Identification and Decompositions in Probit and Logit Models
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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- Wolff, François-Charles, 2012.
"Decomposition of non-linear models using simulated residuals,"
Elsevier, vol. 116(3), pages 346-348.
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- 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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