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

Frank Schorfheide and Dongho Song

No 16760, CEPR Discussion Papers from C.E.P.R. Discussion Papers

Abstract: 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.

Keywords: Bayesian inference; Covid-19; Macroeconomic forecasting; Minnesota prior; Real-time data; Survey of professional forecasters; Vector autoregressions (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Date: 2021-11
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Related works:
Journal Article: Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic (2024)
Working Paper: Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic (2021)
Working Paper: Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic (2020)
Working Paper: Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic (2020)
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