Open AccessForecasting Government Size in Iran Using Artificial Neural Network
Author(s) -
Ahmad Jafari Samimi
Publication year - 2011
Publication title -
journal of economics and behavioral studies
Language(s) - English
Resource type - Journals
ISSN - 2220-6140
DOI - 10.22610/jebs.v3i5.280
Subject(s) - artificial neural network , population , per capita , computer science , artificial intelligence , network architecture , revenue , government (linguistics) , openness to experience , transfer of learning , variable (mathematics) , hyperbolic function , econometrics , mathematics , economics , demography , finance , psychology , social psychology , linguistics , philosophy , mathematical analysis , computer security , sociology
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.