BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Maximilian Böck, Martin Feldkircher, Florian Huber

Publikation: 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.
PublikationsstatusVeröffentlicht - 2020

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

    Publikation: Elektronische/multimediale VeröffentlichungenSoftware

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