ScienceDirect® Home Skip Main Navigation Links
You have guest access to ScienceDirect. Find out more.
 
Home
Browse
My Settings
Alerts
Help
 Quick Search
 Search tips (Opens new window)
    Clear all fields    
advertisementadvertisement
Computational Statistics & Data Analysis
Volume 52, Issue 1, 15 September 2007, Pages 109-120
 
Font Size: Decrease Font Size  Increase Font Size
 Abstract - selected
Article
Purchase PDF (330 K)

 
 
 
Related Articles in ScienceDirect
View More Related Articles
 
View Record in Scopus
 
doi:10.1016/j.csda.2007.06.003    How to Cite or Link Using DOI (Opens New Window)
Copyright © 2007 Elsevier B.V. All rights reserved.

GSA-based maximum likelihood estimation for threshold vector error correction model

Zheng Yanga, Corresponding Author Contact Information, E-mail The Corresponding Author, Zheng Tiana, b and Zixia Yuana

aDepartment of Applied Mathematics, Northwestern Polytechnical University, Xi’an 710072, China bNational Key Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100080, China

Received 4 July 2005; 
revised 28 April 2007; 
accepted 6 June 2007. 
Available online 9 June 2007.

Purchase the full-text article



References and further reading may be available for this article. To view references and further reading you must purchase this article.

Abstract

The log-likelihood function of threshold vector error correction models is neither differentiable, nor smooth with respect to some parameters. Therefore, it is very difficult to implement maximum likelihood estimation (MLE) of the model. A new estimation method, which is based on a hybrid algorithm and MLE, is proposed to resolve this problem. The hybrid algorithm, referred to as genetic-simulated annealing, not only inherits aspects of genetic-algorithms (GAs), but also avoids premature convergence by incorporating elements of simulated annealing (SA). Simulation experiments demonstrate that the proposed method allows to estimate the parameters of larger cointegrating systems. Additionally, numerical results show that the hybrid algorithm does a better job than either SA or GA alone.

Keywords: Threshold; Vector error correction model; Maximum likelihood estimation; Genetic-simulated annealing

Article Outline

1. Introduction
2. Quasi-MLE for TVECM
3. GSA Algorithm
4. Simulation examples
4.1. A simulated data
4.2. Parameter estimation by GSA
4.3. Parameter estimation by MLE
4.4. Robustness of GSA-based MLE
5. Conclusion
Acknowledgements
References




 
Home
Browse
My Settings
Alerts
Help
Elsevier.com (Opens new window)
About ScienceDirect  |  Contact Us  |  Information for Advertisers  |  Terms & Conditions  |  Privacy Policy
Copyright © 2008 Elsevier B.V. All rights reserved. ScienceDirect® is a registered trademark of Elsevier B.V.