Estimation of the long memory parameter in non stationary models: A Simulation Study
AbstractIn 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.
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Date of creation: 23 May 2011
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wavelets; long memory; tapering; non-stationarity; volatility.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-06-04 (All new papers)
- NEP-ECM-2011-06-04 (Econometrics)
- NEP-ETS-2011-06-04 (Econometric Time Series)
- NEP-ORE-2011-06-04 (Operations Research)
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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, 2004.
"LARCH, Leverage, and Long Memory,"
Journal of Financial Econometrics,
Society for Financial Econometrics, vol. 2(2), pages 177-210.
- Tanaka, Katsuto, 1999. "The Nonstationary Fractional Unit Root," Econometric Theory, Cambridge University Press, vol. 15(04), pages 549-582, August.
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