Title: Optimal window selection for forecasting in the presence of recent structural breaks
Authors: Yongli Wang - University of Essex (United Kingdom) [presenting]
Abstract: Two feasible algorithms are proposed to select the optimal window size for forecasting in rolling regression. The proposed methods are developed based on the existing methodology, keeping the asymptotic validity and allowing for the lagged dependent variable in regression and multi-step ahead forecasting. The Monte-Carlo experiments show that the proposed bootstrap method outperforms the original algorithm in the literature in almost all cases. It is also shown that the forecasts from the proposed methods are superior to those from other existing methods in some cases, and close to the best forecasts in other cases. However, when the break occurs far before the time of making forecasts and the break size is significant, using only post-break data is almost always the best strategy.