...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of productivity analysis >Robust stochastic frontier analysis: a Student's f-half normal model with application to highway maintenance costs in England
【24h】

Robust stochastic frontier analysis: a Student's f-half normal model with application to highway maintenance costs in England

机译：鲁棒的随机前沿分析：学生的f-半正态模型及其在英格兰的公路养护费用中的应用

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

The presence of outliers in the data has implications for stochastic frontier analysis, and indeed any performance analysis methodology, because they may lead to imprecise parameter estimates and, crucially, lead to an exaggerated spread of efficiency predictions. In this paper we replace the normal distribution for the noise term in the standard stochastic frontier model with a Student's t distribution, which generalises the normal distribution by adding a shape parameter governing the degree of kurtosis. This has the advantages of introducing flexibility in the heaviness of the tails, which can be determined by the data, as well as containing the normal distribution as a limiting case, and we outline how to test against the standard model. Monte Carlo simulation results for the maximum simulated likelihood estimator confirm that the model recovers appropriate frontier and distributional parameter estimates under various values of the true shape parameter. The simulation results also indicate the influence of a phenomenon we term wrong kurtosis' in the case of small samples, which is analogous to the issue of wrong skewness' previously identified in the literature. We apply a Student's t-half normal cost frontier to data for highways authorities in England, and this formulation is found to be preferred by statistical testing to the comparator normal-half normal cost frontier model. The model yields a significantly narrower range of efficiency predictions, which are non-monotonic at the tails of the residual distribution.
机译：数据中异常值的存在对随机前沿分析以及实际上的任何性能分析方法都具有影响，因为它们可能导致不精确的参数估计，并且至关重要的是，导致效率预测的夸大分布。在本文中，我们用Student t分布替换了标准随机边界模型中噪声项的正态分布，后者通过添加控制峰度的形状参数来概括正态分布。这样做的好处是，可以在尾部的沉重感中引入灵活性，而这种灵活性可以由数据确定，并且包含正态分布作为极限情况，并且我们概述了如何针对标准模型进行测试。最大模拟似然估计器的蒙特卡洛模拟结果证实，该模型在真实形状参数的各种值下恢复了适当的边界和分布参数估计。仿真结果还表明，在小样本情况下，我们称之为错误峰度现象的影响，类似于文献中先前指出的错误偏度问题。我们将学生的t-一半正常成本边界模型应用于英格兰公路当局的数据，并且通过统计测试发现，此公式是比较者正常/一半正常成本边界模型的首选。该模型产生的效率预测范围明显狭窄，在残差分布的尾部是非单调的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号