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Alternative Methodology for Turning-Point Detection in Business Cycle : A Wavelet Approach

Author

Listed:
  • Peter Martey Addo

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, University of Ca’ Foscari [Venice, Italy])

  • Monica Billio

    (University of Ca’ Foscari [Venice, Italy])

  • Dominique Guegan

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, PSE - Paris School of Economics - UP1 - Université Paris 1 Panthéon-Sorbonne - ENS-PSL - École normale supérieure - Paris - PSL - Université Paris Sciences et Lettres - EHESS - École des hautes études en sciences sociales - ENPC - École des Ponts ParisTech - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)

Abstract
We provide a signal modality analysis to characterize and detect nonlinearity schemes in the US Industrial Production Index time series. The analysis is achieved by using the recently proposed 'delay vector variance ' (DVV) method, which examines local predictability of a signal in the phase space to detect the presence of determinism and nonlinearity in a time series. Optimal embedding parameters used in the DVV analysis are obtained via a differential entropy based method using wavelet-based surrogates. A complex Morlet wavelet is employed to detect and characterize the US business cycle. A comprehensive analysis of the feasibility of this approach is provided. Our results coincide with the business cycles peaks and troughs dates published by the National Bureau of Economic Research (NBER).

Suggested Citation

  • Peter Martey Addo & Monica Billio & Dominique Guegan, 2012. "Alternative Methodology for Turning-Point Detection in Business Cycle : A Wavelet Approach," Post-Print halshs-00694420, HAL.
    • Handle: RePEc:hal:journl:halshs-00694420
    Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00694420
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    References listed on IDEAS

    as
    1. Luís Francisco Aguiar & Maria Joana Soares, 2010. "The Continuous Wavelet Transform: A Primer," NIPE Working Papers 23/2010, NIPE - Universidade do Minho.
    2. Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
    3. Gallegati Marco & Gallegati Mauro, 2007. "Wavelet Variance Analysis of Output in G-7 Countries," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 11(3), pages 1-25, September.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

    1. Addo, Peter Martey & Billio, Monica & Guégan, Dominique, 2013. "Nonlinear dynamics and recurrence plots for detecting financial crisis," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 416-435.
    2. Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Turning point chronology for the Euro-Zone: A Distance Plot Approach," Documents de travail du Centre d'Economie de la Sorbonne 13025, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    3. Micha³ Bernardelli, 2015. "The Economic Situation In Poland Through The Prism Of The Situation In The Enterprises On The Basis Of The Business Tendency Survey," GUT FME Conference Publications, in: Blazej Prusak (ed.),ENTERPRISES IN UNSTABLE ECONOMY, chapter 10, pages 109-136, Faculty of Management and Economics, Gdansk University of Technology.

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    More about this item

    Keywords

    business cycles; Nonparametric methods; STAR models; business cycles.; Méthodes non paramétriques; modèle STAR; cycles d'activité.;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C40 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - General
    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles

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