Selection of an Estimation Window in the Presence of Data Revisions and Recent Structural Breaks

Abstract In this paper, we analyze the forecasting performance of a set of widely used window selection methods in the presence of data revisions and recent structural breaks. Our Monte Carlo and empirical results for U.S. real GDP and inflation show that the expanding window estimator often yields the most accurate forecasts after a recent break. It performs well regardless of whether the revisions are news or noise, or whether we forecast first-release or final values. We find that the differences in the forecasting accuracy are large in practice, especially when we forecast inflation after the break of the early 1980s.