Regression analysis aims at the revelation of interdependencies and causalities between variables observed in the population. That is, a structure between regressors and regressants that causes the realization of the finite population is assumed, the so-called data generating process or a...

Many variables that social and economic researchers seek to analyze through regression analysis violate normality assumptions. A standard remedy in that case is the logarithmic transformation. However, taking logarithms is not always sufficient to restablish model assumptions. A more general...

Innovations in statistical methodology is often accompanied by Monte-Carlo studies. In the context of survey statistics two types of inferences have to be considered. First, the classical randomization methods used for developments in statistical modelling. Second, survey data is typically...

In this paper, we estimate a logit mixture vector autoregressive (Logit-MVAR) model describing monetary policy transmission in the euro area over the period 1999..2015. MVARs allow us to differentiate between different states of the economy. In our model, the state weights are determined by an...

An approach to model-based small area estimation under covariate measurement errors is presented. Using a min-max approach, we proof that regularized regression coefficient estimation is equivalent to robust optimization under additive noise. Applying this equivalence, the Fay-Herriot model is...

The joint usage of unit- and area-level data for model-based small area estimation is investigated. The combination of levels within a single model encloses a variety of methodological problems. Firstly, it implies a critical decrease in degrees of freedom due to more model parameters that need...

Within this article, a generalized calibration approach is presented, which provides coherent and efficient estimates considering a high number of constraints on different hierarchical levels. These constraints may be obtained from different sources such as survey data, register data,...

Spatial dynamic microsimulations allow for the multivariate analysis of complex socioeconomic systems with geographic segmentation. For this, a synthetic replica of the system as base population is stochastically projected into future periods. Thereby, the projection is based on micro-level...

Model-based small area predictors are derived under the assumption that data files are complete. In application to real data, files may contain missing values. We introduce a variant of the bivariate Fay-Herriot model that takes into account for missing values in one component of the target...

Microsimulations usually contain various transition processes such as births and deaths, relocations, and change in household characteristics. The estimation, organisation, and implementation of these processes can have a substantial impact on the simulation outcomes. We propose to evaluate a...