We analyze macroeconomic data using univariate and multivariate forecast combining techniques. We simulate forecast errors with different variance-covariance structures. The simulations are used to compare the performance of univariate and multivariate combining techniques.

Cointegration describes the pattern that pairs of time series keep together in long run, although they diverge in short run. A generalisation of this behaviour is the fractional cointegration. Two statistical tests, the M– and ML–test are formulated for fractional cointegration in different...

This note provides a proof of Granger's (1986) error correction model for fractionally cointegrated variables and points out a necessary assumption that has not been noted before. Moreover, a simpler, alternative error correction model is proposed which can be employed to estimate fractionally...

In this paper business cycles are considered as a multivariate phenomenon and not as a univariate one determined e.g. by the GNP. The subject is to look for the number of phases of a business cycle, which can be motivated by the number of clusters in a given dataset of macro-economic variables....

Die finanzwirtschaftliche Bedeutung von Kreditausfällen und Kreditausfall- Prognosen bedarf keiner weiteren Begründung. Dieser Bedeutung angemessen, gibt es inzwischen eine Vielzahl von Modellen und Verfahren, die Ausfallwahrscheinlichkeiten von Bankkrediten oder Industrieanleihen zu...

We simulate forecast errors with different variance-covariance structures based on macroeconomic data. The simulations are used to compare the performance of different forecast combining techniques.