We propose a new method for identifying and estimating the CP-factor models for matrix time series. Unlike the generalized eigenanalysis-based method (J. R. Stat. Soc. Ser. B. Stat. Methodol. 85 (2023) 127-148) for which the convergence rates of the associated estimators 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 colinearity (see Proposition 5 and Section 4.3). Furthermore, unlike the generalized eigenanalysis-based method which requires the two factor loading matrices to be full-ranked, the proposed 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.
Publication: ANNALS OF STATISTICS
http://dx.doi.org/10.1214/25-AOS2608
Author: JINYUAN CHANG，Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics，State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences，China Center for Economic Research, Peking University
Email:changjinyuan@swufe.edu.cn
YUE DU， State Key Laboratory of Mathematical Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences
Email:122071400004@smail.swufe.edu.cn
GUANGLIN HUANG， China Center for Economic Research, Peking University
Email:huanggl@swufe.edu.cn
QIWEI YAO， Department of Statistics, London School of Economics and Political Science
Email:q.yao@lse.ac.uk
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