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Dynamic Binary Outcome Models with Maximal Heterogeneity

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  • Martin Browning

    (Department of Economics, University of Oxford)

  • Jesus M. Carro

    (Universidad Carlos III de Madrid)

Abstract

Most econometric schemes to allow for heterogeneity in micro behaviour have two drawbacks: they do not fit the data and they rule out interesting economic models. In this paper we consider the time homogeneous first order Markov (HFOM) model that allows for maximal heterogeneity. That is, the modelling of the heterogeneity does not impose anything on the data (except the HFOM assumption for each agent) and it allows for any theory model (that gives a HFOM process for an individual observable variable). 'Maximal' means that the joint distribution of initial values and the transition probabilities is unrestricted. We establish necessary and sufficient conditions for the point identification of our heterogeneity structure and show how it depends on the length of the panel. A feasible ML estimation procedure is developed. Tests for a variety of subsidiary hypotheses such as the assumption that marginal dynamic effects are homogeneous are developed. We apply our techniques to a long panel of Danish workers who are very homogeneous in terms of observables. We show that individual unemployment dynamics are very heterogeneous, even for such a homogeneous group. We also show that the impact of cyclical variables on individual unemployment probabilities differs widely across workers. Some workers have unemployment dynamics that are independent of the cycle whereas others are highly sensitive to macro shocks.

Suggested Citation

  • Martin Browning & Jesus M. Carro, 2007. "Dynamic Binary Outcome Models with Maximal Heterogeneity," CAM Working Papers 2009-08, University of Copenhagen. Department of Economics. Centre for Applied Microeconometrics, revised Feb 2009.
  • Handle: RePEc:kud:kuieca:2009_08
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    References listed on IDEAS

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    Cited by:

    1. Stéphane Bonhomme & Elena Manresa, 2015. "Grouped Patterns of Heterogeneity in Panel Data," Econometrica, Econometric Society, vol. 83(3), pages 1147-1184, May.
    2. Manuel Arellano & Stéphane Bonhomme, 2017. "Nonlinear Panel Data Methods for Dynamic Heterogeneous Agent Models," Annual Review of Economics, Annual Reviews, vol. 9(1), pages 471-496, September.
    3. Victor Aguirregabiria & Jiaying Gu & Yao Luo, 2018. "Sufficient Statistics for Unobserved Heterogeneity in Structural Dynamic Logit Models," Papers 1805.04048, arXiv.org.
    4. Plum, Alexander & Ayllón, Sara, 2015. "Heterogeneity in unemployment state dependence," Economics Letters, Elsevier, vol. 136(C), pages 85-87.
    5. Ippei Shibata, 2019. "Labor Market Dynamics: A Hidden Markov Approach," IMF Working Papers 2019/282, International Monetary Fund.
    6. Martin Browning & Jesus M. Carro, 2013. "The Identification of a Mixture of First-Order Binary Markov Chains," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(3), pages 455-459, June.
    7. Bester, C. Alan & Hansen, Christian B., 2016. "Grouped effects estimators in fixed effects models," Journal of Econometrics, Elsevier, vol. 190(1), pages 197-208.
    8. Williams, Benjamin, 2020. "Nonparametric identification of discrete choice models with lagged dependent variables," Journal of Econometrics, Elsevier, vol. 215(1), pages 286-304.

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    More about this item

    Keywords

    discrete choice; Markov processes; nonparametric identification; unemployment dynamics;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models
    • J64 - Labor and Demographic Economics - - Mobility, Unemployment, Vacancies, and Immigrant Workers - - - Unemployment: Models, Duration, Incidence, and Job Search

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