BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

global vector autoregressions, Bayesian inference, time series analysis, R.

This document introduces the R package BGVAR to estimate Bayesian global vector
autoregressions (GVAR) with shrinkage priors and stochastic volatility. The Bayesian
treatment of GVARs allows to include large information sets by mitigating issues related
to overfitting. This often improves inference as well as 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 forecast

Maximilian Boeck

https://www.jstatsoft.org/article/view/v104i09

Vienna School of
International Studies
Vienna University of
Economics and Business

October 2022

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