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The effect of microaggregation by individual ranking on the estimation of moments

Author

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  • Schmid, Matthias
  • Schneeweiss, Hans

Abstract

Microaggregation by individual ranking (IR) is an important technique for masking confidential econometric data. While being a successful method for controlling the disclosure risk of observations, IR also affects the results of statistical analyses. We conduct a theoretical analysis on the estimation of arbitrary moments from a data set that has been anonymized by means of the IR method. We show that classical moment estimators remain both consistent and asymptotically normal under weak assumptions. This theory provides the justification for applying standard statistical estimation techniques to the anonymized data without having to correct for a possible bias caused by anonymization.

Suggested Citation

  • Schmid, Matthias & Schneeweiss, Hans, 2009. "The effect of microaggregation by individual ranking on the estimation of moments," Journal of Econometrics, Elsevier, vol. 153(2), pages 174-182, December.
  • Handle: RePEc:eee:econom:v:153:y:2009:i:2:p:174-182
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    References listed on IDEAS

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    1. Matthias Schmid, 2006. "Estimation of a linear model under microaggregation by individual ranking," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 90(3), pages 419-438, September.
    2. Matthias Schmid & Hans Schneeweiss & Helmut Küchenhoff, 2007. "Estimation of a linear regression under microaggregation with the response variable as a sorting variable," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 61(4), pages 407-431.
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    Citations

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    Cited by:

    1. López, Alberto, 2011. "The effect of microaggregation on regression results: an application to Spanish innovation data," MPRA Paper 30403, University Library of Munich, Germany.
    2. Hans Schneeweiss & Daniel Rost & Matthias Schmid, 2012. "Probability and quantile estimation from individually micro-aggregated data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 75(6), pages 721-742, August.
    3. Flavio Calvino, 2016. "Technological Innovation and the Distribution of Employment Growth: a firm-level analysis," LEM Papers Series 2016/37, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.

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