威尼斯欢迎你welcome建院40周年系列活动之学术讲座第62期
威尼斯欢迎你welcome统计学系列 Seminar 第79期
主题: Adaptive inference for a semiparametric generalized autoregressive conditional heteroskedasticity model
主讲人: 朱柯 教授
主持人：王国长 副教授
会议工具：腾讯会议 221 232 188
会议时间：2020年12月17日下午16:00-17:00
摘要
This paper considers a semiparametric generalized autoregressive conditional heteroscedasticity (S-GARCH) model. For this model, we first estimate the time-varying long run component for unconditional variance by the kernel estimator, and then estimate the non-time-varying parameters in GARCH-type short run component by the quasi maximum likelihood estimator (QMLE). We show that the QMLE is asymptotically normal with the parametric convergence rate. Next, we construct a Lagrange multiplier test for linear parameter constraint and a portmanteau test for model checking, and obtain their asymptotic null distributions. Our entire statistical inference procedure works for the non-stationary data with two important features: first, our QMLE and two tests are adaptive to the unknown form of the long run component; second, our QMLE and two tests share the same efficiency and testing power as those in variance targeting method when the S-GARCH model is stationary.
主讲人简介
朱柯：香港大学统计与精算系, 教授、博士生导师。于2011年获得香港科技大学统计学博士学位。主要研究方向为时间序列计量经济和统计包括：稳健统计、拟合优度检验、变点问题、bootstrap 方法及应用计量经济。目前，他已经发表学术论文20余篇，其中包括 Journal of the American Statistical Association, Annals of Statistics, Journal of the Royal Statistical Society Series B, Journal of Econometrics, Econometric Theory ,Journal of Business and Economic Statistics 等国际顶尖统计和计量经济学期刊。
