Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables

Christian Dustmann and Arthur van Soest

No 218, IZA Discussion Papers from IZA Network @ LISER

Abstract: 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.

Keywords: misclassification error; speaking fluency; Immigrants (search for similar items in EconPapers)
JEL-codes: C14 C35 J15 (search for similar items in EconPapers)
Pages: 45 pages
Date: 2000-11
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Published - published in: Journal of Business and Economic Statistics, 2004, 22 (3), 312-321

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Related works:
Working Paper: Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables (1999)
Working Paper: Parametric and Semiparametric Estimation in Models with Misclassified Categorical Dependent Variables (1999)
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Persistent link: https://EconPapers.repec.org/RePEc:iza:izadps:dp218

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