1# Verification checks (P4)2| check | outcome |3|---|---|4| Clean re-run (deleted outputs, ran twice) -> results.json and forecasts.csv byte-identical | PASS |5| forecasts.csv has 32 rows, 8 per (model,horizon) | PASS |6| Exclusion ledger: 314 - 151 - 1 - 3 = 159 = n_analysed; each step n_before-n_removed=n_after | PASS |7| RMSE >= MAE for every model/horizon; all RMSE > 0 | PASS |8| rel_improvement_rmse_h{1,2} == 1 - rmse_model/rmse_naive | PASS |9| target_date > train_last_date for all rows; vintage_date == origin_date | PASS |10| train_last_value and n_train (naive) equal last value / row count of the origin's vintage file | PASS |11| Naive RMSE independently recomputed from forecasts.csv matches results.json | PASS |12| first_release_vintage_date of every target > origin (assert in analysis.py) | PASS |13| SHA-256 of all inputs match data/README.md | PASS |14| Magnitudes: RMSEs 1.4-2.3 pp, same order as GDP growth volatility; AR(1) phi 0.33-0.34, implied mean 2.6-2.7% — plausible | PASS |15