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Estimation of the Parameter of Long Memory Model

Author: HanZuoYing
Tutor: GaoWei
School: Northeast Normal University
Course: Probability Theory and Mathematical Statistics
Keywords: Long Memory Parameter estimation Semiparametric estimates Spectral density Maximum Likelihood APE LPE QMLE
CLC: O212
Type: Master's thesis
Year: 2007
Downloads: 92
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Abstract


The study found that sometimes the time series data still showing between distant observations related characteristics , such characteristics is generally known as a long memory . The Long Memory frequent in hydrology, climatology , economics and other fields . Long memory time series modeling is also subject to the attention of a growing number of statisticians . Long memory model estimation method is mainly divided into two categories: parametric methods and semi- parametric methods . Semi-parametric method does not require complete variance-covariance model, only interested in the fractional difference parameter d ; model established , you can use the parameter method . Parametric method is more efficient than the semi-parametric methods , but a large amount of computation , and is limited to the identification of model errors ; semiparametric method is less efficient , less computation error identification and robust type of model . In this article , we will have a long memory model three parameter estimates to be presented with five kinds of semi-parametric estimation method . We discuss the EMLE, AMLE, the quadratic approximation MaximumLikelihood contingent of three parameters estimated method APE, GPH, LPE, QMLE, wavelet OLS five semi- parameter estimation method and the various estimation methods excellent elaborate . Finally, in order to more clearly on the long memory parameter estimation method and the semi-parametric method and its excellent fractional white noise ARFIMA (0, d, 0) , for example, where d ∈ ( 0,1 / 2 ) , the the wavelet OLS estimates GPH estimated as to compare and discuss the advantages and disadvantages of each of the two estimators .

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CLC: > Mathematical sciences and chemical > Mathematics > Probability Theory and Mathematical Statistics > Mathematical Statistics
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