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Identification Of Mixtures Of Dynamic Discrete Choices

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  • Higgins, Ayden
  • Jochmans, Koen

Abstract

This paper provides new identification results for finite mixtures of Markov processes. Our arguments are constructive and show that identification can be achieved from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. Our approach is contrasted with the ones taken in prior work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, monotonicity restrictions that link conditional distributions to latent types are not needed. Maximum likelihood is considered for the purpose of estimation and inference. Implementation via the EM algorithm is straightforward. Its performance is evaluated in a simulation exercise.

Suggested Citation

  • Higgins, Ayden & Jochmans, Koen, 2021. "Identification Of Mixtures Of Dynamic Discrete Choices," TSE Working Papers 21-1272, Toulouse School of Economics (TSE), revised Jan 2023.
    • Handle: RePEc:tse:wpaper:126197
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    References listed on IDEAS

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    1. T. Anderson, 1954. "On estimation of parameters in latent structure analysis," Psychometrika, Springer;The Psychometric Society, vol. 19(1), pages 1-10, March.
    2. Hu, Yingyao, 2008. "Identification and estimation of nonlinear models with misclassification error using instrumental variables: A general solution," Journal of Econometrics, Elsevier, vol. 144(1), pages 27-61, May.
    3. Keane, Michael P & Wolpin, Kenneth I, 1997. "The Career Decisions of Young Men," Journal of Political Economy, University of Chicago Press, vol. 105(3), pages 473-522, June.
    4. Marc Henry & Yuichi Kitamura & Bernard Salanié, 2014. "Partial identification of finite mixtures in econometric models," Quantitative Economics, Econometric Society, vol. 5, pages 123-144, March.
    5. Victor Aguirregabiria & Pedro Mira, 2007. "Sequential Estimation of Dynamic Discrete Games," Econometrica, Econometric Society, vol. 75(1), pages 1-53, January.
    6. Hiroyuki Kasahara & Katsumi Shimotsu, 2009. "Nonparametric Identification of Finite Mixture Models of Dynamic Discrete Choices," Econometrica, Econometric Society, vol. 77(1), pages 135-175, January.
    7. Hu, Yingyao & Shum, Matthew, 2012. "Nonparametric identification of dynamic models with unobserved state variables," Journal of Econometrics, Elsevier, vol. 171(1), pages 32-44.
    8. Peter Arcidiacono & Robert A. Miller, 2011. "Conditional Choice Probability Estimation of Dynamic Discrete Choice Models With Unobserved Heterogeneity," Econometrica, Econometric Society, vol. 79(6), pages 1823-1867, November.
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    Citations

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    Cited by:

    1. Jochmans, Koen, 2024. "Nonparametric Identification And Estimation of Stochastic Block Models From Many Small Networks”," TSE Working Papers 24-1514, Toulouse School of Economics (TSE).
    2. Jochmans, Koen & Higgins, Ayden, 2022. "Learning Markov Processes with Latent Variables From Longitudinal Data," TSE Working Papers 22-1366, Toulouse School of Economics (TSE).

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    More about this item

    Keywords

    Discrete choice; heterogeneity; Markov process; mixture; state dependence;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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