Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method
Author
Source
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-22, 22 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-06-10
Country of Publication
Egypt
No. of Pages
22
Main Subjects
Economics & Business Administration
Business Administration
Abstract EN
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.
American Psychological Association (APA)
Ogata, Hiroaki. 2012. Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method. Advances in Decision Sciences،Vol. 2012, no. 2012, pp.1-22.
https://search.emarefa.net/detail/BIM-491940
Modern Language Association (MLA)
Ogata, Hiroaki. Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method. Advances in Decision Sciences No. 2012 (2012), pp.1-22.
https://search.emarefa.net/detail/BIM-491940
American Medical Association (AMA)
Ogata, Hiroaki. Estimation for Non-Gaussian Locally Stationary Processes with Empirical Likelihood Method. Advances in Decision Sciences. 2012. Vol. 2012, no. 2012, pp.1-22.
https://search.emarefa.net/detail/BIM-491940
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-491940