Generalized partially linear regression with misclassified data and an application to labour market transitions
Enno Mammen and
Computational Statistics & Data Analysis, 2017, vol. 110, issue C, 145-159
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)
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Working Paper: Generalised partially linear regression with misclassified data and an application to labour market transitions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:110:y:2017:i:c:p:145-159
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