bsvars: Bayesian Estimation of Structural Vector Autoregressive Models

Efficient algorithms for Bayesian estimation of Structural Vector Autoregressive (SVAR) models via Markov chain Monte Carlo methods. A wide range of SVAR models is considered, including homo- and heteroskedastic specifications and those with non-normal structural shocks. The heteroskedastic SVAR model setup is similar as in Woźniak & Droumaguet (2015) <doi:10.13140/RG.2.2.19492.55687> and Lütkepohl & Woźniak (2020) <doi:10.1016/j.jedc.2020.103862>. The sampler of the structural matrix follows Waggoner & Zha (2003) <doi:10.1016/S0165-1889(02)00168-9>, whereas that for autoregressive parameters follows Chan, Koop, Yu (2022) <>. The specification of Markov switching heteroskedasticity is inspired by Song & Woźniak (2021) <doi:10.1093/acrefore/9780190625979.013.174>, and that of Stochastic Volatility model by Kastner & Frühwirth-Schnatter (2014) <doi:10.1016/j.csda.2013.01.002>.

Version: 1.0.0
Depends: R (≥ 3.5.0)
Imports: Rcpp (≥ 1.0.7), RcppProgress (≥ 0.1), RcppTN, GIGrvg, R6
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, RcppTN
Suggests: tinytest
Published: 2022-09-01
Author: Tomasz Woźniak ORCID iD [aut, cre]
Maintainer: Tomasz Woźniak <wozniak.tom at>
License: GPL (≥ 3)
NeedsCompilation: yes
Materials: README NEWS
In views: TimeSeries
CRAN checks: bsvars results


Reference manual: bsvars.pdf


Package source: bsvars_1.0.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel: not available
macOS binaries: r-release (arm64): bsvars_1.0.0.tgz, r-oldrel (arm64): bsvars_1.0.0.tgz, r-release (x86_64): bsvars_1.0.0.tgz, r-oldrel (x86_64): bsvars_1.0.0.tgz


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