Title: Machine Learning for Economics Research: When What and How? Show Chinese title

Title: 机器学习在经济学研究中的应用：何时、何地以及如何应用？

Abstract: 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 to process nontraditional and unstructured data, capture strong nonlinearity, and improve 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 suggests that ML is becoming an essential addition to the econometrician's toolbox. Show Chinese abstract

Abstract: 本文提供了一份精选论文的综述，这些论文发表在重要的经济学期刊上，使用机器学习（ML）工具进行研究和政策分析。 综述聚焦于三个关键问题：（1）机器学习在经济学中何时被使用，（2）哪些机器学习模型被普遍偏好，（3）它们如何用于经济应用。 综述指出，机器学习特别用于处理非传统和非结构化数据，捕捉强非线性，并提高预测准确性。 深度学习模型适用于非传统数据，而集成学习模型则更适用于传统数据集。 虽然传统计量经济学模型可能足以分析低复杂度数据，但由于快速数字化带来的经济数据复杂性的增加以及相关文献的不断增长，机器学习正逐渐成为计量经济学家工具箱中不可或缺的补充。