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

我们提出了一种新的带有样本选择的随机前沿模型，其中样本选择机制、无效率项和生产方程中的两侧误差之间的依赖关系由三变量高斯 copula 建模。将该模型与 Greene 原始的带有样本选择的随机前沿模型以及基于两个双变量 copula 的替代模型进行比较。使用泰国茉莉花水稻生产的模拟数据和横截面数据分析了三种模型的相对性能。我们表明，我们的三变量高斯 copula 模型在所有模型中具有最佳性能，忽略某些相关性可能会导致估计偏差以及技术效率得分的高估或低估。