Package: BVAR 1.0.5

BVAR: Hierarchical Bayesian Vector Autoregression

Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021) <doi:10.18637/jss.v100.i14>. Implements hierarchical prior selection for conjugate priors in the fashion of Giannone, Lenza & Primiceri (2015) <doi:10.1162/REST_a_00483>. Functions to compute and identify impulse responses, calculate forecasts, forecast error variance decompositions and scenarios are available. Several methods to print, plot and summarise results facilitate analysis.

Authors:Nikolas Kuschnig [aut, cre], Lukas Vashold [aut], Nirai Tomass [ctb], Michael McCracken [dtc], Serena Ng [dtc]

BVAR_1.0.5.tar.gz
BVAR_1.0.5.zip(r-4.7)BVAR_1.0.5.zip(r-4.6)BVAR_1.0.5.zip(r-4.5)
BVAR_1.0.5.tgz(r-4.6-any)BVAR_1.0.5.tgz(r-4.5-any)
BVAR_1.0.5.tar.gz(r-4.7-any)BVAR_1.0.5.tar.gz(r-4.6-any)
BVAR_1.0.5.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
card.svg |card.png
BVAR/json (API)
NEWS

# Install 'BVAR' in R:
install.packages('BVAR', repos = c('https://nk027.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/nk027/bvar/issues

Datasets:
  • fred_md - FRED-MD and FRED-QD: Databases for Macroeconomic Research
  • fred_qd - FRED-MD and FRED-QD: Databases for Macroeconomic Research

On CRAN:

Conda:

bayesianbvarforecastsimpulse-responsesvector-autoregressions

8.16 score 57 stars 2 packages 118 scripts 1.2k downloads 3 mentions 28 exports 1 dependencies

Last updated from:f6b282250f. Checks:7 WARNING, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64WARNING136
source / vignettesOK243
linux-release-x86_64WARNING96
macos-release-arm64WARNING73
macos-oldrel-arm64WARNING73
windows-develWARNING96
windows-releaseWARNING77
windows-oldrelWARNING83
wasm-releaseOK89

Exports:bv_alphabv_dummybv_fcastbv_irfbv_lambdabv_metropolisbv_mhbv_minnesotabv_mnbv_priorsbv_psibv_socbv_surbvarcompanionfevdfevd<-fred_codefred_transformhist_decompindependent_indexirfirf<-lpspar_bvarpredict<-rmseWAIC

Dependencies:mvtnorm

BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Rendered fromarticle.Rnwusingutils::Sweaveon May 28 2026.

Last update: 2026-04-27
Started: 2020-02-16

Readme and manuals

Help Manual

Help pageTopics
BVAR: Hierarchical Bayesian vector autoregressionBVAR-package BVAR
Dummy prior settingsbv_dummy bv_soc bv_sur
Forecast settingsbv_fcast
Impulse response settings and identificationbv_irf
Metropolis-Hastings settingsbv_metropolis bv_mh
Minnesota prior settingsbv_alpha bv_lambda bv_minnesota bv_mn bv_psi
Prior settingsbv_priors
Hierarchical Bayesian vector autoregressionbvar
Methods for 'coda' Markov chain Monte Carlo objectsas.mcmc.bvar as.mcmc.bvar_chains coda
Coefficient and VCOV methods for Bayesian VARscoef.bvar vcov.bvar
Retrieve companion matrix from a Bayesian VARcompanion companion.bvar companion.default
Density methods for Bayesian VARsdensity.bvar independent_index plot.bvar_density
Fitted and residual methods for Bayesian VARsfitted.bvar plot.bvar_resid residuals.bvar
FRED-MD and FRED-QD: Databases for Macroeconomic Researchfred_md fred_qd
FRED transformation and subset helperfred_code fred_transform
Historical decompositionhist_decomp hist_decomp.bvar hist_decomp.default
Impulse response and forecast error methods for Bayesian VARsfevd fevd.bvar fevd.default fevd<- irf irf.bvar irf.default irf<- summary.bvar_irf
Log-Likelihood method for Bayesian VARslogLik.bvar
Parallel hierarchical Bayesian vector autoregressionpar_bvar
Plotting method for Bayesian VARsplot.bvar
Plotting method for Bayesian VAR predictionsplot.bvar_fcast
Plotting method for Bayesian VAR impulse responsesplot.bvar_irf
Predict method for Bayesian VARspredict.bvar predict<- summary.bvar_fcast
Model fit in- and out-of-samplelps lps.bvar lps.default rmse rmse.bvar rmse.default
Summary method for Bayesian VARssummary.bvar
Widely applicable information criterion (WAIC) for Bayesian VARsWAIC WAIC.bvar WAIC.default