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:	2.0.1
Depends:	R (≥ 2.10)
Imports:	abind, bayesm, coda, doParallel, foreach, GIGrvg, graphics, knitr, MASS, Matrix, methods, parallel, Rcpp (≥ 1.0.3), stats, stochvol, utils, xts, zoo
LinkingTo:	Rcpp, RcppArmadillo, RcppProgress, stochvol, GIGrvg
Suggests:	testthat (≥ 2.1.0), rmarkdown
Published:	2020-06-24
Author:	Maximilian Boeck [aut, cre], Martin Feldkircher [aut], Florian Huber [aut], Christopher Sims [ctb]
Maintainer:	Maximilian Boeck <maximilian.boeck at wu.ac.at>
License:	GPL-3
NeedsCompilation:	yes
Language:	en-US
Citation:	BGVAR citation info
Materials:	README NEWS
In views:	TimeSeries
CRAN checks:	BGVAR results

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