Randomized Response and the Binary Probit Model

The paper analyzes eects of randomized response with respect to some binary dependent variable on the estimation of the probit model. This approach is used in interviews when asking sensitive questions. Alternatively randomization can be considered as a means of statistical disclosure control which has been termed post randomization method (PRAM). The paper shows that all properties concerning parameter estimation are maintained although there is a loss in (asymptotic) eciency.Asymptotic Eciency Maximum Likelihood; Post Randomisation; Statistical Disclosure.

A Microeconometric Characterisation of Household Consumption Using Quantile Regression

The paper uses micro cross-section data from the GfK consumer panel for econometric demand analysis of private households in Germany. Contrary to most research which considered \average" behavior we extend this approach to consumer behavior for di®erent \intensities" of consumption. Our analytical tool is quantile regression which allows us to describe the conditional distribu- tion for any quantile including the (conditional) median representing \average" behavior. As an illustrative example we use the demand for beer and wine. The paper shows quite distinct patterns regarding price and income e®ects for di®erent goods which leads us to an extended characterization of household demand.econometric demand analysis, Engel curve, demand system, micro data

Panel Regression with Random Noise

The paper explores the effect of measurement errors on the estimation of a linear panel data model. The conventional fixed effects estimator, which ignores measurement errors, is biased. By correcting for the bias one can construct consistent and asymptotically normal estimators. In addition, we find estimates for the asymptotic variances of these estimators. The paper focuses on multiplicative errors, which are often deliberately added to the data in order to minimize their disclosure risk. They can be analyzed in a similar way as additive errors, but with some important and consequential differences.panel regression, multiplicative measurement errors, bias correction, asymptotic variance, disclosure control

Estimation of the Probit Model from Anonymized Micro Data

The demand of scientists for confidential micro data from official sources has created discussion of how to anonymize these data in such a way that they can be given to the scientific community. We report results from a German project which exploits various options of anonymization for producing such ”scientific-use- files”. The main concern in the project however is whether estimation of stochastic models from these perturbed data is possible and – more importantly – leads to reliable results. In this paper we concentrate on estimation of the probit model under the assumption that only anonymized data are available. In particular we assume that the binary dependent variable has undergone post-randomization (PRAM) and that the set of explanatory variables has been perturbed by addition of noise. We employ a maximum likelihood estimator which is consistent if only the dependent variable has been anonymized by PRAM. The errors-in-variables structure of the regressors then is handled by the simulation extrapolation (SIMEX) estimation procedure where we compare performance of quadratic and nonlinear (rational) extrapolation.anonymization, misclassification, noise addition, post-randomization, SIMEX procedure, statistical disclosure.

Stochastische Überlagerung mit Hilfe der Mischungsverteilung

The paper considers the effect of additive and multiplicative measurement errors on the estimation of linear models.We assume that such measurement errors have been applied to the micro data by purpose in order to protect them against re-identification. In particular measurement errors with a bimodal mixture distribution are analyzed. First the case of cross-section data is assumed. Then for panel data both the "naive' estimator ("within estimator", mixed effects estimator) and IV estimators are considered. In particular the effect of autocorrelation of regressors in short panels is discussed.bimodal mixture distribution

Stochastische Überlagerung mit Hilfe der Mischungsverteilung

openness, growth, German re-unification

IV-Schätzung eines linearen Panelmodells mit stochastisch überlagerten Betriebs- und Unternehmensdaten

Eines der wichtigsten Verfahren zur Anonymisierung von Betriebs- und Unternehmensdaten ist die stochastische Überlagerung. Ihr Einsatz zur Sicherstellung der faktischen Anonymität der Einheiten eines Datensatzes führt jedoch zu inkonsistenten Schätzungen von linearen Panelmodellen und macht die Verwendung von Korrekturverfahren erforderlich. Dieser Beitrag befasst sich mit der Instrumentvariablen-Schätzung (IV-Schätzung) eines linearen Panelmodells mit Individualeffekten und überprüft die Eignung der IV-Methode zur Korrektur der Verzerrung. Als Instrumente werden (a) eine verzögerte Variable, (b) die Differenz von verzögerten Variablen und (c) eine zusätzlich anonymisierte Variable getestet. Wir kommen zum Ergebnis, dass lediglich das letzte Instrument in konsistenten IV-Schätzern resultiert.Instrumentvariablen-Schätzung, additive und multiplikative stochastische Überlagerungen, Anonymisierung von Paneldaten

Remote data access and the risk of disclosure from linear regression

In the endeavor of finding ways for easy data access for external researchers remote data access seems to be an attractive alternative to the current standard of data perturbation or restricted access only at designated data archives or research data centers. However, even if the microdata are not available directly, disclosure of sensitive information is still possible. We illustrate that an ill-intentioned user could use some commonly available background information to reveal sensitive information using simple linear regression. We demonstrate the real risks from this approach with an empirical evaluation based on a German establishment survey, the IAB Establishment Panel

Improved Protective Efficacy of a Species-Specific DNA Vaccine Encoding Mycolyl-Transferase Ag85A from Mycobacterium ulcerans by Homologous Protein Boosting

Vaccination with plasmid DNA encoding Ag85A from M. bovis BCG can partially protect C57BL/6 mice against a subsequent footpad challenge with M. ulcerans. Unfortunately, this cross-reactive protection is insufficient to completely control the infection. Although genes encoding Ag85A from M. bovis BCG (identical to genes from M. tuberculosis) and from M. ulcerans are highly conserved, minor sequence differences exist, and use of the specific gene of M. ulcerans could possibly result in a more potent vaccine. Here we report on a comparison of immunogenicity and protective efficacy in C57BL/6 mice of Ag85A from M. tuberculosis and M. ulcerans, administered as a plasmid DNA vaccine, as a recombinant protein vaccine in adjuvant or as a combined DNA prime-protein boost vaccine. All three vaccination formulations induced cross-reactive humoral and cell-mediated immune responses, although species-specific Th1 type T cell epitopes could be identified in both the NH2-terminal region and the COOH-terminal region of the antigens. This partial species-specificity was reflected in a higher—albeit not sustained—protective efficacy of the M. ulcerans than of the M. tuberculosis vaccine, particularly when administered using the DNA prime-protein boost protocol