文章基本信息

标题：On the Ability to Disentangle the Two Errors in the Normal/Half-Normal Stochastic Frontier Model
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作者：Jose M. Gavilan ; Francisco J. Ortega
期刊名称：Estudios de Economía Aplicada
印刷版ISSN：1133-3197
电子版ISSN：1697-5731
出版年度：2015
卷号：33
期号：2
页码：619-632
语种：English
出版社：Asociación Internacional de Economía Aplicada
摘要：In this paper, a simulation experiment is carried out in the framework of the normal/half-normal stochastic frontier model in order to analyse its ability to disentangle the two types of errors that form the composite error. According to the results obtained through the mean bias and the mean squared error of the parameters and efficiencies, and via Spearman rank correlation between actual and estimated efficiencies, a good performance of the model is only obtained when considering medium-sized or large samples and the variance of the inefficiencies highly contributes to that of the composite error. The problems of wrong skewness and absence of random error are also addressed. The influence on the results of selecting a wrong distribution for the inefficiency term is also analysed.
其他摘要：In this paper, a simulation experiment is carried out in the framework of the normal/half-normal stochastic frontier model in order to analyse its ability to disentangle the two types of errors that form the composite error. According to the results obtained through the mean bias and the mean squared error of the parameters and efficiencies, and via Spearman rank correlation between actual and estimated efficiencies, a good performance of the model is only obtained when considering medium-sized or large samples and the variance of the inefficiencies highly contributes to that of the composite error. The problems of wrong skewness and absence of random error are also addressed. The influence on the results of selecting a wrong distribution for the inefficiency term is also analysed.
关键词：Production Models; Stochastic Frontier; Maximum Likelihood; Monte Carlo.
其他关键词：Production Models; Stochastic Frontier; Maximum Likelihood; Monte Carlo.