报告题目：Bootstrap Tests for High-Dimensional White-Noise

报 告 人：孔婀芳教授（电子科技大学）

报告时间：2022年6月9日  9:00-11:00

报告地点：腾讯会议（859587458，密码：0609）

报告摘要：

The testing of white-noise (WN) is an essential step in time series analysis. In a high dimensional set-up, most existing methods either are computationally infeasible, or suffer from highly distorted Type-I errors, or both. We propose an easy-to-implement bootstrap method for high-dimensional WN test and prove its consistency for a variety of test statistics. Its power properties as well as extensions to WN tests based on fitted residuals are also considered. Simulation results show that compared to the existing methods, the new approach possesses much better power, while maintaining a proper control over the Type-I error. They also provide proofs that even in cases where our method is expected to suffer from lack of theoretical justification, it continues to outperform its competitors. The proposed method is applied to the analysis of the daily stock returns of the top 50 companies by market capitalization listed on the NYSE, and we find strong evidence that the common market factor is the main cause of cross-correlation between stocks.

报告人简介：

孔婀芳，电子科技大学数学科学学院教授、博士生导师。2006年获得新加坡国立大学统计专业博士毕业。曾任教于英国肯特大学。主要研究方向是半参数和非参数统计和渐近理论，计量经济模型和高维统计学习及推断等。研究成果曾发表于Annals of Statistics、JASA、Biometrika、Statistica Sinica、JBES、Econometric Theory等统计和计量经济顶级刊物。