Estimation of the long memory parameter in non stationary models: A Simulation Study
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.
|Date of creation:||23 May 2011|
|Date of revision:|
|Note:||View the original document on HAL open archive server: http://halshs.archives-ouvertes.fr/halshs-00595057/en/|
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References listed on IDEAS
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.:
- Liudas Giraitis, 2004.
"LARCH, Leverage, and Long Memory,"
Journal of Financial Econometrics,
Society for Financial Econometrics, vol. 2(2), pages 177-210.
- Faÿ, Gilles & Moulines, Eric & Roueff, François & Taqqu, Murad S., 2009. "Estimators of long-memory: Fourier versus wavelets," Journal of Econometrics, Elsevier, vol. 151(2), pages 159-177, August.
- Tanaka, Katsuto, 1999. "The Nonstationary Fractional Unit Root," Econometric Theory, Cambridge University Press, vol. 15(04), pages 549-582, August.
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