Robust Frontier and Boundary Estimation: Theory and Application

鲁棒前沿和边界估计：理论与应用

• 批准号: 0906739
• 负责人: Lan Xue
• 金额: $ 3.29万
• 依托单位: Oregon State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2012-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0906739&HistoricalAwards=false
• 关键词: Robust; Frontier; Boundary; Estimation; Theory

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).This research project aims to develop robust estimation methods for various non- and semiparametric frontier models using the efficient polynomial spline quantile smoothing method. More specifically, the objectives of the proposed research include: to propose an easy-to-use robust estimation procedure for non- and semi-parametric frontier models; to develop an innovative penalized polynomial spline quantile regression for variable selection and efficient estimation of non- and semi-parametric frontier models; to study the asymptotic properties and develop efficient numerical algorithms of the proposed methods. The P.I. also plans to investigate polynomial spline estimations of flexible semiparametric ARCH models and formulate a nonparametric likelihood ratio test based on polynomial spline estimation to make inferences of the coefficient functions in the semiparametric ARCH model.The proposed research generates new methods and theories of curve fitting. The results of the proposed research are very useful for researchers in a wide range of fields. For example, it can be used to enhance the understanding of the impact of the macro economic variables on the stock prices and benefit investment banks to help improve risk management. Furthermore, the proposed research will be incorporated into teaching activities through development of a graduate course on nonparametric methods and time series analysis, which promotes involvement of students in the research of current sciences.

该奖项是根据2009年美国复苏和再投资法案(Public Law 111-5)资助的。本研究项目旨在利用高效的多项式多项式分位数平滑方法，为各种非参数和半参数前沿模型开发稳健的估计方法。更具体地说，本研究的目标包括：提出一种易于使用的非参数和半参数前沿面模型的稳健估计方法；发展一种新的用于非参数和半参数前沿面模型的变量选择和有效估计的惩罚多项式样条分位数回归方法；研究所提出方法的渐近性质并开发有效的数值算法。P.I.还计划研究柔性半参数ARCH模型的多项式样条估计，并提出基于多项式样条估计的非参数似然比检验，以推断半参数ARCH模型中的系数函数。所提出的研究结果对广泛领域的研究人员非常有用。例如，它可以用来加强对宏观经济变量对股价影响的了解，并有利于投资银行帮助改善风险管理。此外，拟议的研究将通过开发一门关于非参数方法和时间序列分析的研究生课程纳入教学活动，这将促进学生参与当前科学的研究。