We introduce a multivariate multiplicative error model which is driven by componentspecific observation driven dynamics as well as a common latent autoregressive factor. The model is designed to explicitly account for (information driven) common factor dynamics as well as idiosyncratic effects...

Based on the theory of multiple statistical hypothesis testing, we elaborate simultaneous statistical inference methods in dynamic factor models. In particular, we employ structural properties of multivariate chi-squared distributions in order to construct critical regions for vectors of...

This article evaluates the performance of structural equation models in validating measurement models for hypothetical constructs and deals with specific issues following from the way this methodology is typically applied in scale construction. In particular, controlling for various types of...

Exploratory factor analysis (EFA) is an important tool in data analyses, particularly in social science. Usually four steps are carried out which contain an large number of options. One important option is the number of factors and the association of variables with a factor. Our tools aim to...

The paper proposes a data driven adaptive model selection strategy. The selection crite- rion measures economic exante forecasting content by means of trading implied cash flows. Empirical evidence suggests that the proposed strategy is neither exposed to selection bias nor to the risk of...

Comparing groups with respect to hypothetical constructs requires that the measurement models are equal across groups. Otherwise conclusions drawn from the observed indicators regarding differences at the latent level (mean differences, differences in the structural relations) might be severly...

This paper analyses the link between sustainability-related innovation and sustainability performance and the role that family firms play in this. This theme is particular relevant from a European point of view given the large number of firms that are family-owned. Governments often support...

We give an overview over smooth back tting type estimators in additive models. Moreover we illustrate their wide applicability in models closely related to additive models such as nonparametric regression with dependent error variables where the errors can be transformed to white noise by a...

Principal component analysis (PCA) is a widely used dimension reduction tool in the analysis of high-dimensional data. However, in many applications such as risk quantification in finance or climatology, one is interested in capturing the tail variations rather than variation around the mean. In...