Machine learning in agricultural and applied economics

Hugo Storm, Kathy Baylis and Thomas Heckelei

European Review of Agricultural Economics, 2020, vol. 47, issue 3, 849-892

Abstract: 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.

Keywords: machine learning; econometrics; simulation models; quantitative economic analysis; agri-environmental policy analysis (search for similar items in EconPapers)
Date: 2020
References: Add references at CitEc
Citations: View citations in EconPapers (46)

Downloads: (external link)
http://hdl.handle.net/10.1093/erae/jbz033 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:erevae:v:47:y:2020:i:3:p:849-892.

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

European Review of Agricultural Economics is currently edited by Timothy Richards, Salvatore Di Falco, Céline Nauges and Vincenzina Caputo

More articles in European Review of Agricultural Economics from Oxford University Press and the European Agricultural and Applied Economics Publications Foundation Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK. Contact information at EDIRC.
Bibliographic data for series maintained by Oxford University Press ().