Comparing vector autoregressive (VAR) estimation with combine white noise (CWN) estimation
The purpose of this study is to compare one of the existing models, which is VAR model with the new Combine White Noise model. The VAR models have not been able to model the conditional heteroscedasticity and the leverage effect exhibited by the data. Likewise, GARCH family models cannot model lever...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MAXWELL Science Publication
2016
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| Subjects: | |
| Online Access: | http://repo.uum.edu.my/21522/1/RJASET%2012%205%202016%20544%20549.pdf http://repo.uum.edu.my/21522/ http://doi.org/10.19026/rjaset.12.2682 |
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| Institution: | Universiti Utara Malaysia |
| Language: | English |
| Summary: | The purpose of this study is to compare one of the existing models, which is VAR model with the new Combine White Noise model. The VAR models have not been able to model the conditional heteroscedasticity and the leverage effect exhibited by the data. Likewise, GARCH family models cannot model leverage effect. The Combine White Noise (CWN) has proved more efficient and takes care of these weaknesses. CWN has the minimum information criteria and high log likelihood when compare with VAR estimation. The determinant of the residual covariance matrix value indicates that CWN estimation is efficient. It passes the Levene’s test of equal variances. CWN has a minimum forecast errors which indicates forecast accuracy. All its outcomes outperform all the outcomes of VAR widely. |
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