题目：Adaptive inference for a semiparametric generalized autoregressive conditional heteroscedastic model

主讲人：香港大学 朱柯助理教授

主持人：西南财经大学统计学院 常晋源教授

时间：2020年6月2日（星期二）16:00-17:20

直播平台及会议ID：腾讯会议，563 592 324

报告摘要：

This paper considers a semiparametric generalized autoregressive conditional heteroscedastic (S-GARCH) model. For this model, we first estimate the time-varying long run component by the kernel estimator, and then estimate the non-time-varying parameters in 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 provide a consistent Bayesian information criterion for order selection. Furthermore, 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 target method when the S-GARCH model is stationary.

主讲人简介：

Dr Ke Zhu is an assistant professor at department of statistics & actuarial science in university of Hong Kong. His research interest includes financial time series analysis, econometrics, model diagnostic checking, and causal inference. He has published papers in leading statistics and econometrics journals.