Machine Learning Applications in Economic Forecasting: An Empirical Comparison with Traditional Econometric Models

Abstract

Economic forecasting plays a critical role in policymaking, financial planning, and macroeconomic stabilization. Traditionally, econometric models such as ARIMA, VAR, and regression-based frameworks have dominated forecasting practice. However, the growing availability of large datasets and advances in computational power have facilitated the adoption of machine learning (ML) techniques in economic analysis. This study provides an empirical comparison between machine learning models and traditional econometric approaches in forecasting key macroeconomic indicators. Using time-series data on GDP growth, inflation, and unemployment, the performance of models such as Random Forest, Support Vector Machines, and Artificial Neural Networks is evaluated against conventional econometric benchmarks. Forecast accuracy is assessed using standard error metrics, including RMSE and MAE. The findings indicate that machine learning models generally outperform traditional methods in capturing nonlinear relationships and complex interactions, particularly in volatile economic environments. However, issues of interpretability, data requirements, and model transparency remain significant challenges. The study concludes that machine learning should complement rather than replace traditional econometric modeling in economic forecasting.