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Consistent estimation of the memory parameter for nonlinear time series

By Violetta Dalla, Liudas Giraitis and Javier Hidalgo

Abstract

For linear processes, semiparametric estimation of the memory parameter, based on the log-periodogram and local Whittle estimators, has been exhaustively examined and their properties well established. However, except for some specific cases, little is known about the estimation of the memory parameter for nonlinear processes. The purpose of this paper is to provide the general conditions under which the local Whittle estimator of the memory parameter of a stationary process is consistent and to examine its rate of convergence. We show that these conditions are satisfied for linear processes and a wide class of nonlinear models, among others, signal plus noise processes, nonlinear transforms of a Gaussian process ξt and exponential generalized autoregressive, conditionally heteroscedastic (EGARCH) models. Special cases where the estimator satisfies the central limit theorem are discussed. The finite-sample performance of the estimator is investigated in a small Monte Carlo study

Topics: QA Mathematics
Publisher: Wiley
Year: 2006
DOI identifier: 10.1111/j.1467-9892.2005.00464.x
OAI identifier: oai:eprints.lse.ac.uk:35779
Provided by: LSE Research Online
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