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

Peter Martey Addo, Monica Billio and Dominique Guegan ()
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Dominique Guegan: Centre d'Economie de la Sorbonne - Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan

Documents de travail du Centre d'Economie de la Sorbonne from Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne

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)

Keywords: Nonlinearity analysis; surrogates; Delay Vector Variance (DVV) method; wavelets; business cycle; embedding parameters (search for similar items in EconPapers)
JEL-codes: C14 C22 C40 E32 (search for similar items in EconPapers)
Date: 2012-04
New Economics Papers: this item is included in nep-bec, nep-ecm and nep-ets
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Citations: View citations in EconPapers (6)

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Related works:
Working Paper: Alternative Methodology for Turning-Point Detection in Business Cycle: A Wavelet Approach (2012)
Working Paper: Alternative Methodology for Turning-Point Detection in Business Cycle: A Wavelet Approach (2012)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:12023

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