Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Publication date | 31/12/2004 |
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Host publication | International Workshop on Privacy in Statistical Databases: PSD 2004: Privacy in Statistical Databases |
Editors | Josep Domingo-Ferrer, Vicenc Torra |
Place of Publication | Berlin |
Publisher | Springer-Verlag |
Pages | 187-200 |
Number of pages | 14 |
ISBN (Electronic) | 9783540259558 |
ISBN (Print) | 3540221182, 9783540221180 |
<mark>Original language</mark> | English |
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 3050 |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Statistical disclosure limitation is widely used by data collecting institutions to provide safe individual data. However, the choice of the disclosure limitation method severely affects the quality of the data and limit their use for empirical research. In particular, estimators for nonlinear models based on data which are masked by standard disclosure limitation techniques such as blanking or noise addition lead to inconsistent parameter estimates. This paper investigates to what extent appropriate econometric techniques can obtain parameter estimates of the true data generating process, if the data are masked by noise addition or blanking. Comparing three different estimators - calibration method, the SIMEX method and a semiparametric sample selectivity estimator - we produce Monte-Carlo evidence on how the reduction of data quality can be minimized by masking.