Composite Model Analysis in Forecasting the Malaysian Imports

Abstract

For more than a century, forecasting models have been crucial in a variety of fields. Models can offer the most accurate forecasting outcomes if error terms are normally distributed. Finding out a good statistical model for time series predicting imports in Malaysia is the main target of this study. The decision made during this study mostly addresses Vector Error Correction Method (VECM), composite model (Combined regression-ARIMA), and ARIMA model. The imports of Malaysia from the first quarter of 1991 to the first quarter of 2023 are employed in this study’s quarterly time series data. The forecasting outcomes of the current study demonstrated that the composite model offered more probabilistic data, which improved forecasting the volume of Malaysia’s imports. The (ARIMA) mode, composite model, and VECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of nonlinear and linear models in forecasting.