selection the suitable model for forecasting land, production and price of sugar beet in iran

abstract the aim of this study was to selecting the suitable model for forecast land, production and price of sugar beet in iran. for this purpose, models applied to forecast are arima, single and double exponential smoothing, harmonic, artificial neural network and arch for period 1993-2008. results of durbin-watson tests, land, production and price of sugar beet series were found non stochastic and predictable. based on the lowest forecasting error criterion, arima is the best model for forecast production and price of sugar beet series. but in orther to forecast land of sugar beet, neural network model is the best. hence, using the forecast method can affect on different policy about production via forecasting the fluctuation variables. jel classification: q11 – d12 – c32 – c22