Seite - 164 - in The Austrian Business Cycle in the European Context

164 Based on this method superior to the others, we found that cross- correlation 128 and coherence statistics give support for a broadly coincident behaviour of the whole set of time series. The German GDP seems to lag the Austrian gross value added ( excluding agri- culture and forestry), whereas the German gross value added is found to be coincident. This small lag of German GDP makes for a synchronised comovement of total euro area GDP, whereas it shows a lead when Germany and Austria are excluded from it. However, none of the results are significant for well-founded statements to be made. Interestingly, none of the series covering manufacturing leads the cycle, whereas the sector of financial in- termediation and rental services (NACE J+K) of both countries show a lag, which is not in conflict with theory. Whereas the picture of leading and lagging properties based on cross-correlations and coherences seems to be rather stable across different filtering methods, the Bry-Boschan procedure for detecting turning points reacts rather sensitively in this respect. For series where local minima and maxima are in close neighbour- hood, the criteria used by this dating algorithm are getting arbi- trary. In our study, this is the case for the series including high- frequency variations: the HP-filtered series and especially the first- order-differenced data. In these cases it appears that the first turn- ing point detected in such a series is crucial for dating the others. As an alternative to our ad-hoc determination of the series which carries the reference business cycle information, we set up a dy- namic factor model. The idea behind this procedure is to find one or more common factors reflected by a large part of our observed time series - possibly shifted by different leads or lags - in order to represent a large part of the total variation of the whole data set. These common factors can be regarded as the main driving force behind all economic time series. Therefore they can be regarded 12a Cross-correlations based on band-passed filtered data correspond lo the con- cept of dynamic correlation developed by Croux - Forni - Reich/in ( 1999).