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Autor:innen: 
Erscheinungsjahr: 
2022
Schriftenreihe/Nr.: 
Deutsche Bundesbank Discussion Paper No. 52/2022
Verlag: 
Deutsche Bundesbank, Frankfurt a. M.
Zusammenfassung: 
This paper investigates the ability of several generalized Bayesian vector autoregressions to cope with the extreme COVID-19 observations and discusses their impact on prior calibration for inference and forecasting purposes. It shows that the preferred model interprets the pandemic episode as a rare event rather than a persistent increase in macroeconomic volatility. For forecasting, the choice among outlier-robust error structures is less important, however, when a large cross-section of information is used. Besides the error structure, this paper shows that the standard Minnesota prior calibration is an important source of changing macroeconomic transmission channels during the pandemic, altering the predictability of real and nominal variables. To alleviate this sensitivity, an outlier-robust prior calibration is proposed.
Schlagwörter: 
forecasting
multivariate t errors
common time-varying volatility
outlier-robust prior calibration
JEL: 
C11
C51
C53
ISBN: 
978-3-95729-931-4
Dokumentart: 
Working Paper
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