CRAN/E | bayesianVARs

bayesianVARs

MCMC Estimation of Bayesian Vectorautoregressions

Installation

About

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) . 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.

github.com/luisgruber/bayesianVARs
luisgruber.github.io/bayesianVARs/
Bug report File report

Key Metrics

Version 0.1.2
R ≥ 3.3.0
Published 2024-01-20 106 days ago
Needs compilation? yes
License GPL (≥ 3)
CRAN checks bayesianVARs results

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Maintainer

Maintainer

Luis Gruber

Luis.Gruber@aau.at

Authors

Luis Gruber

cph / aut / cre

Gregor Kastner

ctb

Material

README
NEWS
Reference manual
Package source

Vignettes

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

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

bayesianVARs archive

Depends

R ≥ 3.3.0

Imports

colorspace
factorstochvol ≥ 1.1.0
GIGrvg ≥ 0.7
graphics
MASS
mvtnorm
Rcpp ≥ 1.0.0
scales
stats
stochvol ≥ 3.0.3
utils

Suggests

coda
knitr
rmarkdown
testthat ≥ 3.0.0

LinkingTo

factorstochvol
Rcpp
RcppArmadillo
RcppProgress
stochvol