Bayesian VARs and prior calibration in times of COVID-19

Arbeitspapier

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.

Object Details

• ISBN: 978-3-95729-931-4
• Language: Englisch

References and Relationships

• Bibliographic citation: Series: Deutsche Bundesbank Discussion Paper ; No. 52/2022

Classification and Topics

• Classification: Wirtschaft ; Bayesian Analysis: General ; Model Construction and Estimation ; Forecasting Models; Simulation Methods
• Subject: forecasting ; multivariate t errors ; common time-varying volatility ; outlier-robust prior calibration

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Hartwig, Benny
• Event: Veröffentlichung
• (who): Deutsche Bundesbank
• (where): Frankfurt a. M.
• (when): 2022

Further information

• Handle: http://hdl.handle.net/10419/268252
• Last update: 10.03.2025, 11:43 AM CET