A Numerical Assessment for Predicting Human Development Index (HDI) Trends in the GCC Countries
Mahdi Goldani

TL;DR
This paper employs machine learning, specifically XGBoost with EDR feature selection, to predict HDI trends in GCC countries from 2023 to 2027, highlighting regional development patterns and policy implications.
Contribution
It introduces a novel predictive model for HDI trends in GCC countries using EDR for feature selection and demonstrates its effectiveness with real-world data.
Findings
Kuwait, Bahrain, and UAE likely to see stable or increasing HDI
Saudi Arabia, Qatar, and Oman may experience minimal fluctuations
Model shows strong in-sample accuracy but minor out-of-sample overfitting
Abstract
This study focuses on predicting the Human Development Index (HDI) trends for GCC countries Saudi Arabia, Qatar, Kuwait, Bahrain, United Arab Emirates, and Omanusing machine learning techniques, specifically the XGBoost algorithm. HDI is a composite measure of life expectancy, education, and income, reflecting overall human development. Data was gathered from official government sources and international databases, including the World Bank and UNDP, covering the period from 1996 to 2022. Using the Edit Distance on Real sequence (EDR) method for feature selection, the model analyzed key indicators to predict HDI values over the next five years (2023-2027). The model demonstrated strong predictive accuracy for in-sample data, but minor overfitting issues were observed with out-of-sample predictions, particularly in the case of the UAE. The forecast results suggest that Kuwait, Bahrain,…
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Taxonomy
TopicsEconomic Growth and Development
MethodsFocus
