IDEAS home Printed from https://ideas.repec.org/a/now/jnlcfr/104.00000105.html
   My bibliography  Save this article

Dividend Growth Does Not Help Predict Returns Compared To Likelihood-Based Tests: An Anatomy of the Dog

Author

Listed:
  • Erik Hjalmarsson
  • Tamás Kiss

Abstract

The dividend-growth based test of return predictability, proposed by Cochrane (2008), is similar to a likelihood-based test of the standard return-predictability model, treating the autoregressive (AR) parameter of the dividend-price ratio as known. In comparison to standard OLS-based inference, both tests can achieve power gains by using restrictions or prior information on the value of the AR parameter. When compared to the likelihood-based test, there are no power advantages for the dividend-growth based test. In common implementations, with the AR parameter set equal to the corresponding OLS estimate, Cochrane’s test suffers from severe size distortions.

Suggested Citation

  • Erik Hjalmarsson & Tamás Kiss, 2021. "Dividend Growth Does Not Help Predict Returns Compared To Likelihood-Based Tests: An Anatomy of the Dog," Critical Finance Review, now publishers, vol. 10(3), pages 445-464, August.
    • Handle: RePEc:now:jnlcfr:104.00000105
    DOI: 10.1561/104.00000105
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1561/104.00000105
    Download Restriction: no
    File URL: https://libkey.io/10.1561/104.00000105?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
    ---><---

    Citations

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


    Cited by:

    1. Kiss, Tamás & Mazur, Stepan & Nguyen, Hoang, 2022. "Predicting returns and dividend growth — The role of non-Gaussian innovations," Finance Research Letters, Elsevier, vol. 46(PA).

    More about this item

    Keywords

    Predictive regressions; Present-value relationship; Stock-return predictability;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G1 - Financial Economics - - General Financial Markets

    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:now:jnlcfr:104.00000105. 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: Lucy Wiseman (email available below). General contact details of provider: http://www.nowpublishers.com/ .

    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.