1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "data_issues / gdp_yoy 1998 rows",8 "issue": "The README says gdp_yoy is empty for the first 12 months, but the file has values for 1998. These cannot be recomputed from the levels in the file, and they enter the training sample (from 1998-03). The report says 'agrees to rounding where possible', which correctly implies the 1998 values were not verified, but whether they are valid is still unknown.",9 "suggested_fix": "Add a sensitivity run that starts training in 1999-01 (the first month whose yoy can be checked against the levels), or say explicitly that the 1998 yoy values are unverified."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "report.md Method/Data ('decided with pre-2022 info', 'specification fixed in advance')",15 "issue": "Nothing in the files can confirm that the COVID-exclusion window and the specification were chosen before looking at the evaluation results. Without the exclusion, h=1 RMSE is worse (rel. improvement 0.012) and h=3 RMSE is better (0.304). So the headline choice is not obviously cherry-picked, but the h=1 gain depends on it.",16 "suggested_fix": "Describe the claim as a design statement, and in the claims and summary keep the h=1 point gain clearly tied to the exclusion choice (C4 already does this in part)."17 },18 {19 "id": "R3",20 "severity": "minor",21 "location": "results.json n_analysed",22 "issue": "n_analysed (310) is the h=1 sample at the last origin. The h=3 sample is 306, reported separately under an extra key. This is consistent and disclosed, but the meaning of n_analysed depends on the horizon.",23 "suggested_fix": "State in model_description or the report that n_analysed refers to h=1."24 }25 ],26 "summary": "A fresh copy of analysis.py reproduces results.json and forecasts.csv exactly (192 rows, deterministic). Separate checks confirm that actuals match gdp_yoy of the target month, that the naive forecast equals gdp_yoy(t-2), and that a hand-built direct OLS gives the same model forecast at a sample origin. The code follows the information rules: GDP is truncated at t-2 and employment at t-1 before the lagged rows are built, training targets satisfy tau+h <= t-2, and the plug-in values are y(t-2) and e(t-1). All required keys are present and internally consistent. The point improvements (0.087 at h=1, 0.208 at h=3) come with DM p-values of about 0.18-0.19, which the report correctly calls not significant. It also reports sensitivity to the COVID exclusion and states the current-vintage, short-window and mean-reversion caveats honestly. Only minor issues remain."27}28