Optimal method in multiple regression with structural changes
Abstract
In this paper, we consider an estimation problem of the regression coefficients in multiple regression models with several unknown change-points. Under some 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. As illustrative example, our method is applied to the "gross domestic product" data set of 10 countries whose USA, Canada, UK, France and Germany. The simulation results corroborate our theoretical findings.
Cite
@article{arxiv.1509.05581,
title = {Optimal method in multiple regression with structural changes},
author = {Fuqi Chen and Sévérien Nkurunziza},
journal= {arXiv preprint arXiv:1509.05581},
year = {2016}
}
Comments
Published at http://dx.doi.org/10.3150/14-BEJ642 in the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)