Econometric Modeling Vs. Machine Learning: a Comparative Analysis of Predictive Accuracy and Parameter Interpretability in Economic Forecasting
Year: 2026
Type: article
Abstract: This article examines the theoretical and methodological aspects and practical applications of machine learning algorithms in econometric modeling. It provides a comparative analysis of the characteristics, advantages, and limitations of classical econometric models and modern machine learning algorithms. The study compares linear regression and time series-based econometric approaches with machine learning algorithms such as Random Forest, Gradient Boosting, and XGBoost. The findings provide a methodological basis for selecting an appropriate modeling approach based on data characteristics and research objectives in the analysis of economic indicators.
Author: Isoqov Hasan Abduxalimovich
Institution: Karshi State Technical University
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: green
OpenAlex ID: W7225409726