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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`). 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{"parentUuid":"85efdbf9-0551-425d-910b-92b936f084c1","isSidechain":false,"promptId":"f82d019b-695c-409d-a13b-3b56798626f6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_011LTfxnSUXrMrEB8n31Y9J8","type":"tool_result","content":"# 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 |\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\n(2140, 7)\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]\nfirm_id 0\nyear 0\nleverage 0\ncash_ratio 0\nlog_sales 0\nrating_2025 0\ndefault_next 307\ndtype: int64\n40 40\nyear\n2016 305\n2017 301\n2018 309\n2019 305\n2020 306\n2021 307\n2022 307\nName: count, dtype: int64\ndefault_next\n0.0 1609\nNaN 307\n1.0 224\nName: count, dtype: int64\n firm_id year leverage cash_ratio log_sales rating_2025 default_next\n0 F042 2022 0.5244 0.0709 6.0889 D NaN\n4 F062 2020 0.2496 0.2919 6.3207 B 0.0\n15 F042 2022 0.5244 0.0709 6.0889 D NaN\n17 F297 2018 0.6450 0.3127 4.4769 D 0.0\n35 F084 2020 0.1587 0.0206 5.6992 A 0.0\n36 F121 2019 0.4775 0.0808 5.2710 C 0.0\n50 F066 2021 0.4655 0.1123 3.2088 D 1.0\n62 F127 2022 0.3460 0.2264 6.2420 D NaN\n82 F040 2018 0.6918 0.2139 4.8758 D 1.0\n102 F025 2018 0.2454 0.2691 4.3037 D 0.0\n105 F176 2021 0.1279 0.2350 5.2493 A 0.0\n127 F065 2018 0.8433 0.6880 5.8157 D 0.0\n225 F109 2018 0.1243 0.1150 5.3471 A 0.0\n237 F040 2018 0.6918 0.2139 4.8758 D 1.0\n245 F076 2019 0.3285 0.3043 6.1376 D 0.0\n281 F080 2021 0.3278 0.1733 5.5272 A 0.0\n302 F128 2020 0.3966 0.0526 4.1044 D 0.0\n307 F133 2016 0.5009 0.1289 5.5273 D 1.0\n336 F024 2022 0.4509 0.2859 4.3565 D NaN\n396 F101 2020 0.5337 0.1910 5.7664 D 1.0","is_error":false}]},"uuid":"17c448cf-45b3-457c-b7d6-55fcac3c7298","timestamp":"2026-10-30T04:46:21.675Z","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 |\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\n(2140, 7)\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]\nfirm_id 0\nyear 0\nleverage 0\ncash_ratio 0\nlog_sales 0\nrating_2025 0\ndefault_next 307\ndtype: int64\n40 40\nyear\n2016 305\n2017 301\n2018 309\n2019 305\n2020 306\n2021 307\n2022 307\nName: count, dtype: int64\ndefault_next\n0.0 1609\nNaN 307\n1.0 224\nName: count, dtype: int64\n firm_id year leverage cash_ratio log_sales rating_2025 default_next\n0 F042 2022 0.5244 0.0709 6.0889 D NaN\n4 F062 2020 0.2496 0.2919 6.3207 B 0.0\n15 F042 2022 0.5244 0.0709 6.0889 D NaN\n17 F297 2018 0.6450 0.3127 4.4769 D 0.0\n35 F084 2020 0.1587 0.0206 5.6992 A 0.0\n36 F121 2019 0.4775 0.0808 5.2710 C 0.0\n50 F066 2021 0.4655 0.1123 3.2088 D 1.0\n62 F127 2022 0.3460 0.2264 6.2420 D NaN\n82 F040 2018 0.6918 0.2139 4.8758 D 1.0\n102 F025 2018 0.2454 0.2691 4.3037 D 0.0\n105 F176 2021 0.1279 0.2350 5.2493 A 0.0\n127 F065 2018 0.8433 0.6880 5.8157 D 0.0\n225 F109 2018 0.1243 0.1150 5.3471 A 0.0\n237 F040 2018 0.6918 0.2139 4.8758 D 1.0\n245 F076 2019 0.3285 0.3043 6.1376 D 0.0\n281 F080 2021 0.3278 0.1733 5.5272 A 0.0\n302 F128 2020 0.3966 0.0526 4.1044 D 0.0\n307 F133 2016 0.5009 0.1289 5.5273 D 1.0\n336 F024 2022 0.4509 0.2859 4.3565 D NaN\n396 F101 2020 0.5337 0.1910 5.7664 D 1.0","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"85efdbf9-0551-425d-910b-92b936f084c1","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0162","sessionId":"db95caf8-3a47-49af-b6d4-3a9d30f40784","version":"2.1.314","gitBranch":"HEAD"}
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{"parentUuid":"76b56018-4316-4f8a-83c0-f3277e6d8e76","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpBezUKHMdKEDrK4EGZ","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01F4jJ3Ym9nZGXKMezkg3GXX","name":"Bash","input":{"command":"mkdir -p out; cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting analytic dataset and LPM for next-year default.\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_read = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger, issues = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from firm_panel.csv\", n_read, n_read, \"all data rows (excluding header)\")\n\n# column exclusion: rating_2025 is known only in 2025 and built from realised defaults\nstep(\"1_drop_rating_2025\", \"drop column rating_2025 (known 2025 > end of year t)\", n_read, n_read,\n \"final-vintage field built from realised default history through 2022 and sample-average leverage; \"\n \"look-ahead / outcome leakage; column removal only\")\n\n# exact duplicate rows\nn_dup_exact = int(raw.duplicated().sum())\nd = raw.drop_duplicates().copy()\nstep(\"2_drop_exact_duplicates\", \"remove exact duplicate rows (keep first)\", n_read, len(d),\n \"assembly error: identical firm-year rows repeated\")\nif n_dup_exact:\n issues.append(dict(issue=f\"{n_dup_exact} exact duplicate rows (same firm_id-year, all fields identical)\",\n handling=\"removed, keeping one copy per firm-year\"))\nn_key_dup = int(d.duplicated([\"firm_id\", \"year\"]).sum())\nif n_key_dup: # conflicting duplicates (none expected after exact dedup check)\n issues.append(dict(issue=f\"{n_key_dup} conflicting firm-year duplicates\",\n handling=\"all copies of conflicting firm-years dropped\"))\n bad = d.duplicated([\"firm_id\", \"year\"], keep=False)\n b = len(d); d = d[~bad]\n step(\"2b_drop_conflicting_duplicates\", \"drop firm-years with conflicting rows\", b, len(d), \"cannot tell which row is correct\")\n\n# missing outcome\nb = len(d)\nmiss = d[\"default_next\"].isna()\nmiss_years = sorted(d.loc[miss, \"year\"].unique().tolist())\nd = d[~miss].copy()\nstep(\"3_drop_missing_outcome\", \"drop firm-years with default_next not observed\", b, len(d),\n f\"outcome year t+1 not observed at data cut (end 2022); missing years: {miss_years}; not imputed\")\nissues.append(dict(issue=f\"{int(miss.sum())} firm-years with missing default_next (origin years {miss_years}; \"\n \"outcome year 2023 beyond data cut)\", handling=\"excluded from estimation; not imputed\"))\n\n# missing predictors\nb = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_drop_missing_predictors\", \"drop rows with missing leverage/cash_ratio/log_sales\", b, len(d),\n \"complete-case on predictors\")\nif b != len(d):\n issues.append(dict(issue=f\"{b-len(d)} rows with missing predictors\", handling=\"dropped (complete case)\"))\n\nissues.append(dict(issue=\"rating_2025 is a final-vintage (2025) field derived from realised defaults through 2022 and average sample leverage\",\n handling=\"excluded from analytic dataset and model (information rule)\"))\nnd = d.groupby(\"firm_id\")[\"default_next\"].sum()\nissues.append(dict(issue=f\"default is not absorbing: {int((nd > 1).sum())} firms have default_next=1 in more than one year\",\n handling=\"kept as given (default treated as a repeatable annual event); noted as limitation\"))\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nd[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nX = sm.add_constant(d[PRED]); y = d[\"default_next\"]\ngroups = pd.factorize(d[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nres = dict(\n task_id=\"F1-conf-03\",\n n_analysed=int(len(d)), n_firms=int(d[\"firm_id\"].nunique()),\n predictors_used=PRED,\n variables_excluded=[\n dict(variable=\"rating_2025\", reason=\"known only in 2025; built from realised default history through 2022 and \"\n \"sample-average leverage, so it leaks future outcomes (violates end-of-year-t availability)\"),\n dict(variable=\"default_next\", reason=\"outcome variable (known end of t+1); used as dependent variable, not predictor\"),\n dict(variable=\"firm_id\", reason=\"identifier; used only for clustering\"),\n dict(variable=\"year\", reason=\"origin-year index; not in specified model\"),\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\", feasibility_reason=None,\n data_issues=issues,\n n_input_rows={\"firm_panel.csv\": int(n_read)},\n random_seed=None,\n _extra=dict(coef={k: float(v) for k, v in fit.params.items()},\n se_cluster={k: float(v) for k, v in fit.bse.items()},\n ci95_leverage=[float(x) for x in fit.conf_int().loc[\"leverage\"]],\n p_leverage=float(fit.pvalues[\"leverage\"]),\n cov_type=\"cluster by firm_id (statsmodels default small-sample correction)\"),\n)\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":724,"cache_read_input_tokens":5768,"output_tokens":2384,"output_tokens_details":{"thinking_tokens":42},"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":724},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpBej5y2pKAp6uS6bhp","requestRef":"c6101145-e2aa-461c-a9ec-8819ea8a3b4e","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"3ddf3e81-d1c7-4e3a-9e89-02f1227b8c0f","timestamp":"2026-10-30T04:46:46.811Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0162","sessionId":"db95caf8-3a47-49af-b6d4-3a9d30f40784","version":"2.1.314","gitBranch":"HEAD"}
{"parentUuid":"3ddf3e81-d1c7-4e3a-9e89-02f1227b8c0f","isSidechain":false,"promptId":"f82d019b-695c-409d-a13b-3b56798626f6","type":"user","message":{"role":"user","content":[{"tool_use_id":"toolu_01F4jJ3Ym9nZGXKMezkg3GXX","type":"tool_result","content":"{\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; built from realised default history through 2022 and sample-average leverage, so it leaks future outcomes (violates end-of-year-t availability)\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"outcome variable (known end of t+1); used as dependent variable, not predictor\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"identifier; used only for clustering\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"origin-year index; not in specified model\"\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 exact duplicate rows (same firm_id-year, all fields identical)\",\n \"handling\": \"removed, keeping one copy per firm-year\"\n },\n {\n \"issue\": \"300 firm-years with missing default_next (origin years [2022]; outcome year 2023 beyond data cut)\",\n \"handling\": \"excluded from estimation; not imputed\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage (2025) field derived from realised defaults through 2022 and average sample leverage\",\n \"handling\": \"excluded from analytic dataset and model (information rule)\"\n },\n {\n \"issue\": \"default is not absorbing: 56 firms have default_next=1 in more than one year\",\n \"handling\": \"kept as given (default treated as a repeatable annual event); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"_extra\": {\n \"coef\": {\n \"const\": 0.08997926409935522,\n \"leverage\": 0.2637836102938267,\n \"cash_ratio\": -0.18594837465723182,\n \"log_sales\": -0.007342181807581363\n },\n \"se_cluster\": {\n \"const\": 0.04351602070943679,\n \"leverage\": 0.0407569280434615,\n \"cash_ratio\": 0.0588860837664576,\n \"log_sales\": 0.007626106514531905\n },\n \"ci95_leverage\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage\": 9.663918872288981e-11,\n \"cov_type\": \"cluster by firm_id (statsmodels default small-sample correction)\"\n }\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from firm_panel.csv,2140,2140,0,all data rows (excluding header)\n1_drop_rating_2025,drop column rating_2025 (known 2025 > end of year t),2140,2140,0,final-vintage field built from realised default history through 2022 and sample-average leverage; look-ahead / outcome leakage; column removal only\n2_drop_exact_duplicates,remove exact duplicate rows (keep first),2140,2100,40,assembly error: identical firm-year rows repeated\n3_drop_missing_outcome,drop firm-years with default_next not observed,2100,1800,300,outcome year t+1 not observed at data cut (end 2022); missing years: [2022]; not imputed\n4_drop_missing_predictors,drop rows with missing leverage/cash_ratio/log_sales,1800,1800,0,complete-case on predictors","is_error":false}]},"uuid":"fd90d988-01a4-40b1-98c4-ba53342534be","timestamp":"2026-10-30T04:46:47.760Z","toolUseResult":{"stdout":"{\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; built from realised default history through 2022 and sample-average leverage, so it leaks future outcomes (violates end-of-year-t availability)\"\n },\n {\n \"variable\": \"default_next\",\n \"reason\": \"outcome variable (known end of t+1); used as dependent variable, not predictor\"\n },\n {\n \"variable\": \"firm_id\",\n \"reason\": \"identifier; used only for clustering\"\n },\n {\n \"variable\": \"year\",\n \"reason\": \"origin-year index; not in specified model\"\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 exact duplicate rows (same firm_id-year, all fields identical)\",\n \"handling\": \"removed, keeping one copy per firm-year\"\n },\n {\n \"issue\": \"300 firm-years with missing default_next (origin years [2022]; outcome year 2023 beyond data cut)\",\n \"handling\": \"excluded from estimation; not imputed\"\n },\n {\n \"issue\": \"rating_2025 is a final-vintage (2025) field derived from realised defaults through 2022 and average sample leverage\",\n \"handling\": \"excluded from analytic dataset and model (information rule)\"\n },\n {\n \"issue\": \"default is not absorbing: 56 firms have default_next=1 in more than one year\",\n \"handling\": \"kept as given (default treated as a repeatable annual event); noted as limitation\"\n }\n ],\n \"n_input_rows\": {\n \"firm_panel.csv\": 2140\n },\n \"random_seed\": null,\n \"_extra\": {\n \"coef\": {\n \"const\": 0.08997926409935522,\n \"leverage\": 0.2637836102938267,\n \"cash_ratio\": -0.18594837465723182,\n \"log_sales\": -0.007342181807581363\n },\n \"se_cluster\": {\n \"const\": 0.04351602070943679,\n \"leverage\": 0.0407569280434615,\n \"cash_ratio\": 0.0588860837664576,\n \"log_sales\": 0.007626106514531905\n },\n \"ci95_leverage\": [\n 0.18390149920815163,\n 0.34366572137950174\n ],\n \"p_leverage\": 9.663918872288981e-11,\n \"cov_type\": \"cluster by firm_id (statsmodels default small-sample correction)\"\n }\n}\nstep,rule,n_before,n_after,n_removed,rationale\n0_read,rows read from firm_panel.csv,2140,2140,0,all data rows (excluding header)\n1_drop_rating_2025,drop column rating_2025 (known 2025 > end of year t),2140,2140,0,final-vintage field built from realised default history through 2022 and sample-average leverage; look-ahead / outcome leakage; column removal only\n2_drop_exact_duplicates,remove exact duplicate rows (keep first),2140,2100,40,assembly error: identical firm-year rows repeated\n3_drop_missing_outcome,drop firm-years with default_next not observed,2100,1800,300,outcome year t+1 not observed at data cut (end 2022); missing years: [2022]; not imputed\n4_drop_missing_predictors,drop rows with missing leverage/cash_ratio/log_sales,1800,1800,0,complete-case on predictors","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"3ddf3e81-d1c7-4e3a-9e89-02f1227b8c0f","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0162","sessionId":"db95caf8-3a47-49af-b6d4-3a9d30f40784","version":"2.1.314","gitBranch":"HEAD"}
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cat > out/analysis.py <<'EOF'\n\"\"\"F1-conf-03: information-respecting analytic dataset and LPM for next-year default.\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_read = len(raw)\nPRED = [\"leverage\", \"cash_ratio\", \"log_sales\"]\nledger, issues = [], []\n\ndef step(name, rule, before, after, why):\n ledger.append(dict(step=name, rule=rule, n_before=before, n_after=after,\n n_removed=before - after, rationale=why))\n\nstep(\"0_read\", \"rows read from firm_panel.csv\", n_read, n_read, \"all data rows (excluding header)\")\n\n# column exclusion: rating_2025 is known only in 2025 and built from realised defaults\nstep(\"1_drop_rating_2025\", \"drop column rating_2025 (known 2025 > end of year t)\", n_read, n_read,\n \"final-vintage field built from realised default history through 2022 and sample-average leverage; \"\n \"look-ahead / outcome leakage; column removal only\")\n\n# exact duplicate rows\nn_dup_exact = int(raw.duplicated().sum())\nd = raw.drop_duplicates().copy()\nstep(\"2_drop_exact_duplicates\", \"remove exact duplicate rows (keep first)\", n_read, len(d),\n \"assembly error: identical firm-year rows repeated\")\nif n_dup_exact:\n issues.append(dict(issue=f\"{n_dup_exact} exact duplicate rows (same firm_id-year, all fields identical)\",\n handling=\"removed, keeping one copy per firm-year\"))\nn_key_dup = int(d.duplicated([\"firm_id\", \"year\"]).sum())\nif n_key_dup: # conflicting duplicates (none expected after exact dedup check)\n issues.append(dict(issue=f\"{n_key_dup} conflicting firm-year duplicates\",\n handling=\"all copies of conflicting firm-years dropped\"))\n bad = d.duplicated([\"firm_id\", \"year\"], keep=False)\n b = len(d); d = d[~bad]\n step(\"2b_drop_conflicting_duplicates\", \"drop firm-years with conflicting rows\", b, len(d), \"cannot tell which row is correct\")\n\n# missing outcome\nb = len(d)\nmiss = d[\"default_next\"].isna()\nmiss_years = sorted(d.loc[miss, \"year\"].unique().tolist())\nd = d[~miss].copy()\nstep(\"3_drop_missing_outcome\", \"drop firm-years with default_next not observed\", b, len(d),\n f\"outcome year t+1 not observed at data cut (end 2022); missing years: {miss_years}; not imputed\")\nissues.append(dict(issue=f\"{int(miss.sum())} firm-years with missing default_next (origin years {miss_years}; \"\n \"outcome year 2023 beyond data cut)\", handling=\"excluded from estimation; not imputed\"))\n\n# missing predictors\nb = len(d)\nd = d.dropna(subset=PRED)\nstep(\"4_drop_missing_predictors\", \"drop rows with missing leverage/cash_ratio/log_sales\", b, len(d),\n \"complete-case on predictors\")\nif b != len(d):\n issues.append(dict(issue=f\"{b-len(d)} rows with missing predictors\", handling=\"dropped (complete case)\"))\n\nissues.append(dict(issue=\"rating_2025 is a final-vintage (2025) field derived from realised defaults through 2022 and average sample leverage\",\n handling=\"excluded from analytic dataset and model (information rule)\"))\nnd = d.groupby(\"firm_id\")[\"default_next\"].sum()\nissues.append(dict(issue=f\"default is not absorbing: {int((nd > 1).sum())} firms have default_next=1 in more than one year\",\n handling=\"kept as given (default treated as a repeatable annual event); noted as limitation\"))\n\nd[\"default_next\"] = d[\"default_next\"].astype(int)\nd = d.sort_values([\"firm_id\", \"year\"]).reset_index(drop=True)\nd[[\"firm_id\", \"year\"] + PRED + [\"default_next\"]].to_csv(f\"{OUT}/analytic.csv\", index=False)\npd.DataFrame(ledger).to_csv(f\"{OUT}/exclusions.csv\", index=False)\n\nX = sm.add_constant(d[PRED]); y = d[\"default_next\"]\ngroups = pd.factorize(d[\"firm_id\"])[0]\nfit = sm.OLS(y, X).fit(cov_type=\"cluster\", cov_kwds={\"groups\": groups})\nres = dict(\n task_id=\"F1-conf-03\",\n n_analysed=int(len(d)), n_firms=int(d[\"firm_id\"].nunique()),\n predictors_used=PRED,\n variables_excluded=[\n dict(variable=\"rating_2025\", reason=\"known only in 2025; built from realised default history through 2022 and \"\n \"sample-average leverage, so it leaks future outcomes (violates end-of-year-t availability)\"),\n dict(variable=\"default_next\", reason=\"outcome variable (known end of t+1); used as dependent variable, not predictor\"),\n dict(variable=\"firm_id\", reason=\"identifier; used only for clustering\"),\n dict(variable=\"year\", reason=\"origin-year index; not in specified model\"),\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\", feasibility_reason=None,\n data_issues=issues,\n n_input_rows={\"firm_panel.csv\": int(n_read)},\n random_seed=None,\n _extra=dict(coef={k: float(v) for k, v in fit.params.items()},\n se_cluster={k: float(v) for k, v in fit.bse.items()},\n ci95_leverage=[float(x) for x in fit.conf_int().loc[\"leverage\"]],\n p_leverage=float(fit.pvalues[\"leverage\"]),\n cov_type=\"cluster by firm_id (statsmodels default small-sample correction)\"),\n)\nwith open(f\"{OUT}/results.json\", \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps(res, indent=2))\nEOF\n~/wp29/venv/bin/python out/analysis.py; cat out/exclusions.csv"}}]}}
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{"parentUuid":"15e4f627-faf8-4ec5-ad2a-a0671f9f4200","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpDA3UrzfdLWcwEdNnK","type":"message","role":"assistant","content":[{"type":"tool_use","id":"toolu_01Lbu87Xsrg3vhj8autdvx4p","name":"Bash","input":{"command":"cat > out/report.md <<'EOF'\n# F1-conf-03 — Leverage at end of year t and default in year t+1\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: the coefficient b1 in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e, estimated by OLS with firm-clustered standard errors. The data are **simulated** (a synthetic panel), not observed firms.\n\n## Data and exclusions\n- I read 2,140 data rows from `data/firm_panel.csv`. The panel should have 300 firms × 7 years = 2,100 rows.\n- **Column excluded: `rating_2025`.** It only becomes known in 2025, and it was built from each firm's realised default history through 2022 and from its average leverage over the sample. It therefore contains future outcome information (leakage), so it breaks the end-of-year-t rule. I removed it from the analytic dataset and from the model. `firm_id` is used only for clustering, and `year` is not part of the specified model.\n- **Exact duplicate rows: 40 removed.** These are assembly errors: each firm-year is repeated with identical values in every field. After removing them there are 2,100 rows, with every firm present exactly 7 times. No firm-year had conflicting duplicates.\n- **Missing outcome: 300 removed.** Every origin-2022 firm-year has an empty `default_next`, because the outcome year (2023) falls after the end-2022 data cut. No outcome is missing for any other year. I did not impute these values.\n- **Missing predictors:** none.\n- **Estimation sample:** 1,800 firm-years (origin years 2016–2021) covering 300 firms. The step-by-step ledger is in `out/exclusions.csv`, and the sample itself is in `out/analytic.csv`.\n\n## Method\nOLS of `default_next` on an intercept, `leverage`, `cash_ratio` and `log_sales` (statsmodels). Standard errors are clustered by `firm_id` (300 clusters) using statsmodels' default small-sample correction. No randomness is involved, so no seed is used. Running `out/analysis.py` regenerates every output.\n\n## Results\n| term | coef | cluster SE |\n|---|---|---|\n| intercept | 0.0900 | 0.0435 |\n| leverage | **0.2638** | **0.0408** |\n| cash_ratio | −0.1859 | 0.0589 |\n| log_sales | −0.0073 | 0.0076 |\n\n- R² = 0.0349, n = 1,800 firm-years, 300 firms.\n- Baseline: the default rate in the sample is 0.1211.\n- Interpretation: holding cash ratio and log sales fixed, a leverage that is 0.10 higher (10 percentage points of debt/assets) goes with a next-year default probability about 2.6 percentage points higher.\n\n## Uncertainty\n- The 95% CI for the leverage coefficient (firm-clustered) is [0.184, 0.344], p ≈ 1e-10.\n- Clustering accounts for correlation within each firm across years. It does not account for common shocks within a year, and with only 6 origin years, clustering by year would not be reliable anyway.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage is correlated with omitted firm characteristics.\n- A linear-probability model can produce fitted values outside [0,1], and its errors are heteroskedastic (cluster-robust SEs address the latter).\n- Default is not absorbing in this extract: 56 firms have `default_next = 1` in more than one year. I kept every firm-year as given, treating default as a repeatable annual event. If default were meant to end a firm's history, the years after its first default would have to be dropped, and the estimate would change.\n- Dropping the 2022 origin year is required, because its outcome cannot be observed. It is not a selection on outcomes.\n- The data are synthetic, so the numbers say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Using only end-of-year-t information, the OLS coefficient on leverage is 0.264 (firm-clustered SE 0.041): 10 pp higher leverage is associated with ~2.6 pp higher next-year default probability.\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association in simulated data; LPM; default treated as repeatable event.\"},\n {\"id\":\"C2\",\"text\":\"The estimation sample has 1,800 firm-years from 300 firms after removing 40 exact duplicate rows and 300 firm-years (all origin 2022) with unobserved outcomes.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"rating_2025 was excluded because it is a 2025 field built from realised defaults and sample-average leverage (look-ahead leakage).\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The baseline next-year default rate is 12.1% and the model explains little variance (R2 = 0.035).\",\"results_keys\":[\"default_rate\",\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && ls out"},"caller":{"type":"direct"}}],"container":null,"stop_reason":"tool_use","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":3694,"cache_read_input_tokens":6492,"output_tokens":1938,"output_tokens_details":{"thinking_tokens":44},"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":3694},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpD9mrkwszfuvMAg9nN","requestRef":"05401f04-d863-4e39-9260-deeea9d41dcf","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"a9d494b2-89dd-4e29-96ff-f8498bfdf73d","timestamp":"2026-10-30T04:47:05.779Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0162","sessionId":"db95caf8-3a47-49af-b6d4-3a9d30f40784","version":"2.1.314","gitBranch":"HEAD"}
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Estimand: the coefficient b1 in the linear-probability model default_next = a + b1·leverage + b2·cash_ratio + b3·log_sales + e, estimated by OLS with firm-clustered standard errors. The data are **simulated** (a synthetic panel), not observed firms.\n\n## Data and exclusions\n- I read 2,140 data rows from `data/firm_panel.csv`. The panel should have 300 firms × 7 years = 2,100 rows.\n- **Column excluded: `rating_2025`.** It only becomes known in 2025, and it was built from each firm's realised default history through 2022 and from its average leverage over the sample. It therefore contains future outcome information (leakage), so it breaks the end-of-year-t rule. I removed it from the analytic dataset and from the model. `firm_id` is used only for clustering, and `year` is not part of the specified model.\n- **Exact duplicate rows: 40 removed.** These are assembly errors: each firm-year is repeated with identical values in every field. After removing them there are 2,100 rows, with every firm present exactly 7 times. No firm-year had conflicting duplicates.\n- **Missing outcome: 300 removed.** Every origin-2022 firm-year has an empty `default_next`, because the outcome year (2023) falls after the end-2022 data cut. No outcome is missing for any other year. I did not impute these values.\n- **Missing predictors:** none.\n- **Estimation sample:** 1,800 firm-years (origin years 2016–2021) covering 300 firms. The step-by-step ledger is in `out/exclusions.csv`, and the sample itself is in `out/analytic.csv`.\n\n## Method\nOLS of `default_next` on an intercept, `leverage`, `cash_ratio` and `log_sales` (statsmodels). Standard errors are clustered by `firm_id` (300 clusters) using statsmodels' default small-sample correction. No randomness is involved, so no seed is used. Running `out/analysis.py` regenerates every output.\n\n## Results\n| term | coef | cluster SE |\n|---|---|---|\n| intercept | 0.0900 | 0.0435 |\n| leverage | **0.2638** | **0.0408** |\n| cash_ratio | −0.1859 | 0.0589 |\n| log_sales | −0.0073 | 0.0076 |\n\n- R² = 0.0349, n = 1,800 firm-years, 300 firms.\n- Baseline: the default rate in the sample is 0.1211.\n- Interpretation: holding cash ratio and log sales fixed, a leverage that is 0.10 higher (10 percentage points of debt/assets) goes with a next-year default probability about 2.6 percentage points higher.\n\n## Uncertainty\n- The 95% CI for the leverage coefficient (firm-clustered) is [0.184, 0.344], p ≈ 1e-10.\n- Clustering accounts for correlation within each firm across years. It does not account for common shocks within a year, and with only 6 origin years, clustering by year would not be reliable anyway.\n\n## Limitations and open issues\n- This is an association, not a causal effect. Leverage is correlated with omitted firm characteristics.\n- A linear-probability model can produce fitted values outside [0,1], and its errors are heteroskedastic (cluster-robust SEs address the latter).\n- Default is not absorbing in this extract: 56 firms have `default_next = 1` in more than one year. I kept every firm-year as given, treating default as a repeatable annual event. If default were meant to end a firm's history, the years after its first default would have to be dropped, and the estimate would change.\n- Dropping the 2022 origin year is required, because its outcome cannot be observed. It is not a selection on outcomes.\n- The data are synthetic, so the numbers say nothing about real firms.\nEOF\ncat > out/claims.json <<'EOF'\n[\n {\"id\":\"C1\",\"text\":\"Using only end-of-year-t information, the OLS coefficient on leverage is 0.264 (firm-clustered SE 0.041): 10 pp higher leverage is associated with ~2.6 pp higher next-year default probability.\",\"results_keys\":[\"beta_leverage\",\"se_leverage_cluster\"],\"status\":\"supported\",\"caveat\":\"Association in simulated data; LPM; default treated as repeatable event.\"},\n {\"id\":\"C2\",\"text\":\"The estimation sample has 1,800 firm-years from 300 firms after removing 40 exact duplicate rows and 300 firm-years (all origin 2022) with unobserved outcomes.\",\"results_keys\":[\"n_analysed\",\"n_firms\",\"n_input_rows\",\"data_issues\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C3\",\"text\":\"rating_2025 was excluded because it is a 2025 field built from realised defaults and sample-average leverage (look-ahead leakage).\",\"results_keys\":[\"variables_excluded\",\"predictors_used\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"The baseline next-year default rate is 12.1% and the model explains little variance (R2 = 0.035).\",\"results_keys\":[\"default_rate\",\"r2\"],\"status\":\"supported\",\"caveat\":\"\"}\n]\nEOF\n~/wp29/venv/bin/python out/analysis.py >/dev/null && ls out"}}]}}
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{"parentUuid":"770798fc-3043-457a-9b85-65d0df46b8de","isSidechain":false,"message":{"model":"claude-opus-5-5","id":"msg_011CgXpEZBJcTuumhrHGWkFz","type":"message","role":"assistant","content":[{"type":"text","text":"I finished the task: the leverage coefficient is **0.264** (firm-clustered SE 0.041, 95% CI [0.184, 0.344]). Holding cash ratio and sales fixed, 10 percentage points more debt-to-assets goes with about a 2.6 percentage point higher chance of default the next year. I re-ran `out/analysis.py` and it regenerates the outputs; the data are simulated, so this says nothing about real firms.\n\n**How the sample was built:**\n- **Rows read:** 2,140.\n- **Duplicates:** 40 rows were exact copies of other firm-years, so I removed them. That leaves 2,100 rows, with each of the 300 firms present for all 7 years.\n- **Missing outcomes:** all 300 rows for 2022 have no next-year default recorded, because 2023 is after the data cut. I dropped them and did not fill them in.\n- **Missing predictors:** none.\n- **Estimation sample:** 1,800 firm-years (2016–2021) from 300 firms.\n- **Excluded column:** `rating_2025`. It was assigned in 2025 using each firm's actual defaults through 2022 and its average leverage, so it carries future information and fails the \"known at end of year t\" rule.\n\n**Other results:**\n\n| | value |\n|---|---|\n| Cash ratio coefficient | −0.186 (SE 0.059) |\n| Log sales coefficient | −0.007 (SE 0.008) |\n| R² | 0.035 |\n| Share of firm-years defaulting next year | 12.1% |\n\nThe task is marked complete.\n\n**Limitations:**\n- This is an association, not a causal effect, and a linear probability model can predict values outside 0–1.\n- 56 firms default in more than one year. I kept all their years, treating default as something that can recur. If a default was meant to end a firm's record, the years after its first default should be dropped and the estimate would change.\n\nFiles are in `out/`:\n- `results.json`: also includes an extra `_extra` key with all coefficients, the confidence interval and the p-value.\n- `analytic.csv`\n- `exclusions.csv`\n- `report.md`\n- `claims.json`\n- `analysis.py`"}],"container":null,"stop_reason":"end_turn","stop_sequence":null,"stop_details":null,"usage":{"input_tokens":2,"cache_creation_input_tokens":1976,"cache_read_input_tokens":10186,"output_tokens":750,"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":1976},"inference_geo":"global","iterations":[],"speed":"standard","fallback_credit":null},"input_transformations":[],"diagnostics":null,"context_management":null},"apiBlockIndex":1,"requestId":"req_011CgXpEYvBLgj2rNLT7igMx","requestRef":"579bfb29-cd40-46f3-a252-603d00d1b67f","requestedModel":"claude-opus-5-5","type":"assistant","uuid":"6b33bb76-420e-44eb-9746-15543e85c021","timestamp":"2026-10-30T04:47:14.423Z","effort":"medium","perTurnEffort":"medium","userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0162","sessionId":"db95caf8-3a47-49af-b6d4-3a9d30f40784","version":"2.1.314","gitBranch":"HEAD"}
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