Modeling the error term of regression by combine white noise

• Main Authors: Agboluaje, Ayodele Abraham, Ismail, Suzilah, Yin, Chee Yip
• Format: Article
• Language: English
• Published: 2016
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://repo.uum.edu.my/21534/1/IJARSE%205%2012%202016%2070%2063.pdf; http://repo.uum.edu.my/21534/; https://www.ijarse.com/images/fullpdf/1480581618_1317.pdf
• Institution: Universiti Utara Malaysia

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.