Forecasting Iran’s Rice Imports during 2009-2013
الموضوعات :Mohammad Reza Pakravan 1 , Mohammad Kavoosi Kelashemi 2 , Hamid Reza Alipour 3
1 - PhD student of Agricultural Economics, University of Tehran, Karaj, Iran
2 - PhD student of Agricultural Economics, University of Tehran, Karaj, Iran
3 - Assistant Professor of Islamic Azad University, Rasht Branch, Iran
الکلمات المفتاحية: Iran, Import, ARIMA, Rice, ANN, Trend,
ملخص المقالة :
In the present study Iran’s rice imports trend is forecasted, using artificial neural networks and econometric methods, during 2009 to 2013, and their results are compared. The results showed that feet forward neural network leading with less forecast error and had better performance in comparison to econometric techniques and also, other methods of neural networks, such as Recurrent networks and Multilayer perceptron networks. Moreover, the results showed that the amount of rice import has ascending growth rate in 2009-2013 and maximum growth occurs in 2009-2010 years, which was equal to 25.72 percent. Increasing rice import caused a lot of exchange to exit out of the country and also, irreparable damage in domestic production, both in terms of price and quantity. Considering mentioned conditions, economic policy makers should seek ways to reduce increasing trend of rice import; and more investment and planning for domestic rice producers.
1. Adnan, H., and Nadeem, H. M. 2007. Inflation Forecasting in Pakistan using Artificial Neural Networks, Munich Personal Repec Archive (MPRA) papar, No. 14645. | ||||
2. Box, G. E. P. and Jenkins, G. M. 1976. Time Series Analysis, Forecasting and Control, San Francisco: Holden- Day. | ||||
3. Chu, C. W. and Zhang, G. P. 2003. A comparative study of linear and nonlinear models for aggregate retail sales forecasting", International Journal of Production Economics, 86: 217-231. | ||||
4. Church, k., and Curram, S. P. 1996. Forecasting consumer's expenditure: A comparison between econometric and neural network models, International journal of forecasting, 12: 255-167. | ||||
5. Faria, E. L. and Albuquerque, M. P., and Gonzalez, J. L., and Cavalcante, J. T. P., and Albuquerque, M. P. 2009. Predicting the Brazilian stock market through neural networks and adaptive exponential smoothing method, Expert Systems with Applications. | ||||
6. Haider, A. and Hanif, M. N. 2007. Inflation Forecasting in Pakistan using Artificial Neural Networks, MPRA Paper 8898, University Library of Munich, Germany. | ||||
7. Haykin, S. 1994. Neural Networks – A Coomprehensive foundation. Macmillan College Publishing Company, New York. | ||||
8. Heravi. S., and Osborn, D. R., and Birchenhall, C. R. 2004. Linear versus neural network forecasts for European industrial production series. International Journal of Forecasting, 20: 435–446. | ||||
9. Kazemnejhad, M., and Mehrabi Boshrabadi, H. 1999. Types of rice price analysis, Agricultural Economics and Development, 7: 103-122. | ||||
10. Noori, K. 2002. Defining the production comparative advantages in major rice groups in Gilan and Mazandaran province, Agricultural Economics and Development, 10(4): 25-45 | ||||
11. Portugal, M.S. 1995. Neural networks versus time series methods: a forecasting exercise, Revista Brasileira de Economia, Vol. 49, Issue 4. | ||||
12. Tkacz, G. 2001. Neural network forecasting of Canadian GDP growth, International Journal of Forecasting, 17: 57-69 | ||||
13. White, H. 1988. Economic Perdication Using Neural Networks: The Case of IBM DAILY Stock Returns, Proceeding of The IEEE International Conference on Neural Network II, p. 451-458. |