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

Frank Schorfheide and Dongho Song

PIER Working Paper Archive from Penn Institute for Economic Research, Department of Economics, University of Pennsylvania

Abstract: In this paper we resuscitate the mixed-frequency vector autoregression (MF-VAR) de-veloped in Schorfheide and Song (2015) to generate real-time macroeconomic forecasts for the U.S. during the COVID-19 pandemic. The model combines eleven time series observed at two frequencies: quarterly and monthly. We deliberately do not modify the model speci?cation in view of the recession induced by the COVID-19 outbreak. We ?nd that forecasts based on a pre-crisis estimate of the VAR using data up until the end of 2019 appear to be more stable and reasonable than forecasts based on a sequence of recursive estimates that include the most recent observations. Overall, the MF-VAR outlook is quite pessimistic. The estimated MF-VAR implies that level variables are highly persistent, which means that the COVID-19 shock generates a long-lasting reduction in real activity.

Keywords: Bayesian inference; COVID-19; Macroeconomic Forecasting; Minnesota Prior; Real-time data; Vector autoregressions (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
Pages: 19 pages
Date: 2020-07-08
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Citations: View citations in EconPapers (41)

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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 (2021)
Working Paper: Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic (2020)
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