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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:
Linking:
Please use the canonical form
https://CRAN.R-project.org/package=BVAR
to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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