Comparison of semiparametric and parametric methods for estimating copulas

  • Authors:

  • Gunky Kim;Mervyn J. Silvapulle;Paramsothy Silvapulle

  • Affiliations:

  • Department of Econometrics and Business Statistics, Monash University, Caulfield East, Melbourne 3145, Australia;Department of Econometrics and Business Statistics, Monash University, Caulfield East, Melbourne 3145, Australia;Department of Econometrics and Business Statistics, Monash University, Caulfield East, Melbourne 3145, Australia

  • Venue:
  • Computational Statistics & Data Analysis
  • Year:
  • 2007

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Abstract

Copulas have attracted significant attention in the recent literature for modeling multivariate observations. An important feature of copulas is that they enable us to specify the univariate marginal distributions and their joint behavior separately. The copula parameter captures the intrinsic dependence between the marginal variables and it can be estimated by parametric or semiparametric methods. For practical applications, the so called inference function for margins (IFM) method has emerged as the preferred fully parametric method because it is close to maximum likelihood (ML) in approach and is easier to implement. The purpose of this paper is to compare the ML and IFM methods with a semiparametric (SP) method that treats the univariate marginal distributions as unknown functions. In this paper, we consider the SP method proposed by Genest et al. [1995. A semiparametric estimation procedure of dependence parameters in multivariate families of distributions. Biometrika 82(3), 543-552], which has attracted considerable interest in the literature. The results of an extensive simulation study reported here show that the ML/IFM methods are nonrobust against misspecification of the marginal distributions, and that the SP method performs better than the ML and IFM methods, overall. A data example on household expenditure is used to illustrate the application of various data analytic methods for applying the SP method, and to compare and contrast the ML, IFM and SP methods. The main conclusion is that, in terms of statistical computations and data analysis, the SP method is better than ML and IFM methods when the marginal distributions are unknown which is almost always the case in practice.