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Paper Information

Journal:   EQTESAD-E KESHAVARZI VA TOWSE'E   SPRING 2016 , Volume 24 , Number 93; Page(s) 1 To 26.
 
Paper: 

INVESTIGATING AND SENSITIVITY ANALYSIS OF EFFECTIVE FACTORS ON AGRICULTURAL IMPORT DEMAND USING ARTIFICIAL NEURAL NETWORK MODEL

 
 
Author(s):  FARYADRAS V.A., SHABANZADEH M., ESFANJARI KENARI R.
 
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Abstract: 

In spite of large emphasis on agriculture and food security in the upstream documentation and national development plans, importation of agricultural and food products has had an upward trend mainly influenced by various variables within last three decades. Imports of these products have increased of nearly 5 million tons in 1981 to more than 21 million tons in 2012. Aims of present study is identifying and examining the effective factors on Iranian agricultural import demand for the period 1991-2012 using combination of exploratory data analysis methods (EDA) with Artificial Neural Network. For this purpose, information of eleven goods consist of Wheat, barley, rice, corn, Soybean meal, oil (soybean and sunflower), sugar, eggs, milk, poultry and beef meat have been used. The result of modeling shows that nonoil GDP (29.5%), oil incomes (27.7%), tariff rate (16.5%) and amount of domestic production (14.6%) have the greatest effect on import demand. Also the results revealed that relative price of agricultural products imports (6.6%) and lag of import demand (5.1%) have the lowest effect on import demand.

 
Keyword(s): AGRICULTURAL IMPORT DEMAND, EXPLORATORY DATA ANALYSIS, ARTIFICIAL NEURAL NETWORK
 
References: 
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