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|
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- 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.
- Liudas Giraitis & Remigijus Leipus & Peter M Robinson & Donatas Surgailis, 2003.
"LARCH, Leverage and Long Memory,"
STICERD - Econometrics Paper Series
/2003/460, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
- Tanaka, Katsuto, 1999. "The Nonstationary Fractional Unit Root," Econometric Theory, Cambridge University Press, vol. 15(04), pages 549-582, August.
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