期刊
EUROPEAN REVIEW OF AGRICULTURAL ECONOMICS
卷 47, 期 3, 页码 849-892出版社
OXFORD UNIV PRESS
DOI: 10.1093/erae/jbz033
关键词
machine learning; econometrics; simulation models; quantitative economic analysis; agri-environmental policy analysis
资金
- Deutsche Forschungsgemeinschaft [STO 1087/1-1]
- USDA Hatch project [ILLU-470-333]
This review presents machine learning (ML) approaches from an applied economist's perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.
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