An R package for Bayesian Estimation of Structural Vector Autoregressive Models

CRAN version R-CMD-check

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.

The heteroskedastic SVAR model setup is similar as in Woźniak & Droumaguet (2015) and Lütkepohl & Woźniak (2020). The sampler of the structural matrix follows Waggoner & Zha (2003), whereas that for autoregressive parameters follows Chan, Koop, Yu (2022). The specification of Markov switching heteroskedasticity is inspired by Song & Woźniak (2021), and that of Stochastic Volatility model by Kastner & Frühwirth-Schnatter (2014).


Structural Vector Autoregressions

    Y = AX + E           (VAR equation)
   BE = U                (structural equation)

Simple workflows

Fast and efficient computations

bsvars: Bayesian Structural Vector Autoregressions|
 Gibbs sampler for the SVAR-SV model              |
   Non-centred SV model is estimated              |
 Progress of the MCMC simulation for 1000 draws
    Every 10th draw is saved via MCMC thinning
 Press Esc to interrupt the computations
0%   10   20   30   40   50   60   70   80   90   100%

Start your Bayesian analysis of data

The beginnings are as easy as ABC:

library(bsvars)                       # upload the package
data(us_fiscal_lsuw)                  # upload data
spec      = specify_bsvar_sv$new(us_fiscal_lsuw, p = 4)     # specify the model
burn_in   = estimate(spec, 1000)      # run the burn-in
out       = estimate(burn_in, 50000)  # estimate the model

Starting from bsvars version 2.0.0 a simplified workflow using the |> pipe is possible:

library(bsvars)                       # upload the package
data(us_fiscal_lsuw)                  # upload data
us_fiscal_lsuw |>
  specify_bsvar_sv$new(p = 4) |>      # specify the model
  estimate(S = 1000) |>               # run the burn-in
  estimate(S = 50000) -> out          # estimate the model

Now, you’re ready to analyse your model!


Just open your R and type:


The developer’s version of the package with the newest features can be installed by typing:



The package is under intensive development. Your help is welcome! Please, have a look at the roadmap, discuss package features and applications, or report a bug. Thank you!

About the author

Tomasz is a Bayesian econometrician and a Senior Lecturer at the University of Melbourne. He develops methodology for empirical macroeconomic analyses and programs in R and cpp using Rcpp.