1{2 "verdict": "accept",3 "issues": [4 {5 "id": "R1",6 "severity": "minor",7 "location": "out/claims.json C4; out/report.md Results",8 "issue": "C4 and the report say the gain 'is not attributable to employment' and that employment 'does not help out of sample', and C4 is marked 'supported'. This rests only on point RMSE differences (0.743 vs 0.715 at h=1, 0.902 vs 0.864 at h=3) with no test. The report also states as fact that the gain comes from mean reversion, which is an interpretation.",9 "suggested_fix": "Rephrase as 'in this sample the AR-only diagnostic had lower RMSE than the employment model; no evidence that employment adds accuracy (untested)'. Mark the mean-reversion explanation as an interpretation."10 },11 {12 "id": "R2",13 "severity": "minor",14 "location": "out/analysis.py COVID window; out/report.md Data and exclusions",15 "issue": "Training rows touching 2020-03..2021-12 are excluded (25 rows in the last sample). Nothing in the files can confirm the claim that this was 'pre-specified'. The window boundaries are a judgement call (they use data that is available at every origin, so there is no leakage), and no sensitivity variant is reported.",16 "suggested_fix": "Report the model RMSE/MAE with no exclusion (or with a dummy variable) as a sensitivity check, or soften the 'pre-specified' wording."17 },18 {19 "id": "R3",20 "severity": "minor",21 "location": "out/results.json dm_*",22 "issue": "The DM HAC lag count (h+1) is chosen ad hoc. The test ignores parameter-estimation uncertainty in the expanding-window regression, and the HLN correction is applied together with a nonstandard lag choice. The p-values are therefore approximate. The report already says the lag choice is a judgement call.",23 "suggested_fix": "Note that the DM p-values are approximate. Optionally report how sensitive they are to the lag choice."24 }25 ],26 "summary": "Re-running analysis.py in a scratch copy reproduces results.json and forecasts.csv exactly. The data SHA-256 matches the README. The ledger has 192 rows (48 per model and horizon), and every required key is present. An independent re-estimation with the post-origin data blanked out (GDP only for months <= t-2, employment only for months <= t-1, training targets <= t-2) matches all model forecasts to within 5e-7, so the publication-lag rules are respected. The naive forecast is gdp_yoy[t-2] as specified. The exclusion counts (345 -> 337 -> 335 -> 310) and the last-available dates (2026-02-01, 2026-03-01) are consistent. The headline claims (relative RMSE improvements of 0.087 and 0.208, neither significant by DM) match the numbers and carry suitable caveats. The limitations section (current vintage, single window, COVID exclusion, diagnostic added after the fact) is honest. The remaining issues are minor wording and sensitivity points."27}28