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The Effect of Microaggregation Procedures on the Estimation of Linear Models: A Simulation Study

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  • Matthias Schmid

    ()
    (University of Munich)

  • Hans Schneeweiss

    ()
    (University of Munich)

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    Abstract

    Microaggregation is a set of procedures that distort empirical data in order to guarantee their facutal anonymity. At the same time the information content of data sets should not be reduced too much and should still be useful for scientific research. This paper investigates the effect of mircoaggregation on the estimation of a linear regression by ordinary least squares. It studies, by way of an extensive simulation experiment, the bias of the slope parameter estimator induced by various microaggregation techniques. Some microaggregation procedures lead to consistent estimates while others imply an asymptotic bias for the estimator.

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    Bibliographic Info

    Article provided by Justus-Liebig University Giessen, Department of Statistics and Economics in its journal Journal of Economics and Statistics.

    Volume (Year): 225 (2005)
    Issue (Month): 5 (September)
    Pages: 529-543

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    Handle: RePEc:jns:jbstat:v:225:y:2005:i:5:p:529-543

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    Related research

    Keywords: Microaggregation; disclosure control; simple linear model; bias; consistency;

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    References

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    1. Feige, Edgar L & Watts, Harold W, 1972. "An Investigation of the Consequences of Partial Aggregation of Micro-Economic Data," Econometrica, Econometric Society, vol. 40(2), pages 343-60, March.
    2. Winfried Pohlmeier & Sandra Lechner, 2003. "Schätzung ökonometrischer Modelle auf der Grundlage anonymisierter Daten," CoFE Discussion Paper 03-04, Center of Finance and Econometrics, University of Konstanz.
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    Cited by:
    1. Matthias Schmid, 2006. "Estimation of a linear model under microaggregation by individual ranking," AStA Advances in Statistical Analysis, Springer, vol. 90(3), pages 419-438, September.
    2. López, Alberto, 2011. "The effect of microaggregation on regression results: an application to Spanish innovation data," MPRA Paper 30403, University Library of Munich, Germany.

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