Machine Learning Approaches for Macroeconomic Forecasting: Evaluating the Effectiveness of Ensemble Methods

This paper evaluates the usefulness of machine learning techniques and combination methods in macro forecasts, emphasizing how they did across multiple indicators and forecasting periods. Conventional econometric models, such ARIMA and VAR, were contrasted with advanced machine learning approaches like as Decision Trees, Random Forests, Support Vector Machines (SVM), and Neural Networks. Ensemble instruments, for instance Bagging (Random Forest), Boosting (GBM), and Stacking, were tested for their effect on prediction accuracy. The research applied multiple natural metrics-Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE)-to compare model performance. The results demonstrate that Neural Networks exceeded the background model accuracy, with the Interspersed ensemble approach displaying the most considerable gains across all criteria. The area graphs and line plots demonstrate that ensemble techniques generally exhibited increased performance, notably in lowering MAE and RMSE, and offered a more beneficial error shipment relative to individual models. Subsequent examination of forecasting horizons demonstrated that baseline models were effective for short-term predictions (1-4 quarters), although ensemble techniques had a considerable edge for long-term projections (5-8 quarters). Pie charts demonstrating error distribution within models verified the pre-eminence of ensemble techniques in decreasing prediction errors. This study underlines the importance of merging numerous predictive models via ensemble approaches to increase forecasting precision. The findings have practical significance for projections for money and decision-making, suggesting that applying contemporary machine learning and ensemble methodologies may considerably boost the reliability of macroeconomic predictions. Future work should consider supplemental hybrid models and inclusion of external inputs to boost forecasting precision.