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

Stephan Dlugosz, Enno Mammen and Ralf Wilke

No 15-043, ZEW Discussion Papers from ZEW - Leibniz Centre for European Economic Research

Abstract: We consider the semiparametric generalised linear regression model which has mainstream empirical models such as the (partially) linear mean regression, logistic and multinomial regression as special cases. As an extension to related literature we allow a misclassified covariate to be interacted with a nonparametric function of a continuous covariate. This model is tailormade to address known data quality issues of administrative labour market data. Using a sample of 20m observations from Germany we estimate the determinants of labour market transitions and illustrate the role of considerable misclassification in the educational status on estimated transition probabilities and marginal effects.

Keywords: semiparametric regression; measurement error; side information (search for similar items in EconPapers)
Date: 2015
New Economics Papers: this item is included in nep-ecm
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Related works:
Journal Article: Generalized partially linear regression with misclassified data and an application to labour market transitions (2017)
Working Paper: Generalised partially linear regression with misclassied data and an application to labour market transitions (2015)
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:zewdip:15043

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