Measuring Business Cycles: A Wavelet Analysis of Economic Time Series

By Motohiro Yogo. 2008

137 citations recorded by the source corpus.

Multiresolution wavelet analysis is a natural way to decompose an economic time series into trend, cycle, and noise. The method is illustrated with GDP data. The business-cycle component of the wavelet-filtered series closely resembles the series filtered by the approximate bandpass filter.

Subfields: Economics and Econometrics, Management Science and Operations Research, Signal Processing

View Paper: https://www.semanticscholar.org/paper/b0a72918c8503e6b8c538bb53e5d0cf3e8bf1bc6

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Citations in the corpus

Measuring comovement in the time-frequency space (2010, unknown): “…between stock returns and inflation, Gençay et al. (2005) and Fernandez (2005) study the Capital Asset Pricing Model, Gallegati et al. (2008) and Yogo (2008) resort to wavelets for business cycle analysis, Rua and Nunes (2009) focus on international stock market returns, among others (see, for…”
A wavelet-based assessment of market risk: The emerging markets case (2012, background): “…and futures markets, Gallegati and Gallegati (2007) provide a wavelet variance analysis of output in G-7 countries, Gallegati et al. (2008) and Yogo (2008) resort to wavelets for business cycle analysis, Rua (2011) focuses on forecasting GDP growth in the major euro area countries, and others…”
The US Wage Phillips Curve Across Frequencies and Over Time (2011, methodology): “In fact, it is only recently that applications of wavelet analysis have been used in economics and finance (e.g. Ramsey and Lampart, 1998a,b; Kim and Haueck In, 2003; Crowley and Lee, 2005; Raihan,Wen and Zeng, 2005; Crivellini et al., 2006; Gallegati et al., 2006; Yogo, 2008).”