Consistent estimation of the memory parameter for nonlinear time series

Violetta Dalla (), Liudas Giraitis () and J Hidalgo

Discussion Papers from Department of Economics, University of York

Abstract: For linear processes, semiparametric estimation of the memory parameter, based on the log-periodogram and local Whittle estimators, has been exhaustively examined. 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 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, and 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 Monte-Carlo study.

References: Add references at CitEc
Citations: View citations in EconPapers (29)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
Journal Article: Consistent estimation of the memory parameter for nonlinear time series (2006)
Working Paper: Consistent estimation of the memory parameterfor nonlinear time series (2006)
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:yor:yorken:05/17

Access Statistics for this paper

More papers in Discussion Papers from Department of Economics, University of York Department of Economics and Related Studies, University of York, York, YO10 5DD, United Kingdom. Contact information at EDIRC.
Bibliographic data for series maintained by Paul Hodgson ().