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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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{
  "description": "<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>", 
  "license": "", 
  "creator": [
    {
      "affiliation": "University of Neuch\u00e2tel", 
      "@type": "Person", 
      "name": "David Ardia"
    }, 
    {
      "affiliation": "Vrije Universiteit Amsterdam", 
      "@type": "Person", 
      "name": "Lennart F. Hoogerheide"
    }
  ], 
  "url": "https://zenodo.org/record/231327", 
  "codeRepository": "https://github.com/ArdiaD/bayesGARCH/tree/v2.0.4", 
  "datePublished": "2017-01-05", 
  "version": "v2.0.4", 
  "keywords": [
    "GARCH", 
    "Bayesian", 
    "MCMC", 
    "R software"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.231327", 
  "@id": "https://doi.org/10.5281/zenodo.231327", 
  "@type": "SoftwareSourceCode", 
  "name": "bayesGARCH: Bayesian Estimation of the GARCH(1,1) Model with Student-t Innovations in R"
}
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