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Addressing COVID-19 outliers in BVARs with stochastic volatility

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Bibliographic Details
Authors and Corporations: Carriero, Andrea (Author), Clark, Todd E. (Author), Marcellino, Massimiliano (Author), Mertens, Elmar (Author)
Other Authors: Clark, Todd E. 1963- [Author] • Marcellino, Massimiliano 1970- [Author] • Mertens, Elmar [Author]
Type of Resource: E-Book
Language: English
Frankfurt am Main Deutsche Bundesbank [2022]
Series: Deutsche Bundesbank: Discussion paper ; no 2022, 13
Source: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
ISBN: 9783957298812
Summary: 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.
Physical Description: 1 Online-Ressource (circa 39 Seiten); Illustrationen
ISBN: 9783957298812