Page - 29 - in The Austrian Business Cycle in the European Context
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chanical forecast feature which allows de-trending even at the
endpoints50. The HP filter calculates the trend component and
identifies the cyclical component as the difference between the
original series and the trend component51 • The end-point problem
is therefore concentrated on changes in the trend component.
This makes the HP filter very attractive for practical purposes, but
the end-point problem is solved at the cost of the accuracy of the
filter. Especially at the end points, the filter works imprecisely in that
it shows a stronger leakage. This means that some frequencies be-
longing to the trend can pass, whereas fluctuations of cyclical na-
ture (especially those of low frequency order) are filtered out.
Cogley - Nason ( 1995) and Canova ( 1998) pointed out that de-
spite the HP filter's ability to de-trend difference stationary time se-
ries, it distorts the frequency spectrum so that business cycle ex-
traction could be problematic.
3.2.4.5 The Baxter-King filter
Band-pass filters are understood as frequency filters, which give -
in their ideal representation - zero weight to the frequency band
to be filtered out and unit weight to the rest52. Baxter -King (1995)
proposed such a filter for business cycle filtering, which is an opti-
mal linear approximation to an ideal band-pass filter. They con-
structed a band-pass filter by starting from a low-pass filter, which
allows all frequencies w (below or equal a certain threshold .Q?) to
pass. This requires that all frequencies get a unit weight p if they
are above this threshold and zero otherwise:
50 See Baxter -King ( 1995).
51 See Kranendonk - Bonenl<omp - Verbruggen (2004).
52 In a wider sense, high-pass filters, which filter out all frequencies below a certain
frequency threshold (e.g. the trend) as well as low-poss filters, which let pass only
frequencies below a certain threshold, can be regarded as band-pass filters. But
what is meant here are only the filters which capture the band between two
thresholds not belonging to extreme ends.
Table of contents
- Zusammenfassung V
- Abstract IX
- List of figures and tables XV
- List of abbreviations XVII
- List of variables XIX
- 1. Research motivation and overview 1
- 2. The data 7
- 3. Methods of extracting business cycle characteristics 13
- 4. Identifying the business cycle 41
- 5. Analysing cyclical comovements
- 6. Dating the business cycle 61
- 7. Analysis of turning points 71
- 8. Results 79
- 9. Comparing results with earlier studies on the Austrian business cycle 125
- 9.1 Comparing the results with the study by Altissimo et al. (2001) 126
- 9.2 Comparing the results with the study by Monch -Uhlig (2004) 128
- 9.3 Comparing the results with the study by Cheung -Westermann (1999) 130
- 9.4 Comparing the results with the study by Brandner -Neusser (1992) 131
- 9.5 Comparing the results with the study by Forni - Hallin -Lippi -Reich/in (2000) 132
- 9.6 Comparing the results with the study by Breitung -Eickmeier (2005) 134
- 9.7 Comparing the results with the study by Artis - Marcellino - Proietti (2004) 134
- 9.8 Comparing the results with the study by Vijselaar -Albers (2001) 140
- 9.9 Comparing the results with the study by Artis - Zhang (1999) 142
- 9.10 Comparing the results with the study by Dickerson -Gibson -Tsakalotos (1998) 142
- 9.11 Comparing the results with the study by Artis - Krolzig - Toro (2004) 143
- 9.12 Comparing the results with the dating calendar of the CEPR 146
- 9.13 Comparing the results with the study by Breuss ( 1984) 151
- 9.14 Comparing the results with the study by Hahn - Walterskirchen ( 1992) 153
- 9.15 Comparison of the results of different dating procedures 154
- 9 .15.1 Turning point dates of the Austrian business cycle 155
- 9 .15.2 Turning point dates of the euro area business cycle 156
- 10. Concludlng remarks 161
- References 169
- Annex 177