IDEAS home Printed from https://ideas.repec.org/p/zbw/zewdip/15043.html
   My bibliography  Save this paper

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

Author

Listed:
  • Dlugosz, Stephan
  • Mammen, Enno
  • Wilke, Ralf A.

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.

Suggested Citation

  • Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2015. "Generalised partially linear regression with misclassified data and an application to labour market transitions," ZEW Discussion Papers 15-043, ZEW - Leibniz Centre for European Economic Research.
    • Handle: RePEc:zbw:zewdip:15043
    as

    Download full text from publisher

    File URL: https://www.econstor.eu/bitstream/10419/112759/1/832553174.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Hernandez, Monica & Pudney, Stephen, 2007. "Measurement error in models of welfare participation," Journal of Public Economics, Elsevier, vol. 91(1-2), pages 327-341, February.
    2. Bergemann, Annette & Mertens, Antje, 2000. "Job stability trends, layoffs and quits: An empirical analysis for West Germany," SFB 373 Discussion Papers 2001,102, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
    3. Nina Westerheide & Goran Kauermann, 2014. "Unemployed in Germany: Factors Influencing the Risk of Losing the Job," Research in World Economy, Research in World Economy, Sciedu Press, vol. 5(2), pages 43-55, September.
    4. Manfred Antoni & Stefan Seth, 2012. "ALWA-ADIAB – Linked Individual Survey and Administrative Data for Substantive and Methodological Research," Schmollers Jahrbuch : Journal of Applied Social Science Studies / Zeitschrift für Wirtschafts- und Sozialwissenschaften, Duncker & Humblot, Berlin, vol. 132(1), pages 141-146.
    5. Liang H. & Wang S. & Robins J.M. & Carroll R.J., 2004. "Estimation in Partially Linear Models With Missing Covariates," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 357-367, January.
    6. Wang Q. & Linton O. & Hardle W., 2004. "Semiparametric Regression Analysis With Missing Response at Random," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 334-345, January.
    7. Thierry Magnac & Michael Visser, 1999. "Transition Models With Measurement Errors," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 466-474, August.
    8. Boockmann, Bernhard & Steffes, Susanne, 2005. "Individual and Plant-level Determinants of Job Durations in Germany," ZEW Discussion Papers 05-89, ZEW - Leibniz Centre for European Economic Research.
    9. Manuel Arellano & Costas Meghir, 1992. "Female Labour Supply and On-the-Job Search: An Empirical Model Estimated Using Complementary Data Sets," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 59(3), pages 537-559.
    10. Martin Ladouceur & Elham Rahme & Christian A. Pineau & Lawrence Joseph, 2007. "Robustness of Prevalence Estimates Derived from Misclassified Data from Administrative Databases," Biometrics, The International Biometric Society, vol. 63(1), pages 272-279, March.
    11. Maddala, G S, 1971. "The Likelihood Approach to Pooling Cross-Section and Time-Series Data," Econometrica, Econometric Society, vol. 39(6), pages 939-953, November.
    12. Xiaohong Chen & Han Hong & Elie Tamer, 2005. "Measurement Error Models with Auxiliary Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(2), pages 343-366.
    13. Chen, Xiaohong & Hu, Yingyao & Lewbel, Arthur, 2008. "Nonparametric identification of regression models containing a misclassified dichotomous regressor without instruments," Economics Letters, Elsevier, vol. 100(3), pages 381-384, September.
    14. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521785167, September.
    15. Matthew Blackwell & James Honaker & Gary King, 2017. "A Unified Approach to Measurement Error and Missing Data: Overview and Applications," Sociological Methods & Research, , vol. 46(3), pages 303-341, August.
    16. repec:iab:iabfme:201112(en is not listed on IDEAS
    17. Laura Wichert & Ralf A. Wilke, 2012. "Which factors safeguard employment?: an analysis with misclassified German register data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 175(1), pages 135-151, January.
    18. Kruppe, Thomas & Matthes, Britta & Unger, Stefanie, 2014. "Effectiveness of data correction rules in process-produced data : the case of educational attainment," IAB-Discussion Paper 201415, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany].
    19. Grace Y. Yi & Yanyuan Ma & Donna Spiegelman & Raymond J. Carroll, 2015. "Functional and Structural Methods With Mixed Measurement Error and Misclassification in Covariates," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 110(510), pages 681-696, June.
    20. Dlugosz, Stephan, 2011. "Give missings a chance: Combined stochastic and rule-based approach to improve regression models with mismeasured monotonic covariates without side information," ZEW Discussion Papers 11-013, ZEW - Leibniz Centre for European Economic Research.
    21. Ruppert,David & Wand,M. P. & Carroll,R. J., 2003. "Semiparametric Regression," Cambridge Books, Cambridge University Press, number 9780521780506, September.
    22. Per Johansson & Per Skedinger, 2009. "Misreporting in register data on disability status: evidence from the Swedish Public Employment Service," Empirical Economics, Springer, vol. 37(2), pages 411-434, October.
    23. Inyoung Kim & Noah D. Cohen & Raymond J. Carroll, 2003. "Semiparametric Regression Splines in Matched Case-Control Studies," Biometrics, The International Biometric Society, vol. 59(4), pages 1158-1169, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Dlugosz, Stephan & Mammen, Enno & Wilke, Ralf A., 2015. "Generalised partially linear regression with misclassified data and an application to labour market transitions," ZEW Discussion Papers 15-043, ZEW - Leibniz Centre for European Economic Research.
    2. repec:iab:iabfme:201510(en is not listed on IDEAS
    3. Bravo, Francesco, 2015. "Semiparametric estimation with missing covariates," Journal of Multivariate Analysis, Elsevier, vol. 139(C), pages 329-346.
    4. Takuma Yoshida, 2019. "Two stage smoothing in additive models with missing covariates," Statistical Papers, Springer, vol. 60(6), pages 1803-1826, December.
    5. Chen, Xiaohong, 2007. "Large Sample Sieve Estimation of Semi-Nonparametric Models," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 76, Elsevier.
    6. Göran Kauermann & Mehboob Ali, 2021. "Semi-parametric regression when some (expensive) covariates are missing by design," Statistical Papers, Springer, vol. 62(4), pages 1675-1696, August.
    7. Otto-Sobotka, Fabian & Salvati, Nicola & Ranalli, Maria Giovanna & Kneib, Thomas, 2019. "Adaptive semiparametric M-quantile regression," Econometrics and Statistics, Elsevier, vol. 11(C), pages 116-129.
    8. Timothy K.M. Beatty & Erling Røed Larsen, 2005. "Using Engel curves to estimate bias in the Canadian CPI as a cost of living index," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 38(2), pages 482-499, May.
    9. Arthur Charpentier & Emmanuel Flachaire & Antoine Ly, 2017. "Econom\'etrie et Machine Learning," Papers 1708.06992, arXiv.org, revised Mar 2018.
    10. Hyunju Son & Youyi Fong, 2021. "Fast grid search and bootstrap‐based inference for continuous two‐phase polynomial regression models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(3), May.
    11. Michael Wegener & Göran Kauermann, 2017. "Forecasting in nonlinear univariate time series using penalized splines," Statistical Papers, Springer, vol. 58(3), pages 557-576, September.
    12. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
    13. Zi Ye & Giles Hooker & Stephen P. Ellner, 2021. "Generalized Single Index Models and Jensen Effects on Reproduction and Survival," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 26(3), pages 492-512, September.
    14. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    15. Alexander Dokumentov & Rob J. Hyndman, 2022. "STR: Seasonal-Trend Decomposition Using Regression," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 50-62, April.
    16. Akdeniz Duran, Esra & Härdle, Wolfgang Karl & Osipenko, Maria, 2012. "Difference based ridge and Liu type estimators in semiparametric regression models," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 164-175.
    17. Kalogridis, Ioannis & Van Aelst, Stefan, 2023. "Robust penalized estimators for functional linear regression," Journal of Multivariate Analysis, Elsevier, vol. 194(C).
    18. Krisztin, Tamás, 2018. "Semi-parametric spatial autoregressive models in freight generation modeling," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 114(C), pages 121-143.
    19. Lauren N. Berry & Nathaniel E. Helwig, 2021. "Cross-Validation, Information Theory, or Maximum Likelihood? A Comparison of Tuning Methods for Penalized Splines," Stats, MDPI, vol. 4(3), pages 1-24, September.
    20. Nagler Thomas & Schellhase Christian & Czado Claudia, 2017. "Nonparametric estimation of simplified vine copula models: comparison of methods," Dependence Modeling, De Gruyter, vol. 5(1), pages 99-120, January.
    21. Yukun Zhang & Haocheng Li & Sarah Kozey Keadle & Charles E. Matthews & Raymond J. Carroll, 2019. "A Review of Statistical Analyses on Physical Activity Data Collected from Accelerometers," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 11(2), pages 465-476, July.

    More about this item

    Keywords

    semiparametric regression; measurement error; side information;
    All these keywords.

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:zbw:zewdip:15043. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: ZBW - Leibniz Information Centre for Economics (email available below). General contact details of provider: https://edirc.repec.org/data/zemande.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.