David Ardia
Lennart F. Hoogerheide
2017-01-05
<p>The package bayesGARCH implements in R (R Core Team, 2016) the Bayesian estimation procedure described in Ardia (2008, chapter 5) for the GARCH(1,1) model with Student-t innovations. The approach consists of a Metropolis-Hastings (MH) algorithm where the proposal distributions are constructed from auxiliary ARMA processes on the squared observations. This methodology avoids the time-consuming and difficult task, especially for non-experts, of choosing and tuning a sampling algorithm. We refer the user to Ardia (2008) and Ardia and Hoogerheide (2010) for illustrations. The latest version of the package is available at https://github.com/ArdiaD/bayesGARCH.</p>
https://doi.org/10.5281/zenodo.231327
oai:zenodo.org:231327
Zenodo
https://github.com/ArdiaD/bayesGARCH/tree/v2.0.4
https://doi.org/
info:eu-repo/semantics/openAccess
Other (Open)
The R Journal, 2(2), 41-47, (2017-01-05)
GARCH
Bayesian
MCMC
R software
bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R
info:eu-repo/semantics/other