Seite - 106 - in The Austrian Business Cycle in the European Context

106 Again, calculations on the basis of first-order-differenced data show the smallest number of turning points and of BK-filtered data the highest. Only for gerGVAex, there are two more turning points in the first-order-difference case than for HP-filtered data. Whereas the result for leads and lags when analysing business cy- cle movements on the basis of cross-correlations and mean devia- tions seems to be rather robust over all business cycle extraction methods, things are markedly different for the dating procedure. This result is not unexpected as it seems plausible that the criteria for peaks and troughs requiring a cycle length of at least 6 quar- ters and a phase length of at least 3 quarters are rather hard to ful- fil for time series having a large or even superimposed high- frequency variation. There are so many ups and downs in these series that a proper identification of peaks and troughs becomes a difficult task. Furthermore, it could be expected that for series with a large content of high-frequency variation, the identification of the first turning point is highly determinant for the identification of the others. Interestingly, the difference between HP- and BK- filtered data is not only marginal, as it was in the case of cross- correlations and mean delays. This time, BK-filtered data show sub- stantially more turning points than HP-filtered ones. Again, high- frequency variations in the HP-filtered data seem to be a problem for the identification of turning points, even if they are not super- imposed as in the first-order-difference case. If one has to judge which result is most relevant for economic pol- icy purposes, it seems to be clear that only those peaks and troughs that mark a turn in the medium (or at least not very short) term economic development are relevant. The fact that for time series including high-frequency variations the process of identifica- tion is problematic or that some of the many ups and downs may accidentally be identified as turning points, make these time series a poor guide for economic policy issues. Figure A 1 d shows the turning points for BK-filtered data of the Austrian and German gross value added (excluding agriculture