Auto-regressive Model for Matrix Valued Time Series

In finance, economics and many other fields, observations in a matrix form are often observed over time. For example, several key economic indicators are reported in different countries every quarter. Various financial characteristics of many companies are reported over time. Import-export figures among a group of countries can also be structured in a matrix form. Although it is natural to turn the matrix observations into a long vector then use standard vector time series models, it is often the case that the columns and rows of a matrix re- present different sets of information that are closely inter-played. We propose a novel matrix auto-regressive model that maintains and uti- lizes the matrix structure to achieve greater dimensional reduction as well as easier interpretable results. The model can be further simplified by a set of reduced rank assumptions. Estimation procedure and its theoretical properties are investigated and demonstrated with simulated and real examples.

举办单位：数学与统计学院

发 布 人：科研助理 发布时间： 2017-06-30

发 布 人：科研助理 发布时间： 2017-06-30

Dr. Han Xiao is an associate professor at Department of Statistics and Biostatistics, Rutgers University. He was an assistant professor at Rutgers from 2011-2017. Dr. Xiao obtained a PhD in statistics from the University of Chicago in 2011. Prior to that, he earned a Master in science from National University of Singapore, in 2006, and a Bachelor in mathematics from Peking University, in 2003. Dr. Han Xiao's research interests include algebraic statistics, multivariate and high dimensional time series, nonlinear and nonstationary time series, and random matrix theory.