BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R

Böck M, Feldkircher M, Huber F (2022)

Publication Type: Journal article

Publication year: 2022

Journal

Journal of Statistical Software University of California, Los Angeles

Pages Range: 1-28

Journal Issue: 104 (9)

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

DOI: 10.18637/jss.v104.i09

Abstract

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
forecasts.

Authors with CRIS profile

Maximilian Böck Lehrstuhl für Statistik und Ökonometrie

Involved external institutions

Oesterreichische Nationalbank AT Austria (AT)

How to cite

APA:

Böck, M., Feldkircher, M., & Huber, F. (2022). BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R. Journal of Statistical Software, 104 (9), 1-28. https://doi.org/10.18637/jss.v104.i09

MLA:

Böck, Maximilian, Martin Feldkircher, and Florian Huber. "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R." Journal of Statistical Software 104 (9) (2022): 1-28.

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