Teaching and Statistical Training

The availability of well-educated researchers is necessary for the fruitful analysis of social and economic data. The increased data offer made possible by the creation of the Research Data Centers (RDCs) has resulted in an increased demand for PhD students at the master’s or Diplom levels. Especially in economics, where we find intense competition among the various individual subjects within the course of study, survey statistics has not been very successful in laying claim to a substantial proportion of the coursework and training. The situation is more favorable in sociology faculties. This article argues that the creation of new CAMPUS Files would help foster statistical education by providing public use files covering a wider range of subjects. It also presents some suggestions for new CAMPUS Files along these lines. Additionally, it argues for the establishment of master’s programs in survey statistics to increase the availability of well-trained statisticians. An outline of such a master’s program is presented and current PhD programs are evaluated with respect to training in survey statistics. Training courses are also offered outside the university that promote the use of new data sets as well as expanding the knowledge of new statistical methods or methods that lie outside standard education. These training courses are organized by the RDCs, (i.e. the data producers), the Data Service Centers, or by GESIS (Leibniz Institute for the Social Sciences). The current tendency to strengthen ties and collaborate with universities should be supported by making it possible to earn academic credit for such courses.master’s programs, survey statistics, campus files, statistical training

The stability of simulation based estimation of the multiperiod multinominal probit model with individual specific covariates

The multi-period multinomial Probit model (MMPM) is seen as a flexible tool to explain individual choices among several alternatives over time. There are two versions of this model: a) for each individual the covariates for all alternatives are known and b) for each individual only the parameters of the alternative which was chosen is known. The main difficulty with the MMPM was the calculation of the probability for the individual sequence of chosen alternatives, which requires the computation of the integral over a high dimensional multivariate Normal density. This remedy was removed by the Smooth Recursive Conditional (SRC) simulator. Several simulation studies have investigated the stability of the MMPM estimates with special emphasis to the number of replications of the SRC routine. In contrast to these studies, which use the case of alternative specific covariates, we use the case of the individual specific covariates. We conclude that the MMPM with individual specific covariates is only weakly identified, generalizing Keane's (1992) result for the one period case. As a consequence the maximization of the simulated likelihood often converges to a singular covariance structure so that the SRC-routine stops iterating. This feature cannot be avoided by increasing the number of replications in the SRC-routine. The percentage of these failures rapidly increases with the number of alternatives. --discrete choice models,multi-period multinomial,probit models,simulated maximum likelihood method,smooth recursive conditional simulator,panel data

Assessing the bias due to non-coverage of residential movers in the German microcensus panel: an evaluation using data from the socio-economic panel

The German Microcensus (MC) is a large scale rotating panel survey over three years. The MC is attractive for longitudinal analysis over the entire participation duration because of the mandatory participation and the very high case numbers (about 200 thousand respondents). However, as a consequence of the area sampling that is used for the MC , residential mobility is not covered and consequently statistical information at the new residence is lacking in theMCsample. This raises the question whether longitudinal analyses, like transitions between labour market states, are biased and how different methods perform that promise to reduce such a bias. Based on data of the German Socio-Economic Panel (SOEP), which covers residential mobility, we analysed the effects of missing data of residential movers by the estimation of labour force flows. By comparing the results from the complete SOEP sample and the results from the SOEP, restricted to the non-movers, we concluded that the non-coverage of the residential movers can not be ignored in Rubins sense. With respect to correction methods we analysed weighting by inverse mobility scores and loglinear models for partially observed contingency tables. Our results indicate that weighting by inverse mobility scores reduces the bias to about 60 percent whereas the official longitudinal weights obtained by calibration result in a bias reduction of about 80 percent. The estimation of loglinear models for nonignorable nonresponse leads to very unstable results. --Panel survey,labour market analysis,residential mobility,non-coverage bias,log-linear modelling,inverse probability weighting

Visual data of minor meteor showers limits of the method

Visual meteor observations are carried out on a regular basis by many experienced observers worldwide, thus supplying information about activity of meteor showers. The limits of the method are determined by the accuracy of the detection of the meteor trail. This study shows that visual meteor observations provide reliable data for an observable hourly rate of greater than or equal to 3

Design-oriented weighting of a household panel

The method of inverse sampling probabilities is adopted to calculate weights for a household panel. The method generates longitudinal as well as cross-sectional weights, which reflect the subsequent sampling stages of the panel and the different possibilities of households to enter the panel

Is there a fade-away effect of initial nonresponse bias in EU-SILC?

Nonresponse in surveys may result in a distortion of the distribution of interest. In a panel survey the participation behavior in later waves is different from the participation behavior at the start. With register data that cover also the information for non-respondents one can observe a fade away of the distributional differences between the distribution of the full sample, including nonresponders, and the respondent sample, without the nonrespondents. The mechanics of this effect may be explained by a Markov chain model. Under suitable regularity conditions the distribution on the state space converges to the steady state distribution of the chain, which is independent from the starting distribution of the chain. Therefore the fade- away effect is considered here as the swing-in into the steady state distribution. An essential condition for the fade-away effect assumes the same tran- sition law for the responders and the nonresponders. Such a hypothesis is investigated here for the Finnish subsample of EU-SILC for the equival- ized household net-income. The income is grouped into income brackets which divides the starting sample into quintiles. This analysis is based on register information. For this analysis the null-hypothesis of equal transition behavior between income quintiles for responders and nonre- sponders cannot be rejected. This finding restates a result for Finland for the ECHP (European Community Household Panel). A second condition concerns the selectivity of panel attrition after wave one. Here panel attrition must not depend on the income state of the previous panel wave. The velocity of the swing-in into the steady state distribution depends on the stability to stay in the same income state. The stability may vary among the European countries. Therefore we investigated the transition matrices for 25 EU-SILC countries. We simulated 6 different pattern of nonresponse bias and investigated the fade-away effect across the waves 2006 to 2009. We found remarkable differences between these 25 coun- tries. Expressed by the relative bias, i.e. bias in 2009 divided by bias at start in 2006, we found a reduction down to 26 percent of the initial bias for Bulgaria (foremost reduction) up to 61 percent for Finland (least reduction). Our results vote for longer observation periods in rotation panels like EU-SILC

eine Detektivarbeit

Der Aufsatz setzt sich mit dem empirischen Befund auseinander, dass die unterschiedliche Behandlung der kleinen und der großen Gemeinden beim Zensus 2011 zu unterschiedlich hohen Differenzen zwischen Bevölkerungsfortschreibung und Zensus führt. Dabei weisen die großen Gemeinden mit mehr als 10.000 Einwohnern eine im Schnitt um 1.5 Prozentpunkte niedrigere Zensuszahl als in der Bevölkerungsfortschreibung aus. Dieser von Christensen et al. (2015) gezeigte Befund wird mit einem erweiterten nichtparametrischen Analyseinstrumentarium für jedes einzelne Bundesland separat re-analysiert. Hierbei zeigt sich in drei Bundesländern kein derartiger Methodeneffekt des ab 10.000 Einwohnern benutzten Stichprobenverfahrens. Der Aufsatz untersucht die Frage, warum ein scheinbar so allgemeiner Methodeneffekt sich in drei Bundesländern nicht zeigt. Es zeigt sich, dass plausible Argumente darauf hinweisen, dass die Einwohnermelderegister in diesen drei Bundesländern besser geführt werden als in den anderen Bundesländern. In dieser Sichtweise deckt das Stichprobenverfahren systematische Mängel der Einwohnermelderegister auf, während das formal kaum spezifizierte Klärungsverfahren, das bei den kleinen Gemeinden angewendet wird, diesen Mangel nicht aufdeckt. In diesem Sinne können sich die großen Gemeinden nicht über eine Benachteiligung bei der Schätzung ihrer amtlichen Einwohnerzahl beklagen, da der gezeigte Effekt einem unpräzisen Meldewesen zuzuschreiben ist. Allerdings kommen die kleinen Gemeinden bei dem "Klärungsverfahren" im Schnitt zu gut weg