Abstract
In this study, artificial neural network (ANN) for forecasting government size in Iran is applied. The purpose of the study is comparison various architectures, transfer functions and learning algorithms on the operation of network, for this purpose the annual data from 1971-2007 of selected variable are used. Variables are tax income, oil revenue, population, openness, government expenditure, GDP and GDP per capita; these variables are selected based on economic theories. Result shows that networks with various training algorithms and transfer functions have different results. Best architecture is a network with two hidden layer and twelve (12) neuron in hidden layers with hyperbolic tangent transfer function both in hidden and output layers with Quasi -Newton training algorithm. Base on findings in this study suggested in using neural network must be careful in selecting the architecture, transfer function and training algorithms.
Highlights
Presence of the government in economic is one of the argument among the economists
The result shows that different training algorithm and transfer function have different results based on average test error
According to the findings of this study several conclusions reported; First, the number of hidden layer and neurons in this layer strongly affect the results of network, in this study, best results are gets in networks with two (2) hidden layers, and network performance is the best measurement about the number of neurons
Summary
There are two views regarding the role of government in economic activities and its effect on growth and investment. Some argue that increase in government expenditure on socioeconomic and physical infrastructures encourages economic growth. Many economists have theorized that government production is statically less efficient than private sector They have contended that governmental outputs will be non optimal and produced at higher cost than corresponding private sector outputs. The traditional view argues that government expenditure crowds out private investment. Whether financed with taxes or debt, increases the demand for goods and services, raising interest rates, making capital more expensive and, as such, reducing private investment. Empirical studies: There has been a great interest in studying the artificial neural network (ANN) forecasting in economics, financial, business and engineering. In table (1) comes some study on neural network and their results
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