Moment bounds for non-linear functionals of the periodogram

Gilles Faÿ

Stochastic Processes and their Applications, 2010, vol. 120, issue 6, 983-1009

Abstract: In this paper, we prove the validity of the Edgeworth expansion of the Discrete Fourier transforms of some linear time series. This result is applied to approach moments of non-linear functionals of the periodogram. As an illustration, we give an expression of the mean square error of the slightly modified Geweke and Porter-Hudak estimator of the long memory parameter. We prove that this estimator is rate optimal, extending the result of Giraitis et al. (1997) [12] from Gaussian to linear processes.

Keywords: Linear; processes; Discrete; Fourier; transform; Periodogram; Long; range; dependence; Geweke; and; Porter-Hudak; (GPH); estimator; Log-periodogram; regression (search for similar items in EconPapers)
Date: 2010
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0304-4149(10)00046-3
Full text for ScienceDirect subscribers only

Related works:
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:eee:spapps:v:120:y:2010:i:6:p:983-1009

Ordering information: This journal article can be ordered from
http://http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

Access Statistics for this article

Stochastic Processes and their Applications is currently edited by T. Mikosch

More articles in Stochastic Processes and their Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().