Working Paper Revision

Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Abstract: 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;

https://doi.org/10.26509/frbc-wp-202102r

Bibliographic Information

Provider: Federal Reserve Bank of Cleveland

Part of Series: Working Papers

Publication Date: 2021-08-09

Number: 21-02R

Related Works

• Working Paper Revision (2021-08-09) : You are here.
• Working Paper Original (2021-02-02) : Addressing COVID-19 Outliers in BVARs with Stochastic Volatility