Estimation of the long memory parameter in non stationary models: A Simulation Study

Mohamed Boutahar and Rabeh Khalfaoui2 ()
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Rabeh Khalfaoui2: GREQAM - Groupement de Recherche en Économie Quantitative d'Aix-Marseille - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Authors registered in the RePEc Author Service: Rabeh Khalfaoui ()

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Abstract: In this paper we perform a Monte Carlo study based on three well-known semiparametric estimates for the long memory fractional parameter. We study the efficiency of Geweke and Porter-Hudak, Gaussian semiparametric and wavelet Ordinary Least-Square estimates in both stationary and non stationary models. We consider an adequate data tapers to compute non stationary estimates. The Monte Carlo simulation study is based on different sample size. We show that for d belonging to [1/4,1.25) the Haar estimate performs the others with respect to the mean squared error. The estimation methods are applied to energy data set for an empirical illustration.

Keywords: wavelets; long memory; tapering; non-stationarity; volatility.; volatility (search for similar items in EconPapers)
Date: 2011-05-23
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
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