MACHINE LEARNING AND ECONOMETRICS: BRIDGING THE GAP FOR ENHANCED ECONOMIC ANALYSIS

Abstract

This paper explores the integration of machine learning techniques in econometric analysis, emphasizing the transformative impact on economic research. The study highlights three key applications: predictive modeling, causal inference, and text mining. It illustrates how deep learning models, like neural networks, improve forecasting accuracy by capturing complex patterns in time series data. In causal analysis, machine learning techniques such as random forests and generalized random forests enhance estimation of treatment effects, enabling robust policy evaluation. Additionally, text mining and sentiment analysis unlock insights from unstructured data, including financial news and social media, providing real-time economic indicators and aiding in risk assessment. The paper also discusses challenges associated with model interpretability, data quality, and overfitting, recommending future research to focus on hybrid models that combine traditional econometrics and machine learning approaches. The findings suggest that interdisciplinary collaboration between economists and data scientists will be crucial for advancing economic analysis and translating machine learning innovations into practical economic insights.