THEORETICAL AND APPLIED ASPECTS OF USING MACHINE LEARNING IN THE ANALYSIS OF ECONOMIC DATA

Abstract

The article explores modern approaches to macroeconomic forecasting based on machine learning methods and their integration with classical econometric models. The subject of the study is data-driven forecasting of key macroeconomic indicators under conditions of high dimensionality, structural instability, and limited observability of economic processes. The purpose of the research is to systematize contemporary machine learning tools used in macroeconomic analysis, assess their methodological foundations, and identify the advantages and limitations of their application for forecasting and inference.

The methodological framework combines econometric theory, statistical learning, and modern machine learning techniques. Particular attention is paid to regularization methods, tree-based algorithms, ensemble models, and neural networks, including recurrent architectures designed for time series analysis. The study also considers approaches to model validation, loss minimization, and overfitting control in the context of economic data characterized by noise, nonlinearity, and regime shifts. Alongside predictive performance, the article emphasizes the growing importance of interpretability and causal reasoning in applied macroeconomic modeling.

The results demonstrate that machine learning models substantially improve short- and medium-term forecasting accuracy compared to traditional linear specifications, especially when dealing with large information sets and nonlinear relationships. At the same time, purely predictive models are shown to have limited explanatory power and may produce biased estimates when used for policy evaluation. This has stimulated the development of explainable artificial intelligence and causal machine learning approaches, including double and debiased machine learning, which combine the flexibility of machine learning algorithms with the causal logic of econometrics. These methods enable valid inference on structural and treatment effects in high-dimensional settings while mitigating regularization bias.