# Contract — F3-conf-03

Question. Over 60 rolling origins t = 2021-09 … 2026-08, does a fixed-penalty (λ=50) ridge regression on 8 standardised predictors forecast US IP growth at t+1 better than (a) naive (ip_growth_t) and (b) expanding mean of ip_growth over 1992-04..t? Estimands. Out-of-sample RMSE and MAE (percent/month) of naive, mean, ridge over the 60 h=1 forecasts; rel. improvements 1 − RMSE_ridge/RMSE_baseline. Meaningful under: current-vintage data, publication lags ignored (stylised timing), fixed model spec; 60 forecasts is a small sample. Outputs. out/forecasts.csv (180 rows: origin_date,target_date,horizon,forecast,actual,model); out/results.json (task_id, n_origins, first_origin, last_origin, lambda, normalisation_window, n_features, rmse_/mae_ naive/mean/ridge, rel_improvement_vs_naive/mean, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (Question; Data and exclusions; Method; Results; Uncertainty; Limitations and open issues); out/analysis.py; out/claims.json. Information rules. At origin t use only values dated ≤ t; training rows s ∈ [1992-03, t−1] with target ip_growth_{s+1}; standardisation μ, σ (ddof=1) and ȳ recomputed per origin from training rows only. Invalidity. Any use of data dated > t (incl. full-sample scaling); wrong origin set; missing target months. Impossible if months non-contiguous or required values missing in the training window.