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On the classical estimation of bivariate copula-based Seemingly Unrelated Tobit models through the proposed inference function for augmented margins method

Título:
On the classical estimation of bivariate copula-based Seemingly Unrelated Tobit models through the proposed inference function for augmented margins method
Quando:
Sex, 17. Junho 2016, 14:00 h
Categoria:
Científico

Descrição

A convite do Centro de Ciências Matemáticas Aplicadas à Indústria (CeMEAI), o professor Paulo Henrique Ferreira, do Departamento de Estatística da Universidade Federal da Bahia (UFBA), dará uma palestra na Universidade Federal de São Carlos (UFSCar).

A apresentação será nesta sexta-feira (17 de junho), às 14h, na sala 43 do Departamento de Estatística da UFSCar. O evento é aberto ao público e não é necessário fazer inscrição.

Abaixo, seguem o título e o resumo (em inglês) da palestra:

Título: On the classical estimation of bivariate copula-based Seemingly Unrelated Tobit models through the proposed inference function for augmented margins method

Resumo: This work performs the analysis of the bivariate Seemingly Unrelated (SUR) Tobit by modeling its nonlinear dependence structure through copula and assuming non-normal marginal error distributions. For model estimation, the use of copula methods enables the use of the (classical) Inference Function for Margins (IFM) method by Joe and Xu (1996), which is more computationally attractive (feasible) than the full maximum likelihood approach. However, our simulation study shows that the IFM method provides a biased estimate of the copula parameter in the presence of censored observations in both margins. In order to obtain an unbiased estimate of the copula association parameter, we propose/develop a modified version of the IFM method, which we refer to as Inference Function for Augmented Margins (IFAM). Since the usual asymptotic approach, i.e. the computation of the asymptotic covariance matrix of the parameter estimates, is troublesome, we propose the use of resampling procedures (bootstrap methods) to obtain confidence intervals for the copula-based SUR Tobit model parameters. The satisfactory results from the simulation and empirical studies indicate the adequate performance of our proposed model and methods. We illustrate our procedure using bivariate data on consumption of salad dressings and lettuce by U.S. individuals.