Efficient algorithms for Bayesian estimation of Structural Vector Autoregressive (SVAR) models via Markov chain Monte Carlo methods.
The R package is available on CRAN, GitHub and on the package website.
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>.
Woźniak T (2023). bsvars: Bayesian Estimation of Structural Vector Autoregressive Models. R package version 2.0.0, https://cran.r-project.org/package=bsvars.