Nowcasting GDP in real time: a density combination approach
Journal article, Peer reviewed
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- Scientific articles 
Original versionJournal of Business and Economic Statistics, 32(2014)1: 48-68 10.1080/07350015.2013.844155
In this paper, we use U.S. real-time data to produce combined density nowcasts of quarterly GDP growth, using a system of three commonly used model classes. We update the density nowcast for every new data release throughout the quarter, and highlight the importance of new information for nowcasting. Our results show that the logarithmic score of the predictive densities for U.S. GDP growth increase almost monotonically, as new information arrives during the quarter. While the ranking of the model classes changes during the quarter, the combined density nowcasts always perform well relative to the model classes in terms of both logarithmic scores and calibration tests. The density combination approach is superior to a simple model selection strategy and also performs better in terms of point forecast evaluation than standard point forecast combinations.
This is the authors’ accepted and refereed manuscript to the article. Publisher's webpage: www.tandfonline.com Availability of author's version is delayed until 18 months after first online publication. Unavailable until 2015-06-30. Publisher's policy