IDEAS home Printed from https://ideas.repec.org/p/kan/wpaper/202206.html
   My bibliography  Save this paper

A New Test on Asset Return Predictability with Structural Breaks

Author

Listed:
  • Zongwu Cai

    (Department of Economics, The University of Kansas, Lawrence, KS 66045, USA)

  • Seong Yeon Chang

    (Department of Economics, Soongsil University, Seoul 06978, Korea)

Abstract

This paper considers predictive regressions in which a structural break is allowed on an unknown date. We establish novel testing procedures for asset return predictability using empirical likelihood methods based on weighted-score equations. The theoretical results are useful in practice because our unified framework does not require distinguishing whether the predictor variables are stationary or nonstationary. Simulations show that the empirical likelihood-based tests perform well in terms of size and power in finite samples. As an empirical analysis, we test asset returns predictability using various predictor variables.

Suggested Citation

  • Zongwu Cai & Seong Yeon Chang, 2022. "A New Test on Asset Return Predictability with Structural Breaks," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202206, University of Kansas, Department of Economics, revised Feb 2202.
    • Handle: RePEc:kan:wpaper:202206
    as

    Download full text from publisher

    File URL: http://www2.ku.edu/~kuwpaper/2022Papers/202206.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Autoregressive process; Empirical likelihood; Structural break; Unit root; Weighted estimation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:kan:wpaper:202206. 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: Professor Zongwu Cai (email available below). General contact details of provider: https://edirc.repec.org/data/deuksus.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.