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Generalized partially linear regression with misclassified data and an application to labour market transitions

Stephan Dlugosz, Enno Mammen and Ralf Wilke

Computational Statistics & Data Analysis, 2017, vol. 110, issue C, 145-159

Abstract: Large data sets that originate from administrative or operational activity are increasingly used for statistical analysis as they often contain very precise information and a large number of observations. But there is evidence that some variables can be subject to severe misclassification or contain missing values. Given the size of the data, a flexible semiparametric misclassification model would be good choice but their use in practise is scarce. To close this gap a semiparametric model for the probability of observing labour market transitions is estimated using a sample of 20 m observations from Germany. It is shown that estimated marginal effects of a number of covariates are sizeably affected by misclassification and missing values in the analysis data. The proposed generalized partially linear regression extends existing models by allowing a misclassified discrete covariate to be interacted with a nonparametric function of a continuous covariate.

Keywords: Semiparametric regression; Measurement error; Side information (search for similar items in EconPapers)
Date: 2017
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Working Paper: Generalised partially linear regression with misclassified data and an application to labour market transitions (2015) Downloads
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DOI: 10.1016/j.csda.2017.01.003

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