题目：Identification and Estimation for Matrix Time Series CP-factor Models

报告人：常晋源教授 (西南财经大学)

摘要：We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method of Chang et al. (2023) for which the convergence rates may suffer from small eigengaps as the asymptotic theory is based on some matrix perturbation analysis, the proposed new method enjoys faster convergence rates which are free from any eigengaps. It achieves this by turning the problem into a joint diagonalization of several matrices whose elements are determined by a basis of a linear system, and by choosing the basis carefully to avoid near co-linearity. Furthermore, unlike Chang et al. (2023) which requires the two factor loading matrices to be full-ranked, the new method can handle rank-deficient factor loading matrices. Illustration with both simulated and real matrix time series data shows the advantages of the proposed new method.

报告人简介：常晋源，西南财经大学光华特聘教授、中国科学院数学与系统科学研究院研究员，主要从事超高维数据分析相关的研究。主持了国家自然科学基金重大项目、国家杰出青年科学基金项目等项目。

报告时间：2025年3月19日（周三）下午14:30-15:30

报告地点：教二楼727

联系人：胡涛