• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Mathematics
  • Department of Mathematics
  • View Item
  •   Home
  • Faculty of Mathematics and Natural Sciences
  • Department of Mathematics
  • Department of Mathematics
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Nonlinear spectral analysis via the local Gaussian correlation

Jordanger, Lars Arne; Tjøstheim, Dag
Journal article
Submitted version
Thumbnail
View/Open
Preprint (9.225Mb)
URI
https://hdl.handle.net/1956/16948
Date
2017
Metadata
Show full item record
Collections
  • Department of Mathematics [793]
Abstract
The spectral distribution \(f(\omega)\) of a stationary time series \(\{Y_t\}_{t\in\mathbb{Z}}\) can be used to investigate whether or not periodic structures are present in \(\{Y_t\}_{t\in\mathbb{Z}}\), but \(f(\omega)\) has some limitations due to its dependence on the autocovariances \(\gamma(h)\), For example, \(f(\omega)\) can not distinguish white i.i.d. noise from GARCH-type models (whose terms are dependent, but uncorrelated), which implies that \(f(\omega)\) can be an inadequate tool when \(\{Y_t\}_{t\in\mathbb{Z}}\) contains asymmetries and nonlinear dependencies. Asymmetries between the upper and lower tails of a time series can be investigated by means of the local Gaussian autocorrelations \(\gamma_{v}(h)\) introduced in Tjøstheim and Hufthammer (2013), and these local measures of dependence can be used to construct the local Gaussian spectral density \(f_{v}(\omega)\) that is presented in this paper. A key feature of \(f_{v}(\omega)\) is that it coincides with \(f(\omega)\) for Gaussian time series, which implies that \(f_{v}(\omega)\) can be used to detect non-Gaussian traits in the time series under investigation. In particular, if \(f(\omega)\) is flat, then peaks and troughs of \(f_{v}(\omega)\) can indicate nonlinear traits, which potentially might discover local periodic phenomena that goes undetected in an ordinary spectral analysis.
Copyright
Copyright the Author. All rights reserved

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit