[
 {"id":"C1","text":"The seasonal-dummy AR model has lower out-of-sample RMSE and MAE than the seasonal-naive forecast at h = 1, 2 and 4 over the 40 origins 2015Q4-2025Q3 (relative RMSE improvement 23%, 13%, 19%).","results_keys":["rmse_snaive_h1","rmse_model_h1","rmse_snaive_h2","rmse_model_h2","rmse_snaive_h4","rmse_model_h4","mae_snaive_h1","mae_model_h1","mae_snaive_h2","mae_model_h2","mae_snaive_h4","mae_model_h4","rel_improvement_rmse_h1","rel_improvement_rmse_h2","rel_improvement_rmse_h4"],"status":"supported","caveat":"Point estimates on 40 forecasts; partly driven by 2020-2023 volatility."},
 {"id":"C2","text":"At h = 4 the mean squared-error loss differential (2.82 sq. pp in favour of the model) is not statistically distinguishable from zero at 5% using a Bartlett HAC SE with lag 4 (DM = 1.52, p = 0.14).","results_keys":["loss_diff_mean_h4","loss_diff_se_h4","hac_lags_h4","hac_kernel_h4","dm_stat_h4","dm_pvalue_h4"],"status":"supported","caveat":"Sensitive to truncation lag: Bartlett lags 6 and 8 give p < 0.05; lags 3 (Bartlett/rectangular, HLN) give p 0.11-0.18."},
 {"id":"C3","text":"The model's superiority over seasonal naive is statistically significant at h = 4.","results_keys":["dm_pvalue_h4"],"status":"unsupported","caveat":"Only holds under longer HAC truncation lags chosen after the fact."}
]