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A New Formulation for Latent Class Models

Author

Listed:
  • Sarah Brown

    () (Economics Department, University of Sheffield)

  • William Greene

    (Economics Department, Stern Business School, New York University)

  • Mark N. Harris

    (School of Economics and Finance, Curtin University)

Abstract

Latent class, or finite mixture, modelling has proved a very popular, and relatively easy, way of introducing much-needed heterogeneity into empirical models right across the social sciences. The technique involves (probabilistically) splitting the population into a finite number of (relatively homogeneous) classes, or types. Within each of these, typically, the same statistical model applies, although these are characterised by differing parameters of that distribution. In this way, the same explanatory variables can have differing effects across the classes, for example. A priori, nothing is known about the behaviours within each class; but ex post, researchers invariably label the classes according to expected values, however defined, within each class. Here we propose a simple, yet effective, way of parameterising both the class probabilities and the statistical representation of behaviours within each class, that simultaneously preserves the ranking of such according to class-specific expected values and which yields a parsimonious representation of the class probabilities.

Suggested Citation

  • Sarah Brown & William Greene & Mark N. Harris, 2014. "A New Formulation for Latent Class Models," Working Papers 2014006, The University of Sheffield, Department of Economics.
  • Handle: RePEc:shf:wpaper:2014006
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    File URL: http://www.shef.ac.uk/economics/research/serps/articles/2014_006.html
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    References listed on IDEAS

    as
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    3. David M. Cutler & Edward L. Glaeser & Jesse M. Shapiro, 2003. "Why Have Americans Become More Obese?," Journal of Economic Perspectives, American Economic Association, vol. 17(3), pages 93-118, Summer.
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    7. Chou, Shin-Yi & Grossman, Michael & Saffer, Henry, 2004. "An economic analysis of adult obesity: results from the Behavioral Risk Factor Surveillance System," Journal of Health Economics, Elsevier, vol. 23(3), pages 565-587, May.
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    Cited by:

    1. Burnett, J. Wesley, 2016. "Club convergence and clustering of U.S. energy-related CO2 emissions," Resource and Energy Economics, Elsevier, vol. 46(C), pages 62-84.
    2. Brown, Sarah & Durand, Robert B. & Harris, Mark N. & Weterings, Tim, 2014. "Modelling financial satisfaction across life stages: A latent class approach," Journal of Economic Psychology, Elsevier, vol. 45(C), pages 117-127.

    More about this item

    Keywords

    latent class models; finite mixture models; ordered probability models; expected values; body mass index;

    JEL classification:

    • C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
    • D1 - Microeconomics - - Household Behavior
    • I1 - Health, Education, and Welfare - - Health

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