题目：Identification and Estimation for Matrix Time Series CP-factor Models
报告人：姚琦伟
报告时间：2026年4月27日 15:00
地点：综合楼644会议室
报告人简介：
姚琦伟，英国伦敦政治经济学院 (London School of Economics and Political Sciences) 统计系教授，美国统计协会会士，数理统计学会会士，国际统计研究学会选举会员。姚琦伟教授一直从事统计学的教学和科研工作，主要研究领域为：时间序列分析、时空过程分析、金融计量经济学。他在非线性和高维时间序列方面的研究国际领先。姚琦伟教授迄今已发表学术论文100多篇， 并获得EPSRC, BBSRC等英国国家基金会支持的多项研究基金项目。其专著《非线性时间序列：非参数及参数方法》（与范剑青合著）于2003年由Springer 出版，《计量金融简要》（与范剑青合著）于2017年由剑桥出版社出版。姚琦伟教授已担任Journal of the Royal Statistical Society (Series B) 的联合主编，Annals of Statistics，Journal of the American Statistics Association等多个顶级杂志副主编。姚琦伟教授还曾为巴克莱银行，法国电力公司以及Winton资本等多家企业提供咨询。
报告摘要：
We investigate the identification and the estimation for matrix time series CP-factor models.ow computa Unlike the generalized eigenanalysis-based method of Chang et al. (2023) which requires the two factor loading matrices to be full-ranked, the newly proposed estimation can handle rank-deficient factor loading matrices. The estimation procedure consists of the spectral decomposition of several matrices and a matrix joint diagonalization algorithm, resulting in ltional cost. The theoretical guarantee established without the stationarity assumption shows that the proposed estimation exhibits a faster convergence rate than that of Chang et al. (2023). In fact the new estimator is free from the adverse impact of any eigen-gaps, unlike most eigenanalysis-based methods. Illustration with both simulated and real matrix time series data shows the usefulness of the proposed approach.
