Selecting a Model for Forecasting

We investigate forecasting in models that condition on variables for which future values are unknown. consider the role of significance level because it guides binary decisions whether to include or exclude variables. The analysis is extended by allowing a structural break, either first forecast period just before. Theoretical results derived three-variable static model, but generalized dynamics and many more simulation experiment. show trade-off selecting stationary world, namely should be retained if their noncentralities exceed unity, still applies settings with breaks. This provides support model selection at looser than conventional settings, albeit additional features explaining performance, caveat retaining irrelevant subject location shifts can worsen performance.