Page - 18 - in The Austrian Business Cycle in the European Context

18 smoothing procedure to get rid of high frequency noise variation. The subtraction of deterministic time trends, first order differencing, the Hodrick-Prescott filter, the Christiano-Fitzgerald filter and some ad-hoc moving averages belong to this class. In most cases, sev- eral steps have to be combined in order to single out the business cycle. Model-based approaches estimate all or some components by assuming some specific structure for them. Further model-based approaches concentrate on time series models. As they do not as- sume a specific structure, they can also be regarded as filter techniques. This goes for instance for the Beveridge-Nelson de- composition where the economic time series is represented as a time series model which is factorised after identification in order to extract the business cycle. 3.2.1 Outliers A proper identification of the business cycle requires a consistent data base, adjusted for disturbances caused by outliers. This goes for all approaches, whether they are model- or filter-based, direct or indirect methods. According to the decomposition possibilities outlined above, these outliers are contained in most cases in the error term et of ( 1 ) , together with other high frequency noise. For an initial cleaning of the underlying time series, three different types of outliers had been considered: • additive outliers • level shifts • transitory components. Additive outliers appear at one point in time and vanish thereafter without having any lasting effect on the further development of