Wavelet method for locally stationary seasonal long memory processes

Dominique Guegan () and Zhiping Lu ()
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Dominique Guegan: Centre d'Economie de la Sorbonne - Paris School of Economics, https://cv.archives-ouvertes.fr/dominique-guegan
Zhiping Lu: Centre d'Economie de la Sorbonne et East China Normal University, https://centredeconomiesorbonne.cnrs.fr

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

Abstract: Long memory processes have been extensively studied over the past decades. When dealing with the financial and economic data, seasonality and time-varying long-range dependence can often be observed and thus some kind of non-stationarity can exist inside financial data sets. To take into account this kind of phenomena, we propose a new class of stochastic process: the locally stationary k-factor Gegenbauer process. We describe a procedure of estimating consistently the time-varying parameters by applying the discrete wavelet packet transform (DWPT). The robustness of the algorithm is investigated through simulation study. An application based on the error correction term of fractional cointegration analysis of the Nikkei Stock Average 225 index is proposed

Keywords: Discrete wavelet packet transform; Gegenbauer process; Nikkei Stock Average 225 index; non-stationarity; ordinary least square estimation (search for similar items in EconPapers)
JEL-codes: C13 C14 C15 C22 C63 G15 (search for similar items in EconPapers)
Pages: 25 pages
Date: 2009-03
New Economics Papers: this item is included in nep-ecm and nep-ets
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Citations: View citations in EconPapers (2)

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ftp://mse.univ-paris1.fr/pub/mse/CES2009/09015.pdf (application/pdf)

Related works:
Working Paper: Wavelet Method for Locally Stationary Seasonal Long Memory Processes (2009)
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Persistent link: https://EconPapers.repec.org/RePEc:mse:cesdoc:09015

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