Centered-Residuals-Based Moment Estimator and Test for Stochastic Frontier Models
针对随机前沿模型中两个随机变量的分布假设难以检验的问题，提出一种基于中心残差的矩估计方法，可灵活检验分布假设并估计参数，蒙特卡洛模拟和实证例子验证了其有效性。
The composed error of a stochastic frontier (SF) model consists of two random variables, and the identification of the model relies heavily on the distribution assumptions for each of these variables. While the literature has put much effort into applying various SF models to a wide range of empirical problems, little has been done to test the distribution assumptions of these two variables. In this article, by exploiting the specification structures of the SF model, we propose a centered-residuals-based method of moments which can be easily and flexibly applied to testing the distribution assumptions on both of the random variables and to estimating the model parameters. A Monte Carlo simulation is conducted to assess the performance of the proposed method. We also provide two empirical examples to demonstrate the use of the proposed estimator and test using real data.