Abstract

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.

中文翻译：

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参数随机边界模型的固定点

摘要

使用狄拉克（Dirac）三角理论对正态-半正态随机边界模型上的平稳点结果进行了概括，并建立了无分布条件，以确保当无效率分布的方差变为零时，该平稳点具有可能性。对于模型上的常见参数假设，可以导出或模拟固定点的稳定性和“错误的偏斜”结果。我们讨论识别和扩展到更一般的随机边界模型。