Bgvar: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

26 Pages Posted: 28 Aug 2020

See all articles by Maximilian Bock

Maximilian Bock

Vienna University of Economics and Business

Martin Feldkircher

Oesterreichische Nationalbank (OeNB)

Florian Huber

University of Salzburg

Date Written: August, 2020

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

Suggested Citation

Bock, Maximilian and Feldkircher, Martin and Huber, Florian, Bgvar: Bayesian Global Vector Autoregressions with Shrinkage Priors in R (August, 2020). Globalization Institute Working Paper No. 395, Available at SSRN: https://ssrn.com/abstract=3682355 or http://dx.doi.org/10.24149/gwp395

Maximilian Bock (Contact Author)

Vienna University of Economics and Business

Welthandelsplatz 1
Vienna, Wien 1020
Austria

Martin Feldkircher

Oesterreichische Nationalbank (OeNB)

Otto-Wagner-Platz 3, PO Box 61
Vienna,
1010 Vienna, A-1011
Austria

Florian Huber

University of Salzburg ( email )

Akademiestra├če 26
Salzburg, Salzburg 5020
Austria

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