New Monthly Estimation Approach for Nowcasting GDP Growth: The Case of Japan

This paper proposes a new approach for nowcasting as yet unavailable GDP growth by estimating monthly GDP growth with a large dataset. The model consists of two parts: (i) a few indicators that explain a large part of the variation in GDP growth, and (ii) principal components, which are orthogonal to those indicators and are extracted from a number of GDP source data, capturing the rest of the variation. The approach relies on a static factor model comprising a number of indicators that have a simultaneous relationship with GDP. Applying this approach to data for Japan, we find that our model produces more precise estimates of recent GDP growth at an earlier stage of nowcasting than the nowcasts of professional forecasters.