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Artificial Intelligence And Economic Growth


  • Shigeyuki Hamori

    (Graduate School of Economics, Kobe University, Kobe, Japan)

  • Takahiro Kume

    (Graduate School of Economics, Kobe University, Kobe, Japan)


This paper describes the use of five machine learning methods for predicting economic growth based on a country’s attributes and presents a comparison of their prediction accuracy. The methods used are four neural network (NN) methods with different activation functions, and eXtreme Gradient Boosting (XGBoost). Their performance is compared in terms of their ability to predict the economic growth rate using three measures (prediction accuracy rate, area under the curve (AUC) value, and F-score). The results obtained can be summarized as follows: 1) XGBoost outperforms the NNs in terms of prediction accuracy and F-score for original data; 2) data standardization enhances the reliability of NNs, improving their prediction accuracy, AUC-value, and F-score; 3) XGBoost has smaller standard deviation of prediction accuracy rate than that of NNs; and 4) "Political institution", "Investment and its composition", "Colonial history", and "Trade" are important factors for cross-country economic growth.

Suggested Citation

  • Shigeyuki Hamori & Takahiro Kume, 2018. "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, vol. 22(1), pages 256-278, December.
  • Handle: RePEc:aag:wpaper:v:22:y:2018:i:1:p:256-278

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    References listed on IDEAS

    1. Romer, Paul M, 1986. "Increasing Returns and Long-run Growth," Journal of Political Economy, University of Chicago Press, vol. 94(5), pages 1002-1037, October.
    2. Oberlechner, Thomas & Hocking, Sam, 2004. "Information sources, news, and rumors in financial markets: Insights into the foreign exchange market," Journal of Economic Psychology, Elsevier, vol. 25(3), pages 407-424, June.
    3. T. W. Swan, 1956. "ECONOMIC GROWTH and CAPITAL ACCUMULATION," The Economic Record, The Economic Society of Australia, vol. 32(2), pages 334-361, November.
    4. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    5. Sala-i-Martin, Xavier, 1997. "I Just Ran Two Million Regressions," American Economic Review, American Economic Association, vol. 87(2), pages 178-183, May.
    6. Romer, Paul M, 1990. "Endogenous Technological Change," Journal of Political Economy, University of Chicago Press, vol. 98(5), pages 71-102, October.
    7. Angelini, Eliana & di Tollo, Giacomo & Roli, Andrea, 2008. "A neural network approach for credit risk evaluation," The Quarterly Review of Economics and Finance, Elsevier, vol. 48(4), pages 733-755, November.
    8. Robert M. Solow, 1956. "A Contribution to the Theory of Economic Growth," The Quarterly Journal of Economics, Oxford University Press, vol. 70(1), pages 65-94.
    9. Shigeyuki Hamori & Minami Kawai & Takahiro Kume & Yuji Murakami & Chikara Watanabe, 2018. "Ensemble Learning or Deep Learning? Application to Default Risk Analysis," Journal of Risk and Financial Management, MDPI, Open Access Journal, vol. 11(1), pages 1-14, March.
    10. Shanker, M. & Hu, M. Y. & Hung, M. S., 1996. "Effect of data standardization on neural network training," Omega, Elsevier, vol. 24(4), pages 385-397, August.
    11. Sihem Khemakhem & Younes Boujelbene, 2015. "Credit Risk Prediction: A Comparative Study between Discriminant Analysis and the Neural Network Approach," Journal of Accounting and Management Information Systems, Faculty of Accounting and Management Information Systems, The Bucharest University of Economic Studies, vol. 14(1), pages 60-78, March.
    12. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July.
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    More about this item


    Economic growth; machine learning; XGBoost; neural network;

    JEL classification:

    • E10 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - General
    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics


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