Comparison of econometric models and artificial neural networks to predict of Iran Oilcake imports

Document Type : Research Paper

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faculty

Abstract

The importance of economic variables forecasting for policy makers and planners and firms is obvious. Therefore, in recent decades, several models have been developed. In the present study, the Oilcake import quantity predicted by using econometric Model and artificial neural network for 1400-1394. For this purpose, for predict and education network, the data of the 88-1348 and for test the accuracy of forecasts, the data of the 1393-1389 were used. The results showed that forward neural network compared to ARIMA and exponential smoothing method with fewer errors and better performance for predicting of Oilcake import. The results show that the Oilcake imports in 1394 compared to last year, a 32% increase. Therefore, it is necessary to reduce the amount of imported and domestic needs, be protectionist policies. Also, to control of the steep rise in Oilcake import, policy makers and planners would be necessary programs apply, including increased tariffs. The grants for the production and supply companies in the conversion process to oilcake can be used to increase the productivity and competitiveness of these products to be effective.

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Iranian Journal of Agricultural Economics and Development Research
Volume 47, Issue 3
January 2017
Pages 633-646

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