Seite - 51 - in The Austrian Business Cycle in the European Context

51 models based on 147 series75• This is especially the case if idiosyn- cratic components are not cross-correlated as it is required in the strict factor model approach. In some cases, adding more time- series (which are probably noisier) lowers the average variation of the common component while assigning more to the idiosyncratic parts76. The findings of Ink/oar -Jacobs - Romp (2003) are a further example that a higher number of series for studying business cy- cles does not necessarily deliver better results. In the present study, time series have been carefully selected with regard to their theoretical content concerning business cycle in- formation. Therefore, series belonging to the domain of public ser- vices and agriculture and fores try have been discarded from this analysis. For theoretical reasons, they will not facilitate the identifi- cation process, but instead bring some noisy information into the data base, hampering a proper identification. Therefore, a reduc- tion of the number of time series will probably improve the out- come. The pre-filtering of time series in order to isolate only busi- ness cycle frequencies should improve the identification process of the common component further. Low-order cross correlation of idiosyncratic components is substantially reduced by adjusting for seasonal, working day and irregular variations. The before- mentioned facts warrant the expectation that the restriction to somewhat more than 1 O time series can still produce meaningful results77• 75 A further example is Watson (2000) who found the gain from going beyond 50 time series being only marginal. 16 See Boivin -Ng (2006). 77 The chapters that compare our results to ad-hoc methods and other studies confirm this expectation.