JSM 2005 Online Program

Abstract #304540

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	267
Type:	Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Business and Economics Statistics Section
Abstract - #304540
Title:	Semiparametric Estimation of a Multivariate Distribution Using Copulas when Each Variable Satisfies a Regression Model
Author(s):	Mervyn Silvapulle*+ and Gunky Kim and Paramsothy Silvapulle
Companies:	Monash University and Monash University and Monash University
Address:	Dept of Econometrics and Business Stats, Caulfield East, 3145, Australia
Keywords:	copula ; dependence parameter ; multivariate regression ; semiparametric
Abstract:	The family of copulas offer a flexible approach to modeling the joint distribution of multivariate observations. We study the case when each variable satisfies a linear regression model and the joint distribution of the error terms is specified by a copula. Our main objective is to estimate the parameter of the copula. The marginal distribution of each error term is estimated nonparametrically, but the copula that captures the joint distribution of the error terms is estimated parametrically. We show the estimate of the copula parameter is consistent and asymptotically normal. A formula is obtained to estimate the large sample standard error of the estimate. The results of a simulation study show this semiparametric method has excellent robustness properties against misspecification of the marginal distributions of the error terms.

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