Final published version
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Chapter
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TY - CHAP
T1 - To blank or not to blank?
T2 - A comparison of the effects of disclosure limitation methods on nonlinear regression estimates
AU - Lechner, Sandra
AU - Pohlmeier, Winfried
PY - 2004/12/31
Y1 - 2004/12/31
N2 - 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.
AB - 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.
KW - Blanking
KW - Disclosure limitation
KW - Errors in variables in nonlinear models
KW - Semi-parametric selection models
U2 - 10.1007/978-3-540-25955-8_15
DO - 10.1007/978-3-540-25955-8_15
M3 - Chapter
AN - SCOPUS:35048827490
SN - 3540221182
SN - 9783540221180
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 187
EP - 200
BT - International Workshop on Privacy in Statistical Databases
A2 - Domingo-Ferrer, Josep
A2 - Torra, Vicenc
PB - Springer-Verlag
CY - Berlin
ER -