Estimation of High-Dimensional Matrix Factor Models with Change Points

Abstract In this paper, we focus on the high-dimensional matrix factor models with change points. We first consider the matrix factor model with a single change point, and propose a least squares estimation method to identify the change point. When the number of change points is unknown, a two-step estimation procedure is developed to identify all the change points. Under some mild conditions, the estimator of the number of change points can be proved to be consistent, and the distance between the estimated and true change points can be proved to be stochastically bounded. Monte Carlo simulation study and real data analysis are carried out for illustration.