Evaluation of Artificial Neural Network-Panel Data Hybrid Model in Predicting Iran's Dried Fruits Export Prices

In many studies, in order to predict economic variables, quantitative methods based on time series data or cross-section data are mostly used. Time series data or cross-section data do not control the heterogeneity of countries, and the possibility of obtaining the risk of biased results exists. Panel data provides more informative data and a more degree of freedom which lead to results and predictions that are more precise. In this study, while considering the significant role and proportion of dried fruits in non-oil exports, the synthetic artificial neural network-panel data method has been used to predict the price of pistachio, raisin and date exports. Then the predictions were compared, using the accuracy criteria, with the regression model (the two-way error component model). The data from ten target markets for each of the dried fruits from 1992 to 2012 were used, and the results of this study show that the new and synthetic artificial neural network-panel data method has a better performance in predicting the price of Iran’s pistachio, raisin and date exports than that of the regression method. Therefore, it is suggested that exporters, policy makers, and researchers use this method in order to predict economic variables.