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Neural Networks in Economics: A Selective Review

Author

Listed:
  • Xiaohong Chen

    (Yale University)

  • Luis Hoderlein

    (Yale University)

  • Jonas Lieber

    (Imperial College London)

Abstract

The "deep learning revolution" has led to remarkable success of neural networks in applications across a wide range of fields, such as computer vision, speech recognition, natural language processing, code generation, protein structure prediction, image and video generation, and dynamic control. This review introduces neural networks to economists. Recent advances and challenges in approximation theory, neural network architecture, computation, econometric theory and practice are presented. Finally, we survey the rapidly evolving applications of modern neural networks and Large Language Models in economic research.

Suggested Citation

  • Xiaohong Chen & Luis Hoderlein & Jonas Lieber, 2026. "Neural Networks in Economics: A Selective Review," Cowles Foundation Discussion Papers 2539, Cowles Foundation for Research in Economics, Yale University.
    • Handle: RePEc:cwl:cwldpp:2539
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    File URL: https://cowles.yale.edu/sites/default/files/2026-07/d2539.pdf
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    References listed on IDEAS

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    1. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn & Zhipeng Liao, 2014. "Asymptotic Efficiency of Semiparametric Two-step GMM," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 81(3), pages 919-943.
    2. Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2023. "An Adversarial Approach to Structural Estimation," Econometrica, Econometric Society, vol. 91(6), pages 2041-2063, November.
    3. Andrews, Donald W K, 1994. "Asymptotics for Semiparametric Econometric Models via Stochastic Equicontinuity," Econometrica, Econometric Society, vol. 62(1), pages 43-72, January.
    4. Angelo Mele & Lingjiong Zhu, 2023. "Approximate Variational Estimation for a Model of Network Formation," The Review of Economics and Statistics, MIT Press, vol. 105(1), pages 113-124, January.
    5. Coppejans, Mark, 2004. "On Kolmogorov's representation of functions of several variables by functions of one variable," Journal of Econometrics, Elsevier, vol. 123(1), pages 1-31, November.
    6. Ola Hall & Francis Dompae & Ibrahim Wahab & Fred Mawunyo Dzanku, 2023. "A review of machine learning and satellite imagery for poverty prediction: Implications for development research and applications," Journal of International Development, John Wiley & Sons, Ltd., vol. 35(7), pages 1753-1768, October.
    7. Chamberlain, Gary, 1987. "Asymptotic efficiency in estimation with conditional moment restrictions," Journal of Econometrics, Elsevier, vol. 34(3), pages 305-334, March.
    8. Chen, Xiaohong & Hong, Han & Shum, Matthew, 2007. "Nonparametric likelihood ratio model selection tests between parametric likelihood and moment condition models," Journal of Econometrics, Elsevier, vol. 141(1), pages 109-140, November.
    9. Xiaohong Chen & Oliver Linton & Ingrid Van Keilegom, 2003. "Estimation of Semiparametric Models when the Criterion Function Is Not Smooth," Econometrica, Econometric Society, vol. 71(5), pages 1591-1608, September.
    10. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    11. Sukjin Han & Kyungho Lee, 2025. "Copyright and Competition: Estimating Supply and Demand with Unstructured Data," Bristol Economics Discussion Papers 25/816, School of Economics, University of Bristol, UK.
    12. Ifrach, Bar & Weintraub, Gabriel Y., 2016. "A Framework for Dynamic Oligopoly in Concentrated Industries," Research Papers 3449, Stanford University, Graduate School of Business.
    13. Wayne Gao & Sukjin Han & Annie Liang, 2026. "How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge," Papers 2601.12343, arXiv.org.
    14. Daniel Kahneman & Jack L. Knetsch & Richard H. Thaler, 1991. "Anomalies: The Endowment Effect, Loss Aversion, and Status Quo Bias," Journal of Economic Perspectives, American Economic Association, vol. 5(1), pages 193-206, Winter.
    15. Chen, Xiaohong & Liao, Yuan & Wang, Weichen, 2025. "Inference on time series nonparametric conditional moment restrictions using nonlinear sieves," Journal of Econometrics, Elsevier, vol. 249(PA).
    16. Utku U. Acikalin & Tolga Caskurlu & Gerard Hoberg & Gordon M. Phillips, 2022. "Intellectual Property Protection Lost and Competition: An Examination Using Machine Learning," NBER Working Papers 30671, National Bureau of Economic Research, Inc.
    17. Boskin, Michael J, 1974. "A Conditional Logit Model of Occupational Choice," Journal of Political Economy, University of Chicago Press, vol. 82(2), pages 389-398, Part I, M.
    18. Armstrong, Timothy B. & Bertanha, Marinho & Hong, Han, 2014. "A fast resample method for parametric and semiparametric models," Journal of Econometrics, Elsevier, vol. 179(2), pages 128-133.
    19. Laura Battaglia & Timothy Christensen & Stephen Hansen & Szymon Sacher, 2024. "Inference for Regression with Variables Generated by AI or Machine Learning," Papers 2402.15585, arXiv.org, revised Apr 2025.
    20. Bryan S. Graham & Cristine Campos de Xavier Pinto & Daniel Egel, 2016. "Efficient Estimation of Data Combination Models by the Method of Auxiliary-to-Study Tilting (AST)," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 34(2), pages 288-301, April.
    21. Chan, Joshua C.C. & Yu, Xuewen, 2022. "Fast and Accurate Variational Inference for Large Bayesian VARs with Stochastic Volatility," Journal of Economic Dynamics and Control, Elsevier, vol. 143(C).
    22. Steven T. Berry & Philip A. Haile, 2014. "Identification in Differentiated Products Markets Using Market Level Data," Econometrica, Econometric Society, vol. 82, pages 1749-1797, September.
    23. Aguirregabiria, Victor & Ho, Chun-Yu, 2012. "A dynamic oligopoly game of the US airline industry: Estimation and policy experiments," Journal of Econometrics, Elsevier, vol. 168(1), pages 156-173.
    24. Ran Abramitzky & Leah Boustan & Katherine Eriksson & James Feigenbaum & Santiago Pérez, 2021. "Automated Linking of Historical Data," Journal of Economic Literature, American Economic Association, vol. 59(3), pages 865-918, September.
    25. Chen, Xiaohong & Liu, Ying & Ma, Shujie & Zhang, Zheng, 2024. "Causal inference of general treatment effects using neural networks with a diverging number of confounders," Journal of Econometrics, Elsevier, vol. 238(1).
    26. David Silver & Aja Huang & Chris J. Maddison & Arthur Guez & Laurent Sifre & George van den Driessche & Julian Schrittwieser & Ioannis Antonoglou & Veda Panneershelvam & Marc Lanctot & Sander Dieleman, 2016. "Mastering the game of Go with deep neural networks and tree search," Nature, Nature, vol. 529(7587), pages 484-489, January.
    27. Xiaohong Chen & Zhenxiao Chen & Wayne Yuan Gao, 2025. "Inference on Welfare and Value Functionals under Optimal Treatment Assignment," Papers 2510.25607, arXiv.org.
    28. Daniel Ackerberg & Xiaohong Chen & Jinyong Hahn, 2012. "A Practical Asymptotic Variance Estimator for Two-Step Semiparametric Estimators," The Review of Economics and Statistics, MIT Press, vol. 94(2), pages 481-498, May.
    29. Wayne Gao & Sukjin Han & Annie Liang, 2026. "How Well Do LLMs Predict Human Behavior? A Measure of their Pretrained Knowledge," Bristol Economics Discussion Papers 26/835, School of Economics, University of Bristol, UK.
    30. Xiaohong Chen & Demian Pouzo, 2012. "Estimation of Nonparametric Conditional Moment Models With Possibly Nonsmooth Generalized Residuals," Econometrica, Econometric Society, vol. 80(1), pages 277-321, January.
    31. Jiequn Han & Yucheng Yang & Weinan E, 2026. "DeepHAM: A global solution method for heterogeneous agent models with aggregate shocks," Quantitative Economics, Econometric Society, vol. 17(2), pages 297-341, May.
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