Locally stationary long memory estimation Roueff, Fran├žois; Von Sachs, Rainer

User activity

Share to:
View the summary of this work
Authors
Roueff, Fran├žois ; Von Sachs, Rainer
Appears In
Stochastic Processes and their Applications
Subjects
62m10 (primary), 62m15, 62g05, 60g15 (secondary); mathematics - statistics; semi-parametric estimation
Audience
Academic
Summary
To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.spa.2010.12.004 Byline: Francois Roueff, Rainer von Sachs Abstract: There exists a wide literature on parametrically or semi-parametrically modelling strongly dependent time series using a long-memory parameter d, including more recent work on wavelet estimation. As a generalization of these latter approaches, in this work we allow the long-memory parameter d to be varying over time. We adopt a semi-parametric approach in order to avoid fitting a time-varying parametric model, such as tvARFIMA, to the observed data. We study the asymptotic behavior of a local log-regression wavelet estimator of the time-dependent d. Both simulations and a real data example complete our work on providing a fairly general approach. Article History: Received 7 April 2010; Revised 8 October 2010; Accepted 7 December 2010
Bookmark
http://trove.nla.gov.au/work/191180
Work ID
191180

User activity


e.g. test cricket, Perth (WA), "Parkes, Henry"

Separate different tags with a comma. To include a comma in your tag, surround the tag with double quotes.

Be the first to add a tag for this work

Be the first to add this to a list

Comments and reviews

What are comments? Add a comment

No user comments or reviews for this work

Add a comment


Show comments and reviews from Amazon users