A Recursive Thick Frontier Approach to Estimating Production Efficiency*
提出一种新的面板数据估计技术：递归厚前沿方法（RTFA），允许技术无效率依赖于解释变量，且无需对误差项的无效率成分做分布假设。模拟实验表明，在现实参数设定下，RTFA优于随机前沿法和组内普通最小二乘估计，可作为现有方法的有益补充。
Abstract We introduce a new panel data estimation technique for production and cost functions, the recursive thick frontier approach (RTFA). RTFA has two advantages over existing econometric frontier methods. First, technical inefficiency is allowed to be dependent on the explanatory variables of the frontier model. Secondly, RTFA does not hinge on distributional assumptions on the inefficiency component of the error term. We show by means of simulation experiments that RTFA outperforms the popular stochastic frontier approach and the ‘within’ ordinary least squares estimator for realistic parameterizations of a productivity model. Although RTFAs formal statistical properties are unknown, we argue, based on these simulation experiments, that RTFA is a useful complement to existing methods.