Volume 16, Issue 2 (2008)
J Tax Res 2008, 16(2): 27-56 |
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Khaloozadeh H, Hamidi Alamdari S, Zayer A. Forecasting the Iranian Tax Revenues: An Application of Nonlinear Models. J Tax Res 2008; 16 (2) :27-56
URL: http://taxjournal.ir/article-1-160-en.html
URL: http://taxjournal.ir/article-1-160-en.html
1- Associate Professor of Electrical Engineering at Khajeh Nasiroddin Toosi University
2- M.A. in Economics and a Tax Researcher at the Tax Research Department, INTA
3- M.A. in Economic Systems Planning and a Tax Researcher at the Tax Research Department, INTA
2- M.A. in Economics and a Tax Researcher at the Tax Research Department, INTA
3- M.A. in Economic Systems Planning and a Tax Researcher at the Tax Research Department, INTA
Abstract: (11489 Views)
Taxes are one of the main components of the government budgetand
as such, having a clear image of the attainable amount of taxes is not
only necessary for optimal allocation of scare resources for tax
collection purposes, but also helps the government to develop more
precise tax collection plans. In this article, the structural features of tax
revenues series have first been examined in terms of linearity, chaotic
nonlinearity and stochasticity using Lyapunov Exponent. These series
are: total taxes, direct taxes, indirect taxes, corporate taxes, income
taxes, salary taxes, real estates taxes, business taxes, wealth taxes,
inheritance taxes and goods & services taxes. The results endorse the
existence of chaos in the series of different tax resources in different
extents. Therefore, based on the results obtained, it was found that
more accurate short-term forecasts are possible by applying nonlinear
modeling methods. In the next step, using data of the period 1963-
2006, tax revenues of different resources were forecasted for the
period 2007-2009 by applying both parallel and proposed multipleinput
multiple-output structures of the ANN’s.
Keywords: Tax, forecast, chaos, Lyapunov exponent, artificial neural networks, parallel models, multiple-input multiple-output model
Type of Study: Research |
Received: 2008/04/16 | Accepted: 2008/06/28 | Published: 2014/03/14
Received: 2008/04/16 | Accepted: 2008/06/28 | Published: 2014/03/14
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