bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2023) <doi:10.48550/arXiv.2206.04902>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>.

README.md Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs** Shrinkage Priors for Bayesian Vectorautoregressions featuring Stochastic Volatility Using the **R** Package **bayesianVARs**

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Package details
Author	Luis Gruber [cph, aut, cre] (<https://orcid.org/0000-0002-2399-738X>), Gregor Kastner [ctb] (<https://orcid.org/0000-0002-8237-8271>)
Maintainer	Luis Gruber <Luis.Gruber@aau.at>
License	GPL (>= 3)
Version	0.1.5
URL	https://github.com/luisgruber/bayesianVARs https://luisgruber.github.io/bayesianVARs/
Package repository	View on CRAN
Installation	Install the latest version of this package by entering the following in R: `install.packages("bayesianVARs")`