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The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\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 | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\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.\ntotal 88\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 00:32 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:30 ..\n-rw-r--r-- 1 simon-pierreboucher staff 7014 oct. 30 00:31 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 15076 oct. 30 00:31 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1720 oct. 30 00:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 345 oct. 30 00:31 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4696 oct. 30 00:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2603 oct. 30 00:31 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:32 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:32 state\n{\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": \"2001-01\",\n \"last_month\": \"2026-09\",\n \"n_months_growth\": 308,\n \"n_months_missing_growth\": 1,\n \"n_analysed\": 308,\n \"mean_growth_12m\": 2.5741201901,\n \"mean_growth_12m_2001_2013\": 2.3612153974,\n \"mean_growth_12m_2014_2026\": 2.7926277404,\n \"growth_2014_06\": 2.0976342439,\n \"growth_2016_07\": 0.8683633431,\n \"growth_2020_05\": 0.1982013036,\n \"growth_2022_06\": 8.9793611031,\n \"growth_2026_09\": 3.7172508443,\n \"max_growth_12m\": 8.9793611031,\n \"max_growth_month\": \"2022-06\",\n \"min_growth_12m\": -1.9587610038,\n \"min_growth_month\": \"2009-07\",\n \"feasibility\": \"partial\",\n \"feasibility_reason\": \"g_t not computable for 1 month(s) in the window (2025-10) because the source reports no index level; these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": [\n {\n \"issue\": \"cpi_vintage_2026-10-28.csv: no index value reported for 2025-10 (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"\n },\n {\n \"issue\": \"Vintages overlap for 2014-01..2016-06 (30 months) with differing levels (max abs diff 0.455 index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: months absent: 2016-07\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: months beyond the sources' coverage: 2026-10 (values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: of 955 months shared with the authoritative splice, 151 have a different level and 30 a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"\n }\n ],\n \"n_input_rows\": {\n \"cpi_vintage_2016-07-15.csv\": 834,\n \"cpi_vintage_2026-10-28.csv\": 153,\n \"cpi_assembled.csv\": 957\n },\n \"random_seed\": null,\n \"n_months_2001_2013\": 156,\n \"n_months_2014_2026\": 152,\n \"missing_growth_months\": [\n \"2025-10\"\n ]\n}[\n {\"id\":\"C1\",\"text\":\"Mean 12-month CPI growth over 2001-01..2026-09 (308 computable months) is 2.574%.\",\"results_keys\":[\"mean_growth_12m\",\"n_months_growth\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Excludes 2025-10 (no index level).\"},\n {\"id\":\"C2\",\"text\":\"Mean growth was 2.361% in 2001-2013 (baseline) and 2.793% in 2014-01..2026-09.\",\"results_keys\":[\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_2001_2013\",\"n_months_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Sub-periods use different vintages; 2014-2026 mean excludes 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth was 8.979% in 2022-06; minimum was -1.959% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Growth in named months: 2014-06 2.098%, 2016-07 0.868%, 2020-05 0.198%, 2022-06 8.979%, 2026-09 3.717%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2022_06\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes 2026-vintage numerator with 2016-vintage denominator per the vintage rule.\"},\n {\"id\":\"C5\",\"text\":\"g_t is not computable for one month (2025-10) because the source reports no index level; the task is therefore partially feasible, with all summary estimands computed.\",\"results_keys\":[\"n_months_missing_growth\",\"missing_growth_months\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"cpi_assembled.csv contains assembly errors (old-vintage values in the overlap, missing 2016-07, one-month shift from 2016-08) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]","is_error":false}]},"uuid":"f8d023a7-ea70-4f96-8a7a-446f25b965cc","timestamp":"2026-10-30T04:32:49.518Z","toolUseResult":{"stdout":"# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Scientific question\nCompute the 12-month growth rate of the U.S. consumer price index (CPIAUCSL, seasonally adjusted) for every month from 2001-01 through 2026-09 and summarise it. The estimand for month t is g_t = 100 × (P_t / P_{t−12} − 1) in percent, where P is the index level taken from the most recent data vintage in which that month is available; the summary estimands are the mean of g_t over the window, the sub-period means for 2001-01 … 2013-12 and 2014-01 … 2026-09, a few named months, and the months of the maximum and minimum growth.\n\n## Inputs\n- `data/cpi_vintage_2016-07-15.csv` — observed data: CPIAUCSL as published by BLS/FRED in the ALFRED vintage of 2016-07-15, months 1947-01 to 2016-06 (834 rows). Columns `date` (ISO, first day of month), `cpi` (index, 1982–84 = 100). Source, retrieval date, license and SHA-256 in `data/README.md`.\n- `data/cpi_vintage_2026-10-28.csv` — observed data: CPIAUCSL in the ALFRED vintage of 2026-10-28, extract restricted to months 2014-01 to 2026-09 (153 rows). Same columns. An empty `cpi` cell means that the source reports no value for that month.\n- `data/cpi_assembled.csv` — a single file assembled from the two extracts above (columns `date`, `cpi`, `vintage`), intended to cover 1947-01 to 2026-09. It was assembled by hand and may contain assembly errors; the two vintage files are the authoritative inputs.\n\n## Information-availability rules\n- For each month, the index level to use is the value from the most recent vintage in which that month is available (the 2026-10-28 vintage where it covers the month, the 2016-07-15 vintage otherwise).\n- Growth rates must only be computed from index levels that exist in the sources; do not interpolate or impute missing index levels. Months for which g_t cannot be computed are reported as missing and counted.\n- Use only the files under `data/`.\n\n## Required outputs\n- `out/results.json` with the keys:\n\n| key | type | unit | meaning |\n|---|---|---|---|\n| `task_id` | string | — | \"F1-conf-01\" |\n| `first_growth_month` | string | YYYY-MM | first month of the window with a growth rate (expected \"2001-01\" if computable) |\n| `last_month` | string | YYYY-MM | last month of the window for which an index level exists in the sources |\n| `n_months_growth` | integer | months | months in the window with a computable g_t |\n| `n_months_missing_growth` | integer | months | months in the window without a computable g_t |\n| `n_analysed` | integer | months | same as `n_months_growth` |\n| `mean_growth_12m` | number | percent | mean of g_t over the window |\n| `mean_growth_12m_2001_2013` | number | percent | baseline: mean of g_t over 2001-01 … 2013-12 |\n| `mean_growth_12m_2014_2026` | number | percent | mean of g_t over 2014-01 … 2026-09 |\n| `growth_2014_06`, `growth_2016_07`, `growth_2020_05`, `growth_2022_06`, `growth_2026_09` | number | percent | g_t for those months |\n| `max_growth_12m`, `max_growth_month` | number, string | percent, YYYY-MM | maximum g_t in the window and its month |\n| `min_growth_12m`, `min_growth_month` | number, string | percent, YYYY-MM | minimum g_t in the window and its month |\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 | {file name: number of data rows read (excluding the header)} for every file under `data/` that was read |\n| `random_seed` | integer or null | — | seed used, if any |\n\n- `out/analytic.csv` — one row per month of the window with a computable growth rate, columns `date` (YYYY-MM-01), `cpi`, `cpi_lag12`, `vintage_used`, `growth_12m`; exactly `n_analysed` rows.\n- `out/exclusions.csv` — the exclusion ledger with columns `step`, `rule`, `n_before`, `n_after`, `n_removed`, `rationale`; one row per step from the rows you start from to the `n_analysed` rows of `out/analytic.csv` (`n_before − n_removed = n_after` on every row; each step's `n_before` equals the previous step's `n_after`).\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.\ntotal 88\ndrwxr-xr-x 10 simon-pierreboucher staff 320 oct. 30 00:32 .\ndrwxr-xr-x 7 simon-pierreboucher staff 224 oct. 30 00:30 ..\n-rw-r--r-- 1 simon-pierreboucher staff 7014 oct. 30 00:31 analysis.py\n-rw-r--r-- 1 simon-pierreboucher staff 15076 oct. 30 00:31 analytic.csv\n-rw-r--r-- 1 simon-pierreboucher staff 1720 oct. 30 00:32 claims.json\n-rw-r--r-- 1 simon-pierreboucher staff 345 oct. 30 00:31 exclusions.csv\n-rw-r--r-- 1 simon-pierreboucher staff 4696 oct. 30 00:32 report.md\n-rw-r--r-- 1 simon-pierreboucher staff 2603 oct. 30 00:31 results.json\ndrwxr-xr-x 2 simon-pierreboucher staff 64 oct. 30 00:32 review\ndrwxr-xr-x 8 simon-pierreboucher staff 256 oct. 30 00:32 state\n{\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": \"2001-01\",\n \"last_month\": \"2026-09\",\n \"n_months_growth\": 308,\n \"n_months_missing_growth\": 1,\n \"n_analysed\": 308,\n \"mean_growth_12m\": 2.5741201901,\n \"mean_growth_12m_2001_2013\": 2.3612153974,\n \"mean_growth_12m_2014_2026\": 2.7926277404,\n \"growth_2014_06\": 2.0976342439,\n \"growth_2016_07\": 0.8683633431,\n \"growth_2020_05\": 0.1982013036,\n \"growth_2022_06\": 8.9793611031,\n \"growth_2026_09\": 3.7172508443,\n \"max_growth_12m\": 8.9793611031,\n \"max_growth_month\": \"2022-06\",\n \"min_growth_12m\": -1.9587610038,\n \"min_growth_month\": \"2009-07\",\n \"feasibility\": \"partial\",\n \"feasibility_reason\": \"g_t not computable for 1 month(s) in the window (2025-10) because the source reports no index level; these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": [\n {\n \"issue\": \"cpi_vintage_2026-10-28.csv: no index value reported for 2025-10 (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"\n },\n {\n \"issue\": \"Vintages overlap for 2014-01..2016-06 (30 months) with differing levels (max abs diff 0.455 index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: months absent: 2016-07\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: months beyond the sources' coverage: 2026-10 (values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"\n },\n {\n \"issue\": \"cpi_assembled.csv: of 955 months shared with the authoritative splice, 151 have a different level and 30 a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"\n }\n ],\n \"n_input_rows\": {\n \"cpi_vintage_2016-07-15.csv\": 834,\n \"cpi_vintage_2026-10-28.csv\": 153,\n \"cpi_assembled.csv\": 957\n },\n \"random_seed\": null,\n \"n_months_2001_2013\": 156,\n \"n_months_2014_2026\": 152,\n \"missing_growth_months\": [\n \"2025-10\"\n ]\n}[\n {\"id\":\"C1\",\"text\":\"Mean 12-month CPI growth over 2001-01..2026-09 (308 computable months) is 2.574%.\",\"results_keys\":[\"mean_growth_12m\",\"n_months_growth\",\"n_analysed\"],\"status\":\"supported\",\"caveat\":\"Excludes 2025-10 (no index level).\"},\n {\"id\":\"C2\",\"text\":\"Mean growth was 2.361% in 2001-2013 (baseline) and 2.793% in 2014-01..2026-09.\",\"results_keys\":[\"mean_growth_12m_2001_2013\",\"mean_growth_12m_2014_2026\",\"n_months_2001_2013\",\"n_months_2014_2026\"],\"status\":\"supported\",\"caveat\":\"Sub-periods use different vintages; 2014-2026 mean excludes 2025-10.\"},\n {\"id\":\"C3\",\"text\":\"Maximum 12-month growth was 8.979% in 2022-06; minimum was -1.959% in 2009-07.\",\"results_keys\":[\"max_growth_12m\",\"max_growth_month\",\"min_growth_12m\",\"min_growth_month\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C4\",\"text\":\"Growth in named months: 2014-06 2.098%, 2016-07 0.868%, 2020-05 0.198%, 2022-06 8.979%, 2026-09 3.717%.\",\"results_keys\":[\"growth_2014_06\",\"growth_2016_07\",\"growth_2020_05\",\"growth_2022_06\",\"growth_2026_09\"],\"status\":\"supported\",\"caveat\":\"2014-06 mixes 2026-vintage numerator with 2016-vintage denominator per the vintage rule.\"},\n {\"id\":\"C5\",\"text\":\"g_t is not computable for one month (2025-10) because the source reports no index level; the task is therefore partially feasible, with all summary estimands computed.\",\"results_keys\":[\"n_months_missing_growth\",\"missing_growth_months\",\"feasibility\",\"feasibility_reason\"],\"status\":\"supported\",\"caveat\":\"\"},\n {\"id\":\"C6\",\"text\":\"cpi_assembled.csv contains assembly errors (old-vintage values in the overlap, missing 2016-07, one-month shift from 2016-08) and was not used for estimation.\",\"results_keys\":[\"data_issues\",\"n_input_rows\"],\"status\":\"supported\",\"caveat\":\"\"}\n]","stderr":"","interrupted":false,"isImage":false,"noOutputExpected":false},"sandboxed":false,"sourceToolAssistantUUID":"bcc3914a-ccc7-420c-a0e8-bb677d6ccef0","permissionDecision":{"decision":"accept","source":"config","reasonType":"rule","ruleSource":"cliArg"},"userType":"external","entrypoint":"sdk-cli","cwd":"/Users/simon-pierreboucher/wp29/runs/RC0100","sessionId":"2a1c8ef8-1350-4ede-be71-9ca8ee248868","version":"2.1.314","gitBranch":"HEAD"}15{"type":"api-request-blob","sessionId":"2a1c8ef8-1350-4ede-be71-9ca8ee248868","hash":"d68a4e63f7bcb99870dd1a8d941209ca2077bc8fe4a57abbaad4acff9635504c","message":{"role":"assistant","content":[{"type":"thinking","thinking":"","signature":"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"},{"type":"tool_use","id":"toolu_019cerSXYiSMzG1a3qctv2oY","name":"Bash","input":{"command":"cat TASK.md; 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writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = int((cmp_.vintage != cmp_.vintage_used).sum())\nval_wrong = int((~np.isclose(cmp_.cpi, cmp_.cpi_auth, equal_nan=True)).sum())\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nn2 = int(tab.cpi.notna().sum())\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=n2, n_removed=len(win) - n2,\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()]) or \"none\")),\n dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")),\n]\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=2))\nstep,rule,n_before,n_after,n_removed,rationale\n1,Window 2001-01..2026-09 (all calendar months),309,309,0,Start from every month of the target window\n2,Drop months with no index level P_t in the most recent covering vintage,309,308,1,No imputation allowed: 2025-10\n3,Drop months with no index level P_{t-12},308,308,0,No imputation allowed: none\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\nWhat was the 12-month growth rate g_t = 100 × (P_t / P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month from 2001-01 to 2026-09? P_t comes from the most recent vintage that covers month t: the 2026-10-28 vintage for 2014-01 … 2026-09, and the 2016-07-15 vintage for earlier months. The report also gives the mean over the window, the means for 2001–2013 and 2014–2026, five named months, and the extremes.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `cpi_vintage_2016-07-15.csv` has 834 rows covering 1947-01 … 2016-06. `cpi_vintage_2026-10-28.csv` has 153 rows covering 2014-01 … 2026-09, and its 2025-10 value is empty. `cpi_assembled.csv` has 957 rows. Hashes and the full audit are in `out/state/data_audit.md`.\n- **The hand-assembled file contains errors, so I did not use it for estimation** (`data_issues`):\n - For 2014-01 … 2016-06 it takes values from the *older* vintage, which contradicts the most-recent-vintage rule.\n - It omits 2016-07.\n - From 2016-08 onward every value sits one month later than its true month. As a result it contains a spurious 2026-10 row and shows the missing value at 2025-11 instead of 2025-10.\n - 151 of the 955 months it shares with the correct splice have a wrong level.\n\n I read it only to diagnose these problems. All levels come directly from the two vintage files.\n- The two vintages overlap for 2014-01 … 2016-06, and their levels differ by up to 0.455 index points. I used the 2026 vintage throughout the overlap (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`): the window has 309 months. One month is dropped because P_t is missing (2025-10). No month lacks P_{t−12}. That leaves `n_analysed` = 308 months, with `n_months_missing_growth` = 1 (`missing_growth_months` = [\"2025-10\"]).\n\n## Method\n1. Splice the two vintages month by month, taking the non-empty value from the most recent vintage that covers the month. I did no interpolation or imputation.\n2. Compute g_t = 100 × (P_t / P_{t−12} − 1) wherever both levels exist.\n3. Because of the splice rule, g_t for 2014-01 … 2014-12 divides a 2026-vintage level by a 2016-vintage (2013) level.\n4. Compute the means and extremes over the computable months only.\n\nThe whole procedure is deterministic, uses no random seed (`random_seed` = null), and is implemented in `out/analysis.py`.\n\n## Results\n- The first growth month is 2001-01 (`first_growth_month`). The last month with a level is 2026-09 (`last_month`). g_t can be computed for 308 months (`n_months_growth`).\n- Mean g_t over 2001-01 … 2026-09: **2.574%** (`mean_growth_12m`).\n- Baseline mean for 2001-01 … 2013-12 (156 months): **2.361%** (`mean_growth_12m_2001_2013`, `n_months_2001_2013`).\n- Mean for 2014-01 … 2026-09 (152 months): **2.793%** (`mean_growth_12m_2014_2026`, `n_months_2014_2026`).\n- Named months:\n - 2014-06: 2.098% (`growth_2014_06`)\n - 2016-07: 0.868% (`growth_2016_07`)\n - 2020-05: 0.198% (`growth_2020_05`)\n - 2022-06: 8.979% (`growth_2022_06`)\n - 2026-09: 3.717% (`growth_2026_09`)\n- Maximum: 8.979% in 2022-06 (`max_growth_12m`, `max_growth_month`). Minimum: −1.959% in 2009-07 (`min_growth_12m`, `min_growth_month`).\n- Feasibility is `partial` (`feasibility`, `feasibility_reason`). Every summary estimand was computed, but g_t for 2025-10 cannot be computed because the source has no index level for that month.\n\n## Uncertainty\nThese numbers are exact arithmetic on published index levels, so they have no sampling uncertainty and I report no confidence intervals. The main source of variation is data revision. Where the vintages overlap, levels differ by up to 0.455 index points (`data_issues`), so growth rates for 2014–2016 would change somewhat with a different vintage choice. I did not quantify that sensitivity because the task fixes the vintage rule. The 2001–2013 figures reflect the 2016 vintage and could differ in the current vintage, which the task does not provide.\n\n## Limitations and open issues\n- The 2025-10 level is missing, so g_t for that month is missing. It is left out of the overall mean and the 2014–2026 mean, and is not imputed (`n_months_missing_growth`).\n- The 2014 growth rates mix two vintages (2026 numerator, 2013 denominator from the 2016 vintage). The information rule requires this, but it can introduce a small seasonal-revision artefact.\n- `cpi_assembled.csv` was not used because of the assembly errors listed above. Anyone using it would get mis-dated values from 2016-08 onward.\n- The README's SHA-256 values refer to the raw downloads before reformatting, so I could not check them against the CSVs provided.\n# Data provenance — F1-conf-01\n\nAll three files concern the same series: **CPIAUCSL**, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, seasonally adjusted, index 1982–1984 = 100, monthly, produced by the U.S. Bureau of Labor Statistics and distributed by the Federal Reserve Bank of St. Louis (FRED / ALFRED). Terms: FRED/ALFRED terms of use (BLS data are in the public domain; attribution to BLS and FRED requested). Observed data, not simulated.\n\n| file | content | source URL | retrieved (UTC) | SHA-256 of the raw download |\n|---|---|---|---|---|\n| `cpi_vintage_2016-07-15.csv` | vintage of 2016-07-15 (values as published on that date), months 1947-01 … 2016-06, 834 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2016-07-15 | 2026-10-30T03:22Z | 74c7c4e0e3fa26059421d9620abe12d57df9a7786012acf39d694766cb854f6d |\n| `cpi_vintage_2026-10-28.csv` | vintage of 2026-10-28 (latest), extract restricted to months 2014-01 … 2026-09, 153 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2026-10-28&cosd=2014-01-01 | 2026-10-30T03:24Z | 480c9f9931a48e3194b73a8f6b65b325a51e185232456c1222a260dde64f9730 |\n| `cpi_assembled.csv` | a single monthly file assembled by the task author from the two extracts above, intended to cover 1947-01 … 2026-09 with a `vintage` column recording which extract each value came from | assembled from the two files above | 2026-10-30 | — |\n\nColumns of the vintage files: `date` (first day of the month, ISO), `cpi` (index level, 3 decimals). The assembled file adds `vintage` (the vintage date of the extract the value was taken from). The raw downloads were reformatted (two columns, ISO dates) without changing any value. An empty `cpi` cell means the source publishes no value for that month. The assembled file was produced by hand and may contain assembly errors.\ndata:\ncpi_assembled.csv\ncpi_vintage_2016-07-15.csv\ncpi_vintage_2026-10-28.csv\nREADME.md\n\nout/state:\nchecks.md\ncontract.md\ndata_audit.md\ngate_receipts\nlog.md\nphases.json","is_error":false}]},"uuid":"7890476b-6320-432b-b811-12d2e6d1bccb","timestamp":"2026-10-30T04:32:51.912Z","toolUseResult":{"stdout":"\"\"\"F1-conf-01: 12-month CPI inflation from two ALFRED vintages.\nRun from workspace root: ~/wp29/venv/bin/python out/analysis.py\nReads only data/; writes out/results.json, out/analytic.csv, out/exclusions.csv. No randomness.\"\"\"\nimport json, os\nimport numpy as np\nimport pandas as pd\n\nD, O = \"data\", \"out\"\nF_OLD, F_NEW, F_ASM = \"cpi_vintage_2016-07-15.csv\", \"cpi_vintage_2026-10-28.csv\", \"cpi_assembled.csv\"\nV_OLD, V_NEW = \"2016-07-15\", \"2026-10-28\"\nW0, W1 = pd.Timestamp(\"2001-01-01\"), pd.Timestamp(\"2026-09-01\")\n\nold = pd.read_csv(os.path.join(D, F_OLD), parse_dates=[\"date\"])\nnew = pd.read_csv(os.path.join(D, F_NEW), parse_dates=[\"date\"])\nasm = pd.read_csv(os.path.join(D, F_ASM), parse_dates=[\"date\"])\nn_input_rows = {F_OLD: len(old), F_NEW: len(new), F_ASM: len(asm)}\nissues = []\n\nfor name, df in [(F_OLD, old), (F_NEW, new)]:\n assert not df.date.duplicated().any(), name\n full = pd.date_range(df.date.min(), df.date.max(), freq=\"MS\")\n assert len(full) == len(df), f\"gap in {name}\"\n for d in df.loc[df.cpi.isna(), \"date\"]:\n issues.append({\"issue\": f\"{name}: no index value reported for {d:%Y-%m} (empty cpi cell)\",\n \"handling\": \"Month treated as unavailable in that vintage; the older vintage does not cover it, so P is missing. \"\n \"Not interpolated; g_t for this month (and for t+12 if inside the window) reported as missing.\"})\n\n# Splice: most recent vintage in which the month is available (non-empty value).\no = old.dropna(subset=[\"cpi\"]).assign(vintage_used=V_OLD).set_index(\"date\")\nn = new.dropna(subset=[\"cpi\"]).assign(vintage_used=V_NEW).set_index(\"date\")\nlev = pd.concat([n, o[~o.index.isin(n.index)]]).sort_index()\nov = old.merge(new, on=\"date\", suffixes=(\"_old\", \"_new\")).dropna()\nissues.append({\"issue\": f\"Vintages overlap for {ov.date.min():%Y-%m}..{ov.date.max():%Y-%m} ({len(ov)} months) with differing levels \"\n f\"(max abs diff {np.abs(ov.cpi_new-ov.cpi_old).max():.3f} index points; seasonal-factor/revision differences)\",\n \"handling\": \"2026-10-28 vintage used for all overlap months per the most-recent-vintage rule; growth for 2014-01..2014-12 \"\n \"uses a 2026-vintage numerator and a 2016-vintage (2013) denominator, as the rule implies.\"})\n\n# Diagnose the hand-assembled file against the authoritative splice\na = asm.set_index(\"date\")\nfull_rng = pd.date_range(\"1947-01-01\", \"2026-09-01\", freq=\"MS\")\nmissing_in_asm = [d for d in full_rng if d not in a.index]\nextra_in_asm = [d for d in a.index if d not in full_rng]\nif missing_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months absent: {', '.join(f'{d:%Y-%m}' for d in missing_in_asm)}\",\n \"handling\": \"Assembled file not used for estimation; levels taken from the two vintage files.\"})\nif extra_in_asm:\n issues.append({\"issue\": f\"{F_ASM}: months beyond the sources' coverage: {', '.join(f'{d:%Y-%m}' for d in extra_in_asm)} \"\n \"(values appear shifted one month later from 2016-07 onward)\",\n \"handling\": \"Assembled file not used for estimation.\"})\ncmp_ = a.join(lev[[\"cpi\", \"vintage_used\"]], how=\"inner\", rsuffix=\"_auth\")\nvint_wrong = int((cmp_.vintage != cmp_.vintage_used).sum())\nval_wrong = int((~np.isclose(cmp_.cpi, cmp_.cpi_auth, equal_nan=True)).sum())\nissues.append({\"issue\": f\"{F_ASM}: of {len(cmp_)} months shared with the authoritative splice, {val_wrong} have a different level and \"\n f\"{vint_wrong} a different vintage label (2014-01..2016-06 taken from the older vintage; 2016-07 dropped and later values mis-dated)\",\n \"handling\": \"Assembled file read for diagnostics only; all estimates use the vintage files directly.\"})\n\n# Growth rates\nlev = lev.reindex(pd.date_range(lev.index.min(), lev.index.max(), freq=\"MS\"))\nlag = lev.cpi.shift(12)\ng = 100 * (lev.cpi / lag - 1)\nwin = pd.date_range(W0, W1, freq=\"MS\")\ntab = pd.DataFrame({\"cpi\": lev.cpi, \"cpi_lag12\": lag, \"vintage_used\": lev.vintage_used, \"growth_12m\": g}).reindex(win)\nok = tab.growth_12m.notna()\nanalytic = tab[ok].copy()\nmissing_months = [f\"{d:%Y-%m}\" for d in tab.index[~ok]]\n\nn2 = int(tab.cpi.notna().sum())\nexcl = [\n dict(step=1, rule=\"Window 2001-01..2026-09 (all calendar months)\", n_before=len(win), n_after=len(win), n_removed=0,\n rationale=\"Start from every month of the target window\"),\n dict(step=2, rule=\"Drop months with no index level P_t in the most recent covering vintage\",\n n_before=len(win), n_after=n2, n_removed=len(win) - n2,\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.isna()]) or \"none\")),\n dict(step=3, rule=\"Drop months with no index level P_{t-12}\", n_before=n2, n_after=int(ok.sum()),\n n_removed=n2 - int(ok.sum()),\n rationale=\"No imputation allowed: \" + (\", \".join(f\"{d:%Y-%m}\" for d in tab.index[tab.cpi.notna() & tab.cpi_lag12.isna()]) or \"none\")),\n]\npd.DataFrame(excl).to_csv(os.path.join(O, \"exclusions.csv\"), index=False)\n\nout_a = analytic.reset_index().rename(columns={\"index\": \"date\"})\nout_a[\"date\"] = out_a.date.dt.strftime(\"%Y-%m-%d\")\nout_a.to_csv(os.path.join(O, \"analytic.csv\"), index=False, float_format=\"%.10g\")\n\ngs = analytic.growth_12m\ndef gm(m):\n v = tab.growth_12m.get(pd.Timestamp(m + \"-01\"))\n return None if v is None or pd.isna(v) else round(float(v), 10)\nr = lambda x: round(float(x), 10)\nlast_month = lev.cpi.loc[:W1].dropna().index.max()\nres = {\n \"task_id\": \"F1-conf-01\",\n \"first_growth_month\": f\"{gs.index.min():%Y-%m}\",\n \"last_month\": f\"{last_month:%Y-%m}\",\n \"n_months_growth\": int(ok.sum()),\n \"n_months_missing_growth\": int((~ok).sum()),\n \"n_analysed\": int(ok.sum()),\n \"mean_growth_12m\": r(gs.mean()),\n \"mean_growth_12m_2001_2013\": r(gs.loc[:\"2013-12-01\"].mean()),\n \"mean_growth_12m_2014_2026\": r(gs.loc[\"2014-01-01\":].mean()),\n \"growth_2014_06\": gm(\"2014-06\"), \"growth_2016_07\": gm(\"2016-07\"), \"growth_2020_05\": gm(\"2020-05\"),\n \"growth_2022_06\": gm(\"2022-06\"), \"growth_2026_09\": gm(\"2026-09\"),\n \"max_growth_12m\": r(gs.max()), \"max_growth_month\": f\"{gs.idxmax():%Y-%m}\",\n \"min_growth_12m\": r(gs.min()), \"min_growth_month\": f\"{gs.idxmin():%Y-%m}\",\n \"feasibility\": \"complete\" if ok.all() else \"partial\",\n \"feasibility_reason\": None if ok.all() else\n f\"g_t not computable for {len(missing_months)} month(s) in the window ({', '.join(missing_months)}) because the source reports no index level; \"\n \"these months are excluded from all means and extremes. All summary estimands were computed.\",\n \"data_issues\": issues,\n \"n_input_rows\": n_input_rows,\n \"random_seed\": None,\n \"n_months_2001_2013\": int(gs.loc[:\"2013-12-01\"].size),\n \"n_months_2014_2026\": int(gs.loc[\"2014-01-01\":].size),\n \"missing_growth_months\": missing_months,\n}\nwith open(os.path.join(O, \"results.json\"), \"w\") as f:\n json.dump(res, f, indent=2)\nprint(json.dumps({k: v for k, v in res.items() if k != \"data_issues\"}, indent=2))\nstep,rule,n_before,n_after,n_removed,rationale\n1,Window 2001-01..2026-09 (all calendar months),309,309,0,Start from every month of the target window\n2,Drop months with no index level P_t in the most recent covering vintage,309,308,1,No imputation allowed: 2025-10\n3,Drop months with no index level P_{t-12},308,308,0,No imputation allowed: none\n# F1-conf-01 — 12-month CPI inflation from two data vintages\n\n## Question\nWhat was the 12-month growth rate g_t = 100 × (P_t / P_{t−12} − 1) of CPIAUCSL (seasonally adjusted) for each month from 2001-01 to 2026-09? P_t comes from the most recent vintage that covers month t: the 2026-10-28 vintage for 2014-01 … 2026-09, and the 2016-07-15 vintage for earlier months. The report also gives the mean over the window, the means for 2001–2013 and 2014–2026, five named months, and the extremes.\n\n## Data and exclusions\n- Inputs (`n_input_rows`): `cpi_vintage_2016-07-15.csv` has 834 rows covering 1947-01 … 2016-06. `cpi_vintage_2026-10-28.csv` has 153 rows covering 2014-01 … 2026-09, and its 2025-10 value is empty. `cpi_assembled.csv` has 957 rows. Hashes and the full audit are in `out/state/data_audit.md`.\n- **The hand-assembled file contains errors, so I did not use it for estimation** (`data_issues`):\n - For 2014-01 … 2016-06 it takes values from the *older* vintage, which contradicts the most-recent-vintage rule.\n - It omits 2016-07.\n - From 2016-08 onward every value sits one month later than its true month. As a result it contains a spurious 2026-10 row and shows the missing value at 2025-11 instead of 2025-10.\n - 151 of the 955 months it shares with the correct splice have a wrong level.\n\n I read it only to diagnose these problems. All levels come directly from the two vintage files.\n- The two vintages overlap for 2014-01 … 2016-06, and their levels differ by up to 0.455 index points. I used the 2026 vintage throughout the overlap (`data_issues`).\n- Exclusion ledger (`out/exclusions.csv`): the window has 309 months. One month is dropped because P_t is missing (2025-10). No month lacks P_{t−12}. That leaves `n_analysed` = 308 months, with `n_months_missing_growth` = 1 (`missing_growth_months` = [\"2025-10\"]).\n\n## Method\n1. Splice the two vintages month by month, taking the non-empty value from the most recent vintage that covers the month. I did no interpolation or imputation.\n2. Compute g_t = 100 × (P_t / P_{t−12} − 1) wherever both levels exist.\n3. Because of the splice rule, g_t for 2014-01 … 2014-12 divides a 2026-vintage level by a 2016-vintage (2013) level.\n4. Compute the means and extremes over the computable months only.\n\nThe whole procedure is deterministic, uses no random seed (`random_seed` = null), and is implemented in `out/analysis.py`.\n\n## Results\n- The first growth month is 2001-01 (`first_growth_month`). The last month with a level is 2026-09 (`last_month`). g_t can be computed for 308 months (`n_months_growth`).\n- Mean g_t over 2001-01 … 2026-09: **2.574%** (`mean_growth_12m`).\n- Baseline mean for 2001-01 … 2013-12 (156 months): **2.361%** (`mean_growth_12m_2001_2013`, `n_months_2001_2013`).\n- Mean for 2014-01 … 2026-09 (152 months): **2.793%** (`mean_growth_12m_2014_2026`, `n_months_2014_2026`).\n- Named months:\n - 2014-06: 2.098% (`growth_2014_06`)\n - 2016-07: 0.868% (`growth_2016_07`)\n - 2020-05: 0.198% (`growth_2020_05`)\n - 2022-06: 8.979% (`growth_2022_06`)\n - 2026-09: 3.717% (`growth_2026_09`)\n- Maximum: 8.979% in 2022-06 (`max_growth_12m`, `max_growth_month`). Minimum: −1.959% in 2009-07 (`min_growth_12m`, `min_growth_month`).\n- Feasibility is `partial` (`feasibility`, `feasibility_reason`). Every summary estimand was computed, but g_t for 2025-10 cannot be computed because the source has no index level for that month.\n\n## Uncertainty\nThese numbers are exact arithmetic on published index levels, so they have no sampling uncertainty and I report no confidence intervals. The main source of variation is data revision. Where the vintages overlap, levels differ by up to 0.455 index points (`data_issues`), so growth rates for 2014–2016 would change somewhat with a different vintage choice. I did not quantify that sensitivity because the task fixes the vintage rule. The 2001–2013 figures reflect the 2016 vintage and could differ in the current vintage, which the task does not provide.\n\n## Limitations and open issues\n- The 2025-10 level is missing, so g_t for that month is missing. It is left out of the overall mean and the 2014–2026 mean, and is not imputed (`n_months_missing_growth`).\n- The 2014 growth rates mix two vintages (2026 numerator, 2013 denominator from the 2016 vintage). The information rule requires this, but it can introduce a small seasonal-revision artefact.\n- `cpi_assembled.csv` was not used because of the assembly errors listed above. Anyone using it would get mis-dated values from 2016-08 onward.\n- The README's SHA-256 values refer to the raw downloads before reformatting, so I could not check them against the CSVs provided.\n# Data provenance — F1-conf-01\n\nAll three files concern the same series: **CPIAUCSL**, Consumer Price Index for All Urban Consumers: All Items in U.S. City Average, seasonally adjusted, index 1982–1984 = 100, monthly, produced by the U.S. Bureau of Labor Statistics and distributed by the Federal Reserve Bank of St. Louis (FRED / ALFRED). Terms: FRED/ALFRED terms of use (BLS data are in the public domain; attribution to BLS and FRED requested). Observed data, not simulated.\n\n| file | content | source URL | retrieved (UTC) | SHA-256 of the raw download |\n|---|---|---|---|---|\n| `cpi_vintage_2016-07-15.csv` | vintage of 2016-07-15 (values as published on that date), months 1947-01 … 2016-06, 834 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2016-07-15 | 2026-10-30T03:22Z | 74c7c4e0e3fa26059421d9620abe12d57df9a7786012acf39d694766cb854f6d |\n| `cpi_vintage_2026-10-28.csv` | vintage of 2026-10-28 (latest), extract restricted to months 2014-01 … 2026-09, 153 rows | https://alfred.stlouisfed.org/graph/alfredgraph.csv?id=CPIAUCSL&vintage_date=2026-10-28&cosd=2014-01-01 | 2026-10-30T03:24Z | 480c9f9931a48e3194b73a8f6b65b325a51e185232456c1222a260dde64f9730 |\n| `cpi_assembled.csv` | a single monthly file assembled by the task author from the two extracts above, intended to cover 1947-01 … 2026-09 with a `vintage` column recording which extract each value came from | assembled from the two files above | 2026-10-30 | — |\n\nColumns of the vintage files: `date` (first day of the month, ISO), `cpi` (index level, 3 decimals). The assembled file adds `vintage` (the vintage date of the extract the value was taken from). The raw downloads were reformatted (two columns, ISO dates) without changing any value. An empty `cpi` cell means the source publishes no value for that month. The assembled file was produced by hand and may contain assembly 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print(m,g[m+'-01'])\na=pd.read_csv('out/analytic.csv');print(len(a),a.vintage_used.value_counts())\nE","description":"Rerun script in scratch and independently 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I wrote the review to `out/review/review.json`.\n\nI re-ran `out/analysis.py` in a scratch copy, and it regenerated `results.json`, `analytic.csv` and `exclusions.csv` byte-identically. I also recomputed everything myself from the two vintage files. Every reported value matched: 308 of 309 window months have a growth rate, the overall mean is 2.574%, the 2001–2013 baseline is 2.361%, the 2014–2026 mean is 2.793%, the five named months agree, the maximum is 8.979% in 2022-06 and the minimum is −1.959% in 2009-07.\n\nReading the code confirms it follows the information rules. It takes each month from the most recent vintage that covers it, does not interpolate, and reads only `data/`. The hand-assembled file is used only to diagnose its errors. I spot-checked those errors and they are real:\n- it uses old-vintage values for 2014-01 to 2016-06;\n- it leaves out 2016-07;\n- every later value is shifted one month, so the empty cell shows up at 2025-11 instead of 2025-10.\n\nThe exclusion ledger adds up correctly. The missing 2025-10 value is honestly reported as \"partial\" feasibility, the report and claims stay within what the numbers show, and the limitations are stated plainly.\n\nThree minor issues remain:\n- **`analytic.csv`:** the `vintage_used` column shows the vintage of the current month only. 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