Advances in Nowcasting Economic Activity: Secular Trends, Large Shocks and New Data

Ivan Petrella, Juan Antolin-Diaz and Thomas Drechsel

No 15926, CEPR Discussion Papers from Centre for Economic Policy Research

Abstract: A key question for households, firms, and policy makers is: how is the economy doing now? We develop a Bayesian dynamic factor model and compute daily estimates of US GDP growth. Our framework gives prominence to features of modern business cycles absent in linear Gaussian models, including movements in long-run growth, time-varying uncertainty, and fat tails. We also incorporate newly available high-frequency data on consumer behavior. The model beats benchmark econometric models and survey expectations at predicting GDP growth over two decades, and advances our understanding of macroeconomic data during the recession of spring 2020.

Keywords: Nowcasting; Daily economic index; Dynamic factor models; Real-time data; Bayesian methods; Fat tails (search for similar items in EconPapers)
JEL-codes: C32 E01 E23 E32 O47 (search for similar items in EconPapers)
Date: 2021-03
New Economics Papers: this item is included in nep-mac
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Citations: View citations in EconPapers (31)

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