Advances and Applications in Statistics
Volume 10, Issue 1, Pages 25 - 40
(October 2008)
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CHOICE OF PARAMETRIC FAMILIES OF COPULAS
Mariana Craiu (Romania) and Radu V. Craiu (Canada)
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Abstract: Copulas
have evolved into a popular tool for modeling dependence in a large number of
statistical models. Choosing a copula from an ever increasing set of
possibilities presents difficulties that are well recognized in the literature.
In this paper we investigate via simulation the effect of copula
misspecification on various quantities of interest in the model such as
conditional means and conditional variances. We also investigate methods to
select among a number of candidate families of parametric copulas using
nonparametric kernel smoothing and various distances between distributions. Both
the Kullback-Leibler divergence and the Hellinger distance perform very well in
this setting. |
Keywords and phrases: copulas, Hellinger distance, Kullback-Leibler distance, kernel smoothing, Metropolis-Hastings algorithm. |
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