Two-step robust estimator in heteroscedastic regression model in the presence of outliers
Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS)...
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| Format: | Article |
| Language: | English |
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Academy of Economic Studies
2014
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| Online Access: | http://psasir.upm.edu.my/id/eprint/35908/ http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf |
| _version_ | 1848848185581109248 |
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| author | Midi, Habshah Rana, Md. Sohel Imon, A. H. M. Ramatullah |
| author_facet | Midi, Habshah Rana, Md. Sohel Imon, A. H. M. Ramatullah |
| author_sort | Midi, Habshah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS) procedure is often used to estimate the regression parameters when heteroscedasticity occurs in the data. But there is evidence that the WLS estimators suffer a huge set back in the presence of outliers. Moreover, the use of the WLS requires a known form of the heteroscedastic errors structures. To rectify this problem, we proposed a new method that we call two step robust weighted least squares (TSRWLS) method where prior information on the structure of the heteroscedastic errors is not required. In the proposed procedure, the robust technique is used twice. Firstly, the robust weights are used for solving the heteroscedasic error and secondly, the robust weighting function is used for eliminating the effect of outliers. The performance of the newly proposed estimator is investigated extensively by real data sets and Monte Carlo simulations. |
| first_indexed | 2025-11-15T09:30:29Z |
| format | Article |
| id | upm-35908 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T09:30:29Z |
| publishDate | 2014 |
| publisher | Academy of Economic Studies |
| recordtype | eprints |
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| spelling | upm-359082018-09-19T01:32:57Z http://psasir.upm.edu.my/id/eprint/35908/ Two-step robust estimator in heteroscedastic regression model in the presence of outliers Midi, Habshah Rana, Md. Sohel Imon, A. H. M. Ramatullah Although the ordinary least squares (OLS) estimates are unbiased in the presence of heteroscedasticity, these are no longer efficient. This problem becomes more complicated when the violation of constant error variances comes together with the existence of outliers. The weighted least squares (WLS) procedure is often used to estimate the regression parameters when heteroscedasticity occurs in the data. But there is evidence that the WLS estimators suffer a huge set back in the presence of outliers. Moreover, the use of the WLS requires a known form of the heteroscedastic errors structures. To rectify this problem, we proposed a new method that we call two step robust weighted least squares (TSRWLS) method where prior information on the structure of the heteroscedastic errors is not required. In the proposed procedure, the robust technique is used twice. Firstly, the robust weights are used for solving the heteroscedasic error and secondly, the robust weighting function is used for eliminating the effect of outliers. The performance of the newly proposed estimator is investigated extensively by real data sets and Monte Carlo simulations. Academy of Economic Studies 2014 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf Midi, Habshah and Rana, Md. Sohel and Imon, A. H. M. Ramatullah (2014) Two-step robust estimator in heteroscedastic regression model in the presence of outliers. Economic Computation and Economic Cybernetics Studies and Research, 48 (3). pp. 255-272. ISSN 0424-267X; ESSN: 1842-3264 http://www.ecocyb.ase.ro/Articles2014_3.htm |
| spellingShingle | Midi, Habshah Rana, Md. Sohel Imon, A. H. M. Ramatullah Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title | Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title_full | Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title_fullStr | Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title_full_unstemmed | Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title_short | Two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| title_sort | two-step robust estimator in heteroscedastic regression model in the presence of outliers |
| url | http://psasir.upm.edu.my/id/eprint/35908/ http://psasir.upm.edu.my/id/eprint/35908/ http://psasir.upm.edu.my/id/eprint/35908/1/Two-step%20robust%20estimator%20in%20heteroscedastic%20regression%20model%20in%20the%20presence%20of%20outliers.pdf |