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统计系教授，英国皇家统计学会、美国统计协会（ASA）及数理统计学会(IMS)的Fellow，国际统计学会(ISI)的Elected member。姚琦伟教授是国际知名的统计学家，他的研究兴趣包括：时间序列分析、高维时间序列建模与预测、降维和因子建模、动态网络建模、时空建模和金融计量经济学等，已在包括统计学顶刊JASA、AoS、JRSSB和计量经济学顶刊JoE等上发表论文110余篇，并著有2本专著：《非线性时间序列：非参数及参数方法》和《计量金融简要》。担任了Journal of the Royal Statistical Society (Series B)，Statistica Sinica 的联合主编，及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. 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 low computational 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.
