Quotation Kuschnig, Nikolas, Vashold, Lukas. 2021. BVAR: Bayesian vector autoregressions with hierarchical prior selection in R. Journal of Statistical Software. 100 (14), 1-27.




Vector autoregression (VAR) models are widely used for multivariate time series analysis in macroeconomics, finance, and related fields. Bayesian methods are often employed to deal with their dense parameterization, imposing structure on model coefficients via prior information. The optimal choice of the degree of informativeness implied by these priors is subject of much debate and can be approached via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models with hierarchical prior selection. It implements functionalities and options that permit addressing a wide range of research problems, while retaining an easy-to-use and transparent interface. Features include structural analysis of impulse responses, forecasts, the most commonly used conjugate priors, as well as a framework for defining custom dummy-observation priors. BVAR makes Bayesian VAR models user-friendly and provides an accessible reference implementation.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal Journal of Statistical Software
Citation Index SCI
WU-Journal-Rating new FIN-A
Language English
Title BVAR: Bayesian vector autoregressions with hierarchical prior selection in R
Volume 100
Number 14
Year 2021
Page from 1
Page to 27
URL https://doi.org/10.18637/jss.v100.i14
DOI https://doi.org/10.18637/jss.v100.i14
Open Access Y
Open Access Link https://doi.org/10.18637/jss.v100.i14


Kuschnig, Nikolas (Details)
Vashold, Lukas (Details)
Department of Economics (Crespo Cuaresma) (Details)
Department of Economics (PhD) (Details)
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