The role of oil price in the forecasts of agricultural commodity prices

Abstract

© Springer International Publishing AG 2018. The objective of this paper is to examine whether including oil price to the agricultural prices forecasting model can improve the forecasting performance. We employ linear Bayesian vector autoregressive (BVAR) and Markov switching Bayesian vector autoregressive (MS-BVAR) as innovation tools to generate the out-of-sample forecast for the agricultural prices as well as compare the performance of these two forecasting models. The results show that the model which includes the information of oil price and its shock outperforms other models. More importantly, linear model performs well in one- to three-step-ahead forecasting, while Markov switching model presents greater forecasting accuracy in the longer time horizon.