Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic
Frank Schorfheide () and
Dongho Song ()
No 20-26, Working Papers from Federal Reserve Bank of Philadelphia
In this paper we resuscitate the mixed-frequency vector autoregression (MF-VAR) developed 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 specification in view of the recession induced by the COVID-19 outbreak. We find 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. Regularly updated forecasts are available at www.donghosong.com/
Keywords: Bayesian inference; COVID-19; Macroeconomic Forecasting; Minnesota Prior (search for similar items in EconPapers)
JEL-codes: C11 C32 C53 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ets, nep-for, nep-mac and nep-ore
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