Semiparametric Estimation of the Intensity of Long Memory in Conditional Heteroskedasticity.

The paper is concerned with the estimation of the long memory parameter in a conditionally heteroskedastic model proposed by Giraitis, Robinson and Surgailis (1999). We consider methods based on the partial sums of the squared observations which are similar in spirit to the classicla R/S analysis as well as spectral domain approximate maximum likelihood estimators. The finite sample performance of the estimators is examined by means of a Monte Carlo study.

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Year of publication:	1999
Authors:	Giraitis, L. ; Kokoszka, P. ; Leipus, R. ; Teyssiere, G.
Institutions:	Groupement de Recherche en Économie Quantitative d'Aix-Marseille (GREQAM), Aix-Marseille School of Economics (AMSE)
Subject:	ESTIMATOR \| ECONOMETRICS \| HETEROSKEDASTICITY

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Series:	G.R.E.Q.A.M..
Type of publication:	Book / Working Paper
Notes:	13 pages
Classification:	C13 - Estimation ; C14 - Semiparametric and Nonparametric Methods ; C22 - Time-Series Models
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10005779615