Wavelet: a new tool for business cycle analysis
One basic problem in business-cycle studies is how to deal with nonstationary time series. The market economy is an evolutionary system. Economic time series therefore contain stochastic components that are necessarily time dependent. Traditional methods of business cycle analysis, such as the correlation analysis and the spectral analysis, cannot capture such historical information because they do not take the time-varying characteristics of the business cycles into consideration. In this paper, we introduce and apply a new technique to the studies of the business cycle: the wavelet-based time-frequency analysis that has recently been developed in the field of signal processing. This new method allows us to characterize and understand not only the timing of shocks that trigger the business cycle, but also situations where the frequency of the business cycle shifts in time. Our empirical analyses show that 1973 marks a new era for the evolution of the business cycle.
|Date of creation:||2005|
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- Ramsey James B. & Lampart Camille, 1998. "The Decomposition of Economic Relationships by Time Scale Using Wavelets: Expenditure and Income," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 3(1), pages 1-22, April.
- Ramsey, James B. & Lampart, Camille, 1998. "Decomposition Of Economic Relationships By Timescale Using Wavelets," Macroeconomic Dynamics, Cambridge University Press, vol. 2(01), pages 49-71, March.
- Wen, Yi & Zeng, Bing, 1999. "A simple nonlinear filter for economic time series analysis," Economics Letters, Elsevier, vol. 64(2), pages 151-160, August.
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