DP16760 Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic

Author(s): Frank Schorfheide, Dongo Song
Publication Date: November 2021
Keyword(s): Bayesian inference, COVID-19, Macroeconomic forecasting, Minnesota Prior, real-time data, Survey of Professional Forecasters, vector autoregressions
JEL(s): C11, C32, C53
Programme Areas: Monetary Economics and Fluctuations
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=16760

We resuscitated the mixed-frequency vector autoregression (MF-VAR) developed in Schorfheide and Song (2015, JBES) to generate macroeconomic forecasts for the U.S. during the COVID-19 pandemic in real time. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately did not modify the model specification in view of the COVID-19 outbreak, except for the exclusion of crisis observations from the estimation sample. We compare the MF-VAR forecasts to the median forecast from the Survey of Professional Forecasters (SPF). While the MF-VAR performed poorly during 2020:Q2, subsequent forecasts were at par with the SPF forecasts. We show that excluding a few months of extreme observations is a promising way of handling VAR estimation going forward, as an alternative of a sophisticated modeling of outliers.