1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/report.md (Uncertainty); results.json dm_*",8 "issue": "The naive forecast is a special case (nested) of the ADL model: intercept 0, GDP coefficient 1, employment coefficient 0. The standard Diebold-Mariano test with N(0,1)/t reference values tends to under-reject when models are nested, and no small-sample correction (Harvey-Leybourne-Newbold) is applied despite 48 overlapping errors. The report does call the gains 'not significant' cautiously, but it should say the test is approximate.",9 "suggested_fix": "Add a caveat that the DM p-values are approximate for nested comparisons and are likely conservative. Optionally report a Clark-West test or the HLN-adjusted statistic."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/report.md (Uncertainty); claims.json C2 caveat",15 "issue": "The by-year breakdown ('gain concentrated in 2022 origins') is not produced by analysis.py or stored in the outputs. I recomputed it from forecasts.csv and it holds. At h=1 the 2022 RMSE is 1.285 for the model vs 1.515 for naive, and the model is slightly worse than naive in 2023 and 2025. At h=3 the 2022 RMSE is 1.414 vs 2.123. The claim is still not reproducible from the delivered script.",16 "suggested_fix": "Have analysis.py compute and save the per-year RMSE breakdown, and say explicitly that the model is no better than naive in 2023 and 2025."17 },18 {19 "id": "R3",20 "severity": "minor",21 "location": "out/analysis.py COVID window; report Method",22 "issue": "The 2020-03..2021-12 training exclusion is a researcher choice. It uses no future information, because the window ends before the first origin's information cutoff (2022-03). However, the statements that it was 'fixed a priori' and that the specification was 'fixed in advance' cannot be checked from the deliverables, and no sensitivity check without the exclusion is shown.",23 "suggested_fix": "Report RMSEs with the full sample (no pandemic exclusion), or at least state that results are untested to this choice."24 },25 {26 "id": "R4",27 "severity": "minor",28 "location": "results.json n_analysed",29 "issue": "n_analysed=310 refers to the h=1 sample at the last origin. The h=3 sample has 306 rows. This is disclosed in the report, but the key on its own is ambiguous.",30 "suggested_fix": "Optionally add n_analysed_h3 or state in a data_issues/description field which horizon the key refers to."31 }32 ],33 "summary": "The results are reproducible and the publication-lag rules are respected. Re-running analysis.py in a scratch copy reproduced results.json and forecasts.csv exactly, and the data SHA-256 matches the README. The ledger has 192 rows (48x2x2). Actuals equal gdp_yoy of the target month, and the naive forecast equals gdp_yoy of t-2. Every model row satisfies last_target_date_used <= t-2 and last_predictor_date_used <= t-1, and the code enforces this by date-cutting the training rows and with assertions. All required keys are present and consistent. The README inconsistency about the 1998 yoy values was correctly detected. Claims are appropriately hedged: the gains are point improvements (RMSE about 9% lower at h=1 and 21% at h=3), DM tests are not significant, the employment-specific contribution is explicitly left unestablished, and there is no claim of real-time validity. The remaining issues are minor: DM caveats for nested models, a per-year diagnostic the script does not reproduce, no sensitivity check on the pandemic exclusion, and an ambiguous n_analysed."34}35