具有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
AI总结：
本文提出一种针对截面数据中具有内生性的随机前沿模型的最大似然估计方法，利用控制函数和折叠正态分布假设推导闭合形式似然函数，并给出技术效率估计量，兼具计算高效与易实现性。
摘要：
我们提出并研究了一种适用于截面数据中具有内生性的随机前沿模型的最大似然估计量，其中复合误差项可能与投入和环境变量相关。我们的框架是对具有内生性的正态-半正态随机前沿模型的推广。我们基于三个基本假设以闭合形式推导出似然函数：存在能够完全刻画回归变量与不可观测因素之间依赖关系的控制函数；在给定控制函数的条件下，两个误差分量条件独立；在给定控制函数的条件下，随机无效率项的条件分布为折叠正态分布。我们还提供了技术效率的Battese-Coelli估计量。我们的估计量计算速度快且易于实现。我们研究了其若干渐近性质，并通过蒙特卡洛模拟以及对尼泊尔农民的一项实证应用展示了其有限样本表现。
英文摘要：
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.
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