BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

vector autoregression, VAR, multivariate, time series, macroeconomics, structural analysis, hierarchical model, forecast, impulse response, identification, Minnesota prior,
FRED-MD, Bayesian econometrics

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.

Nikolas Kuschnig

https://www.jstatsoft.org/article/view/v100i14

WU Vienna University
of Economics and Business

November 2021

Fajar bagus W

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English

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