Agboluaje, Ayodele Abraham and Ismail, Suzilah and Chee, Yin Yip (2016) Comparing vector autoregressive (VAR) estimation with combine white noise (CWN) estimation. Research Journal of Applied Sciences, Engineering and Technology, 12 (5). pp. 544-549. ISSN 2040-7459

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	Determinant residual covariance, EGARCH, error term, leverage, log likelihood, minimum forecast errors
Subjects:	Q Science > QA Mathematics
Divisions:	School of Quantitative Sciences
Depositing User:	Dr. Suzilah Ismail
Date Deposited:	05 Apr 2017 08:33
Last Modified:	05 Apr 2017 08:33
URI:	https://repo.uum.edu.my/id/eprint/21522

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