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

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  • Schorfheide, Frank
  • Song, Dongho

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.

Suggested Citation

  • Schorfheide, Frank & Song, Dongho, 2021. "Real-Time Forecasting with a (Standard) Mixed-Frequency VAR During a Pandemic," CEPR Discussion Papers 16760, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:16760
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    More about this item

    Keywords

    Bayesian inference; Covid-19; Macroeconomic forecasting; Minnesota prior; Real-time data; Survey of professional forecasters; Vector autoregressions;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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