Alternative Methodology for Turning-Point Detection in Business Cycle : A Wavelet Approach
AbstractWe 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).
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Bibliographic InfoPaper provided by HAL in its series Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) with number halshs-00694420.
Date of creation: Apr 2012
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Nonparametric methods; STAR models; business cycles.;
Other versions of this item:
- Peter Martey Addo & Monica Billio & Dominique Guegan, 2012. "Alternative Methodology for Turning-Point Detection in Business Cycle: A Wavelet Approach," Documents de travail du Centre d'Economie de la Sorbonne 12023, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- 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
- 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
This paper has been announced in the following NEP Reports:
- NEP-ALL-2012-05-15 (All new papers)
- NEP-BEC-2012-05-15 (Business Economics)
- NEP-ETS-2012-05-15 (Econometric Time Series)
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gallegati, Marco, 2008. "Wavelet analysis of stock returns and aggregate economic activity," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3061-3074, February.
- 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.
- Peter Martey Addo & Monica Billio & Dominique Guegan, 2013.
"Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis,"
Documents de travail du Centre d'Economie de la Sorbonne
13024, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
- Peter Martey Addo & Monica Billio & Dominique Guegan, 2013. "Nonlinear Dynamics and Recurrence Plots for Detecting Financial Crisis," UniversitÃ© Paris1 PanthÃ©on-Sorbonne (Post-Print and Working Papers) halshs-00803450, HAL.
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