Evaluating combine white noise with US and UK GDP quarterly data
The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Condit...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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2016
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| Subjects: | |
| Online Access: | https://repo.uum.edu.my/id/eprint/18610/1/GUJS%2029%202%202016%20365-372.pdf |
| _version_ | 1825804059815182336 |
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| author | Agboluaje, Ayodele Abraham Ismail, Suzilah Yip, Chee Yin |
| author_facet | Agboluaje, Ayodele Abraham Ismail, Suzilah Yip, Chee Yin |
| author_sort | Agboluaje, Ayodele Abraham |
| collection | UUM |
| description | The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model cannot handled.The determinant of the residual co variance matrix values indicates that CWN estimation is efficient for each country.CWN has a minimum forecast errors which indicates forecast accuracy by estimating the countries data individually.The overall results indicate that CWN estimation provide more efficient and better forecast accuracy than EGARCH estimation.This boosts the economy. |
| first_indexed | 2024-07-04T06:08:12Z |
| format | Article |
| id | uum-18610 |
| institution | Universiti Utara Malaysia |
| language | English |
| last_indexed | 2024-07-04T06:08:12Z |
| publishDate | 2016 |
| record_format | eprints |
| spelling | uum-186102016-08-22T08:37:35Z https://repo.uum.edu.my/id/eprint/18610/ Evaluating combine white noise with US and UK GDP quarterly data Agboluaje, Ayodele Abraham Ismail, Suzilah Yip, Chee Yin QA Mathematics The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model cannot handled.The determinant of the residual co variance matrix values indicates that CWN estimation is efficient for each country.CWN has a minimum forecast errors which indicates forecast accuracy by estimating the countries data individually.The overall results indicate that CWN estimation provide more efficient and better forecast accuracy than EGARCH estimation.This boosts the economy. 2016 Article PeerReviewed application/pdf en https://repo.uum.edu.my/id/eprint/18610/1/GUJS%2029%202%202016%20365-372.pdf Agboluaje, Ayodele Abraham and Ismail, Suzilah and Yip, Chee Yin (2016) Evaluating combine white noise with US and UK GDP quarterly data. Gazi University Journal of Science, 29 (2). pp. 365-372. ISSN 2147-1762 http://gujs.gazi.edu.tr/article/view/5000176852 |
| spellingShingle | QA Mathematics Agboluaje, Ayodele Abraham Ismail, Suzilah Yip, Chee Yin Evaluating combine white noise with US and UK GDP quarterly data |
| title | Evaluating combine white noise with US and UK GDP quarterly data |
| title_full | Evaluating combine white noise with US and UK GDP quarterly data |
| title_fullStr | Evaluating combine white noise with US and UK GDP quarterly data |
| title_full_unstemmed | Evaluating combine white noise with US and UK GDP quarterly data |
| title_short | Evaluating combine white noise with US and UK GDP quarterly data |
| title_sort | evaluating combine white noise with us and uk gdp quarterly data |
| topic | QA Mathematics |
| url | https://repo.uum.edu.my/id/eprint/18610/1/GUJS%2029%202%202016%20365-372.pdf |
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