Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/31144
Authors: 
Schmid, Matthias
Schneeweiss, Hans
Küchenhoff, Helmut
Year of Publication: 
2005
Series/Report no.: 
Discussion paper // Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 416
Abstract: 
A problem statistical offices are increasingly faced with is guaranteeing confidentiality when releasing microdata sets. One method to provide safe microdata is to reduce the information content of a data set by means of masking procedures. A widely discussed masking procedure is microaggregation, a technique where observations are grouped and replaced with their corresponding group means. However, while reducing the disclosure risk of data file, microaggregation also affects the results of statistical analyses. We focus on the effect of microaggregation on a simple linear model, In a previous paper we have shown how to correct for the aggregation bias of the naive least-squares estimator that occurs when the dependent variable is used to group the data. The present paper deals with the asymptotic variance of the corrected least-squares estimator and with the asymptotic variance of the naive least-squares estimator when either the dependent variable or the regressor is used to group the data. We derive asymptotic confidence intervals for the slope parameter. Furthermore, we show how to test for the significance of the slope parameter by analyzing the effect of microaggregation on the asymptotic power function of the naive t-test.
Subjects: 
Microaggregation
simple linear model
asymptotic variance
t-test
disclosure control
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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