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Perfect classifiers in partial observability bivariate probit

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  • Poirier, Dale J.

Abstract

There have been numerous applications of partial observability bivariate probit models. These models identify determinants of individual discrete outcomes when all that is observed are collective outcomes and individual characteristics. Numerical difficulties with their likelihood functions are commonly reported. This note looks at one aspect: the existence of perfect classifiers.

Suggested Citation

  • Poirier, Dale J., 2012. "Perfect classifiers in partial observability bivariate probit," Economics Letters, Elsevier, vol. 116(3), pages 361-362.
    • Handle: RePEc:eee:ecolet:v:116:y:2012:i:3:p:361-362
    DOI: 10.1016/j.econlet.2012.04.008
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    References listed on IDEAS

    as
    1. Zellner, Arnold & Rossi, Peter E., 1984. "Bayesian analysis of dichotomous quantal response models," Journal of Econometrics, Elsevier, vol. 25(3), pages 365-393, July.
    2. Zorn, Christopher, 2005. "A Solution to Separation in Binary Response Models," Political Analysis, Cambridge University Press, vol. 13(2), pages 157-170, April.
    3. Poirier, Dale J., 1980. "Partial observability in bivariate probit models," Journal of Econometrics, Elsevier, vol. 12(2), pages 209-217, February.
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    More about this item

    Keywords

    Bayesian; Maximum likelihood; Prior;
    All these keywords.

    JEL classification:

    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General
    • C2 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables
    • C11 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Bayesian Analysis: General

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