A Robust Composite Model Approach For Forecasting Malaysian Imports: A Comparative Study - Mohamed A.H. Milad; Rose Irnawaty Ibrahim (Science Alert, 2024)
Abstract: Objective: With the increasing importance of imports as one of the important factors of economic growth, the current study proposed techniques of more reliable and predictable Malaysian imports of crude material in the future. Specifically, this study proposes composite models for probabilistic imports of crude material forecasting in Malaysia. Methodology: In this study, the proposed composite models (With regression processing of heteroscedasticity), (With regression processing of heteroscedasticity and autocorrelation) were employed to extract information that assists in increasing accurate forecasting of the size of the Malaysian imports as well as forecasting engines and compare it with other commonly used models including regression models and ARIMA models. Results: The forecasting results of the study showed that the composite model (With regression processing of heteroscedasticity) approach provides more probabilistic information for improving forecasting of Malaysian imports of crude material. Conclusion: The results also showed two sets of benefits: The main benefit is that the composite model (Without regression processing) is capable of solving the problem of autocorrelation in residuals but it was unable to solve heteroscedasticity in the residuals. The second benefit is processing the problem of autocorrelation in the composite model in a case when it is not processed in the regression model. However, in the case of the emerging problem of the heteroscedasticity, it can be processed in the regression model prior to the composite model formation.