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Machine learning in agricultural and applied economics
European Review of Agricultural Economics ( IF 3.4 ) Pub Date : 2019-08-21 , DOI: 10.1093/erae/jbz033
Hugo Storm 1 , Kathy Baylis 2 , Thomas Heckelei 1
Affiliation  

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.

中文翻译:

农业和应用经济学中的机器学习

这篇评论从应用经济学家的角度介绍了机器学习 (ML) 方法。我们首先介绍与计量经济学实践建立联系的关键 ML 方法。然后,我们确定计量经济学和模拟模型工具箱在应用经济学中的当前局限性,并探索 ML 提供的潜在解决方案。我们深入研究了不灵活的函数形式、非结构化数据源和预测和因果分析中的大量解释变量等情况,并强调了复杂模拟模型的挑战。最后,我们认为经济学家在解决 ML 在用于定量经济分析时的缺点方面发挥着至关重要的作用。
更新日期:2019-08-21
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