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
Englisch

Bibliographic citation
Series: IZA Discussion Papers ; No. 10530

Classification
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
decompositions
probit
logit
identification

Event
Geistige Schöpfung
(who)
Choe, Chung
Jung, Seeun
Oaxaca, Ronald L.
Event
Veröffentlichung
(who)
Institute of Labor Economics (IZA)
(where)
Bonn
(when)
2017

Handle
Last update
10.03.2025, 11:41 AM CET

Data provider

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Object type

  • Arbeitspapier

Associated

  • Choe, Chung
  • Jung, Seeun
  • Oaxaca, Ronald L.
  • Institute of Labor Economics (IZA)

Time of origin

  • 2017

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