Advanced Search
MyIDEAS: Login

Gaussian Maximum Likelihood Estimation For ARMA Models. I. Time Series

Contents:

Author Info

  • Qiwei Yao
  • Peter J. Brockwell
Registered author(s):

    Abstract

    We provide a direct proof for consistency and asymptotic normality of Gaussian maximum likelihood estimators for causal and invertible autoregressive moving-average (ARMA) time series models, which were initially established by Hannan [Journal of Applied Probability (1973) vol. 10, pp. 130-145] via the asymptotic properties of a Whittle's estimator. This also paves the way to establish similar results for spatial processes presented in the follow-up article by Yao and Brockwell [Bernoulli (2006) in press]. Copyright 2006 The Authors Journal compilation 2006 Blackwell Publishing Ltd.

    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://www.blackwell-synergy.com/doi/abs/10.1111/j.1467-9892.2006.00492.x
    File Function: link to full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by Wiley Blackwell in its journal Journal of Time Series Analysis.

    Volume (Year): 27 (2006)
    Issue (Month): 6 (November)
    Pages: 857-875

    as in new window
    Handle: RePEc:bla:jtsera:v:27:y:2006:i:6:p:857-875

    Contact details of provider:
    Web page: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782

    Order Information:
    Web: http://www.blackwellpublishing.com/subs.asp?ref=0143-9782

    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. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2009. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 71(5), pages 683-713, October.
    2. Søren Johansen & Marco Riani & Anthony C. Atkinson, 2012. "The Selection of ARIMA Models with or without Regressors," CREATES Research Papers 2012-46, School of Economics and Management, University of Aarhus.
    3. Seongman Moon & Carlos Velasco, 2011. "Tests for m-dependence Based on Sample Splitting Methods," Working Papers 1108, Research Institute for Market Economy, Sogang University.
    4. Siem Jan Koopman & Marius Ooms & Irma Hindrayanto, 2006. "Periodic Unobserved Cycles in Seasonal Time Series with an Application to US Unemployment," Tinbergen Institute Discussion Papers 06-101/4, Tinbergen Institute.
    5. Hindrayanto, Irma & Koopman, Siem Jan & Ooms, Marius, 2010. "Exact maximum likelihood estimation for non-stationary periodic time series models," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2641-2654, November.

    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:bla:jtsera:v:27:y:2006:i:6:p:857-875. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Wiley-Blackwell Digital Licensing) or (Christopher F. Baum).

    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.