'Penerbit Universiti Kebangsaan Malaysia (UKM Press)'
Abstract
This paper aims to forecast the performance of crude palm oil price (CPO) in Malaysia by comparing several econometric
forecasting techniques, namely Autoregressive Distributed Lag (ARDL), Autoregressive Integrated Moving Average
(ARIMA) and Autoregressive Integrated Moving Average with exogenous inputs (ARIMAX). Using monthly time series
data spanning from 2008 to 2017, the main results revealed that ARIMAX model is the most accurate and the most
efficient model as compared to ARDL and ARIMA in forecasting the crude palm oil price. The results also show that the
spot price of palm oil is highly influenced by stock of palm oil, crude petroleum oil price and soybean oil price. The
empirical findings provide some insights for decision making and policy implementations, including the formulation
of strategies to help the industry in dealing with the price changes and thus enable the Malaysian palm oil industry to
continue dominating the international market
