DP15926 Advances in Nowcasting Economic Activity: Secular Trends, Large Shocks and New Data

Author(s): Juan Antolin-Diaz, Thomas Drechsel, Ivan Petrella
Publication Date: March 2021
Date Revised: July 2021
Keyword(s): Bayesian methods, Daily economic index, Dynamic factor models, Fat Tails, Nowcasting, real-time data
JEL(s): C32, E01, E23, E32, O47
Programme Areas: Monetary Economics and Fluctuations
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15926

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.