We study the forecasting performance of three alternative large scale approaches for German key macroeconomic variables using a dataset that consists of 123 variables in quarterly frequency. These three approaches handle the dimensionality problem evoked by such a large dataset by aggregating...

The use of large datasets for macroeconomic forecasting has received a great deal of interest recently. Boosting is one possible method of using high-dimensional data for this purpose. It is a stage-wise additive modelling procedure, which, in a linear specification, becomes a variable selection...

Das vorliegende Buch beinhaltet sämtliche Beiträge der 23. CIRET-Konferenz, die 1997 in Helsinki sattfand. Schwerpunkte waren dabei der internationale Wettbewerb, der Dienstleistungssektor, die Erhebung und Verwendung von Umfragedaten sowie Finazindikatoren. Besondere Bedeutung haben dabei die...

This paper uses several macroeconomic and financial indicators within a Markov Switching (MS) framework to predict the turning points of the business cycle. The presented model is applied to monthly German real-time data covering the recession and the recovery after the financial crisis. We show...