Thursday, October 20, 2016 - 3:00pm
209 W. Eighteenth Ave. (EA), Room 170
Factor Model for High Dimensional Matrix Valued Time Series
Rong Chen, Rutgers University
In finance, economics and many other fields, observations in a matrix form are often observed over time. For example, many 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 or factor analysis, it is often the case that the columns and rows of a matrix represent different sets of information that are closely interplayed. We propose a novel factor model that maintains and utilizes the matrix structure to achieve greater dimensional reduction as well as easier interpretable factor structure. Estimation procedure and its theoretical properties are investigated and demonstrated with simulated and real examples.
Joint work with Dong Wang (Rutgers University) and Xialu Liu (San Diego State University)