bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

Version: 0.1.2
Depends: R (≥ 3.3.0)
Imports: colorspace, factorstochvol (≥ 1.1.0), GIGrvg (≥ 0.7), graphics, MASS, mvtnorm, Rcpp (≥ 1.0.0), scales, stats, stochvol (≥ 3.0.3), utils
LinkingTo: factorstochvol, Rcpp, RcppArmadillo, RcppProgress, stochvol
Suggests: coda, knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2024-01-20
Author: Luis Gruber ORCID iD [cph, aut, cre], Gregor Kastner ORCID iD [ctb]
Maintainer: Luis Gruber <Luis.Gruber at aau.at>
BugReports: https://github.com/luisgruber/bayesianVARs/issues
License: GPL (≥ 3)
URL: https://github.com/luisgruber/bayesianVARs, https://luisgruber.github.io/bayesianVARs/
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: bayesianVARs results

Documentation:

Reference manual: bayesianVARs.pdf
Vignettes: Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs**

Downloads:

Package source: bayesianVARs_0.1.2.tar.gz
Windows binaries: r-prerel: bayesianVARs_0.1.2.zip, r-release: bayesianVARs_0.1.2.zip, r-oldrel: bayesianVARs_0.1.2.zip
macOS binaries: r-prerel (arm64): bayesianVARs_0.1.2.tgz, r-release (arm64): bayesianVARs_0.1.2.tgz, r-oldrel (arm64): bayesianVARs_0.1.2.tgz, r-prerel (x86_64): bayesianVARs_0.1.2.tgz, r-release (x86_64): bayesianVARs_0.1.2.tgz
Old sources: bayesianVARs archive

Linking:

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