BVAR-package: BVAR: Hierarchical Bayesian vector autoregression

In BVAR: Hierarchical Bayesian Vector Autoregression

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 [e-mail] (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, \Sexpr[results=rd]{tools:::Rd_expr_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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v100.i14")}.

See Also

Useful links:

• https://github.com/nk027/bvar

• Report bugs at https://github.com/nk027/bvar/issues

BVAR documentation built on May 29, 2024, 5:34 a.m.