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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 yield identification from knowledge of the cross-sectional distribution of three (or more) effective time-series observations under simple conditions. We explain how our approach and results are different from those in previous work by Kasahara and Shimotsu (2009) and Hu and Shum (2012). Most notably, outside information, such as monotonicity restrictions that link conditional distributions to latent types, is not needed.

Suggested Citation

  • Higgins, Ayden & Jochmans, Koen, 2023. "Identification of mixtures of dynamic discrete choices," Journal of Econometrics, Elsevier, vol. 237(1).
    • Handle: RePEc:eee:econom:v:237:y:2023:i:1:s0304407623001562
    DOI: 10.1016/j.jeconom.2023.04.006
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    Cited by:

    1. Koen Jochmans, 2024. "Nonparametric identification and estimation of stochastic block models from many small networks," Post-Print hal-04672521, HAL.
    2. Jochmans, Koen, 2024. "Nonparametric identification and estimation of stochastic block models from many small networks," Journal of Econometrics, Elsevier, vol. 242(2).
    3. 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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