# Data audit (P1)

File: data/UNRATE.csv — SHA-256 621ff6fadf50910cb7a21ee20ba610c247cb043b29b4e05c4c1a72fadca2a06c (matches README and bundle.json).

• 945 data rows + header; 2 columns: observation_date (YYYY-MM-01), UNRATE (percent, SA, float).
• Date range 1948-01-01 … 2026-09-01; monthly grid complete (no missing dates), no duplicate dates.
• Missing values: 1 (2025-10-01, empty field). Non-missing: 944. Range 2.5–14.8; max is 2020-04 (COVID spike, genuine, retained).
• Implications of the missing month: origin 2025-10-01 has no own observation (naive uses 2025-09-01 value); target 2025-10-01 is missing for (origin 2025-09-01, h=1) and (origin 2025-07-01, h=3) → those pairs kept in ledger with empty actual, excluded from stats. Model handles the gap via the Kalman filter (state-space likelihood skips the missing observation).
• Availability: the file is the final vintage (retrieved 2026-10-30), so values may include revisions unavailable at origin dates. This cannot be avoided with the given input; handled by stating results are pseudo-out-of-sample on revised data, no real-time claim. Data-dating rule (≤ t) is enforced in code by truncating the series at each origin.