Advances in Nowcasting Economic Activity: Secular Trends, Large Shocks and New Data
47 Pages Posted: 12 Aug 2020
Date Written: August 8, 2020
The assessment of macroeconomic conditions in real time is challenging. Dynamic factor models, which summarize the comovement across many macroeconomic time series as driven by a small number of shocks, have become the workhorse tool for ‘nowcasting’ activity. This paper develops a novel dynamic factor model that explicitly captures three salient features of modern business cycles: low frequency movements in long-run growth and volatility, lead-lag patterns in the responses of variables to common shocks, and fat-tailed outliers. We use real-time unrevised data for the last two decades and cloud computing technology to conduct an out-of-sample evaluation exercise of the model. The exercise demonstrates the importance of considering these features for forecasting and probability assessment of economic conditions. In an application to the COVID-19 recession, we develop a method to incorporate newly available high-frequency data. The use of such alternative data is essential to track the downturn in activity, but a careful econometric specification is just as important.
Keywords: Nowcasting, Dynamic Factor Models, Real-Time Data, Bayesian Methods, Fat Tails
JEL Classification: E32, E23, O47, C32, E01
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