Comparison of Machine Learning Models with Traditional Econometric Models in Macroeconomic Prediction.

COMPUTER SCIENCE AND APPLICATIONS

This study examines how well machine learning algorithms and conventional econometric models predict macroeconomic variables in comparison. Macroeconomic forecasting is essential to economic planning and policy-making, hence the precision and dependability of these models are crucial. Using a variety of macroeconomic datasets, this research methodically compares the performance of machine learning techniques with that of conventional econometric methodologies. The predictive power of machine learning models vs conventional econometric models for macroeconomic variables including GDP growth, inflation, and unemployment rates is investigated in this study. Accurate and trustworthy models are necessary for macroeconomic forecasting since it is essential for corporate planning, investment decisions, and policy-making. Support vector machines, random forests, neural networks, and other cutting-edge machine learning techniques are assessed alongside conventional econometric methods like ARIMA and VAR. We evaluate these models based on interpretability, robustness to various economic conditions, and forecast accuracy using quarterly data collected over the last 30 years from several nations. Our results show that machine learning models capture complicated, non-linear relationships in the data more effectively than standard econometric models, generally outperforming them in terms of predicted accuracy and resilience. Traditional models, however, continue to be more interpretable because of their theoretical foundation and openness. According to the study, a hybrid strategy that combines the advantages of both model types might provide the best forecasting results. These findings show how machine learning can improve macroeconomic forecasting while also emphasising how conventional econometric techniques are still useful for policy research. Future research directions include the development of hybrid models, integration of real-time data, and advancements in explainable AI to improve model transparency and usability in economic contexts.

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