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Application of artificial neural networks in tendency forecasting of economic growth

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  • Feng, Lihua
  • Zhang, Jianzhen

Abstract

Economic growth results from the synthesis influence of various known or unknown and certain or uncertain factors. Mapping of the stimuli effects and the input and output estimates of artificial neural networks (ANN) are obtained via combinations of nonlinear functions. This approach offers the advantages of self-learning, self-organization, self-adaptation, and fault tolerance, as well as the potential for use in forecasting applications of economic growth. Furthermore, the ANN technology allows the use of multiple variables in both the input and output layers. This capability is very important for economic growth calculations because economic development is often a function of many influential variables. Herein, a forecasting system of economic growth with related application has been proposed, based on ANN. The results show that the Zhejiang proportions of tertiary industry in China for 1995, 1996, and 1997 were 32.305%, 32.174%, and 32.114%, respectively, and the comparative errors eann were 0.64%, −0.08%, and −0.27%, respectively, indicating that the forecast result of ANN was better than that of the GM(1.1) model. This method offered better performance and efficiency.

Suggested Citation

  • Feng, Lihua & Zhang, Jianzhen, 2014. "Application of artificial neural networks in tendency forecasting of economic growth," Economic Modelling, Elsevier, vol. 40(C), pages 76-80.
    • Handle: RePEc:eee:ecmode:v:40:y:2014:i:c:p:76-80
    DOI: 10.1016/j.econmod.2014.03.024
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    References listed on IDEAS

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    7. Marković, Dušan & Petković, Dalibor & Nikolić, Vlastimir & Milovančević, Miloš & Petković, Biljana, 2017. "Soft computing prediction of economic growth based in science and technology factors," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 217-220.
    8. Oscar Claveria & Enric Monte & Salvador Torra, 2015. "“Self-organizing map analysis of agents' expectations. Different patterns of anticipation of the 2008 financial crisis”," IREA Working Papers 201511, University of Barcelona, Research Institute of Applied Economics, revised Mar 2015.
    9. Jahn, Malte, 2020. "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, vol. 91(C), pages 148-154.
    10. Maksimović, Goran & Milosavljević, Valentina & Ćirković, Bratislav & Milošević, Božidar & Jović, Srđan & Alizamir, Meysam, 2017. "Analyzing of economic growth based on electricity consumption from different sources," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 484(C), pages 37-40.
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    12. Đokić, Aleksandar & Jović, Srđan, 2017. "Evaluation of agriculture and industry effect on economic health by ANFIS approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 479(C), pages 396-399.
    13. Braham, Rihem & de Peretti, Christian & Belkacem, Lotfi, 2020. "The role of political patronage in the risk-taking behaviour of banks in the Middle East and North Africa," Research in International Business and Finance, Elsevier, vol. 53(C).
    14. Xu Wang & JinRong Wang & Michal Fečkan, 2020. "BP Neural Network Calculus in Economic Growth Modelling of the Group of Seven," Mathematics, MDPI, vol. 8(1), pages 1-11, January.
    15. Tümer, Abdullah Erdal & Akkuş, Aytekin, 2018. "Forecasting Gross Domestic Product per Capita Using Artificial Neural Networks with Non-Economical Parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 512(C), pages 468-473.
    16. Kordanuli, Bojana & Barjaktarović, Lidija & Jeremić, Ljiljana & Alizamir, Meysam, 2017. "Appraisal of artificial neural network for forecasting of economic parameters," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 515-519.
    17. Jahn, Malte, 2018. "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers 185, Hamburg Institute of International Economics (HWWI).
    18. Wanke, Peter & Barros, Carlos Pestana, 2016. "Efficiency drivers in Brazilian insurance: A two-stage DEA meta frontier-data mining approach," Economic Modelling, Elsevier, vol. 53(C), pages 8-22.
    19. Oscar Claveria & Enric Monte & Salvador Torra, 2016. "Modelling cross-dependencies between Spain’s regional tourism markets with an extension of the Gaussian process regression model," SERIEs: Journal of the Spanish Economic Association, Springer;Spanish Economic Association, vol. 7(3), pages 341-357, August.
    20. Basihos, Seda, 2016. "Nightlights as a Development Indicator: The Estimation of Gross Provincial Product (GPP) in Turkey," MPRA Paper 75553, University Library of Munich, Germany, revised 09 Sep 2016.
    21. Milačić, Ljubiša & Jović, Srđan & Vujović, Tanja & Miljković, Jovica, 2017. "Application of artificial neural network with extreme learning machine for economic growth estimation," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 285-288.
    22. Igor Mladenović & Miloš Milovančević & Svetlana Sokolov-Mladenović, 2017. "RETRACTED ARTICLE: Analyzing of innovations influence on economic growth by fuzzy system," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1297-1304, May.
    23. Maksimović, Goran & Jović, Srđan & Jovanović, Radomir, 2017. "Economic growth rate management by soft computing approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 465(C), pages 520-524.
    24. Malte Jahn, 2023. "Regressing on distributions: The nonlinear effect of temperature on regional economic growth," Papers 2309.10481, arXiv.org.

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