Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables

Arbeitspapier

We 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.

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: IZA Discussion Papers ; No. 218

Classification and Topics

• Classification: Wirtschaft ; Semiparametric and Nonparametric Methods: General ; Economics of Minorities, Races, Indigenous Peoples, and Immigrants; Non-labor Discrimination ; Multiple or Simultaneous Equation Models: Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions
• Subject: Immigrants ; speaking fluency ; misclassification error ; Schätztheorie ; Qualitatives Verfahren ; Statistischer Fehler ; Sprache ; Migranten ; Theorie ; Deutschland

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Dustmann, Christian ; van Soest, Arthur
• Event: Veröffentlichung
• (who): Institute for the Study of Labor (IZA)
• (where): Bonn
• (when): 2000

Further information

• Handle: http://hdl.handle.net/10419/21064
• Last update: 10.03.2025, 11:44 AM CET