A modified version of the inference function for margins and interval estimation for the bivariate Clayton copula SUR Tobit model: An simulation approach

TL;DRAbstract

This paper extends the analysis of bivariate seemingly unrelated regression (SUR) Tobit model by modeling its nonlinear dependence structure through the Clayton copula. The ability in capturing/modeling the lower tail dependence of the SUR Tobit model where some data are censored (generally, at zero point) is an additionally useful feature of the Clayton copula. We propose a modified version of the inference function for margins (IFM) method (Joe and Xu, 1996), which we refer to as MIFM method, to obtain the estimates of the marginal parameters and a better (satisfactory) estimate of the copula association parameter. More specifically, we employ the data augmentation technique in the second stage of the IFM method to generate the censored observations (i.e. to obtain continuous marginal distributions, which ensures the uniqueness of the copula) and then estimate the dependence parameter. Resampling procedures (bootstrap methods) are also proposed for obtaining confidence intervals for

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