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Efficient Estimation of Semiparametric Multivariate Copula
Year Of Publication: 2004
Month Of Publication: September
Pages: 28
Download Count: 332
View Count:
Comment Num: 0
Language: EN
Who Can Read: Free
Date: 3-15-2007
Publisher: Administrator
We propose a sieve maximum likelihood (ML) estimation procedure for a broad class of semiparametricmultivariate distribution models. A joint distribution in this class is characterizedby a parametric copula function evaluated at nonparametric marginal distributions. This classof models has gained popularity in diverse fields due to a) its flexibility in separately modelingthe dependence structure and the marginal behaviors of a multivariate random variable, and b)its circumvention of the “curse of dimensionality” associated with purely nonparametric multivariatedistributions. We show that the plug-in sieve ML estimates of all smooth functionals,including the finite dimensional copula parameters and the unknown marginal distributions,are semiparametrically efficient; and that their asymptotic variances can be estimated consistently.Moreover, prior restrictions on the marginal distributions can be easily incorporatedinto the sieve ML procedure to achieve further efficiency gains. Two such cases are studied inthe paper: (i) the marginal distributions are equal but otherwise unspecified, and (ii) some butnot all marginal distributions are parametric. Monte Carlo studies indicate that the sieve MLestimates perform well in finite samples, especially so when prior information on the marginaldistributions is incorporate
Chen, Xiaohong Sign in to follow this author
Fan, Yanqin Sign in to follow this author
Tsyrennikov, Victor Sign in to follow this author
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