Predictor Preselection for Mixed-Frequency Dynamic Factor Models: A Simulation Study With an Empirical Application to GDP Nowcasting

Abstract

We investigate the performance of dynamic factor model nowcasting with preselected predictors in a mixed-frequency setting. The predictors are selected via the elastic net as it is common in the targeted predictor literature. A simulation study and an application to empirical data are used to evaluate different strategies for variable selection, the influence of tuning parameters, and to determine the optimal way to handle mixed-frequency data. We propose a novel cross-validation approach that connects the preselection and nowcasting step. In general, we find that preselecting provides more accurate nowcasts compared with the benchmark dynamic factor model using all variables. Our newly proposed cross-validation method outperforms the other specifications in most cases.