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) <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:	2.0.0
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:	2023-10-24
Author:	Tomasz Woźniak [aut, cre]
Maintainer:	Tomasz Woźniak <wozniak.tom at pm.me>
BugReports:	https://github.com/bsvars/bsvars/issues
License:	GPL (≥ 3)
NeedsCompilation:	yes
Citation:	bsvars citation info
Materials:	README NEWS
In views:	Bayesian, TimeSeries
CRAN checks:	bsvars results

Documentation:

Reference manual:

bsvars.pdf

Downloads:

Package source:	bsvars_2.0.0.tar.gz
Windows binaries:	r-devel: bsvars_2.0.0.zip, r-release: bsvars_2.0.0.zip, r-oldrel: bsvars_2.0.0.zip
macOS binaries:	r-release (arm64): bsvars_2.0.0.tgz, r-oldrel (arm64): bsvars_2.0.0.tgz, r-release (x86_64): bsvars_2.0.0.tgz, r-oldrel (x86_64): bsvars_1.0.0.tgz
Old sources:	bsvars archive

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