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Advances in Decision Sciences
Volume 2012, Article ID 704693, 22 pages
http://dx.doi.org/10.1155/2012/704693
Research Article

Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method

Waseda University, Tokyo 169-8050, Japan

Received 28 January 2012; Revised 28 March 2012; Accepted 30 March 2012

Academic Editor: David Veredas

Copyright © 2012 Hiroaki Ogata. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

An application of the empirical likelihood method to non-Gaussian locally stationary processes is presented. Based on the central limit theorem for locally stationary processes, we give the asymptotic distributions of the maximum empirical likelihood estimator and the empirical likelihood ratio statistics, respectively. It is shown that the empirical likelihood method enables us to make inferences on various important indices in a time series analysis. Furthermore, we give a numerical study and investigate a finite sample property.