Page - 22 - in The Austrian Business Cycle in the European Context

22 models, this is done either by assuming a fixed trigonometric movement of seasonality36 or by regressing on dummy variables. In both cases, seasonality is modelled as being stable over the whole time span of the series, which seems to be a rather strong assump- tion. So the advantage of isolating the seasonal variation within the same framework used for identification of the business cycle has to be weighed against the advantage of allowing for a flexi- ble trend for seasonal variations. The most popular methods for univariate seasonal extraction are X-12 from the US-Census Bureau and TRAMO-SEATS developed by the Bank of Spain37• Both procedures allow - in contrast to unob- served component models with a basic structure - a variation of the seasonal component over time. This captures the possibility of moving seasonal patterns due to technical progress or changing economic circumstances38 which could be very important for cleaning longer time series like the ones used for business cycle analysis. Weighing the pros and cons of the different approaches, it was decided to separate the seasonal component externally. This was done using the TRAMO-SEATS software. Fiorentini - Planas (2003) have shown that business cycle dating is not sensitive to the use of either X-12 or TR AMO-SEATS. This can be seen in Figure 2. Nevertheless, both approaches, the unobserved component model and the univariate time series model, assume orthogonality between seasonal variations and the business cycle, which should be borne in mind when interpreting the respective results. 36 Artis - Marcellino - Proietti (2004) or Hahn - Walterskirchen ( 1992) are examples for this approach. 37 See Gomez - Marava/1 ( 1992). 38 Reasons for that could be new processing techniques in the construction sector stabilising output during winter periods or changing seasonal patterns in tourism.