Advanced Search
MyIDEAS: Login

Nonparametric regression under dependent errors with infinite variance

Contents:

Author Info

  • Peng, Liang
  • Yao, Qiwei
Registered author(s):

    Abstract

    We consider local least absolute deviation (LLAD) estimation for trend functions of time series with heavy tails which are characterised via a symmetric stable law distribution. The setting includes both causal stable ARMA model and fractional stable ARIMA model as special cases. The asymptotic limit of the estimator is established under the assumption that the process has either short or long memory autocorrelation. For a short memory process, the estimator admits the same convergence rate as if the process has the finite variance. The optimal rate of convergence n−2/5 is obtainable by using appropriate bandwidths. This is distinctly different from local least squares estimation, of which the convergence is slowed down due to the existence of heavy tails. On the other hand, the rate of convergence of the LLAD estimator for a long memory process is always slower than n−2/5 and the limit is no longer normal.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://eprints.lse.ac.uk/22874/1/Nonparametric_regression_under_infinite_variance_dependent_errors%28LSERO%29.pdf
    Download Restriction: no

    Bibliographic Info

    Paper provided by London School of Economics and Political Science in its series Open Access publications from London School of Economics and Political Science with number http://eprints.lse.ac.uk/22874/.

    as in new window
    Length:
    Date of creation: Mar 2004
    Date of revision:
    Publication status: Published in Annals of the Institute of Statistical Mathematics (2004-03) v.56, p.73-86
    Handle: RePEc:ner:lselon:http://eprints.lse.ac.uk/22874/

    Contact details of provider:
    Web page: http://www.lse.ac.uk

    Related research

    Keywords:

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Jia Chen & Li-Xin Zhang, 2010. "Local linear M-estimation for spatial processes in fixed-design models," Metrika, Springer, vol. 71(3), pages 319-340, May.
    2. Ngai Chan & Rongmao Zhang, 2009. "M-estimation in nonparametric regression under strong dependence and infinite variance," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(2), pages 391-411, June.
    3. Toshio Honda, 2010. "Nonparametric estimation of conditional medians for linear and related processes," Annals of the Institute of Statistical Mathematics, Springer, vol. 62(6), pages 995-1021, December.
    4. Toshio Honda, 2009. "Nonparametric density estimation for linear processes with infinite variance," Annals of the Institute of Statistical Mathematics, Springer, vol. 61(2), pages 413-439, June.
    5. Qi Li & Jeffrey Scott Racine, 2006. "Nonparametric Econometrics: Theory and Practice," Economics Books, Princeton University Press, edition 1, volume 1, number 8355.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:ner:lselon:http://eprints.lse.ac.uk/22874/

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (LSE Research Online).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.