BGVAR: Bayesian Global Vector Autoregressions

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available.

Version: 2.1.5
Depends: R (≥ 2.10)
Imports: abind, bayesm, coda, GIGrvg, graphics, knitr, MASS, Matrix, methods, parallel, Rcpp (≥ 1.0.3), stats, stochvol, utils, xts, zoo
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol, GIGrvg
Suggests: rmarkdown, testthat (≥ 2.1.0)
Published: 2020-11-15
Author: Maximilian Boeck ORCID iD [aut, cre], Martin Feldkircher ORCID iD [aut], Florian Huber ORCID iD [aut], Christopher Sims [ctb]
Maintainer: Maximilian Boeck <maximilian.boeck at wu.ac.at>
License: GPL-3
NeedsCompilation: yes
SystemRequirements: C++11
Language: en-US
Citation: BGVAR citation info
Materials: README NEWS
CRAN checks: BGVAR results

Downloads:

Reference manual: BGVAR.pdf
Vignettes: BGVAR: Bayesian Global Vector Autoregression
Package source: BGVAR_2.1.5.tar.gz
Windows binaries: r-devel: BGVAR_2.1.5.zip, r-release: BGVAR_2.1.5.zip, r-oldrel: BGVAR_2.1.5.zip
macOS binaries: r-release: BGVAR_2.1.5.tgz, r-oldrel: BGVAR_2.1.5.tgz
Old sources: BGVAR archive

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