Seite - 91 - in The Austrian Business Cycle in the European Context

91 Furthermore, both definitions of German gross value added are again found to be coincident, but GDP shows a lag of one period. This result is in line with theory, since net taxes on production should lag the cycle, although this should again not be over-emphasised because there is hardly any difference between the highest cross- correlation tmn and the coincident one. In comparison with the HP- and first-order-difference-filtered data, the Austrian financial intermediation services and real estate, renting and business ac- tivities (autJK) are found to have some lag, but again the differ- ence between the maximum and the coincident cross-correlation is quite small, which goes for the German construction industry (gerF), too. In accordance with the HP and first-order-difference method, gerGHI and ger JK show a lag, which is one period less for the first series. Therefore, the results concerning the lead and lag structure of the observed time series seem to be quite similar between all methods, especially between the ones containing the irregular component, i.e. the HP and the first-order-difference filter. The BK filter shows that the picture is not as clear-cut as the other filters suggest. This might be a hint that part of the cross-correlation be- tween the time series should not be attributed to the business cy- cle but to higher-frequency dynamics, like special events or weather effects. Defining the business cycle by a narrower fre- quency band, e.g. only cycles above 8 quarters, will probably show a more pronounced difference between the BK filter and the others. A check for robustness can be made not only for the leading and lagging properties of the time series, but also for the size of the cross-correlations. The higher the cross-correlation, the stronger is the cyclical comovement between them. This property is impor- tant for economic policy decisions, as it signals whether such deci- sions influence other sectors. Table 2 shows for each series the dif- ferences between the maximum cross-correlations (rmax) - for all methods.