Multivariate autoregressive models for forecasting seaborne trade flows

This paper contributes to the literature on forecasting seaborne trade flows by presenting multivariate autoregressive time series models that can be used to produce long-term forecasts. The models are applied to forecasting the trade flows of four commodity markets (crude oil, iron ore, grain and coal) on major trade routes. The empirical results indicate that the models can produce long-term seaborne trade flow estimates that have relatively small forecast errors.