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Machine learning and structural econometrics: contrasts and synergies

Author

Listed:
  • Fedor Iskhakov
  • John Rust
  • Bertel Schjerning

Abstract

SummaryWe 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.

Suggested Citation

  • Fedor Iskhakov & John Rust & Bertel Schjerning, 2020. "Machine learning and structural econometrics: contrasts and synergies," The Econometrics Journal, Royal Economic Society, vol. 23(3), pages 81-124.
    • Handle: RePEc:oup:emjrnl:v:23:y:2020:i:3:p:s81-s124.
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    File URL: http://hdl.handle.net/10.1093/ectj/utaa019
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    Citations

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    Cited by:

    1. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie 21.14, Université de Lausanne, Faculté des HEC, Département d’économie.
    2. Zegners, Dainis & Sunde, Uwe & Strittmatter, Anthony, 2020. "Decisions and Performance Under Bounded Rationality: A Computational Benchmarking Approach," Rationality and Competition Discussion Paper Series 263, CRC TRR 190 Rationality and Competition.
    3. Smeele, Nicholas V.R. & Chorus, Caspar G. & Schermer, Maartje H.N. & de Bekker-Grob, Esther W., 2023. "Towards machine learning for moral choice analysis in health economics: A literature review and research agenda," Social Science & Medicine, Elsevier, vol. 326(C).
    4. Luigi Biagini & Simone Severini, 2021. "The role of Common Agricultural Policy (CAP) in enhancing and stabilising farm income: an analysis of income transfer efficiency and the Income Stabilisation Tool," Papers 2104.14188, arXiv.org.
    5. Ugo Bolletta & Laurens Cherchye & Thomas Demuynck & Bram De Rock & Luca Paolo Merlino, 2024. "Identifying Marriage Markets," Working Papers ECARES 2024-12, ULB -- Universite Libre de Bruxelles.
    6. Ren, Xiyuan & Chow, Joseph Y.J., 2022. "A random-utility-consistent machine learning method to estimate agents’ joint activity scheduling choice from a ubiquitous data set," Transportation Research Part B: Methodological, Elsevier, vol. 166(C), pages 396-418.
    7. Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021. "Deep Structural Estimation: With an Application to Option Pricing," Papers 2102.09209, arXiv.org.
    8. Thomas J. Sargent & John Stachurski, 2024. "Dynamic Programming: Finite States," Papers 2401.10473, arXiv.org.
    9. Duo Qin, 2022. "Redirect the Probability Approach in Econometrics Towards PAC Learning," Working Papers 249, Department of Economics, SOAS University of London, UK.

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