Semiparametric estimation of the intensity of long memory in conditional heteroskedasticity - Liudas Giraitis; Piotr Kokoszka; Remigijus Leipus; Gilles Teyssière (Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes, 1999)
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 classical 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.
urn:nbn:de:kobv:11-10046689
Keywords: 330; long memory; ARCH models; semiparametric estimation; modified R/S; KPSS and V/S statistics; periodogram