# Contract (P0)

Question: At end of month t (48 origins 2022-05..2026-04), how accurately can Canadian monthly real GDP y/y growth for month t+h (h=1,3) be forecast using only data published by then, and does a model adding LFS employment y/y growth beat the naive forecast? Estimands: out-of-sample RMSE and MAE (pp) per horizon for (a) naive = gdp_yoy(t-2), (b) one chosen model; relative improvement 1 - RMSE_model/RMSE_naive. Meaningful under: current-vintage data (simulated publication timing only, not real time), fixed origin set, squared/absolute loss. Info rules: at origin t, GDP columns usable only for months <= t-2, employment only for months <= t-1; applies to estimation sample, training targets, model selection, transforms, plug-in values. Outputs: out/forecasts.csv (192 rows: origin_date,target_date,horizon,forecast,actual,model,last_target_date_used,last_predictor_date_used); out/results.json (keys task_id, model_name, model_description, n_origins, first_origin, last_origin, last_target_date_available, last_predictor_date_available, rmse/mae naive & model h1/h3, rel_improvement_rmse_h1/h3, feasibility, feasibility_reason, data_issues, n_input_rows, n_analysed, random_seed); out/report.md (6 sections); out/analysis.py; out/claims.json. Invalidity: any use of GDP > t-2 or employment > t-1; missing actuals for targets (last target 2026-07 is published, so OK); tuning the model on the evaluation errors.