This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.

Abstract Details

Activity Number: 173
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Business and Economic Statistics Section
Abstract - #308300
Title: Doubly Constrained Factor Models: Estimation and Applications
Author(s): Henghsiu Tsai*+ and Ruey S. Tsay
Companies: Academia Sinica and The University of Chicago
Address: 128 Academia Road Sec.2, Nankang, Taipei, International, 115, Taiwan
Keywords: Approximate factor model ; Constrained principal component analysis ; Least squares estimation ; Likelihood ratio test ; Eigenvalues ; Noise-to-signal ratio

Factor models have been widely used in recent years to improve the accuracy of forecasting when many explanatory variables are available. However, the models often encounter the difficulties of over-parameterization and factor interpretation. In this paper, we first consider constrained factor analysis to obtain a parsimonious factor model and propose likelihood ratio statistics to test the adequacy of factor constraints. We then extend the constrained models to the doubly constrained factor models by incorporating external information on both rows (e.g., subjects) and columns (e.g., variables) of a data matrix. Real and simulated examples are used to demonstrate the proposed analysis. In an application, we show that the doubly constrained factor analysis can provide a deeper understanding of variations in monthly financial asset returns.

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