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bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R

David Ardia; Lennart F. Hoogerheide


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  <dc:creator>David Ardia</dc:creator>
  <dc:creator>Lennart F. Hoogerheide</dc:creator>
  <dc:date>2017-01-05</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://zenodo.org/record/231327</dc:identifier>
  <dc:identifier>10.5281/zenodo.231327</dc:identifier>
  <dc:identifier>oai:zenodo.org:231327</dc:identifier>
  <dc:relation>url:https://github.com/ArdiaD/bayesGARCH/tree/v2.0.4</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:source>The R Journal 2(2) 41-47</dc:source>
  <dc:subject>GARCH</dc:subject>
  <dc:subject>Bayesian</dc:subject>
  <dc:subject>MCMC</dc:subject>
  <dc:subject>R software</dc:subject>
  <dc:title>bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
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