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# Data audit (P1)

file SHA-256 (matches README) data rows cols range NaN dup dates gaps >1q min/max
current_vintage.csv 1af2590d…0562 ✓ 318 2 1947-04-01..2026-07-01 0 0 0 -28.0/34.9
first_release.csv ab843c9f…95bb ✓ 13 3 2023-07-01..2026-07-01 0 0 0 -0.3/4.9
vintage 2024-01-31 7ce218fb… ✓ 307 2 ..2023-10-01 0 0 0 -28.0/34.8
vintage 2024-04-30 62c1dfce… ✓ 308 2 ..2024-01-01 0 0 0 -28.0/34.8
vintage 2024-07-31 6f942591… ✓ 309 2 ..2024-04-01 0 0 0 -28.0/34.8
vintage 2024-10-31 c4c51edee… ✓ 310 2 ..2024-07-01 0 0 0 -28.1/35.2
vintage 2025-01-31 10a5e02e… ✓ 311 2 ..2024-10-01 0 0 0 -28.1/35.2
vintage 2025-04-30 1f19c975… ✓ 312 2 ..2025-01-01 0 0 0 -28.1/35.2
vintage 2025-07-31 f1e7c556… ✓ 313 2 ..2025-04-01 0 0 0 -28.0/35.2 (max)
vintage 2025-12-31 c111c070… ✓ 314 2 ..2025-07-01 0 0 0 -28.0/34.9

All vintages start 1947-04-01. Units: percent, SAAR. All values have one decimal. Anomalies: (1) COVID outliers 2020Q2 (≈-28) and 2020Q3 (≈+35) in every vintage — handled by excluding them from the AR estimation sample (decided a priori). (2) Vintages revise earlier quarters (e.g. 2024Q4 2.3→2.4→1.9; 2025Q2 3.0→3.8); per rules each origin uses its own vintage values. (3) 8th origin is 2025-12-31 (shutdown delayed 2025Q3 advance release); 2025Q4 first release dated 2026-02-28 (also delayed). Fine: actuals are only used for evaluation. Availability: all first-release vintage dates of targets are after the respective origin (checked in analysis.py). current_vintage.csv is read only for the revision statistic. No input violates the rules.

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