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Bayesian VARs and prior calibration in times of COVID-19

Author

Listed:
  • Hartwig, Benny
Abstract
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.

Suggested Citation

  • Hartwig, Benny, 2022. "Bayesian VARs and prior calibration in times of COVID-19," Discussion Papers 52/2022, Deutsche Bundesbank.
  • Handle: RePEc:zbw:bubdps:522022
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    File URL: https://www.econstor.eu/bitstream/10419/268252/1/1830347187.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Colunga-Ramos, Luis Fernando & Cepeda, Leonardo E. Torre, 2024. "Regional supply, demand and labor shocks on the manufacturing sector during COVID-19 in Mexico," Latin American Journal of Central Banking (previously Monetaria), Elsevier, vol. 5(2).
    2. Granados, Camilo & Parra-Amado, Daniel, 2024. "Estimating the output gap after COVID: How to address unprecedented macroeconomic variations," Economic Modelling, Elsevier, vol. 135(C).
    3. Colunga L. Fernando & Torre Cepeda Leonardo, 2023. "Effects of Supply, Demand, and Labor Market Shocks in the Mexican Manufacturing Sector," Working Papers 2023-10, Banco de México.
    4. Ter Steege, Lucas, 2024. "Variational inference for Bayesian panel VAR models," Working Paper Series 2991, European Central Bank.

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    More about this item

    Keywords

    forecasting; multivariate t errors; common time-varying volatility; outlier-robust prior calibration;
    All these keywords.

    JEL classification:

    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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