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>
License: GPL-3 | file LICENSE
NeedsCompilation: no
Citation: BVAR citation info
Materials: NEWS
In views: Bayesian, TimeSeries
CRAN checks: BVAR results


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:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): BVAR_1.0.1.tgz, r-release (x86_64): BVAR_1.0.1.tgz, r-oldrel: BVAR_1.0.1.tgz
Old sources: BVAR archive

Reverse dependencies:

Reverse depends: BVARverse


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