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-4.5)BVAR_1.0.5.zip(r-4.4)BVAR_1.0.5.zip(r-4.3)
BVAR_1.0.5.tgz(r-4.4-any)BVAR_1.0.5.tgz(r-4.3-any)
BVAR_1.0.5.tar.gz(r-4.5-noble)BVAR_1.0.5.tar.gz(r-4.4-noble)
BVAR_1.0.5.tgz(r-4.4-emscripten)BVAR_1.0.5.tgz(r-4.3-emscripten)
BVAR.pdf |BVAR.html✨
BVAR/json (API)
NEWS

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

Peer review:

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:

bayesianbvarforecastsimpulse-responsesvector-autoregressions

7.48 score 50 stars 1 packages 65 scripts 1.0k downloads 3 mentions 28 exports 1 dependencies

Last updated 10 days agofrom:2324bdca1a. Checks:OK: 1 NOTE: 6. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 12 2024
R-4.5-winNOTENov 12 2024
R-4.5-linuxNOTENov 12 2024
R-4.4-winNOTENov 12 2024
R-4.4-macNOTENov 12 2024
R-4.3-winNOTENov 12 2024
R-4.3-macNOTENov 12 2024

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 Nov 12 2024.

Last update: 2021-11-23
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