Bottom-up or direct? Forecasting German GDP in a data-rich environment

This paper conducts a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information, and finds that the direct forecast performs relatively well.

Abstract

Abstract In this paper, we investigate whether there are benefits in disaggregating GDP into its components when nowcasting GDP. To answer this question, we conduct a realistic out-of-sample experiment that deals with the most prominent problems in short-term forecasting: mixed frequencies, ragged-edge data, asynchronous data releases and a large set of potential information. We compare a direct leading indicator-based GDP forecast with two bottom-up procedures—that is, forecasting GDP components from the production side or from the demand side. Generally, we find that the direct forecast performs relatively well. Among the disaggregated procedures, the production side seems to be better suited than the demand side to form a disaggregated GDP nowcast.