报告人: 黎德元 (复旦大学)

时  间: 2018年7月11日  15:00—16:00

地  点: 理科楼 LD201

摘  要:：Quantile autoregresive model is a useful extension to classical autoregresive models as it can capture the influences of conditioning variables on the location, scale and shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this paper, assuming quantile autoregresive models, we develop a new estimator for extreme conditional quantiles of time series data based on extreme value theory. We build the connection between the second-order conditions for the autoregression coefficients and for the conditional quantile functions, and establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study and the analysis of US retail gasoline price.

报告人简介:黎德元，复旦大学管理学院统计学系教授，博士生导师。1997年、2000年毕业于北京大学数学科学学院概率统计系，分别获得学士学位和硕士学位；2004年毕业于荷兰Erasmus大学经济学院，获得博士学位；2005年至2007年在瑞士伯尔尼大学统计学系做博士后；2008年至今任教于复旦大学管理学院统计学系。专业方向为：极值统计。目前已在Annals of Statistics、 JASA等国际统计学顶级期刊，Journal of Economic Theory和Econometric Theory等国际经济学一级期刊上发表学术论文10余篇；获得国家自然科学基金三项、教育部基金一项。

学院联系人: 杨 虎

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