Seite - 56 - in The Austrian Business Cycle in the European Context
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56
correlation is significantly higher than for other leads and lags in
close neighbourhood.
5.2 Frequency domain statistics for analysing
comovements
Sometimes it is easier to interpret comovements not in the time
domain, with discrete time data points, but in the frequency do-
main. This is especially the case in business cycle analysis where
special frequencies or frequency bands are the focus of interest.
These statistics can be derived by the Fourier transformation of
time domain statistics. So is the cross-spectra the frequency- do-
main-equivalent of the cross correlation Pb.i (.), like the spectral
density function is the equivalent of the auto-correlation function.
The cross-spectra of two series for a certain frequency tu is defined
as
(23) I <X)
Yb.s (w) = -2 LPb,s (r) e-iwr
,r r=-oo
with tu being a frequency within [-n;.n) and Pb., being the cross-
correlation as defined in (22). As can be seen in (23), the cross-
spectrum contains a complex part which does not cancel out be-
cause the cross-correlation function is not symmetric, i.e.
Pb.s (,)*Pb.,(-,). Therefore, this statistic cannot be interpreted in
order to determine leads and lags directly, but has to be trans-
formed into another statistic like the coherence, the phase spec-
tra or the mean delay.
The Austrian Business Cycle in the European Context
Forschungsergebnisse der Wirtschaftsuniversitat Wien
- Titel
- The Austrian Business Cycle in the European Context
- Autor
- Marcus Scheiblecker
- Verlag
- PETER LANG - lnternationaler Verlag der Wissenschaften
- Ort
- Frankfurt
- Datum
- 2008
- Sprache
- englisch
- Lizenz
- CC BY 4.0
- ISBN
- 978-3-631-75458-0
- Abmessungen
- 14.8 x 21.0 cm
- Seiten
- 236
- Schlagwörter
- Economy, Wirtschaft, WIFO, Vienna
- Kategorien
- International
- Recht und Politik
Inhaltsverzeichnis
- 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