Page - 44 - in The Austrian Business Cycle in the European Context

44 phase-average-trends as presented in 3.2.4.2.) and special heuris- tic weighting schemes are employed66. It is easy to see that the whole process is rather time consuming and hard to track, apart from its high degree of arbitrariness. Koopmans (1947) criticised it fiercely for being "measurement without theory". For all these reasons, this method is not considered to be the right one for the present study and will not be pursued hereafter. 4. 1.2 Index models In order to deal with the more or less arbitrary procedure of con- structing business cycle indicators in the line of the empirical NBER approach, several more sophisticated multivariate time series ap- proaches have been proposed. The concept behind using index (or factor) models is the estimation of a common driving force (considered to be the business cycle) in a multivariate manner67. This multivariate framework for analysing the business cycle can be understood as weighing all time series together in order to identify their common driving force. Weights for aggregating all time series can be determined such as to maximise the variability content of the aggregate in relation to the one of the whole data set. In the last few years, factor models have become more and more popular in several areas of macro economic analysis. They have been applied for constructing economic indicators68, in forecast- 66 Green - Beckman ( 1992) give a description of the method employed by the US Bureau of Economic Analysis (BEA) together with some critical remarks. 67 The idea of extracting common factors for business cycle analysis goes back to Geweke (1977) and Sargent-Sims (1977). 68 Goyer - Genet (2006) used this approach to derive a weighting scheme for the Joint Harmonized EU Programme of Business and Consumer Survey.