Forecasting Wheat Commodity Prices using a Global Vector Autoregressive model
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In this paper the performance of a Global Vector Autoregression model in forecasting export wheat prices is evaluated in comparison to different benchmark models. Forecast evaluation results are based on different statistics including RMSE, MAPE, the Diebold-Mariano (DM) tests and turning points forecast accuracy. The results show that the GVAR forecasts tend to outperform forecasts based on the benchmark models, emphasizing the interdependencies in the global wheat market.