An adaptive optimal estimate of the tail index for MA(1) time series
AbstractFor samples of random variables with a regularly varying tail estimating the tail index has received much attention recently.For the proof of asymptotic normality of the tail index estimator second order regular variation is needed. In this paper we first supplement earlier results on convolution given by Geluk et al. (1997). Secondly we propose a simple estimator of the tail index for finite moving average time series. We also give a subsampling procedure in order to estimate the optimal sample fraction in the sense of minimal mean squared error.
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Bibliographic InfoPaper provided by Erasmus University Rotterdam, Econometric Institute in its series Econometric Institute Report with number EI 9910-/A.
Date of creation: 30 Mar 1999
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tail index; regular variation; asymptotic normality; minimal mean squared error; optimal sample fraction;
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