We generalize the refinement ordering for well calibrated probability forecasters to the case were the debtors under consideration are not necessarily identical. This ordering is consistent with many well known skill scores used in practice. We also add an illustration using default predictions...

We propose a new method (implemented in an R-program) to simulate long-range daily stock-price data. The program reproduces various stylized facts much better than various parametric models from the extended GARCH-family. In particular, the empirically observed changes in unconditional variance...

We plot aggregated daily stock returns with absolute value less than x against x and show empirically that this produces a typical spoon-shaped pattern which indicates a special type of asymmetry which has not been discussed before. This pattern disappears when individual returns are averaged; it...

We show that a recent appendix to the Gini-coefficient to make the latter more sensitive to asymmetric income distributions can be viewed as an abstract measure of skewness. We develop some of its properties and apply it to the US-income distribution in 1974 and 2010.

This article takes issue with a recent book by Ziliak and McCloskey (2008) of the same title. Ziliak and McCloskey argue that statistical significance testing is a barrier rather than a booster for empirical research in many fields and should therefore be abandoned altogether. The present...

For decades, there has been a heated debate about whether or not nuclear power plants contribute to childhood cancer in their respective neighbourhoods, with statisticians testifying on both sides. The present paper points to some flaws in the pro-arguments, taking a recent study prepared for...

We decompose the generalized Lorenz order into a size and a distribution component. The former is represented by stochastic dominance, the latter by the standard Lorenz order. We show that it is always possible, given generalized Lorenz dominance between two distributions F and G, to find...