An improved LCAO-MO-SCF π-electron method.pdfVIP

To Blank or Not to Blank? A Comparison of the Effects of Disclosure Limitation Methods on Nonlinear Regression Estimates Sandra Lechner and Winfried Pohlmeier Department of Economics, Box D124, University of Konstanz 78457 Konstanz, Germany winfried.pohlmeier@uni-konstanz.de Phone ++49-7531-88-2660, Fax -4450 Abstract. Statistical disclosure limitation is widely used by data col- lecting 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 dis- closure limitation techniques such as blanking or noise addition lead to inconsistent parameter estimates. This paper investigates to what ex- tent appropriate econometric techniques can obtain parameter estimates of the true data generating process, if the data are masked by noise ad- dition 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. Keywords: disclosure limitation, blanking, semi-parametric selection models, errors in variables in nonlinear models 1 Introduction Over the last decades empirical research in the social sciences showed an in- creasing interest in the analysis of microdata. While the focus was originally more on individual and household data, a gr