Extreme Quantile Estimation for Autoregressive Models

：黎德元教授
：2019-04-08 16:00
：实验楼108

       Speaker：Prof.Deyuan Li

                        Fudan University

Title: Extreme Quantile Estimation for Autoregressive Models

Time：08 April 2019,16:00

Location：海韵实验楼108

Abstract: A quantile autoregresive model is a useful extension of classical autoregresive models as it can capture the influences of conditioning variables on the location, scale, shape of the response distribution. However, at the extreme tails, standard quantile autoregression estimator is often unstable due to data sparsity. In this article, 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 for the conditional quantile functions, establish the asymptotic properties of the proposed estimator. The finite sample performance of the proposed method is illustrated through a simulation study the analysis of U.S. retail gasoline price.

Speaker Introduction：黎德元，男，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。主持国家自然科学基金项目三项。多次短期访问香港中文大学统计系、美国佐治亚理工学院数学系、美国北卡州立大学统计学系，美国麻省理工学院斯隆商学院等。