Optimal Method in Multiple Regression with Structural Changes

• Activity Number: 419
• Type: Contributed
• Date/Time: Tuesday, August 5, 2014 : 2:00 PM to 3:50 PM
• Sponsor: SSC
• Author(s): Sévérien Nkurunziza*+ and Fuqi Chen
• Companies: University of Windsor and University of Windsor
• Keywords: ADB ; ADR ; change-points ; multiple regression ; restricted estimator ; shrinkage estimators

Abstract

In this paper, we consider an estimation problem in regression model with several unknown change-points, when an uncertain prior information is available. In particular, the regression coefficients are suspected to be restricted to a certain subspace while the "change-points" are treated as nuisance parameters to be estimated as well. Also, we relax some assumptions which are commonly given in literature about the linear model with change-points, and under these realistic assumptions, we propose a class of estimators which includes as a special cases shrinkage estimators (SEs) as well as the unrestricted estimator (UE) and the restricted estimator (RE). We also derive a more general condition for the SEs to dominate the UE. To this end, we generalize some identities for the evaluation of the bias and risk functions of shrinkage-type estimators. The proposed methodology works for both matrix and vector parameters cases. Finally, in order to illustrate the performance of the proposed method, we present some simulation studies.