Page - 69 - in The Austrian Business Cycle in the European Context
Image of the Page - 69 -
Text of the Page - 69 -
69
disadvantage at the same time. Usually, there remains a - hope-
fully small - number of time series points, for which it is inconclusive
in which state the economy is. As they are located between ex-
pansions and contractions, such points become the focus of inter-
est. If they have to be identified, only a judgemental evaluation
can help.
6.4 Threshold autoregressive models
Threshold autoregressive models {TAR} represent a further method
of a model-based dating procedure. Here, different regimes {in
the case of business cycle analysis there will again be two of them:
contraction and expansion} are modelled, with a threshold r for
their identification and with a certain threshold delay d to which
the threshold refers. The structure is basically the same as in (31)
(34) z, =µ,1 + f ¢,, z,_; +&:1
i=l
but the process of determining the state is different, in that it de-
pends on a certain threshold {the threshold delay}, which has to
be exceeded
ifz,-d>r
(35)
ifz ,-d < r
This approach is rather flexible, as for various regimes different
auto-correlation behaviours can be modelled as well as separate
error term variances. The TAR model is a piecewise linear model
which shows non-linear global behaviour. As first steps, the thresh-
old delay d, the threshold r and the order of the AR polynoms for
either state have to be estimated. After applying an identification
The Austrian Business Cycle in the European Context
Forschungsergebnisse der Wirtschaftsuniversitat Wien
- Title
- The Austrian Business Cycle in the European Context
- Author
- Marcus Scheiblecker
- Publisher
- PETER LANG - lnternationaler Verlag der Wissenschaften
- Location
- Frankfurt
- Date
- 2008
- Language
- English
- License
- CC BY 4.0
- ISBN
- 978-3-631-75458-0
- Size
- 14.8 x 21.0 cm
- Pages
- 236
- Keywords
- Economy, Wirtschaft, WIFO, Vienna
- Categories
- International
- Recht und Politik
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