Author(s)

Mohamed A. H. Milad^1*, Hanan Moh. B. Duzan²

¹Libyan Authority and Science Research, Tripoli, Libya.
²The Higher Institute of Science and Technology-Souq Algoma, Tripoli, Libya.

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 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 the unrestricted error correction model (UECM), and composite model (Combined regression—ARIMA). The imports of Malaysia from the first quarter of 1991 to the third quarter of 2022 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 composite model, and the UECM model in this study are linear models based on responses to Malaysia’s imports. Future studies might compare the performance of linear and nonlinear models in forecasting.

Composite Model, UECM, ARIMA, Forecasting, Malaysia

Milad, M. and Duzan, H. (2024) Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports. Open Journal of Statistics, 14, 163-178. doi: 10.4236/ojs.2024.142008.