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Record Nr. |
UNINA9910297053803321 |
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Autore |
Scheiblecker Marcus <1967-> |
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Titolo |
The Austrian business cycle in the European context / / Marcus Scheiblecker |
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Pubbl/distr/stampa |
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Bern, : Peter Lang International Academic Publishing Group, 2018 |
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Frankfurt am Main, Germany : , : Peter Lang, , [2008] |
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Frankfurt am Main, Germany : , : Peter Lang, , [2018] |
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©2008 |
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ISBN |
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Edizione |
[First edition.] |
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Descrizione fisica |
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1 online resource (XIX, 207 pages) : illustrations, charts; digital, PDF file(s) |
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Collana |
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Forschungsergebnisse der Wirtschaftsuniversität Wien ; ; Band 25. |
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Disciplina |
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Soggetti |
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Business cycles - Austria - History - 20th century |
Business cycles - Austria - History - 21st century |
Business cycles - Germany - History |
Business cycles - European Union countries - History |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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Cover -- Zusammenfassung -- Abstract -- List of figures and tables -- List of abbreviations -- List of variables -- 1. Research motivation and overview -- 2. The data -- 3. Methods of extracting business cycle characteristics -- 3.1 Defining the business cycle -- 3.1.1 The classical business cycle definition -- 3.1.2 The deviation cycle definition -- 3.2 Isolation of business cycle frequencies -- 3.2.1 Outliers -- 3.2.2 Calendar effects -- 3.2.3 Seasonal variations -- 3.2.4 The trend -- 4. Identifying the business cycle -- 4.1 Construction of composite economic indices -- 4.1.1 The empirical NBER approach -- 4.1.2 Index models -- 4.2 Univariate determination of the business cycle -- 5. Analysing cyclical comovements -- 5.1 Time domain statistics for analysing comovements -- 5.2 Frequency domain statistics for analysing comovements -- 5.2.1 Coherence -- 5.2.2 Phase spectra and mean delay -- 5.2.3 Dynamic correlation -- 5.2.4 Cohesion -- 6. Dating the business cycle -- 6.1 The expert approaches -- 6.2 The Bry-Boschan routine -- 6.3 Hidden Markovian-switching processes -- |
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6.4 Threshold autoregressive models -- 7. Analysis of turning points -- 7.1 Mean and average leads and lags -- 7.2 Contingency tables for turning points -- 7.3 The intrinsic lead and lag classification of dynamic factor models -- 7.4 Concordance indicator -- 7.5 Standard deviation of the cycle -- 7.6 Mean absolute deviation -- 7.7 Triangle approximation -- 8. Results -- 8.1 Isolation of business cycle frequencies -- 8.1.1 First-order differences -- 8.1.2 The HP filter -- 8.1.3 The BK filter -- 8.2 Determination of the reference business cycle -- 8.2.1 Ad-hoc selection of the business cycle reference series -- 8.2.2 Determination of the business cycle by a dynamic factor model approach -- 8.3 Dating the business cycle. |
8.3.1 Dating the business cycle in the ad-hoc selection framework -- 8.3.2 Dating the business cycle in the dynamic factor model framework -- 9. Comparing results with earlier studies on the Austrian business cycle -- 9.1 Comparing the results with the study by Altissimo et al. (2001) -- 9.2 Comparing the results with the study by Mönch - Uhlig (2004) -- 9.3 Comparing the results with the study by Cheung - Westermann (1999) -- 9.4 Comparing the results with the study by Brandner - Neusser (1992) -- 9.5 Comparing the results with the study by Forni - Hallin - Lippi - Reichlin (2000) -- 9.6 Comparing the results with the study by Breitung - Eickmeier (2005) -- 9.7 Comparing the results with the study by Artis - Marcellino - Proietti (2004) -- 9.8 Comparing the results with the study by Vijselaar - Albers (2001) -- 9.9 Comparing the results with the study by Artis - Zhang (1999) -- 9.10 Comparing the results with the study by Dickerson - Gibson - Tsakalotos (1998) -- 9.11 Comparing the results with the study by Artis - Krolzig - Toro (2004) -- 9.12 Comparing the results with the dating calendar of the CEPR -- 9.13 Comparing the results with the study by Breuss (1984) -- 9.14 Comparing the results with the study by Hahn - Walterskirchen (1992) -- 9.15 Comparison of the results of different dating procedures -- 9.15.1 Turning point dates of the Austrian business cycle -- 9.15.2 Turning point dates of the euro area business cycle -- 10. Concluding remarks -- References -- Annex. |
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Sommario/riassunto |
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Dating business cycle turning points is still an important task for economic policy decisions. This study does this for the Austrian economy for the period between 1976 and 2005, using only quarterly national accounts data of Austria, Germany and the euro area. Three different filtering methods are applied: first-order differences, the Hodrick-Prescott filter, and the Baxter-King filter. To all of them, two different methods of determining the business cycle are applied: the ad-hoc determination of the business cycle and a dynamic factor model, taking into account the common variations of Austria, the euro area and the German business cycle movements. The results of both methods are dated by the Bry-Boschan algorithm in order to locate peaks and troughs of the cycle. The results are interpreted and compared to already exiting studies on the euro area and the Austrian business cycle. |
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