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

Author

Listed:
  • Dlugosz, Stephan

    (ZEW Mannheim)

  • Mammen, Enno

    (Institute for Applied Mathematics, Heidelberg)

  • Wilke, Ralf A.

    (Copenhagen Business School)

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 tailor- made 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." (Author's abstract, IAB-Doku) ((en))

Suggested Citation

  • Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2015. "Generalised partially linear regression with misclassied data and an application to labour market transitions," FDZ-Methodenreport 201510 (en), Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    • Handle: RePEc:iab:iabfme:201510(en)
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    References listed on IDEAS

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