Seite - 165 - in The Austrian Business Cycle in the European Context

165 as business cycle variations. This method was originally developed to reduce the complexity of large data sets but all of them cover- ing only a short time span. Recent research on this topic129 has shown that a reduction in the number of time series used does not necessarily lower the quality of the output, but that a reasonably selective approach eliminating series with a high content of idio- syncratic behaviour can indeed improve the results. In the present study, only economic data deemed to react to the business cycle have been used to set up the dynamic factor model. Furthermore, all series describing similar aggregates have been cancelled, leaving only one of them inside. This should avoid a bias of the common component towards special variations ap- pearing in several time series. As an example, only gross value added excluding agriculture and forestry of one country has been included, but not total gross value added or GDP, too. In order to avoid a bias towards the German business cycle, only series for Austria, Germany and the euro area adjusted for both countries have been considered. We extracted two dynamic factors on the basis of their eigen val- ues ordered by size. With these two factors, we were able to ex- plain more than 60 percent of the total variation of the data set. Again, results for BK-filtered data were highest (close to 70 per- cent), suggesting that idiosyncratic variability is an issue more im- portant for higher frequency components. Apart from this, the pat- tern of highly synchronised co-moving series across all observed frequencies was confirmed by our dynamic factor model results. Only the financial intermediation and real estate service sector shows a considerable lead of four quarters vis-a-vis the Austrian business cycle. Generally, dating the common component included in each of the time series reduces the number of cycles detected by the Bry- 129 See e.g. Boivin -Ng (2006) ortnklaar-Jacobs -Romp (2003).