TITLE:
Comparison among the UECM Model, and the Composite Model in Forecasting Malaysian Imports
AUTHORS:
Mohamed A. H. Milad, Hanan Moh. B. Duzan
KEYWORDS:
Composite Model, UECM, ARIMA, Forecasting, Malaysia
JOURNAL NAME:
Open Journal of Statistics,
Vol.14 No.2,
March
29,
2024
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 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.