Nowcasting GDP in Real Time: A Density Combination Approach

Knut Are Aastveit, Karsten R. Gerdrup, Anne Sofie Jore and Leif Thorsrud

Journal of Business & Economic Statistics, 2014, vol. 32, issue 1, 48-68

Abstract: In this article, we use U.S. real-time data to produce combined density nowcasts of quarterly Gross Domestic Product (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.

Date: 2014
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Related works:
Working Paper: Nowcasting GDP in real-time: A density combination approach (2011)
Working Paper: Nowcasting GDP in Real-Time: A Density Combination Approach (2011)
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DOI: 10.1080/07350015.2013.844155

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