Identification and Decompositions in Probit and Logit Models
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Other versions of this item:
- Choe, Chung & Jung, Seeun & Oaxaca, Ronald L., 2017. "Identification and Decompositions in Probit and Logit Models," IZA Discussion Papers 10530, Institute of Labor Economics (IZA).
References listed on IDEAS
- Wolff, François-Charles, 2012.
"Decomposition of non-linear models using simulated residuals,"
Elsevier, vol. 116(3), pages 346-348.
- François-Charles Wolff, 2012. "Decomposition of non-linear models using simulated residuals," Working Papers hal-00694421, HAL.
- Jung, SeEun & Choe, Chung & Oaxaca, Ronald L., 2018.
"Gender wage gaps and risky vs. secure employment: An experimental analysis,"
Elsevier, vol. 52(C), pages 112-121.
- Jung, Seeun & Choe, Chung & Oaxaca, Ronald L., 2016. "Gender Wage Gaps and Risky vs. Secure Employment: An Experimental Analysis," IZA Discussion Papers 10132, Institute of Labor Economics (IZA).
- SeEun Jung & Chung Choe & Ronald L. Oaxaca, 2017. "Gender Wage Gaps and Risky vs. Secure Employment: An Experimental Analysis," Inha University IBER Working Paper Series 2017-7, Inha University, Institute of Business and Economic Research, revised Jul 2017.
- Thomas Bauer & Mathias Sinning, 2008.
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More about this item
KeywordsDecompositions; Probit; Logit; Identification;
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- J16 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Gender; Non-labor Discrimination
- D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty
- J71 - Labor and Demographic Economics - - Labor Discrimination - - - Hiring and Firing
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