Improved Inference for Moving Average Disturbances in Nonlinear Regression Models
Author
Source
Journal of Probability and Statistics
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-02-13
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
This paper proposes an improved likelihood-based method to test for first-order moving average inthe disturbances of nonlinear regression models.
The proposed method has a third-order distributionalaccuracy which makes it particularly attractive for inference in small sample sizes models.
Compared tothe commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samplesand asymptotic properties of the maximum likelihood estimation, the proposed method has remarkableaccuracy.
Monte Carlo simulations are provided to show how the proposed method outperforms the existingones.
Two empirical examples including a power regression model of aggregate consumption and aGompertz growth model of mobile cellular usage in the US are presented to illustrate the implementationand usefulness of the proposed method in practice.
American Psychological Association (APA)
Nguimkeu, Pierre. 2014. Improved Inference for Moving Average Disturbances in Nonlinear Regression Models. Journal of Probability and Statistics،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1042813
Modern Language Association (MLA)
Nguimkeu, Pierre. Improved Inference for Moving Average Disturbances in Nonlinear Regression Models. Journal of Probability and Statistics No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1042813
American Medical Association (AMA)
Nguimkeu, Pierre. Improved Inference for Moving Average Disturbances in Nonlinear Regression Models. Journal of Probability and Statistics. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1042813
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1042813