Does modeling a structural break improve forecast accuracy?
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.
|Keywords||Forecasting, Squared error loss, Structural break test|
|JEL||Hypothesis Testing (jel C12), Forecasting and Other Model Applications (jel C53)|
|Persistent URL||dx.doi.org/10.1016/j.jeconom.2019.07.007, hdl.handle.net/1765/119740|
|Journal||Journal of Econometrics|
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