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 | fileLICENSE |
| URL: | https://github.com/nk027/bvar |
| NeedsCompilation: | no |
| Citation: | BVAR citation info |
| Materials: | README,NEWS |
| In views: | Bayesian,TimeSeries |
| CRAN checks: | BVAR results |
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