Machine Learning for Applied Economic Analysis: Gaining Practical Insights

Matthew Smith and Francisco Alvarez

No 2025-03, Working Papers from FEDEA

Abstract: Machine learning (ML) is becoming an essential tool in economics, offering powerful methods for prediction, classification, and decision-making. This paper provides an intuitive introduction to two widely used families of ML models: tree-based methods (decision trees, Random Forests, boosting techniques) and neural networks. The goal is to equip practitioners with a clear understanding of how these models work, their strengths and limitations, and their applications in economics. Additionally, we briefly discuss some other methods, as support vector machines (SVMs) and Shapley values, highlighting their relevance in economic research. Rather than providing an exhaustive survey, this paper focuses on practical insights to help economists effectively apply ML in their work.

Date: 2025-04
New Economics Papers: this item is included in nep-acc, nep-geo, nep-pbe, nep-pub and nep-ure
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