Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/161153
Authors: 
Choe, Chung
Jung, Seeun
Oaxaca, Ronald L.
Year of Publication: 
2017
Series/Report no.: 
IZA Discussion Papers 10530
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.
Subjects: 
decompositions
probit
logit
identification
JEL: 
C35
J16
D81
J71
Document Type: 
Working Paper

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