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Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.

This article evaluates the performance of two estimators namely, the Maximum Likelihood Estimator (MLE) and Whittle's Estimator (WE), through a simulation study for the Generalised Autoregressive (GAR) model. As expected, it is found that for the parameters and σ2, the MLE and WE have a be...

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Main Authors: Shitan, Mahendran, Peiris, Shelton
Format: Article
Published: Taylor & Francis 2008
Online Access:http://psasir.upm.edu.my/id/eprint/7027/
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spelling my.upm.eprints.70272014-10-29T07:27:45Z http://psasir.upm.edu.my/id/eprint/7027/ Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study. Shitan, Mahendran Peiris, Shelton This article evaluates the performance of two estimators namely, the Maximum Likelihood Estimator (MLE) and Whittle's Estimator (WE), through a simulation study for the Generalised Autoregressive (GAR) model. As expected, it is found that for the parameters and σ2, the MLE and WE have a better performance than Method of Moments (MOM) estimator. For the parameter δ, MOM sometimes appears to have a slightly better performance than MLE and WE, possibly due to truncation approximations associated with the hypergeometric functions for calculating the autocorrelation function. However, the MLE and WE can be used in practice without loss of efficiency. Taylor & Francis 2008 Article PeerReviewed Shitan, Mahendran and Peiris, Shelton (2008) Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study. Communications in Statistics: Simulation and Computation, 37 (3). pp. 560-570. ISSN 0361-0918; ESSN: 1532-4141 10.1080/03610910701649598
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description This article evaluates the performance of two estimators namely, the Maximum Likelihood Estimator (MLE) and Whittle's Estimator (WE), through a simulation study for the Generalised Autoregressive (GAR) model. As expected, it is found that for the parameters and σ2, the MLE and WE have a better performance than Method of Moments (MOM) estimator. For the parameter δ, MOM sometimes appears to have a slightly better performance than MLE and WE, possibly due to truncation approximations associated with the hypergeometric functions for calculating the autocorrelation function. However, the MLE and WE can be used in practice without loss of efficiency.
format Article
author Shitan, Mahendran
Peiris, Shelton
spellingShingle Shitan, Mahendran
Peiris, Shelton
Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
author_facet Shitan, Mahendran
Peiris, Shelton
author_sort Shitan, Mahendran
title Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
title_short Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
title_full Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
title_fullStr Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
title_full_unstemmed Generalized Autoregressive (GAR) Model: A Comparison of Maximum Likelihood and Whittle Estimation Procedures Using a Simulation Study.
title_sort generalized autoregressive (gar) model: a comparison of maximum likelihood and whittle estimation procedures using a simulation study.
publisher Taylor & Francis
publishDate 2008
url http://psasir.upm.edu.my/id/eprint/7027/
_version_ 1643823600943235072
score 13.211869

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