Stationary Points for Parametric Stochastic Frontier Models
利用狄拉克δ理论推广了正态-半正态随机前沿模型的驻点结果，建立了当无效率分布方差趋近零时似然函数存在驻点的分布无关条件，并推导或模拟了常见参数假设下驻点的稳定性及“错误偏斜”现象。
Stationary point results on the normal–half-normal stochastic frontier model are generalized using the theory of the Dirac delta, and distribution-free conditions are established to ensure a stationary point in the likelihood as the variance of the inefficiency distribution goes to zero. Stability of the stationary point and “wrong skew” results are derived or simulated for common parametric assumptions on the model. We discuss identification and extensions to more general stochastic frontier models.