Economics at your fingertips  

BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Maximilian Böck, Martin Feldkircher and Florian Huber

No 395, Globalization Institute Working Papers from Federal Reserve Bank of Dallas

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 (search for similar items in EconPapers)
JEL-codes: C30 C50 C87 F40 (search for similar items in EconPapers)
Pages: 26
Date: 2020-08-20
New Economics Papers: this item is included in nep-ets and nep-for
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed

Downloads: (external link) Full text (application/pdf) Code (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link:

DOI: 10.24149/gwp395

Access Statistics for this paper

More papers in Globalization Institute Working Papers from Federal Reserve Bank of Dallas Contact information at EDIRC.
Bibliographic data for series maintained by Amy Chapman ().

Page updated 2022-06-30
Handle: RePEc:fip:feddgw:88639