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Jackknife empirical likelihood for parametric copulas

Author

Listed:
  • Ruodu Wang
  • Liang Peng
  • Jingping Yang

Abstract

For fitting a parametric copula to multivariate data, a popular way is to employ the so-called pseudo maximum likelihood estimation proposed by Genest, Ghoudi, and Rivest. Although interval estimation can be obtained via estimating the asymptotic covariance of the pseudo maximum likelihood estimation, we propose a jackknife empirical likelihood method to construct confidence regions for the parameters without estimating any additional quantities such as the asymptotic covariance. A simulation study shows the advantages of the new method in case of strong dependence or having more than one parameter involved.

Suggested Citation

  • Ruodu Wang & Liang Peng & Jingping Yang, 2013. "Jackknife empirical likelihood for parametric copulas," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2013(5), pages 325-339.
    • Handle: RePEc:taf:sactxx:v:2013:y:2013:i:5:p:325-339
    DOI: 10.1080/03461238.2011.611893
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