Mean square forecast error loss implies a bias–variance trade-off that suggests that structural breaks of small magnitude should be ignored. In this paper, we provide a test to determine whether modeling a structural break improves forecast accuracy. The test is near optimal even when the date of a local-to-zero break is not consistently estimable. The results extend to forecast combinations that weight the post-break sample and the full sample forecasts by our test statistic. In a large number of macroeconomic time series, we find that structural breaks that are relevant for forecasting occur much less frequently than existing tests indicate.

Forecasting, Squared error loss, Structural break test
Hypothesis Testing (jel C12), Forecasting and Other Model Applications (jel C53),
Journal of Econometrics
Erasmus School of Economics

Boot, T. (Tom), & Pick, A. (2019). Does modeling a structural break improve forecast accuracy?. Journal of Econometrics. doi:10.1016/j.jeconom.2019.07.007