Machine learning in agricultural and applied economics

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 exp...

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Bibliographic Details
Published in:	European review of agricultural economics 2020-07, Vol.47 (3), p.849-892
Main Authors:	Storm, Hugo, Baylis, Kathy, Heckelei, Thomas
Format:	Article
Language:	eng
Subjects:	Agricultural economics Agronomy Applied economics Causality Cognitive style Computer simulation Econometrics Economic analysis Economic conditions Economic models Economics Economists Identification methods Learning algorithms Machine learning Predictions Simulation Unstructured data
Online Access:	Get full text
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Summary:	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.
ISSN:	0165-1587 1464-3618