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Addressing COVID-19 Outliers in BVARs with Stochastic Volatility

Andrea Carriero, Todd Clark, Massimiliano Marcellino and Elmar Mertens ()

No 21-02R, Working Papers from Federal Reserve Bank of Cleveland

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 (search for similar items in EconPapers)
JEL-codes: C53 E17 E37 F47 (search for similar items in EconPapers)
Pages: 51
Date: 2021-02-02, Revised 2021-08-09
New Economics Papers: this item is included in nep-ecm, nep-ets, nep-for, nep-mac and nep-ore
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Working Paper: Addressing COVID-19 outliers in BVARs with stochastic volatility (2022) Downloads
Working Paper: Addressing COVID-19 Outliers in BVARs with Stochastic Volatility (2021) Downloads
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DOI: 10.26509/frbc-wp-202102r

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