Multivariate autoregressive models for forecasting seaborne trade flows

This paper contributes to the literature on forecasting seaborne trade flow s by presenting multivariate autoregressive time series models that can be used to produce long-term forecasts. The models are applied to forecasting the Grade flows of four commodity markets (crude oil, iron ore, grain and c oal) on major trade routes. The empirical results indicate that the models can produce long-term seaborne trade flow estimates that have relatively sm all forecast errors. (C) 2001 Elsevier Science Ltd. All rights reserved.