Mixed-frequency approaches to nowcasting GDP: An application to Japan

Abstract In this paper, we discuss the approaches to nowcasting Japanese GDP quarterly growth rates. We look at nowcasting quarterly GDP growth rates using monthly indicators, ranging from hard data to soft data. In addition, we compare a variety of mixed frequency approaches including a bridge equation approach, Mixed-Data Sampling (MIDAS) and factor-augmented version of these approaches. In doing so, we examine the usefulness of a novel sparse principal component analysis (SPCA) approach in extracting factors from the dataset. We also discuss the usefulness of forecast combination, considering various ways to combine forecasts from models and surveys. Our findings are summarized as follows. First, some of the mixed frequency models discussed in this paper record out-of-sample performance superior to a naive constant growth model. Second, albeit small, the SPCA approach of extracting factors improves predictive power compared with traditional principal component approach. Furthermore, we find that there is a gain from combining model forecasts and professional survey forecasts.