On the robust parameter estimation for linear model with autocorrelated errors
The Ordinary Least Squares (OLS) estimates become inefficient in the presence of autocorrelation problems. The Cochrane-Orcutt Prais-Winsten iterative method (COPW) is the most commonly used remedial measure to remedy this problem. However, this procedure is based on the OLS estimates, which is not...
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American Scientific Publishers
2013
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my.upm.eprints.303432015-10-08T06:38:15Z http://psasir.upm.edu.my/id/eprint/30343/ On the robust parameter estimation for linear model with autocorrelated errors Midi, Habshah Lim, Hock Ann Rana, Md. Sohel The Ordinary Least Squares (OLS) estimates become inefficient in the presence of autocorrelation problems. The Cochrane-Orcutt Prais-Winsten iterative method (COPW) is the most commonly used remedial measure to remedy this problem. However, this procedure is based on the OLS estimates, which is not robust and therefore easily affected by high leverage points (outliers in the x-direction). In this paper, we propose a robust Cochrane-Orcutt Prais-Winsten iterative method (RCOPW) based on MM estimator for the estimation of linear regression parameters in the situation where autocorrelated errors come together with the existence of outliers. The performance of this RCOPW is investigated extensively by real example and Monte Carlo simulation. The results of the study indicate that the RCOPW is more consistent and efficient as compared to COPW. It also provides a better one step ahead forecast than the OLS and COPW regression models. American Scientific Publishers 2013 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/30343/1/On%20the%20robust%20parameter%20estimation%20for%20linear%20model%20with%20autocorrelated%20errors.pdf Midi, Habshah and Lim, Hock Ann and Rana, Md. Sohel (2013) On the robust parameter estimation for linear model with autocorrelated errors. Advanced Science Letters, 19 (8). pp. 2494-2496. ISSN 1936-6612; ESSN: 1936-7317 10.1166/asl.2013.4945 English |
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The Ordinary Least Squares (OLS) estimates become inefficient in the presence of autocorrelation problems. The Cochrane-Orcutt Prais-Winsten iterative method (COPW) is the most commonly used remedial measure to remedy this problem. However, this procedure is based on the OLS estimates, which is not robust and therefore easily affected by high leverage points (outliers in the x-direction). In this paper, we propose a robust Cochrane-Orcutt Prais-Winsten iterative method (RCOPW) based on MM estimator for the estimation of linear regression parameters in the situation where autocorrelated errors come together with the existence of outliers. The performance of this RCOPW is investigated extensively by real example and Monte Carlo simulation. The results of the study indicate that the RCOPW is more consistent and efficient as compared to COPW. It also provides a better one step ahead forecast than the OLS and COPW regression models. |
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Midi, Habshah Lim, Hock Ann Rana, Md. Sohel |
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Midi, Habshah Lim, Hock Ann Rana, Md. Sohel On the robust parameter estimation for linear model with autocorrelated errors |
author_facet |
Midi, Habshah Lim, Hock Ann Rana, Md. Sohel |
author_sort |
Midi, Habshah |
title |
On the robust parameter estimation for linear model with autocorrelated errors |
title_short |
On the robust parameter estimation for linear model with autocorrelated errors |
title_full |
On the robust parameter estimation for linear model with autocorrelated errors |
title_fullStr |
On the robust parameter estimation for linear model with autocorrelated errors |
title_full_unstemmed |
On the robust parameter estimation for linear model with autocorrelated errors |
title_sort |
on the robust parameter estimation for linear model with autocorrelated errors |
publisher |
American Scientific Publishers |
publishDate |
2013 |
url |
http://psasir.upm.edu.my/id/eprint/30343/1/On%20the%20robust%20parameter%20estimation%20for%20linear%20model%20with%20autocorrelated%20errors.pdf http://psasir.upm.edu.my/id/eprint/30343/ |
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