Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

Knowledge and technological change are often assumed to be the driving forces for long run economic growth. Regions with a higher level of knowledge compared to other regions exhibit a higher per-capita income on average. Agglomeration effects can lead to a steady increase of income and widen...

The aim of this paper is to introduce a new model selection mechanism for cross sectional spatial models. This method is more fexible than the approach proposed by Florax et al. (2003) since it controls for spatial dependence as well as for spatial heterogeneity. In particular, Bayesian and...

The aim of this paper is to introduce a new model selection mechanism for cross sectional spatial models. This method is more flexible than the approach proposed by Florax et al. (2003) since it controls for spatial dependence as well as for spatial heterogeneity. In particular, Bayesian and...

Several approaches for subset recovery and improved forecasting accuracy have been proposed and studied. One way is to apply a regularization strategy and solve the model selection task as a continuous optimization problem. One of the most popular approaches in this research field is given by...

This paper introduces a new approach to modelling the conditional variance in a multivariate setting. It is essentially a combination of the popular GARCH model class with a spatial component, inspired by generalized space-time models. The resulting spatial GARCH model takes into account both...

Wir beschäftigen uns mit dem Auftreten von Autokorrelationseffekten bei der Messung langfristiger Markrpreisrisiken. Dazu diskutieren wir zwei verbreitete Methoden der Value at Risk (VaR) Schätzung, nämlich die Historische Simulation und die Wurzel-t-Regel. Beide Verfahren werden aufgrund...