IDEAS home Printed from https://ideas.repec.org/a/oup/rfinst/v33y2020i9p4403-4443..html
   My bibliography  Save this article

Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

Author

Listed:
  • Ke-Li Xu
  • Lauren Cohen

Abstract

Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium.Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

Suggested Citation

  • Ke-Li Xu & Lauren Cohen, 2020. "Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based," The Review of Financial Studies, Society for Financial Studies, vol. 33(9), pages 4403-4443.
    • Handle: RePEc:oup:rfinst:v:33:y:2020:i:9:p:4403-4443.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/rfs/hhz135
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Christis Katsouris, 2023. "Predictability Tests Robust against Parameter Instability," Papers 2307.15151, arXiv.org.
    2. Deshui Yu & Yayi Yan, 2023. "Joint dynamics of stock returns and cash flows: A time‐varying present‐value framework," Financial Management, Financial Management Association International, vol. 52(3), pages 513-541, September.
    3. Erik Hjalmarsson & Tamas Kiss, 2022. "Long‐run predictability tests are even worse than you thought," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(7), pages 1334-1355, November.
    4. Yu, Deshui & Huang, Difang, 2023. "Cross-sectional uncertainty and expected stock returns," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 321-340.
    5. Xu, Ke-Li, 2021. "On the serial correlation in multi-horizon predictive quantile regression," Economics Letters, Elsevier, vol. 200(C).
    6. Demetrescu, Matei & Rodrigues, Paulo M.M. & Taylor, A.M. Robert, 2023. "Transformed regression-based long-horizon predictability tests," Journal of Econometrics, Elsevier, vol. 237(2).
    7. Yu, Deshui & Huang, Difang & Chen, Li & Li, Luyang, 2023. "Forecasting dividend growth: The role of adjusted earnings yield," Economic Modelling, Elsevier, vol. 120(C).
    8. Jeong, Minsoo, 2022. "Modelling persistent stationary processes in continuous time," Economic Modelling, Elsevier, vol. 109(C).
    9. Yu, Deshui & Chen, Li & Li, Luyang, 2023. "Time-varying predictability of the long horizon equity premium based on semiparametric regressions," Economics Letters, Elsevier, vol. 224(C).

    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:oup:rfinst:v:33:y:2020:i:9:p:4403-4443.. 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: Oxford University Press (email available below). General contact details of provider: https://edirc.repec.org/data/sfsssea.html .

    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.