Modeling large dimensional matrix time series with partially known and latent factors
含部分已知因子与潜在因子的高维矩阵时间序列建模
This article considers to model large-dimensional matrix time series by introducing a regression term to the matrix factor model. This is an extension of classic matrix factor model to incorporate the information of known factors or useful covariates. We establish the convergence rates of coefficient matrix, loading matrices and the signal part. The theoretical results coincide with the rates in Wang et al. (2019). We conduct numerical studies to verify the performance of…