Machine learning and structural econometrics: contrasts and synergies. Issue 3 (29th August 2020)

• Record Type: Journal Article
• Authors: Iskhakov, Fedor; Rust, John; Schjerning, Bertel
• Is Part Of: Econometrics journal. Volume 23:Issue 3(2020)
• Page Start: S81
• Page End: S124
• Publication Date: 2020-08-29
• Subjects: Machine learning -- structural econometrics -- curse of dimensionality -- bounded rationality -- counterfactual predictions; Econometrics -- Periodicals; 330.015195
• DOI: 10.1093/ectj/utaa019
• Languages: English
• ISSNs: 1368-4221

Abstract

Summary: We contrast machine learning (ML) and structural econometrics (SE), focusing on areas where ML can advance the goals of SE. Our views have been informed and inspired by the contributions to this special issue and by papers presented at the second conference on dynamic structural econometrics at the University of Copenhagen in 2018, 'Methodology and Applications of Structural Dynamic Models and Machine Learning'. ML offers a promising class of techniques that can significantly extend the set of questions we can analyse in SE. The scope, relevance and impact of empirical work in SE can be improved by following the lead of ML in questioning and relaxing the assumption of unbounded rationality. For the foreseeable future, however, ML is unlikely to replace the essential role of human creativity and knowledge in model building and inference, particularly with respect to the key goal of SE, counterfactual prediction.