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116 Looking at the results of the just filtered series, the peak following the trough at the beginning of 1980 is dated at the fourth quarter of 1981 , with the following trough at the end of 1983. The equiva- lent peak in the common component is dated at the fourth quar- ter of 1982, with the following trough only at the end of 1986. No other filtering method confirms either of these turning points. For both approaches, HP-filtered data show the same date of the above-mentioned peak as a trough, whereas the subsequent cy- cle is dated by both approaches the same way. Again differences exist at the end of the series. For reasons of space, observed leads and lags of turning points for all other series with respect to the reference series are not inter- preted here. They can be deducted from Table A 8 and com- pared with the results for just filtered series. Noteworthy seems only the fact that for turning point analysis based on common compo- nents, the number of observed extra cycles seems to be higher than for just filtered series. Obviously, the cleaning for idiosyncratic cycles did not reduce the number of turning points, but makes the common cyclicality more visible, so that it becomes easier for the Bry-Boschan algorithm to locate them. Considering the case of BK-filtered data, the number of turning points found is the smallest. This is due to the fact, that it starts dat- ing very late, at the beginning of 1980. Both other methods, the first-order differences and the BK filter, have already identified at least one complete cycle at that time. This interesting fact has al- ready been observed for just filtered data in Table A 2. In the dy- namic factor approach as well as for the just filtered data, the first turning point is a peak in the first quarter of 1980. The following trough is dated differently. In the case of just filtered data it is lo- cated at the end of 1981, whereas for the common component it occurs one year later. The following phase is dated exactly the same way, whereas the subsequent one harmonises only with re- spect to the year of occurrence. For the rest of the series, at least one turning point of a phase is reflected in both calendars. Again, plenty of differences can be found concerning the number of cy-
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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

  1. Zusammenfassung V
  2. Abstract IX
  3. List of figures and tables XV
  4. List of abbreviations XVII
  5. List of variables XIX
  6. 1. Research motivation and overview 1
  7. 2. The data 7
  8. 3. Methods of extracting business cycle characteristics 13
    1. 3. 1 Defining the business cycle 13
      1. 3. 1 . 1 The classical business cycle definition 13
      2. 3.1.2 The deviation cycle definition 15
    2. 3.2 Isolation of business cycle frequencies 16
      1. 3.2. l Outliers 18
      2. 3.2.2 Calendar effects 20
      3. 3.2.3 Seasonal variations 21
      4. 3.2.4 The trend 23
  9. 4. Identifying the business cycle 41
    1. 4.1 Construction of composite economic indices 42
      1. 4. l . l The empirical NBER approach 42
      2. 4.1 .2 Index models 44
    2. 4.2 Univariate determination of the business cycle 52
  10. 5. Analysing cyclical comovements
    1. 5. 1 Time domain statistics for analysing comovements 55
    2. 5.2 Frequency domain statistics for analysing comovements 56
      1. 5.2.1 Coherence 57
      2. 5.2.2 Phase spectra and mean delay 58
      3. 5.2.3 Dynamic correlation 58
      4. 5.2.4 Cohesion 59
  11. 6. Dating the business cycle 61
    1. 6.1 The expert approaches 63
    2. 6.2 The Bry-Boschan routine 65
    3. 6.3 Hidden Markovian-switching processes 67
    4. 6.4 Threshold autoregressive models 69
  12. 7. Analysis of turning points 71
    1. 7.1 Mean and average leads and lags 71
    2. 7.2 Contingency tab/es for turning points 72
    3. 7.3 The intrinsic lead and lag classification of dynamic factor models 74
    4. 7.4 Concordance indicator 74
    5. 7.5 Standard deviation of the cycle 75
    6. 7.6 Mean absolute deviation 76
    7. 7.7 Triangle approximation 76
  13. 8. Results 79
    1. 8.1 Isolation of business cycle frequencies 79
      1. 8.1.1 First-order differences 79
      2. 8.1.2 The HP filter 80
      3. 8.1.3 The BK filter 80
    2. 8.2 Determination of the reference business cycle 85
      1. 8.2.1 Ad-hoc selection of the business cycle reference series 86
      2. 8.2.2 Determination of the business cycle by a dynamic factor model approach 97
    3. 8.3 Dating the business cycle 104
      1. 8.3.1 Dating the business cycle in the ad-hoc selection framework 104
      2. 8.3.2 Dating the business cycle in the dynamic factor model framework 115
  14. 9. Comparing results with earlier studies on the Austrian business cycle 125
    1. 9.1 Comparing the results with the study by Altissimo et al. (2001) 126
    2. 9.2 Comparing the results with the study by Monch -Uhlig (2004) 128
    3. 9.3 Comparing the results with the study by Cheung -Westermann (1999) 130
    4. 9.4 Comparing the results with the study by Brandner -Neusser (1992) 131
    5. 9.5 Comparing the results with the study by Forni - Hallin -Lippi -Reich/in (2000) 132
    6. 9.6 Comparing the results with the study by Breitung -Eickmeier (2005) 134
    7. 9.7 Comparing the results with the study by Artis - Marcellino - Proietti (2004) 134
    8. 9.8 Comparing the results with the study by Vijselaar -Albers (2001) 140
    9. 9.9 Comparing the results with the study by Artis - Zhang (1999) 142
    10. 9.10 Comparing the results with the study by Dickerson -Gibson -Tsakalotos (1998) 142
    11. 9.11 Comparing the results with the study by Artis - Krolzig - Toro (2004) 143
    12. 9.12 Comparing the results with the dating calendar of the CEPR 146
    13. 9.13 Comparing the results with the study by Breuss ( 1984) 151
    14. 9.14 Comparing the results with the study by Hahn - Walterskirchen ( 1992) 153
    15. 9.15 Comparison of the results of different dating procedures 154
    16. 9 .15.1 Turning point dates of the Austrian business cycle 155
    17. 9 .15.2 Turning point dates of the euro area business cycle 156
  15. 10. Concludlng remarks 161
  16. References 169
  17. Annex 177
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