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Efficient maximum likelihood estimation of copula based meta t-distributions

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  • Zhang, Ran
  • Czado, Claudia
  • Min, Aleksey

Abstract

Recently an efficient fixed point algorithm, called maximization by parts (MBP), for finding maximum likelihood estimates has been applied to models based on Gaussian copulas. It requires a decomposition of a likelihood function into two parts and their iterative maximization by solving score equations. For the first time, the MBP algorithm is applied to multivariate meta t-distributions based on t-copulas. Since score equations for meta t-distributions do not have closed forms the proposed MBP algorithm in two variations maximizes the decomposed parts of the likelihood iteratively. Superiority of the proposed MBP algorithm over standard estimation methods such as inference for margins and direct maximization is illustrated in a simulation study. The usefulness of the proposed algorithm is shown in two data applications.

Suggested Citation

  • Zhang, Ran & Czado, Claudia & Min, Aleksey, 2011. "Efficient maximum likelihood estimation of copula based meta t-distributions," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1196-1214, March.
  • Handle: RePEc:eee:csdana:v:55:y:2011:i:3:p:1196-1214
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    References listed on IDEAS

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    Cited by:

    1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 90-110.
    2. Zhang, Kong-Sheng & Lin, Jin-Guan & Xu, Pei-Rong, 2016. "A new class of copulas involving geometric distribution: Estimation and applications," Insurance: Mathematics and Economics, Elsevier, vol. 66(C), pages 1-10.

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