Machine learning in agricultural and applied economics

Hugo Storm, Kathy Baylis, Thomas Heckelei

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Pages (from-to)849-892
Number of pages44
JournalEuropean Review of Agricultural Economics
Issue number3
StatePublished - Jun 15 2020
Externally publishedYes


  • agri-environmental policy analysis
  • econometrics
  • machine learning
  • quantitative economic analysis
  • simulation models

ASJC Scopus subject areas

  • Agricultural and Biological Sciences (miscellaneous)
  • Economics and Econometrics


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