In dieser Arbeit werden zwei Methoden zur Ermittlung der Eigenkapitalunterlegung von Risikopositionen und die Auswirkungen unterschiedlicher Verteilungsannahmen auf das Value-at-Risk (VaR) untersucht. Die empirischen Ergebnisse basieren auf zwei Beispielportfolios aus DAX-Aktien und einer...

Additive regression models have a long history in nonparametric regression. It is well known that these models can be estimated at the one dimensional rate. Until recently, however, these models have been estimated by a backfitting procedure. Although the procedure converges quickly, its...

We consider the component analysis problem for a regression model with an additive structure. The problem is to check the hypothesis of linearity for each component without specifying the structure of the remaining components. In this paper we show that under mild conditions on the design and...

In this article we highlight the main differences of available methods for the analysis of regression functions that are probably additive separable. We first discuss definition and interpretation of the most common estimators in practice. This is done by explaining the different ideas of...

The discussion about the use of semiparametric analysis in empirical research in economics is as old as the methods are. This article can certainly not be more than a small contribution to the polemic question how useful is non- or semiparametric statistics for applied econometrics. The goal is...

Economists use partial and general equilibrium trade simulation models to estimate the impact of changes in domestic policies and international trade rules. During the WTO Doha Development Agenda (DDA) negotiations economists have produced many different estimates of the gains that would result...

Imagine we have two different samples and are interested in doing semi- or nonparametric regression analysis in each of them, possibly on the same econometric model. In this article we consider the problem of testing whether a specific covariate has different impacts on the regression curve in...

Over the last four decades, several methods for selecting the smoothing parameter, generally called the bandwidth, have been introduced in kernel regression. They differ quite a bit, and although there already exist more selection methods than for any other regression smoother we can still see...