Machine Learning for Economic Policy

IF 4 3区经济学 Q1 ECONOMICS

Journal of Econometrics Pub Date : 2025-05-01 DOI:10.1016/j.jeconom.2025.105970

Maryam Haghighi , Andreas Joseph , George Kapetanios , Christopher Kurz , Michele Lenza , Juri Marcucci

引用次数: 0

Abstract

The Themed Issue Machine Learning for Economic Policy consists of 12 papers at the intersection of machine learning, nontraditional data sources and economic policymaking. We will introduce the Themed Issue and review its contributions.

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经济政策的机器学习

主题议题机器学习经济政策由12篇论文组成，涉及机器学习，非传统数据源和经济政策制定的交叉。我们将介绍主题期并回顾其贡献。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Econometrics 社会科学-数学跨学科应用

CiteScore

8.60

自引率

1.60%

发文量

220

审稿时长

3-8 weeks

期刊介绍： The Journal of Econometrics serves as an outlet for important, high quality, new research in both theoretical and applied econometrics. The scope of the Journal includes papers dealing with identification, estimation, testing, decision, and prediction issues encountered in economic research. Classical Bayesian statistics, and machine learning methods, are decidedly within the range of the Journal''s interests. The Annals of Econometrics is a supplement to the Journal of Econometrics.