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Data Masking by Noise Addition and the Estimation of Nonparametric Regression Models

Author

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  • Lechner Sandra

    (Department of Economics, Box D124, University of Konstanz, D-78457 Konstanz, Germany)

  • Pohlmeier Winfried

    (Department of Economics, Box D124, University of Konstanz, D-78457 Konstanz, Germany)

Abstract

Data collecting institutions use a large range of masking procedures in order to protect data against disclosure. Generally, a masking procedure can be regarded as a kind of data filter that transforms the true data generating process. Such a transformation severely affects the quality of the data and limits its use for empirical research. A popular masking procedure is noise addition, which leads to inconsistent estimates if the additional measurement errors are ignored.This paper investigates to what extent appropriate econometric techniques can obtain consistent estimates of the true data generating process for parametric and nonparametric models when data is masked by noise addition. We show how the reduction of the data quality can be minimized using the local polynomial Simulation-Extrapolation (SIMEX) estimator. Evidence is provided by a Monte-Carlo study and by an application to firm-level data, where we analyze the impact of innovative activity on employment.

Suggested Citation

  • 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.
    • Handle: RePEc:jns:jbstat:v:225:y:2005:i:5:p:517-528
    DOI: 10.1515/jbnst-2005-0503
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    References listed on IDEAS

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    1. John Staudenmayer & David Ruppert, 2004. "Local polynomial regression and simulation–extrapolation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 66(1), pages 17-30, February.
    2. Susanne M. Schennach, 2004. "Estimation of Nonlinear Models with Measurement Error," Econometrica, Econometric Society, vol. 72(1), pages 33-75, January.
    3. Hong, Han & Tamer, Elie, 2003. "A simple estimator for nonlinear error in variable models," Journal of Econometrics, Elsevier, vol. 117(1), pages 1-19, November.
    4. 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).
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    Cited by:

    1. 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.
    2. Gerd Ronning & Martin Rosemann, 2006. "Estimation of the Probit Model from Anonymized Micro Data," IAW Discussion Papers 25, Institut für Angewandte Wirtschaftsforschung (IAW).

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