Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables
AbstractWe consider both a parametric and a semiparametric method to account for classification errors on the dependent variable in an ordered response model. The methods are applied to the analysis of self-reported speaking fluency of male immigrants in Germany. We find that a parametric model which explicitly allows for misclassification performs better than a standard ordered probit model and than a model with random thresholds. We find some substantial differences in parameter estimates and predictions of the different models.
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Bibliographic InfoPaper provided by Institute for the Study of Labor (IZA) in its series IZA Discussion Papers with number 218.
Length: 45 pages
Date of creation: Nov 2000
Date of revision:
Publication status: published in: Journal of Business and Economic Statistics, 2004, 22 (3), 312-321
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Other versions of this item:
- Dustmann, C. & Soest, A.H.O. van, 1999. "Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables," Discussion Paper 1999-51, Tilburg University, Center for Economic Research.
- C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
- C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
- J15 - Labor and Demographic Economics - - Demographic Economics - - - Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
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