RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer

Petra Karanikić, Igor Mladenović (), Svetlana Sokolov-Mladenović and Meysam Alizamir
Additional contact information
Petra Karanikić: University of Rijeka
Igor Mladenović: University of Niš, Faculty of Economics
Svetlana Sokolov-Mladenović: University of Niš, Faculty of Economics
Meysam Alizamir: Islamic Azad University

Quality & Quantity: International Journal of Methodology, 2017, vol. 51, issue 3, No 27, 1395-1401

Abstract: Abstract The purpose of this research is to develop and apply the extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. Economic growth may be developed on the basis on combination of different factors. In this investigation was analyzed the economic growth prediction based on the science and technology transfer. The main goal was to analyze the influence of number of granted European patents on the economic growth by field of technology. GDP was used as economic growth indicator. The ELM results are compared with genetic programming (GP) and artificial neural network (ANN). The reliability of the computational models were accessed based on simulation results and using several statistical indicators. Coefficient of determination for ELM method is 0.9841, for ANN method it is 0.7956 and for the GP method it is 0.7561. Based upon simulation results, it is demonstrated that ELM can be utilized effectively in applications of GDP forecasting.

Keywords: GDP; Forecasting; Extreme learning machine; Economic growth (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11135-016-0337-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0337-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11135

DOI: 10.1007/s11135-016-0337-y

Access Statistics for this article

Quality & Quantity: International Journal of Methodology is currently edited by Vittorio Capecchi

More articles in Quality & Quantity: International Journal of Methodology from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().