion step 1).\"},\n {\"issue\": \"Some firms have default_next = 1 in several years (default not absorbing in the panel).\",\n \"handling\": \"Kept as recorded; codebook does not define default as absorbing. Noted as a limitation.\"},\n {\"issue\": \"leverage bounded exactly at 0.02 and 0.95 (possible truncation in the source).\",\n \"handling\": \"Kept as recorded; noted as a limitation.\"},\n ],\n \"n_input_rows\": {\"firm_panel.csv\": int(n_raw)},\n \"random_seed\": None,\n \"supplementary\": {\n \"intercept\": float(fit.params[\"const\"]),\n \"beta_cash_ratio\": float(fit.params[\"cash_ratio\"]),\n \"beta_log_sales\": float(fit.params[\"log_sales\"]),\n \"se_cash_ratio_cluster\": float(fit.bse[\"cash_ratio\"]),\n \"se_log_sales_cluster\": float(fit.bse[\"log_sales\"]),\n \"ci95_leverage_cluster\": [float(v) for v in fit.conf_int().loc[\"leverage\"]],\n \"p_leverage_cluster\": float(fit.pvalues[\"leverage\"]),\n \"se_leverage_hc1\": float(fit_hc.bse[\"leverage\"]),\n \"n_clusters\": int(len(np.unique(groups))),\n \"cluster_se_note\": \"statsmodels cluster-robust covariance with default small-sample correction (G/(G-1))*((N-1)/(N-K)); CI uses normal critical values.\",\n },\n}\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(f\"n={res['n_analysed']} firms={res['n_firms']} beta_lev={res['beta_leverage']:.4f} se={res['se_leverage_cluster']:.4f} r2={res['r2']:.4f} rate={res['default_rate']:.4f}\")\nEOF\n~/wp29/venv/bin/python out/analysis.py && cat out/exclusions.csv && cat out/results.json | head -80 && head -3 out/analytic.csv","description":"Write and run analysis 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out/results.json /tmp/r1.json; rm out/results.json out/analytic.csv out/exclusions.csv; ~/wp29/venv/bin/python out/analysis.py >/dev/null; cmp /tmp/r1.json out/results.json && echo IDENTICAL\n~/wp29/venv/bin/python -c \"\nimport pandas as pd,numpy as np,json\nr=json.load(open('out/results.json'));a=pd.read_csv('out/analytic.csv');e=pd.read_csv('out/exclusions.csv')\nprint('rows',len(a)==r['n_analysed'],'firms',a.firm_id.nunique()==r['n_firms'],'cols',list(a.columns))\nprint('ledger',(e.n_before-e.n_removed==e.n_after).all(),(e.n_before.iloc[1:].values==e.n_after.iloc[:-1].values).all(),e.n_before.iloc[0]==2140,e.n_after.iloc[-1]==r['n_analysed'])\nprint('rate',a.default_next.mean()==r['default_rate'],'max year',a.year.max(),'dupkeys',a.duplicated(['firm_id','year']).sum())\nX=np.column_stack([np.ones(len(a)),a[['leverage','cash_ratio','log_sales']]]);y=a.default_next.values\nXtXi=np.linalg.inv(X.T@X);b=XtXi@X.T@y;u=y-X@b\nM=sum(np.outer(X[g].T@u[g],X[g].T@u[g]) for g in a.groupby('firm_id').indices.values())\nG=300;N,K=X.shape;V=XtXi@M@XtXi*G/(G-1)*(N-1)/(N-K)\nprint('manual b',b[1],'se',np.sqrt(V[1,1]),'r2',1-(u@u)/((y-y.mean())@(y-y.mean())))\nci=r['supplementary']['ci95_leverage_cluster'];print('ci contains',ci[0]<r['beta_leverage']<ci[1])\n\"","description":"Rerun reproducibility and consistency 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True firms True cols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next']\nledger True True True True\nrate True max year 2021 dupkeys 0\nmanual b 0.26378361029382646 se 0.04075692804346157 r2 0.034905642122167624\nci contains True","is_error":false}]},"uuid":"72563e13-6b01-47da-85d1-f2dd5862b646","timestamp":"2026-10-30T04:47:03.259Z","toolUseResult":{"stdout":"IDENTICAL\nrows True firms True cols ['firm_id', 'year', 'leverage', 'cash_ratio', 'log_sales', 'default_next']\nledger True True True True\nrate True max year 2021 dupkeys 0\nmanual b 0.26378361029382646 se 0.04075692804346157 r2 0.034905642122167624\nci contains 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a.groupby('firm_id').indices.values())\nG=300;N,K=X.shape;V=XtXi@M@XtXi*G/(G-1)*(N-1)/(N-K)\nprint('manual b',b[1],'se',np.sqrt(V[1,1]),'r2',1-(u@u)/((y-y.mean())@(y-y.mean())))\nci=r['supplementary']['ci95_leverage_cluster'];print('ci contains',ci[0]<r['beta_leverage']<ci[1])\n\"","description":"Rerun reproducibility and consistency 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> out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run (deleted results.json, analytic.csv, exclusions.csv; re-ran analysis.py): results.json byte-identical (`cmp`). PASS\n2. analytic.csv rows (1800) == n_analysed; distinct firms (300) == n_firms; columns = firm_id, year, leverage, cash_ratio, log_sales, default_next (no rating_2025). PASS\n3. Exclusion ledger: n_before - n_removed = n_after on every row; chained; starts at 2140 = n_input_rows; ends at 1800 = n_analysed. 2140 - 40 - 300 = 1800. PASS\n4. default_rate equals mean of default_next in analytic.csv. PASS\n5. Information timing: max origin year in sample 2021 (outcome 2022 <= data cut); no predictor known after end of t. PASS\n6. No duplicate firm-year keys in analytic sample. PASS\n7. Independent numpy re-computation of OLS beta, CR1 cluster SE and R2 matches statsmodels (0.26378, 0.04076, 0.03491). PASS\n8. SE > 0; 95% CI [0.184, 0.344] contains estimate. PASS\n9. Magnitude vs baseline: default_rate 0.121; beta 0.264 per unit leverage => moving leverage 0.2->0.6 changes predicted PD by ~0.105, plausible relative to baseline. PASS\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1012,"cache_read_input_tokens":15648,"output_tokens":638,"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":1012},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":0,"requestId":"req_011CgXpEHWUojqHe3vbJFJKv","requestRef":"608f65c6-391a-4029-baa6-24708e9c15e1","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"5e0273cc-53a5-490f-bd5a-e4c06a5269bc","timestamp":"2026-10-30T04:47:09.188Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}56{"parentUuid":"5e0273cc-53a5-490f-bd5a-e4c06a5269bc","isSidechain":false,"promptId":"b00a89ff-be83-4cfc-b9dd-ba9d011937c4","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_013cUybRp9GHX63v2BadkENY","type":"tool_result","content":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/analytic.csv: present\n [ok] file:out/exclusions.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:n_analysed: ok\n [ok] key:n_firms: ok\n [ok] key:predictors_used: ok\n [ok] key:variables_excluded: ok\n [ok] key:beta_leverage: ok\n [ok] key:se_leverage_cluster: ok\n [ok] key:r2: ok\n [ok] key:default_rate: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 4 steps consistent\n [ok] positive:se_leverage_cluster: 0.0407569280434615\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0161/out/state/gate_receipts/20261030T044710.159828Z.json\n20261030T044710.159828Z.json","is_error":false}]},"uuid":"7e966690-6a7d-4c40-818f-4a48e901b71f","timestamp":"2026-10-30T04:47:10.170Z","toolUseResult":{"stdout":"PGAR GATE pgar_gate/1.0 — FAILED (10 of 32 checks failed)\n [ok] file:out/results.json: present\n [FAIL] file:out/report.md: missing or empty\n [ok] file:out/analysis.py: present\n [FAIL] file:out/claims.json: missing or empty\n [ok] file:out/analytic.csv: present\n [ok] file:out/exclusions.csv: present\n [ok] results_json: parsed\n [ok] key:task_id: ok\n [ok] key:n_analysed: ok\n [ok] key:n_firms: ok\n [ok] key:predictors_used: ok\n [ok] key:variables_excluded: ok\n [ok] key:beta_leverage: ok\n [ok] key:se_leverage_cluster: ok\n [ok] key:r2: ok\n [ok] key:default_rate: ok\n [ok] key:feasibility: ok\n [ok] key:feasibility_reason: ok\n [ok] key:data_issues: ok\n [ok] key:n_input_rows: ok\n [ok] key:random_seed: ok\n [FAIL] section:Question: heading not found\n [FAIL] section:Data and exclusions: heading not found\n [FAIL] section:Method: heading not found\n [FAIL] section:Results: heading not found\n [FAIL] section:Uncertainty: heading not found\n [FAIL] section:Limitations and open issues: heading not found\n [FAIL] claims_json: out/claims.json missing\n [FAIL] phase_ledger: phases not terminal: ['P1', 'P2', 'P3', 'P4', 'P5']\n [ok] exclusion_ledger: 4 steps consistent\n [ok] positive:se_leverage_cluster: 0.0407569280434615\n [ok] reproducibility: regenerated in 0.9s; identical within tolerance\nreceipt: /Users/simon-pierreboucher/wp29/runs/RC0161/out/state/gate_receipts/20261030T044710.159828Z.json\n20261030T044710.159828Z.json","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"5e0273cc-53a5-490f-bd5a-e4c06a5269bc","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}57{"type":"api-request-blob","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","hash":"6c9ff1134d64d3158ff8c7c64c58166afeb737de1f5363f2930510e7dbb3eba8","message":{"role":"assistant","content":[{"type":"tool_use","id":"toolu_013cUybRp9GHX63v2BadkENY","name":"Bash","input":{"command":"cat > out/state/checks.md <<'EOF'\n# Verification checks (P4)\n1. Clean re-run (deleted results.json, analytic.csv, exclusions.csv; re-ran analysis.py): results.json byte-identical (`cmp`). PASS\n2. analytic.csv rows (1800) == n_analysed; distinct firms (300) == n_firms; columns = firm_id, year, leverage, cash_ratio, log_sales, default_next (no rating_2025). PASS\n3. Exclusion ledger: n_before - n_removed = n_after on every row; chained; starts at 2140 = n_input_rows; ends at 1800 = n_analysed. 2140 - 40 - 300 = 1800. PASS\n4. default_rate equals mean of default_next in analytic.csv. PASS\n5. Information timing: max origin year in sample 2021 (outcome 2022 <= data cut); no predictor known after end of t. PASS\n6. No duplicate firm-year keys in analytic sample. PASS\n7. Independent numpy re-computation of OLS beta, CR1 cluster SE and R2 matches statsmodels (0.26378, 0.04076, 0.03491). PASS\n8. SE > 0; 95% CI [0.184, 0.344] contains estimate. PASS\n9. Magnitude vs baseline: default_rate 0.121; beta 0.264 per unit leverage => moving leverage 0.2->0.6 changes predicted PD by ~0.105, plausible relative to baseline. PASS\nEOF\n~/wp29/venv/bin/python ~/wp29/gates/pgar_gate.py . ; ls out/state/gate_receipts","description":"Write checks and run gate"}}]}}58{"type":"api-request","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","id":"cb35b240-aead-42f6-b259-6a5b0d97eff0","timestamp":"2026-10-30T04:47:10.173Z","version":"2.1.314","querySource":"sdk","shapeHash":"da6115fd787775b0904fc3311407834e54e877c29e199ba0d9f9f497fa30ef0f","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":"608f65c6-391a-4029-baa6-24708e9c15e1","keep":18,"tail":["6c9ff1134d64d3158ff8c7c64c58166afeb737de1f5363f2930510e7dbb3eba8","60b138f541243953b0eb7a5af6d0093c604b0dd36410b22de24d785cf032e14d"],"cacheControl":[{"path":["system",0],"value":{"type":"ephemeral"}},{"path":["system",1],"value":{"type":"ephemeral"}},{"path":["messages",19,"content",0],"value":{"type":"ephemeral"}}],"requestDigest":"446df3f7902a40e38c7baa27d7ca709abdc6fb7ff6e8fa6c2065798f18f4de6e"}59{"parentUuid":"7e966690-6a7d-4c40-818f-4a48e901b71f","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEoW2Aa6h1dbaQdWcx","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":1361,"cache_read_input_tokens":16660,"output_tokens":1907,"output_tokens_details":{"thinking_tokens":29},"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":1361},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"thinkingDurationMs":291,"thinkingDisplay":"updates","apiBlockIndex":0,"requestId":"req_011CgXpEoE9vQv73YYPK7AqY","requestRef":"cb35b240-aead-42f6-b259-6a5b0d97eff0","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"2b8673a5-b986-4152-991b-fa8d7c50cc12","timestamp":"2026-10-30T04:47:11.813Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0161","sessionId":"f2d806c4-94bc-4c30-ad77-6c51bae951ed","version":"2.1.314","gitBranch":"HEAD"}60{"parentUuid":"2b8673a5-b986-4152-991b-fa8d7c50cc12","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEoW2Aa6h1dbaQdWcx","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01M327sKhged8bGvwydWrdnA","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Next-year default and leverage (information-respecting analytic dataset)\n\n## Question\nHow is a firm's leverage at the end of year t associated with default in year t+1, using only information available at the end of year t? Estimand: coefficient b1 on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e, OLS on firm-years with observed outcome, SEs clustered by firm. The data are simulated (synthetic), not observed.\n\n## Data and exclusions\n`data/firm_panel.csv` has 2140 data rows (`n_input_rows`). Steps (see `out/exclusions.csv`):\n1. Column `rating_2025` removed (0 rows): it is assigned in 2025 from the firm's realised default history through 2022, so it was not known at the end of year t (`variables_excluded`). In the data every default carries rating D and A/B/C firms never default, confirming the leakage.\n2. 40 rows that exactly duplicated another row (same firm-year, identical values) dropped → 2100 rows = 300 firms × 7 years (`data_issues`).\n3. 300 firm-years with empty `default_next` dropped (all origin year 2022; outcome year 2023 lies beyond the end-2022 data cut). No outcomes imputed.\n4. Missing predictors: none (0 removed).\n\nEstimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021. Predictors: `predictors_used` = leverage, cash_ratio, log_sales. firm_id and year are not regressors (firm_id is used only for clustering).\n\n## Method\nOLS of default_next on an intercept and the three predictors (statsmodels), with a cluster-robust covariance by `firm_id` (CR1, small-sample factor G/(G−1)·(N−1)/(N−K), 300 clusters). Baseline: unconditional default rate in the estimation sample (`default_rate`). No randomness (`random_seed` = null). Estimates were checked against an independent numpy computation (out/state/checks.md).\n\n## Results\n- Baseline default rate: `default_rate` = 0.121.\n- `beta_leverage` = 0.264: holding cash_ratio and log_sales fixed, a 0.1 increase in leverage (debt/assets) goes with about 2.6 percentage points higher probability of default next year.\n- `r2` = 0.035. Other coefficients, reported in `supplementary` (not required): cash_ratio −0.186, log_sales −0.0073.\n\n## Uncertainty\n`se_leverage_cluster` = 0.0408 (firm-clustered). The 95% CI is [0.184, 0.344] (`supplementary.ci95_leverage_cluster`), with p < 1e-9 (`supplementary.p_leverage_cluster`). The heteroskedasticity-robust SE without clustering is 0.0374 (`supplementary.se_leverage_hc1`), so clustering makes the SE somewhat larger.\n\n## Limitations and open issues\n- The result is an association, not a causal effect. The LPM can produce fitted probabilities outside [0,1]. R² is low, so leverage explains only a small share of the variation in default.\n- Some firms have default_next = 1 in more than one year. The codebook does not say that default is absorbing, so these rows were kept. If default should end a firm's time at risk, the sample definition would change.\n- Leverage is bounded exactly at 0.02 and 0.95, which suggests truncation in the source. Values were kept as recorded.\n- Duplicates were treated as assembly errors because the copies were identical on every column, so no conflicts had to be resolved.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimation sample has 1800 firm-years from 300 firms after removing 40 exact duplicates and 300 firm-years with unobserved outcomes from 2140 input rows.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates treated as assembly errors.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 violates the information-availability rule (known in 2025, built from realised defaults) and is excluded.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"Conditional on cash_ratio and log_sales, leverage is positively associated with next-year default: beta_leverage = 0.264 (cluster SE 0.041, 95% CI 0.184 to 0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"supplementary\"],\"status\":\"supported\",\"caveat\":\"Association only; linear probability model; synthetic data.\"},\n {\"id\":\"C4\",\"text\":\"The baseline default rate is 0.121 and the model R2 is 0.035.\",\"results_keys\":[\"default_rate\",\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\necho \"- 2026-10-30 P2 baseline default_rate; P3 analysis.py done; P4 checks pass, gate run 1 (P5 items missing as expected). 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Estimand: coefficient b1 on `leverage` in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e, OLS on firm-years with observed outcome, SEs clustered by firm. The data are simulated (synthetic), not observed.\n\n## Data and exclusions\n`data/firm_panel.csv` has 2140 data rows (`n_input_rows`). Steps (see `out/exclusions.csv`):\n1. Column `rating_2025` removed (0 rows): it is assigned in 2025 from the firm's realised default history through 2022, so it was not known at the end of year t (`variables_excluded`). In the data every default carries rating D and A/B/C firms never default, confirming the leakage.\n2. 40 rows that exactly duplicated another row (same firm-year, identical values) dropped → 2100 rows = 300 firms × 7 years (`data_issues`).\n3. 300 firm-years with empty `default_next` dropped (all origin year 2022; outcome year 2023 lies beyond the end-2022 data cut). No outcomes imputed.\n4. Missing predictors: none (0 removed).\n\nEstimation sample: `n_analysed` = 1800 firm-years, `n_firms` = 300, origin years 2016–2021. Predictors: `predictors_used` = leverage, cash_ratio, log_sales. firm_id and year are not regressors (firm_id is used only for clustering).\n\n## Method\nOLS of default_next on an intercept and the three predictors (statsmodels), with a cluster-robust covariance by `firm_id` (CR1, small-sample factor G/(G−1)·(N−1)/(N−K), 300 clusters). Baseline: unconditional default rate in the estimation sample (`default_rate`). No randomness (`random_seed` = null). Estimates were checked against an independent numpy computation (out/state/checks.md).\n\n## Results\n- Baseline default rate: `default_rate` = 0.121.\n- `beta_leverage` = 0.264: holding cash_ratio and log_sales fixed, a 0.1 increase in leverage (debt/assets) goes with about 2.6 percentage points higher probability of default next year.\n- `r2` = 0.035. Other coefficients, reported in `supplementary` (not required): cash_ratio −0.186, log_sales −0.0073.\n\n## Uncertainty\n`se_leverage_cluster` = 0.0408 (firm-clustered). The 95% CI is [0.184, 0.344] (`supplementary.ci95_leverage_cluster`), with p < 1e-9 (`supplementary.p_leverage_cluster`). The heteroskedasticity-robust SE without clustering is 0.0374 (`supplementary.se_leverage_hc1`), so clustering makes the SE somewhat larger.\n\n## Limitations and open issues\n- The result is an association, not a causal effect. The LPM can produce fitted probabilities outside [0,1]. R² is low, so leverage explains only a small share of the variation in default.\n- Some firms have default_next = 1 in more than one year. The codebook does not say that default is absorbing, so these rows were kept. If default should end a firm's time at risk, the sample definition would change.\n- Leverage is bounded exactly at 0.02 and 0.95, which suggests truncation in the source. Values were kept as recorded.\n- Duplicates were treated as assembly errors because the copies were identical on every column, so no conflicts had to be resolved.\n- Feasibility: complete (`feasibility`).\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"The estimation sample has 1800 firm-years from 300 firms after removing 40 exact duplicates and 300 firm-years with unobserved outcomes from 2140 input rows.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"Duplicates treated as assembly errors.\"},\n {\"id\":\"C2\",\"text\":\"rating_2025 violates the information-availability rule (known in 2025, built from realised defaults) and is excluded.\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"Conditional on cash_ratio and log_sales, leverage is positively associated with next-year default: beta_leverage = 0.264 (cluster SE 0.041, 95% CI 0.184 to 0.344).\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\",\"supplementary\"],\"status\":\"supported\",\"caveat\":\"Association only; linear probability model; synthetic data.\"},\n {\"id\":\"C4\",\"text\":\"The baseline default rate is 0.121 and the model R2 is 0.035.\",\"results_keys\":[\"default_rate\",\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\necho \"- 2026-10-30 P2 baseline default_rate; P3 analysis.py done; P4 checks pass, gate run 1 (P5 items missing as expected). 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Higher leverage at the end of year t goes with a higher chance of default in year t+1: the leverage coefficient is **0.264** (firm-clustered SE 0.041, 95% CI 0.184 to 0.344). In plain terms, 0.1 more debt-to-assets is associated with about 2.6 percentage points higher default probability the next year, holding cash ratio and log sales fixed. This is an association, not a causal effect, and the data are simulated.\n\nFor comparison, the overall default rate in the sample is 0.121. The model's R² is only 0.035, so leverage and the other two predictors explain a small share of who defaults. I checked the coefficient, standard error and R² against a separate hand calculation and they match. Re-running `out/analysis.py` from scratch gives identical results, and the final gate check passed with no failures.\n\n**How the sample was built (2140 rows read → 1800 used, 300 firms):**\n- **`rating_2025` left out:** it was assigned in 2025 using each firm's actual default history, so it wasn't known at the end of year t. The data confirm the leak: every default is rated D and no A, B or C firm ever defaults.\n- **40 duplicate rows removed:** each was an exact copy of another row, same firm, year and values.\n- **300 rows without an outcome removed:** these are all the 2022 rows, whose outcome year (2023) is after the data end. No outcomes were filled in.\n\n**Limitations:**\n- Some firms default in more than one year. The codebook doesn't say a default ends a firm's time in the panel, so I kept those rows; if it should, the sample would change.\n- Leverage sits exactly at 0.02 and 0.95 at its extremes, which suggests the source capped the values. 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