A trivariate Gaussian copula stochastic frontier model with sample selection

We propose a new stochastic frontier model with sample selection, in which the dependencies between selection mechanism, inefficiency term and two-sided error production equation are modeled by trivariate Gaussian copula. This is compared to Greene's original an alternative based on two bivariate copulas. The relative performances of three models analyzed using simulated data cross-sectional about Jasmine rice Thailand. show that our copula has best performance among all models, ignoring some correlations may cause estimation bias as well over or underestimation technical efficiency scores.