Maximum likelihood estimation for bivariate SUR Tobit modeling in presence of two right-censored dependent variables

This article extends the analysis of the Seemingly Unrelated Regression (SUR) Tobit model for two right-censored dependent variables by modeling its nonlinear dependence structure through the rotated by 180 degrees version of the Clayton copula. An advantage of our approach is to provide unbiased point estimates of the marginal and copula parameters. Moreover, we discuss the construction of confidence intervals using bootstrap resampling procedures. The results of the performed simulation study demonstrate the good performance of the proposed methods. We illustrate our procedures using bivariate customer churn data from a Brazilian commercial bank.