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  [cph,
aut, cre],
Gregor Kastner
[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 |