Reduced-Rank and Sparse-Structured Autoregressive Models for Matrix Time Series

The next statistics seminar will be presented by Professor Philip L.H. Yu from the Education University of Hong Kong.

Title: Reduced-Rank and Sparse-Structured Autoregressive Models for Matrix Time Series
Speaker: Professor Philip L.H. Yu
Time and location : 2-3pm in Carslaw Lecture Theatre 173 or Zoom
Abstract :

Matrix-valued time series models have been investigated as a solution to the dimensionality challenges in high-dimensional time series analysis. These models leverage multi-classification structures within data variables to decompose large interaction networks into smaller, more manageable components. To further address dimension reduction, recent studies have explored imposing structural constraints on the individual coefficient matrices of matrix-valued time series models. The RR-S-MAR model is introduced, a matrix autoregressive (MAR) model of order one featuring a reduced-rank structure on the left matrix and a sparse structure on the right matrix. An alternating least-squares method is developed for estimating the constrained model, while a bootstrapping approach is employed for statistical inference. Additionally, an extended Bayesian information criterion is proposed for selecting tuning parameters within the model. Simulations are conducted to evaluate the performance of the estimation algorithm and the model selection criterion in finite samples. Finally, the model is applied to economic data to illustrate real-world analysis and interpretations. This is a joint work with Dr. Xiaohang Wang and Ling Xin.