IDEAS home Printed from https://ideas.repec.org/p/iaw/iawdip/25.html
   My bibliography  Save this paper

Estimation of the Probit Model from Anonymized Micro Data

Author

Listed:
  • Gerd Ronning
  • Martin Rosemann

Abstract

The demand of scientists for confidential micro data from official sources has created discussion of how to anonymize these data in such a way that they can be given to the scientific community. We report results from a German project which exploits various options of anonymization for producing such ”scientific-use- files”. The main concern in the project however is whether estimation of stochastic models from these perturbed data is possible and – more importantly – leads to reliable results. In this paper we concentrate on estimation of the probit model under the assumption that only anonymized data are available. In particular we assume that the binary dependent variable has undergone post-randomization (PRAM) and that the set of explanatory variables has been perturbed by addition of noise. We employ a maximum likelihood estimator which is consistent if only the dependent variable has been anonymized by PRAM. The errors-in-variables structure of the regressors then is handled by the simulation extrapolation (SIMEX) estimation procedure where we compare performance of quadratic and nonlinear (rational) extrapolation.

Suggested Citation

  • Gerd Ronning & Martin Rosemann, 2006. "Estimation of the Probit Model from Anonymized Micro Data," IAW Discussion Papers 25, Institut für Angewandte Wirtschaftsforschung (IAW).
    • Handle: RePEc:iaw:iawdip:25
    as

    Download full text from publisher

    File URL: http://www.iaw.edu/RePEc/iaw/pdf/iaw_dp_25.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pohlmeier, Winfried & Lechner, Sandra, 2003. "Schätzung ökonometrischer Modelle auf der Grundlage anonymisierter Daten," CoFE Discussion Papers 03/04, University of Konstanz, Center of Finance and Econometrics (CoFE).
    2. Ronning, Gerd, 2005. "Randomized response and the binary probit model," Economics Letters, Elsevier, vol. 86(2), pages 221-228, February.
    3. Frazis, Harley & Loewenstein, Mark A., 2003. "Estimating linear regressions with mismeasured, possibly endogenous, binary explanatory variables," Journal of Econometrics, Elsevier, vol. 117(1), pages 151-178, November.
    4. Lechner Sandra & Pohlmeier Winfried, 2005. "Data Masking by Noise Addition and the Estimation of Nonparametric Regression Models," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(5), pages 517-528, October.
    5. Hausman, J. A. & Abrevaya, Jason & Scott-Morton, F. M., 1998. "Misclassification of the dependent variable in a discrete-response setting," Journal of Econometrics, Elsevier, vol. 87(2), pages 239-269, September.
    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. Ronning Gerd & Rosemann Martin & Strotmann Harald, 2005. "Post-Randomization Under Test: Estimation of the Probit Model," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 225(5), pages 544-566, October.
    2. Gerd Ronning, 2006. "Microeconometric models and anonymized micro data," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(1), pages 153-166, March.
    3. Nguimkeu, Pierre & Denteh, Augustine & Tchernis, Rusty, 2019. "On the estimation of treatment effects with endogenous misreporting," Journal of Econometrics, Elsevier, vol. 208(2), pages 487-506.
    4. Adele Bergin, 2015. "Employer Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable," LABOUR, CEIS, vol. 29(2), pages 194-223, June.
    5. Craig Gundersen & Brent Kreider, 2008. "Food Stamps and Food Insecurity: What Can Be Learned in the Presence of Nonclassical Measurement Error?," Journal of Human Resources, University of Wisconsin Press, vol. 43(2), pages 352-382.
    6. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    7. Wossen, Tesfamicheal & Abay, Kibrom A. & Abdoulaye, Tahirou, 2022. "Misperceiving and misreporting input quality: Implications for input use and productivity," Journal of Development Economics, Elsevier, vol. 157(C).
    8. Augustine Denteh & D'esir'e K'edagni, 2022. "Misclassification in Difference-in-differences Models," Papers 2207.11890, arXiv.org, revised Jul 2022.
    9. Lorenzo Almada & Ian McCarthy & Rusty Tchernis, 2016. "What Can We Learn about the Effects of Food Stamps on Obesity in the Presence of Misreporting?," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 98(4), pages 997-1017.
    10. Dye, Richard F. & McMillen, Daniel P., 2007. "Teardowns and land values in the Chicago metropolitan area," Journal of Urban Economics, Elsevier, vol. 61(1), pages 45-63, January.
    11. Adele Bergin, 2013. "Job Changes and Wage Changes: Estimation with Measurement Error in a Binary Variable," Economics Department Working Paper Series n240-13.pdf, Department of Economics, National University of Ireland - Maynooth.
    12. Takahide Yanagi, 2019. "Inference on local average treatment effects for misclassified treatment," Econometric Reviews, Taylor & Francis Journals, vol. 38(8), pages 938-960, September.
    13. Ronning, Gerd, 2005. "Randomized response and the binary probit model," Economics Letters, Elsevier, vol. 86(2), pages 221-228, February.
    14. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "On Mis-measured Binary Regressors: New Results And Some Comments on the Literature, Second Version," PIER Working Paper Archive 15-039, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 11 Nov 2015.
    15. Brent Kreider & John V. Pepper & Manan Roy, 2020. "Does The Women, Infants, And Children Program Improve Infant Health Outcomes?," Economic Inquiry, Western Economic Association International, vol. 58(4), pages 1731-1756, October.
    16. Francis DiTraglia & Camilo Garcia-Jimeno, 2015. "On Mis-measured Binary Regressors: New Results And Some Comments on the Literature, Third Version," PIER Working Paper Archive 15-040, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 24 Nov 2015.
    17. López, Alberto, 2011. "The effect of microaggregation on regression results: an application to Spanish innovation data," MPRA Paper 30403, University Library of Munich, Germany.
    18. Brachet, Tanguy, 2008. "Maternal Smoking, Misclassification, and Infant Health," MPRA Paper 21466, University Library of Munich, Germany.
    19. Orville Mondal & Rui Wang, 2024. "Partial Identification of Binary Choice Models with Misreported Outcomes," Papers 2401.17137, arXiv.org.
    20. Tommasi, Denni & Zhang, Lina, 2024. "Bounding program benefits when participation is misreported," Journal of Econometrics, Elsevier, vol. 238(1).

    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:iaw:iawdip:25. 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: Rolf Kleimann (email available below). General contact details of provider: https://edirc.repec.org/data/iawtude.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.