BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

RePEc Short ID: pbc7
ORCID iD: 0000-0001-6024-8305

Böck, Maximilian; Feldkircher, Martin; Huber, Florian

doi:10.24149/gwp395

Working Paper

BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Abstract: This document introduces the R library BGVAR to estimate Bayesian global vector autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian treatment of GVARs allows us to include large information sets by mitigating issues related to overfitting. This improves inference and often leads to better out-of-sample forecasts. Computational efficiency is achieved by using C++ to considerably speed up time-consuming functions. To maximize usability, the package includes numerous functions for carrying out structural inference and forecasting. These include generalized and structural impulse response functions, forecast error variance and historical decompositions as well as conditional forecasts.

Keywords: Global Vector Autoregressions; Bayesian inference; time series analysis; R;

JEL Classification: C30; C50; C87; F40;

https://doi.org/10.24149/gwp395