论文标题
与内生性的随机前沿模型的最大似然估计
Maximum Likelihood Estimation of Stochastic Frontier Models with Endogeneity
论文作者
论文摘要
当复合误差项可能与输入和环境变量相关时，我们提出并研究了具有内生性的随机前沿模型的最大似然估计器。我们的框架是对具有内生性的正常半正常随机前沿模型的概括。我们使用三个基本假设以封闭形式得出了可能性函数：控制功能的存在，这些函数的存在完全捕获了回归器和不可观察者之间的依赖性；给定控制函数的两个误差组件的条件独立性；并且，鉴于控制函数是折叠的正态分布，随机效率低效率项的条件分布。我们还提供了技术效率的Battese-Coelli估计器。我们的估计器在计算中快速易于实现。我们研究了其一些渐近特性，并在蒙特卡洛模拟中展示了其有限的样本行为，并向尼泊尔的农民提供了经验应用。
We propose and study a maximum likelihood estimator of stochastic frontier models with endogeneity in cross-section data when the composite error term may be correlated with inputs and environmental variables. Our framework is a generalization of the normal half-normal stochastic frontier model with endogeneity. We derive the likelihood function in closed form using three fundamental assumptions: the existence of control functions that fully capture the dependence between regressors and unobservables; the conditional independence of the two error components given the control functions; and the conditional distribution of the stochastic inefficiency term given the control functions being a folded normal distribution. We also provide a Battese-Coelli estimator of technical efficiency. Our estimator is computationally fast and easy to implement. We study some of its asymptotic properties, and we showcase its finite sample behavior in Monte-Carlo simulations and an empirical application to farmers in Nepal.
