Giraitis, Liudas Kokoszka, Piotr Leipus, Remigijus Teyssière, Gilles
Year of Publication:
Discussion Papers, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes 1999,81
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.
long memory ARCH models semiparametric estimation modified R/S KPSS and V/S statistics periodogram