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Addressing COVID-19 outliers in BVARs with stochastic volatility

Author

Listed:
  • Carriero, Andrea
  • Clark, Todd E.
  • Marcellino, Massimiliano
  • Mertens, Elmar
Abstract
The COVID-19 pandemic has led to enormous data movements that strongly affect parameters and forecasts from standard VARs. To address these issues, we propose VAR models with outlier-augmented stochastic volatility (SV) that combine transitory and persistent changes in volatility. The resulting density forecasts are much less sensitive to outliers in the data than standard VARs. Predictive Bayes factors indicate that our outlier-augmented SV model provides the best data fit for the pandemic period, as well as for earlier subsamples of relatively high volatility. In historical forecasting, outlier-augmented SV schemes fare at least as well as a conventional SV model.

Suggested Citation

  • Carriero, Andrea & Clark, Todd E. & Marcellino, Massimiliano & Mertens, Elmar, 2022. "Addressing COVID-19 outliers in BVARs with stochastic volatility," Discussion Papers 13/2022, Deutsche Bundesbank.
    • Handle: RePEc:zbw:bubdps:132022
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    References listed on IDEAS

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    More about this item

    Keywords

    Bayesian VARs; stochastic volatility; outliers; pandemics; forecasts;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • F47 - International Economics - - Macroeconomic Aspects of International Trade and Finance - - - Forecasting and Simulation: Models and Applications

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