Nonlinear Dynamics and Wavelets for Business Cycle Analysis

Peter Martey Addo (), Monica Billio and Dominique Guégan
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Peter Martey Addo: Université Paris 1 - Panthéon-Sorbonne
Dominique Guégan: Université Paris I—Panthéon Sorbonne

A chapter in Wavelet Applications in Economics and Finance, 2014, pp 73-100 from Springer

Abstract: 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 Fourier and 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: Business Cycle; Discrete Wavelet Transform; Continuous Wavelet Transform; Morlet Wavelet; Original Time Series (search for similar items in EconPapers)
Date: 2014
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Related works:
Working Paper: Nonlinear Dynamics and Wavelets for Business Cycle Analysis (2014)
Working Paper: Nonlinear Dynamics and Wavelets for Business Cycle Analysis (2014)
Working Paper: Nonlinear Dynamics and Wavelets for Business Cycle Analysis (2014)
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DOI: 10.1007/978-3-319-07061-2_4

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