Package: BGVAR
Type: Package
Title: Bayesian Global Vector Autoregressions
Version: 2.5.5
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>),
  Darjus Hosszejni [ctb] (<https://orcid.org/0000-0002-3803-691X>)
Authors@R: c(person("Maximilian","Boeck",role=c("aut","cre"),email="maximilian.boeck@unibocconi.it",
                    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("Darjus","Hosszejni", role="ctb",
                    comment = c(ORCID = "0000-0002-3803-691X")))
Maintainer: Maximilian Boeck <maximilian.boeck@unibocconi.it>
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. 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>.
Encoding: UTF-8
License: GPL-3
Language: en-US
URL: https://github.com/mboeck11/BGVAR
BugReports: https://github.com/mboeck11/BGVAR/issues
Depends: R (>= 3.5.0)
SystemRequirements: GNU make
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
Suggests: rmarkdown, testthat (>= 2.1.0)
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppParallel, stochvol,
        GIGrvg
RoxygenNote: 7.2.3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2023-12-13 15:37:22 UTC; mboeck
Repository: CRAN
Date/Publication: 2023-12-13 22:30:08 UTC
Built: R 4.2.3; x86_64-w64-mingw32; 2024-04-24 02:42:36 UTC; windows
ExperimentalWindowsRuntime: ucrt
Archs: x64
