IDEAS home Printed from https://ideas.repec.org/a/eee/spapps/v46y1993i1p91-113.html
   My bibliography  Save this article

Asymptotic optimal inference for a class of nonlinear time series models

Author

Listed:
  • Young Hwang, Sun
  • Basawa, I. V.

Abstract

The local asymptotic normality (LAN) of the log-likelihood ratio for a class of Markovian nonlinear time series models is established using the approach of quadratic mean differentiability. The error process in the models considered is not necessarily Gaussian. As a consequence of the LAN property, asymptotically optimal estimators of the model parameters are derived. Also, asymptotically efficient tests for linearity are constructed. Several examples are discussed as special cases.

Suggested Citation

  • Young Hwang, Sun & Basawa, I. V., 1993. "Asymptotic optimal inference for a class of nonlinear time series models," Stochastic Processes and their Applications, Elsevier, vol. 46(1), pages 91-113, May.
    • Handle: RePEc:eee:spapps:v:46:y:1993:i:1:p:91-113
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0304-4149(93)90086-J
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    Citations

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


    Cited by:

    1. Kara-Terki, Nesrine & Mourid, Tahar, 2016. "On local asymptotic normality for functional autoregressive processes," Journal of Multivariate Analysis, Elsevier, vol. 148(C), pages 120-140.
    2. Hwang, Sun Y. & Basawa, I. V., 2001. "Nonlinear time series contiguous to AR(1) processes and a related efficient test for linearity," Statistics & Probability Letters, Elsevier, vol. 52(4), pages 381-390, May.
    3. Hwang, S. Y. & Basawa, I. V., 1997. "The local asymptotic normality of a class of generalized random coefficient autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 34(2), pages 165-170, June.
    4. Hwang, S. Y. & Woo, Mi-Ja, 2001. "Threshold ARCH(1) processes: asymptotic inference," Statistics & Probability Letters, Elsevier, vol. 53(1), pages 11-20, May.
    5. Hwang, S. Y. & Basawa, I. V., 1999. "Inference for a binary lattice Markov process," Statistics & Probability Letters, Elsevier, vol. 43(1), pages 75-85, May.
    6. Hwang, S.Y. & Basawa, I.V., 2011. "Godambe estimating functions and asymptotic optimal inference," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1121-1127, August.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:spapps:v:46:y:1993:i:1:p:91-113. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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 RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/505572/description#description .

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

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.