Access

You are not currently logged in.

Access your personal account or get JSTOR access through your library or other institution:

login

Log in to your personal account or through your institution.

If You Use a Screen Reader

This content is available through Read Online (Free) program, which relies on page scans. Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.

Semiparametric Estimation by Model Selection for Locally Stationary Processes

Sébastien Van Bellegem and Rainer Dahlhaus
Journal of the Royal Statistical Society. Series B (Statistical Methodology)
Vol. 68, No. 5 (2006), pp. 721-746
Published by: Wiley for the Royal Statistical Society
Stable URL: http://www.jstor.org/stable/3879272
Page Count: 26
  • Read Online (Free)
  • Download ($29.00)
  • Subscribe ($19.50)
  • Cite this Item
Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Semiparametric Estimation by Model Selection for Locally Stationary Processes
Preview not available

Abstract

Over recent decades increasingly more attention has been paid to the problem of how to fit a parametric model of time series with time-varying parameters. A typical example is given by autoregressive models with time-varying parameters. We propose a procedure to fit such time-varying models to general non-stationary processes. The estimator is a maximum Whittle likelihood estimator on sieves. The results do not assume that the observed process belongs to a specific class of time-varying parametric models. We discuss in more detail the fitting of time-varying AR(p) processes for which we treat the problem of the selection of the order p, and we propose an iterative algorithm for the computation of the estimator. A comparison with model selection by Akaike's information criterion is provided through simulations.

Page Thumbnails

  • Thumbnail: Page 
[721]
    [721]
  • Thumbnail: Page 
722
    722
  • Thumbnail: Page 
723
    723
  • Thumbnail: Page 
724
    724
  • Thumbnail: Page 
725
    725
  • Thumbnail: Page 
726
    726
  • Thumbnail: Page 
727
    727
  • Thumbnail: Page 
728
    728
  • Thumbnail: Page 
729
    729
  • Thumbnail: Page 
730
    730
  • Thumbnail: Page 
731
    731
  • Thumbnail: Page 
732
    732
  • Thumbnail: Page 
733
    733
  • Thumbnail: Page 
734
    734
  • Thumbnail: Page 
735
    735
  • Thumbnail: Page 
736
    736
  • Thumbnail: Page 
737
    737
  • Thumbnail: Page 
738
    738
  • Thumbnail: Page 
739
    739
  • Thumbnail: Page 
740
    740
  • Thumbnail: Page 
741
    741
  • Thumbnail: Page 
742
    742
  • Thumbnail: Page 
743
    743
  • Thumbnail: Page 
744
    744
  • Thumbnail: Page 
745
    745
  • Thumbnail: Page 
746
    746