Addressing COVID-19 Outliers in BVARs with Stochastic Volatility
51 Pages Posted: 3 Feb 2021 Last revised: 10 Aug 2021
Date Written: August 9, 2021
The COVID-19 pandemic has led to enormous movements in economic data that strongly affect parameters and forecasts obtained from standard VARs. One way to address these issues is to model extreme observations as random shifts in the stochastic volatility (SV) of VAR residuals. Specifically, we propose VAR models with outlier-augmented SV that combine transitory and persistent changes in volatility. The resulting density forecasts for the COVID-19 period are much less sensitive to outliers in the data than standard VARs. Evaluating forecast performance over the last few decades, we find that outlier-augmented SV schemes do at least as well as a conventional SV model. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the period since the pandemic’s outbreak, as well as for earlier subsamples of relatively high volatility.
Keywords: Bayesian VARs, stochastic volatility, outliers, pandemics, forecasts
JEL Classification: C53, E17, E37, F47
Suggested Citation: Suggested Citation