Crude palm oil price forecasting in Malaysia: An econometric approach

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 set 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.