BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Maximilian Böck, Martin Feldkircher, Florian Huber

Publication: Working/Discussion PaperWorking Paper/Preprint


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.
Original languageEnglish
Publication statusPublished - 2020

    Böck, M., Feldkircher, M., Huber, F. & Hosszejni, D., 6 Nov 2021

    Publication: Non-textual formSoftware

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