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Identification of Dynamic Discrete Choice Models

Author

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  • Jaap H. Abbring

    (CentER, Department of Econometrics and OR, Tilburg University, 5000 LE Tilburg, The Netherlands)

Abstract
Econometric models of dynamic discrete choice processes are applied to a wide variety of economic problems. Recent research on their empirical content has brought important new insights. It has clarified the conditions for their identification from choice and covariate panel data in the absence of dynamic selection on unobservables. It has provided important new identification results for discrete-time models with unobserved heterogeneity and unobserved states. Finally, it has enhanced the attractiveness of continuous-time models, by developing new insights on the identification of continuous-time optimal stopping models. Current developments in the literature promise to shed further light on the specification and identification of models with unobserved state variables, theory-based nonproportional hazard models, continuous-time optimal stopping models with time-varying covariates, and dynamic games in discrete and continuous time.

Suggested Citation

  • Jaap H. Abbring, 2010. "Identification of Dynamic Discrete Choice Models," Annual Review of Economics, Annual Reviews, vol. 2(1), pages 367-394, September.
    • Handle: RePEc:anr:reveco:v:2:y:2010:p:367-394
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    Cited by:

    1. An, Yonghong & Hu, Yingyao & Xiao, Ruli, 2021. "Dynamic decisions under subjective expectations: A structural analysis," Journal of Econometrics, Elsevier, vol. 222(1), pages 645-675.
    2. Jaap H. Abbring, 2012. "Mixed Hitting‐Time Models," Econometrica, Econometric Society, vol. 80(2), pages 783-819, March.
    3. Michele Fioretti & Alexander Vostroknutov & Giorgio Coricelli, 2022. "Dynamic Regret Avoidance," American Economic Journal: Microeconomics, American Economic Association, vol. 14(1), pages 70-93, February.
    4. Sebastian Galiani & Juan Pantano, 2021. "Structural Models: Inception and Frontier," NBER Working Papers 28698, National Bureau of Economic Research, Inc.
    5. Zheng, Kun, 2019. "Essays on duration analysis and labour economics," Other publications TiSEM 6467229e-a10b-4d8d-acaf-c, Tilburg University, School of Economics and Management.
    6. Jaap H. Abbring & Øystein Daljord, 2020. "Identifying the discount factor in dynamic discrete choice models," Quantitative Economics, Econometric Society, vol. 11(2), pages 471-501, May.
    7. Sasaki, Yuya, 2015. "Heterogeneity and selection in dynamic panel data," Journal of Econometrics, Elsevier, vol. 188(1), pages 236-249.
    8. Wu, Tong & Lawell, C.Y. Cynthia Lin & Just, David R. & Zhao, Jiancheng & Fei, Zhangjun & Wei, Qiang, 2022. "Optimal Forest Management for Interdependent Products: A Nested Dynamic Bioeconomic Model and Application to Bamboo," 2022 Annual Meeting, July 31-August 2, Anaheim, California 322164, Agricultural and Applied Economics Association.
    9. Schneider, Ulrich, 2019. "Identification of Time Preferences in Dynamic Discrete Choice Models: Exploiting Choice Restrictions," MPRA Paper 102137, University Library of Munich, Germany, revised 29 Jul 2020.
    10. Jaap H. Abbring & Tim Salimans, 2019. "The Likelihood of Mixed Hitting Times," Papers 1905.03463, arXiv.org, revised Apr 2021.
    11. Jean-Pierre Dubé & Günter Hitsch & Pranav Jindal, 2014. "The Joint identification of utility and discount functions from stated choice data: An application to durable goods adoption," Quantitative Marketing and Economics (QME), Springer, vol. 12(4), pages 331-377, December.
    12. Gabriel Ulyssea, 2018. "Firms, Informality, and Development: Theory and Evidence from Brazil," American Economic Review, American Economic Association, vol. 108(8), pages 2015-2047, August.
    13. Clemens, Michael A., 2021. "Violence, development, and migration waves: Evidence from Central American child migrant apprehensions," Journal of Urban Economics, Elsevier, vol. 124(C).
    14. Abbring, Jaap H. & Salimans, Tim, 2021. "The likelihood of mixed hitting times," Journal of Econometrics, Elsevier, vol. 223(2), pages 361-375.
    15. Jean-Pierre H. Dube & Günter J. Hitsch & Pranav Jindal, 2012. "The Joint Identification of Utility and Discount Functions From Stated Choice Data: An Application to Durable Goods Adoption," NBER Working Papers 18393, National Bureau of Economic Research, Inc.
    16. Taiga Tsubota, 2021. "Identifying Dynamic Discrete Choice Models with Hyperbolic Discounting," Papers 2111.10721, arXiv.org, revised Oct 2024.
    17. Jaap H. Abbring & {O}ystein Daljord, 2018. "Identifying the Discount Factor in Dynamic Discrete Choice Models," Papers 1808.10651, arXiv.org, revised Sep 2019.
    18. Philip Marx & Elie Tamer & Xun Tang, 2022. "Parallel Trends and Dynamic Choices," Papers 2207.06564, arXiv.org, revised Aug 2023.
    19. Stéphane Gregoir & Tristan‐Pierre Maury, 2013. "The Impact Of Social Housing On The Labour Market Status Of The Disabled," Health Economics, John Wiley & Sons, Ltd., vol. 22(9), pages 1124-1138, September.
    20. Otero, Karina V., 2016. "Nonparametric identification of dynamic multinomial choice games: unknown payoffs and shocks without interchangeability," MPRA Paper 86784, University Library of Munich, Germany.

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    More about this item

    Keywords

    discrete decision process; optimal stopping; hazard; hitting time; heterogeneity;
    All these keywords.

    JEL classification:

    • C25 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions; Probabilities
    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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