This paper extends the fixed effect panel stochastic frontier models to allow group heterogeneity in the slope coefficients. We propose the first-difference penalized maximum likelihood (FDPML) and control function penalized maximum likelihood (CFPML) methods for classification and estimation of latent group structures in the frontier as well as inefficiency. Monte Carlo simulations show that the proposed approach performs well in finite samples. An empirical application is presented to show the advantages of data-determined identification of the heterogeneous group structures in practice.

中文翻译：

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面板随机边界模型中未知的潜在结构和效率低下

本文扩展了固定效应面板随机边界模型，以允许组群在斜率系数上的异质性。我们提出一阶罚分最大似然法（FDPML）和控制函数罚分最大似然法（CFPML），用于对边界中潜在群体结构的分类和估计以及效率低下。蒙特卡洛仿真表明，该方法在有限样本中表现良好。提出了一个经验应用程序，以展示在实践中以数据确定的异类组结构识别的优势。