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Identification and Decompositions in Probit and Logit Models

Listed author(s):
  • Choe, Chung

    ()

    (Hanyang University)

  • Jung, Seeun

    ()

    (Inha University)

  • Oaxaca, Ronald L.

    ()

    (University of Arizona)

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.

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File URL: http://ftp.iza.org/dp10530.pdf
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Paper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 10530.

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Length: 13 pages
Date of creation: Jan 2017
Handle: RePEc:iza:izadps:dp10530
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References listed on IDEAS
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  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., 2016. "Gender Wage Gaps and Risky vs. Secure Employment: An Experimental Analysis," IZA Discussion Papers 10132, Institute for the Study of Labor (IZA).
  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.
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