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

Stephan Dlugosz, Enno Mammen and Ralf Wilke
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Enno Mammen: Institute for Applied Mathematics, Heidelberg

No 201510 (en), FDZ-Methodenreport from Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany]

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))

Keywords: Bundesrepublik Deutschland; Datenqualität; Fehler; IAB-Beschäftigtenstichprobe; IAB-Datensatz Arbeiten und Lernen; Methodenliteratur; Regressionsanalyse (search for similar items in EconPapers)
Pages: 27 pages
Date: 2015
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Published in/as: Computational Statistics and Data Analysis, Vol. 110, No. June (2017), S. 145-159, doi:10.1016/j.csda.2017.01.003

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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:iab:iabfme:201510(en)

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