Dynamic Normal Copula and Predictive Regression

主讲人

Liang Peng

简介

<p class="MsoNormal"><strong><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">Abstract:</span></span></strong><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;"> </span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><o:p></o:p></span></p> <p class="MsoNormal"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">&nbsp;</span></span></p> <p class="MsoNormal" style="text-indent:21.0pt"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">Normal copula underestimates extreme events due to its asymptotic independence property. In the first part of this talk, we show that dynamic normal copulas are able to catch both asymptotic independence and asymptotic dependence so as to predict extreme events accurately. Further we propose both parametric and nonparametric inference procedures for the involved correlation function.</span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><o:p></o:p></span></p> <p>&nbsp;</p> <p class="MsoNormal" style="text-indent:21.0pt"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">In the second part of this talk, we will provide a uniform test for testing predictability for a regression model with dependent AR(p) errors rather than independent errors. We propose &nbsp;empirical likelihood method for the case of small p. When p is large, empirical likelihood method is quite computationally intensive. So we further propose a jackknife empirical likelihood method to reduce computation. Simulation study shows the proposed methods are effective.</span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><o:p></o:p></span></p>

时间

2015-07-21(星期二)16:30-18:00

地点

N302, Econ Building

讲座语言

English

主办单位

SOE&WISE

承办单位

统计系

类型

独立讲座

联系人信息

主持人

专题网站

专题

主讲人简介

<p class="MsoNormal"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">Speaker: Liang Peng</span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><o:p></o:p></span></p> <p class="MsoNormal"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">Affiliation: Department of Risk Management and Insurance in the Robinson College of Business at Georgia State University</span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><o:p></o:p></span></p> <p class="MsoNormal"><span style="font-size: medium;"><span lang="EN-US" style="font-family: 'Times New Roman', serif;">CV:</span></span><span lang="EN-US" style="font-family:&quot;Times New Roman&quot;,&quot;serif&quot;"><a href="/EventsMgr/Upload/File/2015/7/20150716054521570.pdf"><span style="font-size: medium;">EventsMgr/Upload/File/2015/7/20150716054521570.pdf</span></a></span></p>

期数

主讲人: Liang Peng
主讲人简介:

Speaker: Liang Peng

Affiliation: Department of Risk Management and Insurance in the Robinson College of Business at Georgia State University

CV:EventsMgr/Upload/File/2015/7/20150716054521570.pdf

简介:
独立讲座
时间: 2015-07-21(星期二)16:30-18:00
地点: N302, Econ Building
主办单位: SOE&WISE
承办单位: 统计系
类型: 独立讲座