Does modeling a structural break improve forecast accuracy? Tom Boot∗ Andreas Picky December 13, 2017 Abstract Mean square forecast error loss implies a bias-variance trade-off that suggests that struc- tural breaks of small magnitude should be ignored. In this paper, we provide a test to determine whether modeling a break improves forecast accuracy. The test is near opti- mal 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. JEL codes: C12, C53 Keywords: structural break test, forecasting, squared error loss ∗University of Groningen,
[email protected] yErasmus University Rotterdam, Tinbergen Institute, De Nederlandsche Bank, and CESifo Institute,
[email protected]. We thank Graham Elliott, Bart Keijsers, Alex Koning, Robin Lumsdaine, Agnieszka Markiewicz, Michael McCracken, Allan Timmermann, participants of seminars at CESifo Institute, Tinbergen Institute, University of Nottingham, and conference participants at BGSE summer forum, ESEM, IAAE annual conference, NESG meeting, RMSE workshop, and SNDE conference for helpful comments. The paper previously circulated under the title \A near optimal test for structural breaks when forecasting under square error loss." 1 1 Introduction Many macroeconomic and financial time series contain structural breaks as documented by Stock and Watson (1996). Yet, Stock and Watson also find that forecasts are not substan- tially affected by the presence of structural breaks.