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.
Version: |
1.0.5 |
Depends: |
R (≥ 3.3.0) |
Imports: |
mvtnorm, stats, graphics, utils, grDevices |
Suggests: |
coda, vars, tinytest |
Published: |
2024-02-16 |
DOI: |
10.32614/CRAN.package.BVAR |
Author: |
Nikolas Kuschnig
[aut, cre],
Lukas Vashold
[aut],
Nirai Tomass [ctb],
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: |
README NEWS |
In views: |
Bayesian, TimeSeries |
CRAN checks: |
BVAR results |
Documentation:
Downloads:
Reverse dependencies:
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