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Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations

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  • Andriy Norets

Abstract

I propose a method for inference in dynamic discrete choice models (DDCM) that utilizes Markov chain Monte Carlo (MCMC) and artificial neural networks (ANNs). MCMC is intended to handle high-dimensional integration in the likelihood function of richly specified DDCMs. ANNs approximate the dynamic-program (DP) solution as a function of the parameters and state variables prior to estimation to avoid having to solve the DP on each iteration. Potential applications of the proposed methodology include inference in DDCMs with random coefficients, serially correlated unobservables, and dependence across individual observations. The article discusses MCMC estimation of DDCMs, provides relevant background on ANNs, and derives a theoretical justification for the method. Experiments suggest this to be a promising approach.

Suggested Citation

  • Andriy Norets, 2012. "Estimation of Dynamic Discrete Choice Models Using Artificial Neural Network Approximations," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 84-106.
    • Handle: RePEc:taf:emetrv:v:31:y:2012:i:1:p:84-106
    DOI: 10.1080/07474938.2011.607089
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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," Papers 2102.09209, arXiv.org.
    2. Haoying Wang & Guohui Wu, 2022. "Modeling discrete choices with large fine-scale spatial data: opportunities and challenges," Journal of Geographical Systems, Springer, vol. 24(3), pages 325-351, July.
    3. Ertian Chen, 2025. "Model-Adaptive Approach to Dynamic Discrete Choice Models with Large State Spaces," Papers 2501.18746, arXiv.org, revised Mar 2026.
    4. Kristensen, Dennis & Mogensen, Patrick K. & Moon, Jong Myun & Schjerning, Bertel, 2021. "Solving dynamic discrete choice models using smoothing and sieve methods," Journal of Econometrics, Elsevier, vol. 223(2), pages 328-360.
    5. Victor Duarte & Diogo Duarte & Dejanir H. Silva, 2024. "Machine Learning for Continuous-Time Finance," CESifo Working Paper Series 10909, CESifo.
    6. Marlon Azinovic-Yang & Jan Zemlicka, 2025. "Deep Learning in the Sequence Space," CERGE-EI Working Papers wp802, The Center for Economic Research and Graduate Education - Economics Institute, Prague.
    7. Kristensen, Dennis & Salanié, Bernard, 2017. "Higher-order properties of approximate estimators," Journal of Econometrics, Elsevier, vol. 198(2), pages 189-208.
    8. Matthew Osborne, 2011. "Consumer learning, switching costs, and heterogeneity: A structural examination," Quantitative Marketing and Economics (QME), Springer, vol. 9(1), pages 25-70, March.
    9. Eftekhari, Aryan & Juillard, Michel & Rion, Normann & Scheidegger, Simon, 2026. "Scalable global solution techniques for high-dimensional models in Dynare," Journal of Economic Dynamics and Control, Elsevier, vol. 182(C).
    10. Marlon Azinovic & Luca Gaegauf & Simon Scheidegger, 2022. "Deep Equilibrium Nets," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 63(4), pages 1471-1525, November.
    11. Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022. "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers 17391, C.E.P.R. Discussion Papers.
    12. Marlon Azinovic-Yang & Jan v{Z}emliv{c}ka, 2025. "Deep Learning in the Sequence Space," Papers 2509.13623, arXiv.org, revised Mar 2026.
    13. Aguirregabiria, Victor & Magesan, Arvind, 2013. "Euler Equations for the Estimation of Dynamic Discrete Choice Structural," MPRA Paper 46056, University Library of Munich, Germany.
    14. Easton K. Huch & Michael P. Keane, 2026. "Amortized Inference for Correlated Discrete Choice Models via Equivariant Neural Networks," NBER Working Papers 35037, National Bureau of Economic Research, Inc.
    15. Ben Deaner, 2020. "Approximation-Robust Inference in Dynamic Discrete Choice," Papers 2010.11482, arXiv.org.
    16. Ahmed Khwaja & Sonal Srivastava, 2026. "Reinforcement Learning Based Computationally Efficient Conditional Choice Simulation Estimation of Dynamic Discrete Choice Models," Papers 2601.02069, arXiv.org.
    17. Enoch H. Kang & Hema Yoganarasimhan & Lalit Jain, 2025. "An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model," Papers 2502.14131, arXiv.org, revised Jan 2026.

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