Semiparametric Estimation of Stochastic Production Frontier Models
将经典线性随机前沿模型扩展为半参数形式，不指定生产前沿的函数形式，用核估计构造伪似然估计量，蒙特卡洛模拟显示有限样本表现良好，并给出实证应用。
This article extends the linear stochastic frontier model proposed by Aigner, Lovell, and Schmidt to a semiparametric frontier model in which the functional form of the production frontier is unspecified and the distributions of the composite error terms are of known form. Pseudolikelihood estimators of the parameters characterizing the two error terms of the model are constructed based on kernel estimation of the conditional mean function. The Monte Carlo results show that the proposed estimators perform well in finite samples. An empirical application is presented. Extensions to a partially linear frontier function and to more flexible one-sided error distributions than the half-normal are discussed