Bayesian Global Vector Autoregressions R package BGVAR version 2.5.9 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>. Software Depends: R (>= 3.5.0) Imports: abind, bayesm, coda, GIGrvg, graphics, knitr, MASS, Matrix, methods, parallel, Rcpp (>= 1.0.3), RcppParallel, readxl, stats, stochvol (>= 3.0.3), utils, xts, zoo LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppParallel, stochvol, GIGrvg Suggests: rmarkdown, testthat (>= 2.1.0) Maximilian Boeck <maximilian.boeck@fau.de> Comprehensive R Archive Network (CRAN) Maximilian Boeck [aut, cre] (ORCID: <https://orcid.org/0000-0001-6024-8305>), Martin Feldkircher [aut] (ORCID: <https://orcid.org/0000-0002-5511-9215>), Florian Huber [aut] (ORCID: <https://orcid.org/0000-0002-2896-7921>), Darjus Hosszejni [ctb] (ORCID: <https://orcid.org/0000-0002-3803-691X>) GPL-3 2025-09-22 application/tgz https://CRAN.R-project.org/package=BGVAR doi:10.32614/CRAN.package.BGVAR en-US