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Identification and Decompositions in Probit and Logit Models

Chung Choe (), SeEun Jung () and Ronald Oaxaca

No 2017-8, Inha University IBER Working Paper Series from Inha University, Institute of Business and Economic Research

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.

Keywords: Decompositions; Probit; Logit; Identification (search for similar items in EconPapers)
JEL-codes: C35 J16 D81 J71 (search for similar items in EconPapers)
Pages: 11 pages
Date: 2017-07, Revised 2017-07
New Economics Papers: this item is included in nep-dcm and nep-ore
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https://drive.google.com/open?id=0B39YVuPWzf0ZM1hlUUE4R0ZRZG8 First version, 2017 (application/pdf)

Related works:
Journal Article: Identification and decompositions in probit and logit models (2020) Downloads
Working Paper: Identification and Decompositions in Probit and Logit Models (2017) Downloads
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