FORECASTING THE GDP IN IRAN BASED ON GMDH NEURAL NETWORK

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

ABRISHAMI H.; MEHRARA MOHSEN; AHRARI MAHDI; MIRGHASEMI S.

Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Journal of Economic Research Year:2009 | Volume:44 | Issue:88 Page(s): 1-24

Download Full-Text

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Persian Verion

View:

1,343

Download:

Cites:

Information Journal Paper

Title

FORECASTING THE GDP IN IRAN BASED ON GMDH NEURAL NETWORK

Author(s)

ABRISHAMI H. | MEHRARA MOHSEN | AHRARI MAHDI | MIRGHASEMI S. | Issue Writer Certificate

Keywords

GDP GROWTHQ3

GMDH NEURAL NETWORKQ3

DEDUCTIVE PROCESSQ2

FUNDAMENTAL METHODSQ3

FORECASTINGQ3

Abstract

This study employs a GMDH NEURAL NETWORK model, which has high capability in recognition of complicated non-linear trends especially with small samples, for modeling and predicting Iranian GDP GROWTH.First a fundamental model containing 7 independent variables together with dependent variable is designed and then by using DEDUCTIVE PROCESS and omission of one variable at a time, a total of 18 models are estimated. The results shows that omission of total export growth, oil export growth and trading volume growth variables from the fundamental model have the most impact in terms of reducing prediction errors. Moreover, the effect of government expenditure growth on the objective variables confirms recent researches in oil rich countries.In the end, it is shown that the GMDH NEURAL NETWORK has better predictive power than ARIMA method in prediction GDP GROWTH based on error criteria.

Cites

References

Cite

APA: Copy

ABRISHAMI, H., MEHRARA, MOHSEN, AHRARI, MAHDI, & MIRGHASEMI, S.. (2009). FORECASTING THE GDP IN IRAN BASED ON GMDH NEURAL NETWORK. TAHGHIGHAT-E-EGHTESADI, 44(88), 1-24. SID. https://sid.ir/paper/12028/en

Vancouver: Copy

ABRISHAMI H., MEHRARA MOHSEN, AHRARI MAHDI, MIRGHASEMI S.. FORECASTING THE GDP IN IRAN BASED ON GMDH NEURAL NETWORK. TAHGHIGHAT-E-EGHTESADI[Internet]. 2009;44(88):1-24. Available from: https://sid.ir/paper/12028/en

IEEE: Copy

H. ABRISHAMI, MOHSEN MEHRARA, MAHDI AHRARI, and S. MIRGHASEMI, “FORECASTING THE GDP IN IRAN BASED ON GMDH NEURAL NETWORK,” TAHGHIGHAT-E-EGHTESADI, vol. 44, no. 88, pp. 1–24, 2009, [Online]. Available: https://sid.ir/paper/12028/en

Related Journal Papers