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RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer

Author

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  • 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)

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.

Suggested Citation

  • Petra Karanikić & Igor Mladenović & Svetlana Sokolov-Mladenović & Meysam Alizamir, 2017. "RETRACTED ARTICLE: Prediction of economic growth by extreme learning approach based on science and technology transfer," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1395-1401, May.
    • Handle: RePEc:spr:qualqt:v:51:y:2017:i:3:d:10.1007_s11135-016-0337-y
    DOI: 10.1007/s11135-016-0337-y
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    References listed on IDEAS

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    Cited by:

    1. Joao J. M. Ferreira & Cristina Fernandes & Vanessa Ratten, 2019. "The effects of technology transfers and institutional factors on economic growth: evidence from Europe and Oceania," The Journal of Technology Transfer, Springer, vol. 44(5), pages 1505-1528, October.

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