Generalized partially linear regression with misclassified data and an application to labour market transitions
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...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
27 January 2017
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| In: |
Computational statistics & data analysis
Year: 2017, Volume: 110, Pages: 145-159 |
| DOI: | 10.1016/j.csda.2017.01.003 |
| Online Access: | Verlag, Volltext: http://dx.doi.org/10.1016/j.csda.2017.01.003 Verlag, Volltext: http://www.sciencedirect.com/science/article/pii/S0167947317300166 
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| Author Notes: | Stephan Dlugosz, Enno Mammen, Ralf A. Wilke |
| Summary: | 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. |
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| Item Description: | Gesehen am 15.01.2018 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.csda.2017.01.003 |