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How Do You Make A Time Series Sing Like a Choir? Extracting Embedded Frequencies from Economic and Financial Time Series using Empirical Mode Decomposition

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  • Crowley Patrick M.

    (Texas A&M University - Corpus Christi)

Abstract

Empirical Mode Decomposition (EMD) was developed late last century, but has still to be introduced to the vast majority of economists. EMD was originally one of the components of Hilbert Huang Transform (HHT) which was a process of extracting the frequency mode features of cycles embedded in any time series using an adaptive data method which can be applied without making any assumption about stationarity or linear data-generating properties of time series. This paper introduces economists to the two constituent parts of the HHT transform, namely EMD and the Hilbert Spectrum, and also a new variant of this methodology, Ensemble EMD (EEMD). Several illustrative applications using the methodology are also included.

Suggested Citation

  • Crowley Patrick M., 2012. "How Do You Make A Time Series Sing Like a Choir? Extracting Embedded Frequencies from Economic and Financial Time Series using Empirical Mode Decomposition," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 16(5), pages 1-31, December.
    • Handle: RePEc:bpj:sndecm:v:16:y:2012:i:5:p:1-31:n:7
    DOI: 10.1515/1558-3708.2080
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    References listed on IDEAS

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    1. repec:zbw:bofrdp:2010_006 is not listed on IDEAS
    2. Crowley, Patrick M., 2010. "Long cycles in growth : explorations using new frequency domain techniques with US data," Research Discussion Papers 6/2010, Bank of Finland.
    3. Patrick M. Crowley, 2007. "A Guide To Wavelets For Economists," Journal of Economic Surveys, Wiley Blackwell, vol. 21(2), pages 207-267, April.
    4. Benati, Luca, 2009. "Long run evidence on money growth and inflation," Working Paper Series 1027, European Central Bank.
    5. repec:zbw:bofrdp:2011_013 is not listed on IDEAS
    6. Patrick M. Crowley & Andrew Hughes Hallett, 2014. "The Great Moderation Under the Microscope: Decomposition of Macroeconomic Cycles in US and UK Aggregate Demand," Dynamic Modeling and Econometrics in Economics and Finance, in: Marco Gallegati & Willi Semmler (ed.), Wavelet Applications in Economics and Finance, edition 127, pages 47-71, Springer.
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    Cited by:

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    2. Crowley, Patrick M. & Hallett, Andrew Hughes, 2018. "What causes business cycles to elongate, or recessions to intensify?," Journal of Macroeconomics, Elsevier, vol. 57(C), pages 338-349.
    3. Afanasyev, Dmitriy O. & Fedorova, Elena A. & Popov, Viktor U., 2015. "Fine structure of the price–demand relationship in the electricity market: Multi-scale correlation analysis," Energy Economics, Elsevier, vol. 51(C), pages 215-226.
    4. Jason Angelopoulos, 2017. "Creating and assessing composite indicators: Dynamic applications for the port industry and seaborne trade," Maritime Economics & Logistics, Palgrave Macmillan;International Association of Maritime Economists (IAME), vol. 19(1), pages 126-159, March.
    5. He, Kaijian & Liu, Youjin & Yu, Lean & Lai, Kin Keung, 2016. "Multiscale dependence analysis and portfolio risk modeling for precious metal markets," Resources Policy, Elsevier, vol. 50(C), pages 224-233.
    6. He, Kaijian & Yu, Lean & Tang, Ling, 2015. "Electricity price forecasting with a BED (Bivariate EMD Denoising) methodology," Energy, Elsevier, vol. 91(C), pages 601-609.

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