| BVAR-package {BVAR} | R Documentation |
BVAR: Hierarchical Bayesian vector autoregression
Description
Estimation of hierarchical Bayesian vector autoregressive models following Kuschnig & Vashold (2021). 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.
Author(s)
Maintainer: Nikolas Kuschnig nikolas.kuschnig@wu.ac.at (ORCID)
Authors:
Lukas Vashold (ORCID)
Other contributors:
Nirai Tomass [contributor]
Michael McCracken [data contributor]
Serena Ng [data contributor]
References
Giannone, D. and Lenza, M. and Primiceri, G. E. (2015) Prior Selection for Vector Autoregressions. The Review of Economics and Statistics, 97:2, 436-451, doi:10.1162/REST_a_00483.
Kuschnig, N. and Vashold, L. (2021) BVAR: Bayesian Vector Autoregressions with Hierarchical Prior Selection in R. Journal of Statistical Software, 14, 1-27, doi:10.18637/jss.v100.i14.
See Also
Useful links: