Package: bayesianVARs 0.1.5
Luis Gruber
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>.
Authors:
bayesianVARs_0.1.5.tar.gz
bayesianVARs_0.1.5.tar.gz(r-4.5-noble)bayesianVARs_0.1.5.tar.gz(r-4.4-noble)
bayesianVARs.pdf |bayesianVARs.html✨
bayesianVARs/json (API)
NEWS
| # Install 'bayesianVARs' in R: |
| install.packages('bayesianVARs', repos = 'https://cloud.r-project.org') |
Bug tracker:https://github.com/luisgruber/bayesianvars/issues2 issues
Pkgdown site:https://luisgruber.github.io
- usmacro_growth - Data from the US-economy
Last updated 5 months agofrom:f4eabc524e. Checks:1 OK, 2 WARNING. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 14 2025 |
| R-4.5-linux-x86_64 | WARNING | Mar 14 2025 |
| R-4.4-linux-x86_64 | WARNING | Mar 14 2025 |
Exports:bvarmy_gigposterior_heatmapspecify_prior_phispecify_prior_sigmastable_bvar
Dependencies:clicodacolorspacecorrplotfactorstochvolfarverGIGrvggluelabelinglatticelifecycleMASSmunsellmvtnormR6RColorBrewerRcppRcppArmadilloRcppProgressrlangscalesstochvolviridisLite
Citation
To cite package ‘bayesianVARs’ in publications use:
Gruber L (2024). bayesianVARs: MCMC Estimation of Bayesian Vectorautoregressions. R package version 0.1.5, https://CRAN.R-project.org/package=bayesianVARs.
Corresponding BibTeX entry:
@Manual{,
title = {bayesianVARs: MCMC Estimation of Bayesian
Vectorautoregressions},
author = {Luis Gruber},
year = {2024},
note = {R package version 0.1.5},
url = {https://CRAN.R-project.org/package=bayesianVARs},
}
Readme and manuals
bayesianVARs
Estimation of Bayesian vectorautoregressions with/without stochastic volatility.
Implements several modern hierarchical shrinkage priors, amongst them Dirichlet-Laplace prior (DL), hierarchical Minnesota prior (HM), Horseshoe prior (HS), normal-gamma prior (NG), $R^2$-induced-Dirichlet-decomposition prior (R2D2) and stochastic search variable selection prior (SSVS).
Concerning the error-term, the user can either specify an order-invariant factor structure or an order-variant cholesky structure.
Installation
Install CRAN version:
install.packages("bayesianVARs")
Install latest development version directly from GitHub:
devtools::install_github("luisgruber/bayesianVARs")
Usage
The main workhorse to conduct Bayesian inference for
vectorautoregression models in this package is the function bvar().
Some features:
- Prediction, plotting, extraction of model parameters and extraction of
fitted values with the usual generic functions
predict(),plot(),coef(),vcov()andfitted(). - Configure prior distributions with helper functions
specify_prior_phi()andspecify_prior_sigma().
Demonstration
set.seed(537)
# load package
library(bayesianVARs)
# Load data
train_data <-100 * usmacro_growth[1:237,c("GDPC1", "PCECC96", "GPDIC1", "AWHMAN", "GDPCTPI", "CES2000000008x", "FEDFUNDS", "GS10", "EXUSUKx", "S&P 500")]
test_data <-100 * usmacro_growth[238:241,c("GDPC1", "PCECC96", "GPDIC1", "AWHMAN", "GDPCTPI", "CES2000000008x", "FEDFUNDS", "GS10", "EXUSUKx", "S&P 500")]
# Estimate model using default prior settings
mod <- bvar(train_data, lags = 2L, draws = 2000, burnin = 1000, sv_keep = "all")
# Out of sample prediction and log-predictive-likelihood evaluation
pred <- predict(mod, ahead = 1:4, LPL = TRUE, Y_obs = test_data)
# Visualize in-sample fit plus out-of-sample prediction intervals
plot(mod, predictions = pred)
Documentation
bayesianVARs - Shrinkage Priors for Bayesian Vectorautoregressions in R