Title
ARTIFICIAL INTELLIGENCE AND ECONOMIC GROWTH
Authors
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