EVALUATING COMBINE WHITE NOISE WITH US AND UK GDP QUARTERLY DATA

Authors : Ayodele Abraham Agboluaje, Suzilah bt Ismail, Chee Yin Yip

Pages : 365-372

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Publication Date : 2016-06-20

Article Type : Conference Paper

Abstract :The main objective of this study is to evaluate the Combine White Noise insert ignore into journalissuearticles values(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 insert ignore into journalissuearticles values(EGARCH); model cannot handled. The determinant of the residual covariance 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.
Keywords : Combine white noise, Efficient, Forecast accuracy, Log likelihood