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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) <https://www.joshuachan.org/papers/OISV.pdf>. 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: | 2.1.0 |
Depends: | R (≥ 3.5.0) |
Imports: | Rcpp (≥ 1.0.7), RcppProgress (≥ 0.1), RcppTN, GIGrvg, R6, stochvol |
LinkingTo: | Rcpp, RcppProgress, RcppArmadillo, RcppTN |
Suggests: | tinytest |
Published: | 2023-12-11 |
Author: | Tomasz Woźniak [aut, cre] |
Maintainer: | Tomasz Woźniak <wozniak.tom at pm.me> |
BugReports: | https://github.com/bsvars/bsvars/issues |
License: | GPL (≥ 3) |
URL: | https://bsvars.github.io/bsvars/ |
NeedsCompilation: | yes |
Citation: | bsvars citation info |
Materials: | README NEWS |
In views: | Bayesian, TimeSeries |
CRAN checks: | bsvars results |
Reference manual: | bsvars.pdf |
Package source: | bsvars_2.1.0.tar.gz |
Windows binaries: | r-devel: bsvars_2.1.0.zip, r-release: bsvars_2.1.0.zip, r-oldrel: bsvars_2.1.0.zip |
macOS binaries: | r-release (arm64): bsvars_2.1.0.tgz, r-oldrel (arm64): bsvars_2.1.0.tgz, r-release (x86_64): bsvars_2.1.0.tgz, r-oldrel (x86_64): bsvars_2.1.0.tgz |
Old sources: | bsvars archive |
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