This article provides a curated review of selected papers published in prominent economics journals that use machine learning (ML) tools for research and policy analysis. The review focuses on three key questions: (1) when ML is used in economics, (2) what ML models are commonly preferred, and (3) how they are used for economic applications. The review highlights that ML is particularly used in processing nontraditional and unstructured data, capturing strong nonlinearity, and improving prediction accuracy. Deep learning models are suitable for nontraditional data, whereas ensemble learning models are preferred for traditional datasets. While traditional econometric models may suffice for analyzing low-complexity data, the increasing complexity of economic data due to rapid digitalization and the growing literature suggest that ML is becoming an essential addition to the econometrician's toolbox.

翻译：本文对发表在顶级经济学期刊上运用机器学习工具进行研究与政策分析的精选论文进行了系统性综述。综述聚焦于三个关键问题：（1）机器学习何时应用于经济学研究，（2）哪些机器学习模型被普遍偏好，（3）这些模型如何应用于经济领域。综述指出，机器学习特别适用于处理非传统与非结构化数据、捕捉强非线性关系以及提升预测精度。深度学习模型适合处理非传统数据，而集成学习模型更受传统数据集青睐。尽管传统计量模型可能足以分析低复杂度数据，但数字化的快速发展及日益增长的文献表明，经济数据复杂性的持续提升正使得机器学习成为计量经济学工具箱中不可或缺的补充。