Predictive analysis of Somalia's economic indicators using advanced machine learning models - Bashir Mohamed Osman; Abdillahi Mohamoud Sheikh Muse (Cogent Economics & Finance, 2024)
Accurate Gross Domestic Product (GDP) prediction is essential for economic planning and policy formulation. This paper evaluates the performance of three machine learning models-Random Forest Regression (RFR), XGBoost, and Prophet-in predicting Somalia's GDP. Historical economic data, including GDP per capita, population, inflation rate, and current account balances, were used in training and testing. Among the models, RFR achieved the best accuracy with the lowest MAE (0.6621%), MSE (1.3220%), RMSE (1.1497%), and R-squared of 0.89. The Diebold-Mariano p-value for RFR (0.042) confirmed its higher predictive accuracy. XGBoost performed well but with slightly higher error, yielding an R-squared of 0.85 and p-value of 0.063. In contrast, Prophet had the highest forecast errors, with an R-squared of 0.78 and p-value of 0.015. For enhanced interpretability, SHapley Additive exPlanations (SHAP) were applied to RFR, identifying lagged current account balance, GDP per capita, and lagged population as key predictors, along with total population and government net lending/borrowing. SHAP plots provided insights into these features' contributions to GDP predictions. This study highlights RFR's effectiveness in economic forecasting and emphasizes the importance of current and lagged economic indicators. This study presents a critical advancement in economic forecasting for Somalia by comparing the performance of three machine learning models-Random Forest Regression, XGBoost, and Prophet-in predicting Gross Domestic Product (GDP) based on historical economic data. The findings underscore the superior accuracy of the Random Forest Regression model, which yielded the lowest error rates and highest interpretability through SHapley Additive exPlanations (SHAP). Key economic indicators, including lagged current account balances, GDP per capita, and population data, were identified as significant predictors of GDP. By enhancing the accuracy and interpretability of GDP forecasts, this research provides valuable insights for policymakers, aiding in data-driven economic planning and policy formulation to support sustainable development in Somalia.
Keywords: 330; economic indicators; GDP forecasting; machine learning; random forest regression; SHAP; Somalia