Forecasting Wheat Commodity Prices using a Global Vector Autoregressive model

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.


Issue Date:
2015-06
Publication Type:
Conference Paper/ Presentation
Record Identifier:
http://ageconsearch.umn.edu/record/207264
PURL Identifier:
http://purl.umn.edu/207264
Total Pages:
19
JEL Codes:
G14; Q14; C12; C15




 Record created 2017-04-01, last modified 2018-01-23

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