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Artificial Intelligence And Economic Growth

Author

Listed:
  • Shigeyuki Hamori

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

  • Takahiro Kume

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

Abstract

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

    as
    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.
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    More about this item

    Keywords

    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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