报告人：林路 教授，博导，山东大学金融研究院副院长
时间：2016年5月10日(周二)下午15:00-16:00
地点：闵行校区统计楼105室
题目：Consistent Estimation for Distribution-uncertainty Regressions via Cross-sample and Semiparametric Methodologies
摘要：
Motivating by the famous Ellsberg paradox, ambiguity (distribution-uncertainty) is quantitively and qualitatively important in behavior finance. We consider a type of distribution-uncertainty regressions that contains endogenous variable regression and semiparametric regression as its special cases. For such models, however, classical estimating function does involve infinitely many nuisance parameters caused by the uncertain distributions. Consequently, the parameters of interest cannot be consistently estimated and the corresponding prediction is imprecise, even aimless. In this paper, cross-sample and semiparametric techniques, together with a hidden-constant function, are proposed for dealing with the infinitely many nuisance parameters. The resultant estimating function only contains the parameters of interest, and the estimators of them are always consistent and normally distributed with standard convergence rate. Moreover, the newly proposed methodologies can avoid the use of instrumental variable or nonparametric estimation even if actually the model under study contains endogenous variables or nonparametric components. On the other hand, the methodologies for numerical computation are simple, and the corresponding computation procedures are somewhat similar to those for the distribution-certainty models. The main difference from the classical regression analysis is that the estimation efficiency is related to the level of distribution-uncertainty.
林路教授简介：
山东大学金融研究院教授、博士生导师、副院长；在南开大学获得博士学位后,先在南开大学任教，然后到山东大学任教至今；在高维统计、非参数和半参数统计以及金融统计等方面，取得许多重要的研究成果，在国际统计学和机器学习顶级刊物Annals of Statistics, Journal of Machine Learning Research和其它重要期刊发表研究论文70余篇；是国家973项目、国家创新群体和教育部创新团队的核心成员，教育部应用统计专业硕士教育指导委员会成员，山东省应用统计学会副会长，山东省政府参事；主持过多项国家自然科学基金课题、博士点专项基金课题、山东省自然科学基金重点项目等。