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Modeling the error term of regression by combine white noise


Agboluaje, Ayodele Abraham and Ismail, Suzilah and Yin, Chee Yip (2016) Modeling the error term of regression by combine white noise. International Journal of Advance Research in Science and Engineering, 5 (12). pp. 63-70. ISSN 2319-8346

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Abstract

This paper examines the utilization of combination model technique to model the standardized residual exponential generalized autoregressive conditional heteroscedastic (EGARCH) errors.The technique combine white noise (CWN) is found to be more efficient and overcome EGARCH weaknesses. The estimation results using Combine White Noise model satisfies stability condition and passes stationary, serial correlation, and the ARCH effect tests.It fails the histogram-Normality tests but passes the Levene’s test of equal variances. Combine White Noise has minimum values of information criteria. From the results of the dynamic evaluation forecast errors, Combine White Noise has the minimum forecast errors which are indications of better results when compare with the EGARCH model dynamic evaluation forecast errors. Combine White Noise processes show the best fit with forecast accuracy.

Item Type: Article
Uncontrolled Keywords: Combine white noise, More efficient, Minimum information criteria, Minimum forecast error, Best fit, Forecast accuracy
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: School of Quantitative Sciences
Depositing User: Dr. Suzilah Ismail
Date Deposited: 06 Apr 2017 04:38
Last Modified: 06 Apr 2017 04:38
URI: http://repo.uum.edu.my/id/eprint/21534

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