Package: bsvars
Type: Package
Title: Bayesian Estimation of Structural Vector Autoregressive Models
Version: 1.0.0
Date: 2022-08-29
Authors@R: person("Tomasz", "Woźniak", , "wozniak.tom@pm.me", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0003-2212-2378"))
Description: 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>.
License: GPL (>= 3)
Maintainer: Tomasz Woźniak <wozniak.tom@pm.me>
Encoding: UTF-8
Imports: Rcpp (>= 1.0.7), RcppProgress (>= 0.1), RcppTN, GIGrvg, R6
Suggests: tinytest
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, RcppTN
RoxygenNote: 7.2.1
BugReports: https://github.com/donotdespair/bsvars/issues
NeedsCompilation: yes
Packaged: 2022-09-01 05:11:11 UTC; twozniak
Author: Tomasz Woźniak [aut, cre] (<https://orcid.org/0000-0003-2212-2378>)
Depends: R (>= 3.5.0)
Repository: CRAN
Date/Publication: 2022-09-01 15:40:06 UTC
Built: R 4.1.1; aarch64-apple-darwin20; 2022-09-01 18:07:29 UTC; unix
Archs: bsvars.so.dSYM
