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: 0.1.5 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: 2024-11-13 DOI: 10.32614/CRAN.package.bayesianVARs 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: Bayesian, TimeSeries CRAN checks: bayesianVARs resultsRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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