Forecasting Wheat Commodity Prices using a Global Vector Autoregressive model

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dc.creatorGutierrez, Luciano
dc.creatorPiras, Francesco
dc.creatorOlmeo, Maria Grazia
dc.date2017-04-01T19:21:23Z
dc.date.accessioned2026-07-09T09:27:05Z
dc.descriptionIn 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.
dc.identifierdoi:10.22004/ag.econ.207264
dc.identifierhttps://ageconsearch.umn.edu/record/207264/files/Forecasting%20Wheat%20Commodity%20Prices%20using%20a%20Global%20Vector%20Autoregressive%20model.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/207264
dc.identifier.urihttp://hdl.handle.net/123456789/609054
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/207264
dc.titleForecasting Wheat Commodity Prices using a Global Vector Autoregressive model
dc.typeText

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