Estimation of the long-memory parameter, based on a multivariate central limit theorem
Estimation of the long-memory parameter, based on a multivariate central limit theorem
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1994
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Terrin, Norma
Herausgeber:innen
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Journal of Time Series Analysis ; 15 (1994), 3. - S. 269-278. - ISSN 0143-9782
Zusammenfassung
Long memory is known to occur in many fields of statistical application. Stationary processes whose correlations decay asymptotically like ‖k‖2H-2, where k is the lag and Hε (0.5, 1), provide useful parsimonious models with long memory. The parameter H characterizes the long-memory features of the data. For long time series, maximum likelihood estimation of H can be costly in terms of CPU time. In this paper, we show that, for disjoint stretches of the data, estimates of H and other parameters that characterize the dependence structure are asymptotically independent. Averaging these estimates leads to a fast and efficient approximate maximum likelihood method.
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510 Mathematik
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Long-range dependence,fractional Gaussian noise,fractional ARIMA,self-similar,quadratic forms,maximum likelihood estimator
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BERAN, Jan, Norma TERRIN, 1994. Estimation of the long-memory parameter, based on a multivariate central limit theorem. In: Journal of Time Series Analysis. 15(3), pp. 269-278. ISSN 0143-9782. Available under: doi: 10.1111/j.1467-9892.1994.tb00192.xBibTex
@article{Beran1994Estim-18821, year={1994}, doi={10.1111/j.1467-9892.1994.tb00192.x}, title={Estimation of the long-memory parameter, based on a multivariate central limit theorem}, number={3}, volume={15}, issn={0143-9782}, journal={Journal of Time Series Analysis}, pages={269--278}, author={Beran, Jan and Terrin, Norma} }
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