We propose a new multivariate GARCH model with Dynamic Conditional Correlations that extends previous models by admitting multivariate thresholds in conditional volatilitiesand correlations. The model estimation is feasible in large dimensions and the positive definiteness of the conditional...

We propose a multivariate nonparametric technique for generating reliable short-term historical yield curve scenarios and confidence intervals. The approach is based on a Functional Gradient Descent (FGD) estimation of the conditional mean vector and covariance matrix of a multivariate interest...

In Austria active job-search programs were introduced on a large scale in 1999. These programs aim at activating unemployed at an early stage and bringing them back to work by training job-search related skills. We evaluate the impact of active labour market programs in Austria on individual...

Assuming a normal-Wishart modelling framework we compare two methods for finding outliers in a multivariate regression (MR) system. One method is the add-1-dummy approach which needs fewer parameters and a model choice criterion while the other method estimates the outlier probability for each...

This paper discusses a large-scale factor model for the German economy. Following the recent literature, a data set of 121 time series is used via principal component analysis to determine the factors, which enter a dynamic model for German GDP. The model is compared with alternative univariate...

We propose a methodology for comparing poverty over multiple periods across time and space without arbitrarily aggregating income over various years or relying on arbitrarily specified poverty lines. Following Duclos et al. (2006a), we use the multivariate stochastic dominance methodology to...