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Identification and Decompositions in Probit and Logit Models

Author

Listed:
  • Chung Choe

    (Hanyang University)

  • SeEun Jung

    (Department of Economics, Inha University)

  • Ronald L. Oaxaca

    (University of Arizona)

Abstract

Probit and logit models typically require a normalization on the error variance for model identification. This paper shows that in the context of sample mean probability decompositions, error variance normalizations preclude estimation of the effects of group differences in the latent variable model parameters. An empirical example is provided for a model in which the error variances are identified. This identification allows the effects of group differences in the latent variable model parameters to be estimated.

Suggested Citation

  • Chung Choe & SeEun Jung & Ronald L. Oaxaca, 2017. "Identification and Decompositions in Probit and Logit Models," Inha University IBER Working Paper Series 2017-8, Inha University, Institute of Business and Economic Research, revised Jul 2017.
  • Handle: RePEc:inh:wpaper:2017-8
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    References listed on IDEAS

    as
    1. Wolff, François-Charles, 2012. "Decomposition of non-linear models using simulated residuals," Economics Letters, Elsevier, vol. 116(3), pages 346-348.
    2. Jung, SeEun & Choe, Chung & Oaxaca, Ronald L., 2018. "Gender wage gaps and risky vs. secure employment: An experimental analysis," Labour Economics, Elsevier, vol. 52(C), pages 112-121.
    3. Thomas Bauer & Mathias Sinning, 2008. "An extension of the Blinder–Oaxaca decomposition to nonlinear models," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 92(2), pages 197-206, May.
    Full references (including those not matched with items on IDEAS)

    More about this item

    Keywords

    Decompositions; Probit; Logit; Identification;

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
    • J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
    • J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing

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