IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v47y2020i1p117-131.html
   My bibliography  Save this article

Likelihood-based quantile autoregressive distributed lag models and its applications

Author

Listed:
  • Yuzhu Tian
  • Liyong Wang
  • Manlai Tang
  • Yanchao Zang
  • Maozai Tian

Abstract

Time lag effect exists widely in the course of economic operation. Some economic variables are affected not only by various factors in the current period but also by various factors in the past and even their own past values. As a class of dynamical models, autoregressive distributed lag (ARDL) models are frequently used to conduct dynamic regression analysis. In this paper, we are interested in the quantile regression (QR) modeling of the ARDL model in a dynamic framework. By combining the working likelihood of asymmetric Laplace distribution (ALD) with the expectation–maximization (EM) algorithm into the considered ARDL model, the iterative weighted least square estimators (IWLSE) are derived. Some Monte Carlo simulations are implemented to evaluate the performance of the proposed estimation method. A dataset of the consumption of electricity by residential customers is analyzed to illustrate the application.

Suggested Citation

  • Yuzhu Tian & Liyong Wang & Manlai Tang & Yanchao Zang & Maozai Tian, 2020. "Likelihood-based quantile autoregressive distributed lag models and its applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(1), pages 117-131, January.
    • Handle: RePEc:taf:japsta:v:47:y:2020:i:1:p:117-131
    DOI: 10.1080/02664763.2019.1633285
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2019.1633285
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2019.1633285?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    More about this item

    Statistics

    Access and download statistics

    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:taf:japsta:v:47:y:2020:i:1:p:117-131. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.