Package: BGVAR
Type: Package
Title: Bayesian Global Vector Autoregressions
Version: 2.0.1
Author: Maximilian Boeck [aut, cre] (<https://orcid.org/0000-0001-6024-8305>),
  Martin Feldkircher [aut] (<https://orcid.org/0000-0002-5511-9215>),
  Florian Huber [aut] (<https://orcid.org/0000-0002-2896-7921>),
  Christopher Sims [ctb]
Authors@R: c(person("Maximilian","Boeck",role=c("aut","cre"),email="maximilian.boeck@wu.ac.at",
                    comment = c(ORCID = "0000-0001-6024-8305")),
             person("Martin","Feldkircher", role="aut",
                    comment = c(ORCID = "0000-0002-5511-9215")),
             person("Florian","Huber", role="aut",
                    comment = c(ORCID = "0000-0002-2896-7921")),
             person("Christopher","Sims", role="ctb"))
Maintainer: Maximilian Boeck <maximilian.boeck@wu.ac.at>
Description: 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.
Encoding: UTF-8
License: GPL-3
Language: en-US
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
Suggests: testthat (>= 2.1.0), rmarkdown
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, stochvol, GIGrvg
RoxygenNote: 7.1.0
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2020-06-24 10:12:21 UTC; mboeck
Repository: CRAN
Date/Publication: 2020-06-24 12:10:14 UTC
Built: R 4.0.2; x86_64-w64-mingw32; 2020-08-02 10:33:17 UTC; windows
Archs: i386, x64
