Improved Inference for Moving Average Disturbances in Nonlinear Regression Models
This paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared...
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2014-01-01
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| Series: | Journal of Probability and Statistics |
| Online Access: | http://dx.doi.org/10.1155/2014/207087 |
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| _version_ | 1832564522574938112 |
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| author | Pierre Nguimkeu |
| author_facet | Pierre Nguimkeu |
| author_sort | Pierre Nguimkeu |
| collection | DOAJ |
| description | This paper proposes an improved likelihood-based method to test for first-order moving average in
the disturbances of nonlinear regression models. The proposed method has a third-order distributional
accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to
the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples
and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable
accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing
ones. Two empirical examples including a power regression model of aggregate consumption and a
Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation
and usefulness of the proposed method in practice. |
| format | Article |
| id | doaj-art-2036aa0406e24dc99f89ba290447b15e |
| institution | Kabale University |
| issn | 1687-952X 1687-9538 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Probability and Statistics |
| spelling | doaj-art-2036aa0406e24dc99f89ba290447b15e2025-02-03T01:10:50ZengWileyJournal of Probability and Statistics1687-952X1687-95382014-01-01201410.1155/2014/207087207087Improved Inference for Moving Average Disturbances in Nonlinear Regression ModelsPierre Nguimkeu0Department of Economics, Andrew Young School of Policy Studies, Georgia State University, P.O. Box 3992, Atlanta, GA 30302-3992, USAThis paper proposes an improved likelihood-based method to test for first-order moving average in the disturbances of nonlinear regression models. The proposed method has a third-order distributional accuracy which makes it particularly attractive for inference in small sample sizes models. Compared to the commonly used first-order methods such as likelihood ratio and Wald tests which rely on large samples and asymptotic properties of the maximum likelihood estimation, the proposed method has remarkable accuracy. Monte Carlo simulations are provided to show how the proposed method outperforms the existing ones. Two empirical examples including a power regression model of aggregate consumption and a Gompertz growth model of mobile cellular usage in the US are presented to illustrate the implementation and usefulness of the proposed method in practice.http://dx.doi.org/10.1155/2014/207087 |
| spellingShingle | Pierre Nguimkeu Improved Inference for Moving Average Disturbances in Nonlinear Regression Models Journal of Probability and Statistics |
| title | Improved Inference for Moving Average Disturbances in Nonlinear Regression Models |
| title_full | Improved Inference for Moving Average Disturbances in Nonlinear Regression Models |
| title_fullStr | Improved Inference for Moving Average Disturbances in Nonlinear Regression Models |
| title_full_unstemmed | Improved Inference for Moving Average Disturbances in Nonlinear Regression Models |
| title_short | Improved Inference for Moving Average Disturbances in Nonlinear Regression Models |
| title_sort | improved inference for moving average disturbances in nonlinear regression models |
| url | http://dx.doi.org/10.1155/2014/207087 |
| work_keys_str_mv | AT pierrenguimkeu improvedinferenceformovingaveragedisturbancesinnonlinearregressionmodels |