The paper “Identification and estimation for matrix time series CP-factor models”, co-authored by Prof. Jinyuan Chang, doctoral student Yue Du, postdoctoral fellow Guanglin Huang and Prof. Qiwei Yao from the London School of Economics and Political Science (UK), has been officially accepted by the Annals of Statistics, one of the world’s leading journals in the field of statistics.
Abstract
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 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 co-linearity (see Proposition 5 and Section 4.3). Furthermore, unlike Chang et al. (2023) 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.
Author Introduction
Jinyuan Chang is the Executive Director of the Joint Laboratory of Data Science and Business Intelligence at Southwestern University of Finance and Economics. He is a Guanghua Chair Professor and a recipient of the National Science Fund for Distinguished Young Scholars of China. His research primarily focuses on ultra-high dimensional data analysis and high-frequency financial data analysis.
Yue Du is a doctoral student at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. Her main research interests include hypothesis testing for ultra-high-dimensional data and matrix time series analysis.
Guanglin Huang is a postdoctoral researcher at the Joint Laboratory of Data Science and Business Intelligence, Southwestern University of Finance and Economics. His main research interests include time series analysis and financial risk management.
Qiwei Yao is a Chair Professor at the London School of Economics. His research interests include time series analysis, dimensionality reduction, factor modeling, dynamic network modeling, spatiotemporal modeling, financial econometrics, and nonparametric regression.
