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Modeling the heteroscedasticity in data distribution

The main objective of this study is to provide a model that will uplift the weaknesses of the existing model for efficient estimation. Generalized autoregressive conditional heteroscedasticity (GARCH) family models weaknesses were overcome by the new Combine White Noise (CWN) model which proved to...

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Main Authors: Agboluaje, Ayodele Abraham, Ismail, Suzilah, Chee, Yin Yip
Format: Article
Language:English
Published: Research India Publications 2016
Subjects:
QA Mathematics
Online Access:http://repo.uum.edu.my/21520/1/GJPAM%2012%201%202016%20313%20322.pdf
http://repo.uum.edu.my/21520/
http://www.ripublication.com/gjpam16/gjpamv12n1_27.pdf
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spelling my.uum.repo.215202017-04-05T08:19:56Z http://repo.uum.edu.my/21520/ Modeling the heteroscedasticity in data distribution Agboluaje, Ayodele Abraham Ismail, Suzilah Chee, Yin Yip QA Mathematics The main objective of this study is to provide a model that will uplift the weaknesses of the existing model for efficient estimation. Generalized autoregressive conditional heteroscedasticity (GARCH) family models weaknesses were overcome by the new Combine White Noise (CWN) model which proved to be more efficient.CWN estimation passed stability condition, stationary, serial correlation, the ARCH effect tests and it also passed the Levene’s test of equal variances using both Australia (A.U.) and United States (U.S.) GDP data sets. The CWN estimation produced better results with minimum information criteria and high log likelihood values in both U.S. and A.U. data estimation.CWN has the minimum forecast errors which were better results when compare with the GARCH model dynamic evaluation forecast errors in both countries.The determinant of the residual of covariance matrix values revealed that CWN was efficient in the two countries, but A.U.was more efficient.Based on every result in the empirical analysis of the two countries, CWN was the more appropriate model. Research India Publications 2016 Article PeerReviewed application/pdf en http://repo.uum.edu.my/21520/1/GJPAM%2012%201%202016%20313%20322.pdf Agboluaje, Ayodele Abraham and Ismail, Suzilah and Chee, Yin Yip (2016) Modeling the heteroscedasticity in data distribution. Global Journal of Pure and Applied Mathematics, 12 (1). pp. 313-322. ISSN 0973-1768 http://www.ripublication.com/gjpam16/gjpamv12n1_27.pdf
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Institutionali Repository
url_provider http://repo.uum.edu.my/
language English
topic QA Mathematics
spellingShingle QA Mathematics
Agboluaje, Ayodele Abraham
Ismail, Suzilah
Chee, Yin Yip
Modeling the heteroscedasticity in data distribution
description The main objective of this study is to provide a model that will uplift the weaknesses of the existing model for efficient estimation. Generalized autoregressive conditional heteroscedasticity (GARCH) family models weaknesses were overcome by the new Combine White Noise (CWN) model which proved to be more efficient.CWN estimation passed stability condition, stationary, serial correlation, the ARCH effect tests and it also passed the Levene’s test of equal variances using both Australia (A.U.) and United States (U.S.) GDP data sets. The CWN estimation produced better results with minimum information criteria and high log likelihood values in both U.S. and A.U. data estimation.CWN has the minimum forecast errors which were better results when compare with the GARCH model dynamic evaluation forecast errors in both countries.The determinant of the residual of covariance matrix values revealed that CWN was efficient in the two countries, but A.U.was more efficient.Based on every result in the empirical analysis of the two countries, CWN was the more appropriate model.
format Article
author Agboluaje, Ayodele Abraham
Ismail, Suzilah
Chee, Yin Yip
author_facet Agboluaje, Ayodele Abraham
Ismail, Suzilah
Chee, Yin Yip
author_sort Agboluaje, Ayodele Abraham
title Modeling the heteroscedasticity in data distribution
title_short Modeling the heteroscedasticity in data distribution
title_full Modeling the heteroscedasticity in data distribution
title_fullStr Modeling the heteroscedasticity in data distribution
title_full_unstemmed Modeling the heteroscedasticity in data distribution
title_sort modeling the heteroscedasticity in data distribution
publisher Research India Publications
publishDate 2016
url http://repo.uum.edu.my/21520/1/GJPAM%2012%201%202016%20313%20322.pdf
http://repo.uum.edu.my/21520/
http://www.ripublication.com/gjpam16/gjpamv12n1_27.pdf
_version_ 1644283260662972416
score 13.211869

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