Arbeitspapier
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.
- Language
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Englisch
- Bibliographic citation
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Series: IZA Discussion Papers ; No. 10530
- Classification
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Wirtschaft
Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
Economics of Gender; Non-labor Discrimination
Criteria for Decision-Making under Risk and Uncertainty
Labor Discrimination
- Subject
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decompositions
probit
logit
identification
- Event
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Geistige Schöpfung
- (who)
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Choe, Chung
Jung, Seeun
Oaxaca, Ronald L.
- Event
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Veröffentlichung
- (who)
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Institute of Labor Economics (IZA)
- (where)
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Bonn
- (when)
-
2017
- Handle
- Last update
-
10.03.2025, 11:41 AM CET
Data provider
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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Arbeitspapier
Associated
- Choe, Chung
- Jung, Seeun
- Oaxaca, Ronald L.
- Institute of Labor Economics (IZA)
Time of origin
- 2017
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