BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Kuschnig, Nikolas and Vashold, Lukas (2019) BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R. Department of Economics Working Paper Series, 296. WU Vienna University of Economics and Business, Vienna.


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Vector autoregression (VAR) models are widely used models for multivariate time series analysis, but often suffer from their dense parameterization. Bayesian methods are commonly employed as a remedy by imposing shrinkage on the model coefficients via informative priors, thereby reducing parameter uncertainty. The subjective choice of the informativeness of these priors is often criticized and can be alleviated via hierarchical modeling. This paper introduces BVAR, an R package dedicated to the estimation of Bayesian VAR models in a hierarchical fashion. It incorporates functionalities that permit addressing a wide range of research problems while retaining an easy-to-use and transparent interface. It features the most commonly used priors in the context of multivariate time series analysis as well as an extensive set of standard methods for analysis. Further functionalities include a framework for defining custom dummy-observation priors, the computation of impulse response functions, forecast error variance decompositions and forecasts.

Item Type: Paper
Keywords: Vector autoregression, VAR, Bayesian, multivariate, hierarchical, R, package
Classification Codes: JEL C87, C30, C11
Divisions: Departments > Volkswirtschaft
Depositing User: Claudia Tering-Raunig
Date Deposited: 22 Oct 2019 11:08
Last Modified: 07 Jul 2020 10:55


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