首页    期刊浏览 2024年11月08日 星期五
登录注册

文章基本信息

  • 标题:On the Ability to Disentangle the Two Errors in the Normal/Half-Normal Stochastic Frontier Model
  • 本地全文:下载
  • 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.
国家哲学社会科学文献中心版权所有