Forecasting Government Size in Iran Using Artificial Neural Network

Ahmad Jafari Samimi

Journal of Economics and Behavioral Studies, 2011, vol. 3, issue 5, 274-278

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.

Date: 2011
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ojs.amhinternational.com/index.php/jebs/article/view/280/280 (application/pdf)
https://ojs.amhinternational.com/index.php/jebs/article/view/280 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:rnd:arjebs:v:3:y:2011:i:5:p:274-278

DOI: 10.22610/jebs.v3i5.280

Access Statistics for this article

More articles in Journal of Economics and Behavioral Studies from AMH International
Bibliographic data for series maintained by Muhammad Tayyab ().