Author(s) Details:
Ayodele Abraham Agboluaje
Department of Mathematics Sciences, Faculty of Natural Sciences, Ibrahim Badamasi Babangida University, Lapai, Nigeria and School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.
Suzilah Ismail
School of Quantitative Sciences, College of Arts and Sciences, Universiti Utara Malaysia, Malaysia.
Chee Yin Yip
Department of Economics, Faculty of Business and Finance, Universiti Tuanku Abdul Rahman, Malaysia.
This section is a part of the chapter: Improving GDP Error Term Modeling: Application of the Combine White Noise Model to Australia
The objective of the study is to improve the Gross Domestic Product (GDP) error term modeling, with the Combine White Noise (CWN) Model in Australia. The estimation of the Combine White Noise model passes the stability condition, stationary, serial correlation, and the CWN model estimation yields the best results with minimum information criteria and higher log-likelihood values than EGARCH and VAR models. The determinant of the residual of the covariance matrix value indicates that CWN is efficient while the determinant of the residual of the covariance matrix value indicates that VAR is not efficient. CWN has the least forecast errors which are indications of the best results when compared with the EGARCH and VAR models dynamic evaluation forecast errors. The minimum forecast error values indicate forecast accuracy. The total results testified that CWN is the right model. To model conditional heteroscedasticity data with leverage effect in Australia and other nations powerfully, CWN is acclaimed. The contribution of this study to the scientific community is that the CWN gives suitable results that improve the weaknesses of the existing models. The CWN forecast output is more reasonable for effective policy making. Implementation of this CWN will boost the economy of the society.
How to Cite
Agboluaje, A. A., Ismail, S., & Yip, C. Y. (2025). Improving GDP Error Term Modeling: Application of the Combine White Noise Model to Australia. Science and Technology: Developments and Applications Vol. 5, 101–114. https://doi.org/10.9734/bpi/stda/v5/4274