bsvars
We develop R packages for Bayesian Structural Vector Autoregressions using frontier econometric methods and compiled code written in cpp.
bsvars
An R package for Bayesian Estimation of Structural Vector Autoregressive Models
bsvars v2.1.0 is now on CRAN
This package provides 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. All models include three-level equation-specific local-global hierarchical prior distribution for the estimated level of shrinkage for autoregressive and structural parameters. Additionally, the package facilitates predictive and structural analyses with a variety of tools and methods.
bsvarTVPs
An R package for Bayesian Estimation of Heteroskedastic Structural Vector Autoregressions with Markov-Switching and Time-Varying Identification of the Structural Matrix
by Tomasz Woźniak and Annika Camehl
Efficient algorithms for Bayesian estimation of Structural Vector Autoregressions with Stochastic Volatility heteroskedasticity, Markov-switching and Time-Varying Identification of the Structural Matrix, and a three-level global-local hierarchical prior shrinkage for the structural and autoregressive matrices. The models were developed for a paper by Camehl & Woźniak (2023) Time-Varying Identification of Monetary Policy Shocks
bsvarSIGNs
An R package for Bayesian Estimation of Structural Vector Autoregressive Models Identified by Sign and Narrative Restrictions
by Xiaolei Wang and Tomasz Woźniak
Implements efficient algorithms for the Bayesian estimation of Stuructural Vector Autoregressive models identified by sign and narrative restrictions following Rubio-Ramírez, Waggoner & Zha (2010) and Antolín-Díaz & Rubio-Ramírez (2018).