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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: | 0.1.3 |
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: | 2024-06-25 |
DOI: | 10.32614/CRAN.package.bayesianVARs |
Author: | Luis Gruber |
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 |
Reference manual: | bayesianVARs.pdf |
Vignettes: |
Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs** |
Package source: | bayesianVARs_0.1.3.tar.gz |
Windows binaries: | r-devel: bayesianVARs_0.1.3.zip, r-release: bayesianVARs_0.1.3.zip, r-oldrel: bayesianVARs_0.1.3.zip |
macOS binaries: | r-release (arm64): bayesianVARs_0.1.3.tgz, r-oldrel (arm64): bayesianVARs_0.1.3.tgz, r-release (x86_64): bayesianVARs_0.1.3.tgz, r-oldrel (x86_64): bayesianVARs_0.1.3.tgz |
Old sources: | bayesianVARs archive |
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