Identification and Decompositions in Probit and Logit Models
Chung Choe (),
SeEun Jung () and
No 2017-8, Inha University IBER Working Paper Series from Inha University, Institute of Business and Economic Research
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
References: Add references at CitEc
Citations: Track citations by RSS feed
Downloads: (external link)
https://drive.google.com/open?id=0B39YVuPWzf0ZM1hlUUE4R0ZRZG8 First version, 2017 (application/pdf)
Journal Article: Identification and decompositions in probit and logit models (2020)
Working Paper: Identification and Decompositions in Probit and Logit Models (2017)
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:inh:wpaper:2017-8
Access Statistics for this paper
More papers in Inha University IBER Working Paper Series from Inha University, Institute of Business and Economic Research Contact information at EDIRC.
Bibliographic data for series maintained by Hyunduk Suh ().