A Comparative Statistical Analysis of Machine Learning Regression Models for Economic Indicator Forecasting

Abstract

Accurate forecasting of economic indicators is essential for informed policy-making and strategic financial planning. This study presents a comprehensive comparative statistical analysis of multiple machine learning regression models—including linear regression, ridge regression, lasso regression, support vector regression, and random forest regression—for predicting key economic indicators such as GDP growth, unemployment rate, and inflation. Using a simulated dataset of 1000 samples with multiple economic features, each model’s performance was evaluated using mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination ($R^2$). Results indicate that ensemble methods such as random forest provide superior predictive accuracy, while regularized models outperform ordinary linear regression. Visualizations of predicted versus actual values, residual analyses, and statistical comparisons are provided to support the findings. Implications for model selection and economic forecasting are discussed.