Package: BGVAR 2.5.9

Maximilian Boeck

BGVAR: Bayesian Global Vector Autoregressions

Estimation of Bayesian Global Vector Autoregressions (BGVAR) with different prior setups and the possibility to introduce stochastic volatility. Built-in priors include the Minnesota, the stochastic search variable selection and Normal-Gamma (NG) prior. For a reference see also Crespo Cuaresma, J., Feldkircher, M. and F. Huber (2016) "Forecasting with Global Vector Autoregressive Models: a Bayesian Approach", Journal of Applied Econometrics, Vol. 31(7), pp. 1371-1391 <doi:10.1002/jae.2504>. Post-processing functions allow for doing predictions, structurally identify the model with short-run or sign-restrictions and compute impulse response functions, historical decompositions and forecast error variance decompositions. Plotting functions are also available. The package has a companion paper: Boeck, M., Feldkircher, M. and F. Huber (2022) "BGVAR: Bayesian Global Vector Autoregressions with Shrinkage Priors in R", Journal of Statistical Software, Vol. 104(9), pp. 1-28 <doi:10.18637/jss.v104.i09>.

Authors:Maximilian Boeck [aut, cre], Martin Feldkircher [aut], Florian Huber [aut], Darjus Hosszejni [ctb]

BGVAR_2.5.9.tar.gz
BGVAR_2.5.9.tar.gz(r-4.7-arm64)BGVAR_2.5.9.tar.gz(r-4.7-x86_64)BGVAR_2.5.9.tar.gz(r-4.6-arm64)BGVAR_2.5.9.tar.gz(r-4.6-x86_64)
BGVAR_2.5.9.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BGVAR/json (API)
NEWS

# Install 'BGVAR' in R:
install.packages('BGVAR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mboeck11/bgvar/issues

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:
  • dominant - PesaranData
  • EB.weights - Monthly EU / G8 countries macroeconomic dataset
  • eerData - Example data set to replicate Feldkircher and Huber
  • monthlyData - Monthly EU / G8 countries macroeconomic dataset
  • OC.weights - Monthly EU / G8 countries macroeconomic dataset
  • pesaranData - PesaranData
  • pesaranDiff - PesaranData
  • tA - PesaranData
  • testdata - Example data set to show functionality of the package
  • USexpectations - Example data set to replicate Feldkircher and Huber
  • W - Monthly EU / G8 countries macroeconomic dataset
  • W.8016 - PesaranData
  • W.list - Example data set to replicate Feldkircher and Huber
  • W.test - Example data set to show functionality of the package
  • W.trade0012 - Example data set to replicate Feldkircher and Huber

On CRAN:

Conda:

openblascpp

4.26 score 2 stars 181 scripts 511 downloads 16 exports 38 dependencies

Last updated from:50ab45df05. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK195
linux-devel-x86_64OK202
source / vignettesOK400
linux-release-arm64OK226
linux-release-x86_64OK193
wasm-releaseOK147

Exports:add_shockinfoavg.pair.ccbgvarconv.diagdicexcel_to_listfevdget_shockinfogfevdhdirflist_to_matrixlpsmatrix_to_listresid.corr.testrmse

Dependencies:abindbayesmcellrangerclicodacpp11crayonevaluateGIGrvggluehighrhmsknitrlatticelifecyclemagrittrMASSMatrixpillarpkgconfigprettyunitsprogressR6RcppRcppArmadilloRcppParallelRcppProgressreadxlrematchrlangstochvoltibbleutf8vctrsxfunxtsyamlzoo

BGVAR: Bayesian Global Vector Autoregression

Maximilian Böck and Martin Feldkircher and Florian Huber

Rendered fromexamples.Rmdusingknitr::rmarkdownon May 21 2026.

Last update: 2024-07-04
Started: 2020-06-19

Readme and manuals

Help Manual

Help pageTopics
BGVAR: Bayesian Global Vector AutoregressionsBGVAR-package
Adding shocks to 'shockinfo' argumentadd_shockinfo
Average Pairwise Cross-Sectional Correlationsavg.pair.cc
Estimation of Bayesian GVARbgvar
Extract Model Coefficients of Bayesian GVARcoef coef.bgvar coefficients.bgvar
MCMC Convergence Diagnosticsconv.diag
Deviance Information Criteriondic dic.bgvar
Example data set to replicate Feldkircher and Huber (2016)eerData USexpectations W.list W.trade0012
Read Data from Excelexcel_to_list
Forecast Error Variance Decompositionfevd fevd.bgvar.irf
Extract Fitted Values of Bayesian GVARfitted fitted.bgvar
Create 'shockinfo' argumentget_shockinfo
Generalized Forecast Error Variance Decompositiongfevd gfevd.bgvar
Historical Decompositionhd hd.bgvar.irf
Impulse Response Functionirf irf.bgvar
Convert Input List to Matrixlist_to_matrix
Extract Log-likelihood of Bayesian GVARlogLik logLik.bgvar
Compute Log-Predictive Scoreslps lps.bgvar.pred
Convert Input Matrix to Listmatrix_to_list
Monthly EU / G8 countries macroeconomic datasetEB.weights monthlyData OC.weights W
pesaranDatadominant pesaranData pesaranDiff tA W.8016
Graphical Summary of Output Created with 'bgvar'plot plot.bgvar plot.bgvar.fevd plot.bgvar.irf plot.bgvar.pred plot.bgvar.resid
Predictionspredict predict.bgvar
Residual Autocorrelation Testresid.corr.test
Extract Residuals of Bayesian GVARresid.bgvar residuals residuals.bgvar
Compute Root Mean Squared Errorsrmse rmse.bgvar.pred
Summary of Bayesian GVARsummary summary.bgvar
Example data set to show functionality of the packagetestdata W.test
Extract Variance-covariance Matrix of Bayesian GVARvcov vcov.bgvar