Tax Revenues Forecasting By Applying PSO Optimization Algoritm

Asadi, Morteza; Hamidi Alamdari, Saeedeh; Khaloozadeh, Hamid

doi:10.29252/jemr.8.30.147

Volume 8, Issue 30 (12-2017) jemr 2017, 8(30): 147-169 | Back to browse issues page

‎ 10.29252/jemr.8.30.147

‎ 20.1001.1.22286454.1396.8.30.2.3

Mendeley

Zotero

RefWorks

Asadi M, Hamidi Alamdari S, Khaloozadeh H. Tax Revenues Forecasting By Applying PSO Optimization Algoritm. jemr 2017; 8 (30) :147-169
URL: http://jemr.khu.ac.ir/article-1-1133-en.html

Tax Revenues Forecasting By Applying PSO Optimization Algoritm

Morteza Asadi ^*

¹, Saeedeh Hamidi Alamdari²

, Hamid Khaloozadeh³

1- Kharazmi University , asadi@ues.ac.ir
2- Kharazmi University
3- Khaje Nasirooldin Tousi University

Abstract: (6270 Views)

Forecasting tax revenues is vitally important issue for optimal allocation of taxable resources, planning and budgeting in national and regional levels and knowing the potential national participation in public expenditures. The classical optimization based on mathematical methods may not be reliable in real world and mostly inefficient and inapplicable in complicated world due to their restricted assumptions. The smart optimization may help us to find the solution. This essay based on modified PSO methodology .The initial trial based on the data during 1971- 2007 in case of various direct and indirect taxes , and using updated data during 2008- 2012 for final forecasting , to estimate tax revenues for upcoming next three years (2013 up to 2016) by MATLAB software.

Keywords: Tax, Forecast, Neural Net Modeling, Optimization, PSO and Modified PSO

Full-Text [PDF 569 kb] (2245 Downloads)

Type of Study: توسعه ای | Subject: بخش عمومی
Received: 2016/10/27 | Accepted: 2017/11/25 | Published: 2018/03/6

References

1.  Hamidi ,S. (2006), Modeling and Forecasting Business Tax in Iran, Application of Neutral Nets, Graduate Dissertation, Ferdousi University , Mashhad , Iran

2.  Hamidi ,S , Khaloozade, H. (2010), Non Linear Modeling for forcasting Personal Tax , An application of Neutral Nets, Economic Research Centre, Ministry of Economics ,Iran

3.  Hamidi ,S , Khaloozade, H. Asadollahzade ,R. (2009), Forcasting Tax Revenues in fifth development plan, Tax Research center , Tehran, Iran

4.  Khaloozade, H. Khaki, A (2004). A survey of Stock Market Forecasting Methods, A non Linear Neural Nets , Journal of Economic Research, No.63 , pp43-85.

5.  Khaloozade, H., Hamidi ,S , Zaer, A. (2009), Non linear Modeling and Forecasting of Various Tax Revenues, The Second Tax Conference , Iran Tax Administration.

6.  Hamidi ,S , Zaer, A. Gholami,E. (2009), Methods of Tax Forecasting , Tax Research Centre , Iran Tax Administration.

7.  Arabmazar , A. Hamidi ,S , Zaer, A. Gholami,E. (2009), An Estimation of Tax Capacity, Tax Research Centre , Iran Tax Administration.

8.  Falahi, M.A. ,Khaloozade, H., Hamidi ,S ,(2007),Non Linear Modeling and Forecasting of Iran Business Tax , Journal of Economic Research, No.63 , PP. 143-167

9.  Ghetmiri , M.A., Islamlooian ,K. ,(2007) Iran Tax Capacity Estimation , Case of Selected Developing Countries, Research Project sponsored by Iran Tax Administration.

10.  Aminrashti,N. Rezaiee,G. (2010) Forcasting of Direct Tax Revenue In fifth Development Plan , Tax Journal , No.6 , PP. 67-82

11.  Monjazeb, M.R., Soleimani, P.(2006) Economic Research Letter , No.16 , pp.139-162

12.  Farzbod, J. Mollaiipour ,M. , Salimifar,M. (2002), Tax Potentiality of Khorasan , , Province, Case of Spare Parts, Economic Magazine , No.4-5, pp.7-11.

13.  Samati,M., Teyebi,S.K., Heidari,S. (2009), Impact of Government Revenue on Inflation and Real Growth of Iran Economy , Tax Journal , Iran Tax Administration , No.2 , pp.17-32

14.  Sarlak, A. ,(2007) Estimation of Tax Capacity in Markazi Province of Iran, Economic Magazine No. 61, pp15-40.

15.  Lezgi, F., Amini A. , Shomali ,L , Najafi A. (2009),, Forcast of Tax Revenue in Ghazvin Province , Journal of Research and Economic Policy, No.47 pp.121-153

16.  Abraham, B. and Ledolter, J. [1983]. Statistical Methods for Forecasting. Wiley, Hoboken, NJ.

17.  Durbin, J. and Watson, G. S. [1950]. Testing for serial correlation in least squares regression I. Biometrika, 37, 409--438. [DOI:10.2307/2332391]

18.  Chen, An-S. and Mark T. Leung (2004), "Regression Neural Network for error Correction in Foreign Exchange Forecasting and Trading", Elsevier, pp.1049-1068. [DOI:10.1016/S0305-0548(03)00064-9]

19.  Diebold, F.X. (1998),. "The Past, Present and Future of Macroeconomic Forecasting". Journal of Economic Perspectives, 12, 175-192 [DOI:10.1257/jep.12.2.175]

20. ♣ Fllareiov, G.F and E.O. Averehenkov; "Using Neural Nets for Time Series Forecasting "; IEEE, pp. 249-253, 1999.

21.  Fllareiov, G.F. and E.O. Averehenkov (1999), "Using Neutral Nets for Time Series Forecasting", IEEE, pp. 249-253.

22. ♣ Garliauskas, A.; "Neural Network Chaos and Computational Algorithms of Forecast in Finance "; IEEE, pp. 638-643, 1999.

23.  H. Choi, S. Ohmori, K. Yoshimoto and H. Ohtake, "Improvement of Particle Swarm Optimization" the 8th IEEE International Conference on Supply Chain Management and Information Systems, pp. 6-9 2010.

24.  J. Kennedy and R. Mendes, "population Structure and Particle Swarm Performance" in Proceeding of IEEE International Conference on Evolutionary Computation, pp. 1671-1676, 2002.