bsvars - Bayesian Estimation of Structural Vector Autoregressive Models
Provides fast and efficient procedures for Bayesian
analysis of Structural Vector Autoregressions. This package
estimates a wide range of models, including homo-,
heteroskedastic, and non-normal specifications. Structural
models can be identified by adjustable exclusion restrictions,
time-varying volatility, or non-normality. They all include a
flexible 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 such as impulse responses, forecast error variance and
historical decompositions, forecasting, verification of
heteroskedasticity, non-normality, and hypotheses on
autoregressive parameters, as well as analyses of structural
shocks, volatilities, and fitted values. Beautiful plots,
informative summary functions, and extensive documentation
including the vignette by Woźniak (2024)
<doi:10.48550/arXiv.2410.15090> complement all this. The
implemented techniques align closely with those presented in
Lütkepohl, Shang, Uzeda, & Woźniak (2024)
<doi:10.48550/arXiv.2404.11057>, Lütkepohl & Woźniak (2020)
<doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021)
<doi:10.1093/acrefore/9780190625979.013.174>. The 'bsvars'
package is aligned regarding objects, workflows, and code
structure with the R package 'bsvarSIGNs' by Wang & Woźniak
(2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they
constitute an integrated toolset.
