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

Author

Ogata, Hiroaki

Source

Advances in Decision Sciences

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