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Activity Number: 61 - New Developments in Complex Time Series Data
Type: Topic Contributed
Date/Time: Sunday, July 30, 2017 : 4:00 PM to 5:50 PM
Sponsor: IMS
Abstract #323256 View Presentation
Title: Constrained Factor Model for High-Dimensional Matrix-Variate Time Series
Author(s): Yi Chen* and Rong Chen
Companies: Rutgers Univ Statistics Dept and Rutgers University
Keywords: Matrix-Variate Time Series ; Factor Model ; Eigen-analysis ; Dimension Reduction ; Convergence in L2-norm

In finance, meteorology, and many other fields, high-dimensional matrix-variate data are often observed overtime. Matrix-variate factor model is an effective dimension-reduction method that incorporates the structured interrelation between columns and rows. Exploration of the natural structures in loading matrices will improve performances of estimators and enhance interpretations of the estimated common factors. Thus we consider estimation and application of constrained and partially constrained factor model for matrix-variate time series. For estimation, we employ eigen-decomposition of non-negative definite matrices constructed from auto-covariance matrices at nonzero lags. We establish the asymptotic properties as the dimension and the number of constraints go to infinity with sample size; and propose likelihood ratio statistics to test the adequacy of factor constraints. We show that the rates of convergence of the constrained factor loading matrices are much faster than those of the conventional factor analysis. The proposed method and their asymptotic properties are further illustrated in a simulation study. An application to a corporate financial dataset is also reported.

Authors who are presenting talks have a * after their name.

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