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Advances in Decision Sciences (ADS)

Advances in Decision Sciences (ADS)

Published by Asia University, Taiwan; Scientific and Business World

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Artificial Intelligence and Economic Growth

Artificial Intelligence and Economic Growth

Title

ARTIFICIAL INTELLIGENCE AND ECONOMIC GROWTH

Authors

  • 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 economicgrowth 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
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.

Keywords

Economic growth, machine learning, XGBoost, neural network

Classification-JEL

E10, C45

Pages

256-278

https://doi.org/10.47654/v22y2018i1p256-278

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ISSN 2090-3359 (Print)
ISSN 2090-3367 (Online)

Asia University, Taiwan

Scientific and Business World

4.7
2023CiteScore
 
86th percentile
Powered by  Scopus
SCImago Journal & Country Rank
Q2 in Scopus
CiteScore 2023 = 4.7
CiteScoreTracker 2024 = 8.5
SNIP 2023 = 0.799
SJR Quartile = Q1
SJR 2024 = 0.814
H-Index = 20

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