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Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity |
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| Authors: | Liudas Giraitis Piotr Kokoszka Remigijus Leipus Gilles Teyssière |
| Institution: | (1) Department of Economics, London School of Economics, Houghton Street, London, WC2A 2AE, United Kingdom;(2) Department of Mathematical Sciences, University of Liverpool, Liverpool, L69 3BX, United Kingdom;(3) Department of Mathematics and Informatics, Vilnius University, Naugarduko 24, Vilnius, 2600, Lithuania;(4) European Commission, and GREQAM Joint Research Centre, ISIS, TP 361, Via E. Fermi, 1, I-21020 Ispra (VA), Italy |
| Abstract: | The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis et al. (1999b). We consider estimation methods based on the partial sums of the squared observations, which are similar in spirit to the classical R / S analysis, as well as spectral domain approximate maximum likelihood estimators. We review relevant theoretical results and present an empirical simulation study. |
| Keywords: | long memory ARCH models semiparametric estimation modified R / S KPSS and V / S statistics periodogram |
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