Maximum likelihood estimation of stochastic frontier models by the Fourier transform
利用傅里叶变换进行随机前沿模型的最大似然估计AI 解读 ↓
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利用傅里叶变换进行随机前沿模型的最大似然估计
- 研究问题
- 当随机前沿模型的复合误差密度与似然没有闭式表达时，如何实现估计，并容纳非标准单侧误差及多种前沿设定？
- 方法与思路
- 作者通过傅里叶变换反演可处理的特征函数，近似复合误差密度；另提出基于对数似然渐近正态形式的估计程序。
- 材料陈述的贡献
- 摘要提出适用于投入导向、双层、潜在类别等前沿模型的计算方法，并实现评估相关程序，以银行、公用事业和调查数据示范。
- 阅读关注点
- 应核对各模型的误差分布和依赖设定、数值反演精度、似然优化稳定性，以及近似程序的渐近效率如何界定。
- 阅读边界
- 仅据所给出版社摘要，数值截断、计算成本及效率条件未说明；不能从应用示例推断方法在所有前沿模型中更准确或更快。
原文摘要
The paper is concerned with several kinds of stochastic frontier models whose likelihood function is not available in closed form. First, with output-oriented stochastic frontier models whose one-sided errors have a distribution other than the standard ones (exponential or half-normal). The gamma and beta distributions are leading examples. Second, with input-oriented stochastic frontier models which are common in theoretical discussions but not in econometric applications. Third, with two-tiered stochastic frontier models when the one-sided error components follow gamma distributions. Fourth, with latent class models with gamma distributed one-sided error terms. Fifth, with models whose two-sided error component is distributed as stable Paretian and the one-sided error is gamma. The principal aim is to propose approximations to the density of the composed error based on the inversion of the characteristic function (which turns out to be manageable) using the Fourier transform. Procedures that are based on the asymptotic normal form of the log-likelihood function and have arbitrary degrees of asymptotic efficiency are also proposed, implemented and evaluated in connection with output-oriented stochastic frontiers. The new methods are illustrated using data for US commercial banks, electric utilities, and a sample from the National Youth Longitudinal Survey.