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✨
DESCRIPTION |NEWS
card.svg |card.png
BVAR/json (API)

# 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:

bayesian bvar forecasts impulse-responses vector-autoregressions

8.13 score 57 stars 2 packages 118 scripts 1.1k downloads 3 mentions 28 exports 1 dependencies

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

Target	Result	Time
linux-devel-x86_64	WARNING	122
source / vignettes	OK	256
linux-release-x86_64	WARNING	161
macos-release-arm64	WARNING	85
macos-oldrel-arm64	WARNING	74
windows-devel	WARNING	67
windows-release	WARNING	62
windows-oldrel	WARNING	71
wasm-release	OK	94

Exports:bv_alpha bv_dummy bv_fcast bv_irf bv_lambda bv_metropolis bv_mh bv_minnesota bv_mn bv_priors bv_psi bv_soc bv_sur bvar companion fevd fevd<-fred_code fred_transform hist_decomp independent_index irf irf<-lps par_bvar predict<-rmse WAIC

Dependencies:mvtnorm

BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R

Rendered fromarticle.Rnwusingutils::Sweave

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

Help page	Topics
BVAR: Hierarchical Bayesian vector autoregression	BVAR-package BVAR
Dummy prior settings	bv_dummy bv_soc bv_sur
Forecast settings	bv_fcast
Impulse response settings and identification	bv_irf
Metropolis-Hastings settings	bv_metropolis bv_mh
Minnesota prior settings	bv_alpha bv_lambda bv_minnesota bv_mn bv_psi
Prior settings	bv_priors
Hierarchical Bayesian vector autoregression	bvar
Methods for 'coda' Markov chain Monte Carlo objects	as.mcmc.bvar as.mcmc.bvar_chains coda
Coefficient and VCOV methods for Bayesian VARs	coef.bvar vcov.bvar
Retrieve companion matrix from a Bayesian VAR	companion companion.bvar companion.default
Density methods for Bayesian VARs	density.bvar independent_index plot.bvar_density
Fitted and residual methods for Bayesian VARs	fitted.bvar plot.bvar_resid residuals.bvar
FRED-MD and FRED-QD: Databases for Macroeconomic Research	fred_md fred_qd
FRED transformation and subset helper	fred_code fred_transform
Historical decomposition	hist_decomp hist_decomp.bvar hist_decomp.default
Impulse response and forecast error methods for Bayesian VARs	fevd fevd.bvar fevd.default fevd<- irf irf.bvar irf.default irf<- summary.bvar_irf
Log-Likelihood method for Bayesian VARs	logLik.bvar
Parallel hierarchical Bayesian vector autoregression	par_bvar
Plotting method for Bayesian VARs	plot.bvar
Plotting method for Bayesian VAR predictions	plot.bvar_fcast
Plotting method for Bayesian VAR impulse responses	plot.bvar_irf
Predict method for Bayesian VARs	predict.bvar predict<- summary.bvar_fcast
Model fit in- and out-of-sample	lps lps.bvar lps.default rmse rmse.bvar rmse.default
Summary method for Bayesian VARs	summary.bvar
Widely applicable information criterion (WAIC) for Bayesian VARs	WAIC WAIC.bvar WAIC.default

Package: BVAR 1.0.5

BVAR: Hierarchical Bayesian Vector Autoregression

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