Evaluating combine white noise with US and UK GDP quarterly data

• Main Authors: Agboluaje, Ayodele Abraham, Ismail, Suzilah, Yip, Chee Yin
• Format: Article
• Language: English
• Published: 2016
• Subjects: QA Mathematics
• Online Access: http://repo.uum.edu.my/18610/1/GUJS%2029%202%202016%20365-372.pdf; http://repo.uum.edu.my/18610/; http://gujs.gazi.edu.tr/article/view/5000176852
• Institution: Universiti Utara Malaysia

The main objective of this study is to evaluate the Combine White Noise (CWN) model for the confirmation of its effectiveness in addressing the error term challenges.CWN models the leverage effect appropriately with better estimation results of which the Exponential Generalized Autoregressive Conditional Heteroscedasticity (EGARCH) model cannot handled.The determinant of the residual co variance matrix values indicates that CWN estimation is efficient for each country.CWN has a minimum forecast errors which indicates forecast accuracy by estimating the countries data individually.The overall results indicate that CWN estimation provide more efficient and better forecast accuracy than EGARCH estimation.This boosts the economy.