Fast and Efficient Bayesian Analysis of Structural Vector Autoregressions Using the R Package bsvars

TL;DR

The bsvars R package enables fast, flexible Bayesian analysis of Structural Vector Autoregressions, supporting complex models with heteroskedastic shocks and hierarchical priors for macroeconomic and financial data.

Contribution

It introduces a comprehensive R package that combines econometric techniques, hierarchical priors, and C++ efficiency for structural VAR analysis with advanced features.

Findings

Fast estimation with C++ implementation

Supports complex identification strategies

Enables detailed structural and predictive analysis

Abstract

The R package bsvars provides a wide range of tools for empirical macroeconomic and financial analyses using Bayesian Structural Vector Autoregressions. It uses frontier econometric techniques and C++ code to ensure fast and efficient estimation of these multivariate dynamic structural models, possibly with many variables, complex identification strategies, and non-linear characteristics. The models can be identified using adjustable exclusion restrictions and heteroskedastic or non-normal shocks. They feature 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, statistical verification of identification and hypotheses on autoregressive parameters, and analyses of structural shocks, volatilities, and fitted values. These features differentiate bsvars from existing R packages that either focus on a specific structural model, do not consider heteroskedastic shocks, or lack the implementation using compiled code.