BVAR: Hierarchical Bayesian Vector Autoregression

Estimation of hierarchical Bayesian vector autoregressive models. 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.

Version: 1.0.1
Depends: R (≥ 3.3.0)
Imports: mvtnorm, stats, graphics, utils, grDevices
Suggests: coda, vars, tinytest
Published: 2020-09-27
Author: Nikolas Kuschnig ORCID iD [aut, cre], Lukas Vashold ORCID iD [aut], Michael McCracken [dtc], Serena Ng [dtc]
Maintainer: Nikolas Kuschnig <nikolas.kuschnig at wu.ac.at>
BugReports: https://github.com/nk027/bvar/issues
License: GPL-3 | file LICENSE
URL: https://github.com/nk027/bvar
NeedsCompilation: no
Citation: BVAR citation info
Materials: NEWS
In views: Bayesian, TimeSeries
CRAN checks: BVAR results

Downloads:

Reference manual: BVAR.pdf
Vignettes: BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R
Package source: BVAR_1.0.1.tar.gz
Windows binaries: r-devel: BVAR_1.0.0.zip, r-release: BVAR_1.0.0.zip, r-oldrel: BVAR_1.0.0.zip
macOS binaries: r-release: BVAR_1.0.0.tgz, r-oldrel: BVAR_1.0.0.tgz
Old sources: BVAR archive

Reverse dependencies:

Reverse depends: BVARverse

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