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

中文翻译：

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选择预测模型

我们研究了以未来值未知的变量为条件的模型中的预测。我们考虑显着性水平的作用，因为它指导二元决策是包含还是排除变量。通过允许在第一个预测期或之前的结构性中断来扩展分析。理论结果是针对三变量静态模型得出的，但在模拟实验中推广到包括动力学和更多变量。结果表明，在静态世界的预测模型中选择变量的权衡，即如果变量的非中心性超过统一，则应保留变量，仍然适用于结构中断的设置。这为模型选择提供了比传统设置更宽松的支持，