Parametric and Semiparametric Estimation in Models with Misclassified Categorical Variables
Christian Dustmann, Arthur van Soest · 2004
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. (This abstract was borrowed from another version of this item.)