Page - 50 - in The Austrian Business Cycle in the European Context

50 the vector containing the original, de-trended data z,, at certain frequencies? 4• This general dynamic factor model approach has the big advan- tage that the analysis of leading and lagging features of all time series is not done as usual after determining the business cycle, but that instead it constitutes an essential part of the identification procedure itself. Unlike the dynamic factor model approach proposed by Stock - Watson ( 1989, 1991), the business cycle is not modelled specifically by demanding some special auto-correlation of some specific or- der for the factors. This potentially allows common variations that do not belong to the business cycle to be included in the com- mon component, if the data used are not pre-filtered for high fre- quency movements. An example would be a terrorist attack which influences economic growth for a short period and is possi- bly felt in a large part of observed time series. Whereas this ap- pears as a common shock - and therefore influences the estima- tion of the common components - this cannot be regarded as a business cycle phenomenon. The same argument goes for other high-frequency common or idiosyncratic components like sea- sonal or working day variations. In the context of the present study, the low number of time series could be a problem for the identification of the common factors. To obtain consistent estimates of the common factors, the number of series should be considerably higher than the number of obser- vations and theoretically go to infinity. This allows idiosyncratic components to be separated from common variations. Recent re- search by Boivin - Ng (2006) shows that under certain circum- stances, the number of observed variables can be reduced with- out losing explanatory power. The authors have shown that in real forecasting exercises 40 time series can yield better results than 14 See Forni et al. (2000).