Empirical Processes Techniques for the Spectral Estimation of Fractional Processes

Philippe Soulier ()
Additional contact information
Philippe Soulier: Université d’Evry Val d’Essonne, Département de Mathématiques

A chapter in Empirical Process Techniques for Dependent Data, 2002, pp 299-321 from Springer

Abstract: Abstract Empirical processes techniques have only recently been applied to the spectral analysis of time series. Dahlhaus (1988) and Mikosch and Norvaiša (1997) proved a functional central limit theorem for the empirical spectral measure under weak dependence assumptions. This paper presents new applications of empirical processes techniques to the spectral analysis of time series. Different empirical contrast functions are considered, and applied to strongly dependent processes. Statistical applications are presented, including parametric estimation, goodness-of fit tests and adaptive estimation.

Keywords: Spectral Density; Central Limit Theorem; Gaussian Process; Spectral Estimation; Edgeworth Expansion (search for similar items in EconPapers)
Date: 2002
References: Add references at CitEc
Citations:

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

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:spr:sprchp:978-1-4612-0099-4_11

Ordering information: This item can be ordered from
http://www.springer.com/9781461200994

DOI: 10.1007/978-1-4612-0099-4_11

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().