New international evidence on the cyclical behaviour of output: Kuznets swings reconsidered

New international evidence on the cyclical behaviour of output: Kuznets swings reconsidered Spectral analysis is a particularly valuable method for seeking dependences expressed as lags between different magnitudes. Its use in this article was first determined by the search for maximum objectivity in the observation of time series. The possibility of applying it to a large number of series was then examined. This twin requirement resulted from a desire to avoid the criticism generally levelled at statistical studies concerning cyclical movements of the economy. Spectral analysis is based on the theory of stochastic processes. It starts with the core hypothesis that a given time series consists of a large number of sinusoidal components with different frequencies (univariate spectral analysis). It makes it possible to divide a particular category of records into a set of oscillations of different frequencies and then to show the links between the components with the same frequency in the various series examined (cross-spectral or bivariate spectral analysis). It has had limited applications in cliometrics to date. It is used here to determine the frequency of GDP series of several OECD countries. A reminder of the method Sect. 2 is followed by successive examination of the various series chosen, the treatment of these series and the results of spectral analysis Sect. 3. It is then possible as a conclusion to show the prospects of this type of approach and to synthesise a completely new major result for understanding economic dynamics in nineteenth and twentieth centuries, that is to say the existence of a single intermediate cycle with 15–0-year frequency that calls into question or even partially contradicts previous work on economic cycles. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Quality & Quantity Springer Journals

New international evidence on the cyclical behaviour of output: Kuznets swings reconsidered

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Publisher
Springer Journals
Copyright
Copyright © 2007 by Springer Science + Business Media B.V.
Subject
Social Sciences, general; Methodology of the Social Sciences; Social Sciences, general
ISSN
0033-5177
eISSN
1573-7845
D.O.I.
10.1007/s11135-006-9064-0
Publisher site
See Article on Publisher Site

Abstract

Spectral analysis is a particularly valuable method for seeking dependences expressed as lags between different magnitudes. Its use in this article was first determined by the search for maximum objectivity in the observation of time series. The possibility of applying it to a large number of series was then examined. This twin requirement resulted from a desire to avoid the criticism generally levelled at statistical studies concerning cyclical movements of the economy. Spectral analysis is based on the theory of stochastic processes. It starts with the core hypothesis that a given time series consists of a large number of sinusoidal components with different frequencies (univariate spectral analysis). It makes it possible to divide a particular category of records into a set of oscillations of different frequencies and then to show the links between the components with the same frequency in the various series examined (cross-spectral or bivariate spectral analysis). It has had limited applications in cliometrics to date. It is used here to determine the frequency of GDP series of several OECD countries. A reminder of the method Sect. 2 is followed by successive examination of the various series chosen, the treatment of these series and the results of spectral analysis Sect. 3. It is then possible as a conclusion to show the prospects of this type of approach and to synthesise a completely new major result for understanding economic dynamics in nineteenth and twentieth centuries, that is to say the existence of a single intermediate cycle with 15–0-year frequency that calls into question or even partially contradicts previous work on economic cycles.

Journal

Quality & QuantitySpringer Journals

Published: Jan 25, 2007

References

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