IDEAS home Printed from https://ideas.repec.org/p/kan/wpaper/202101.html
   My bibliography  Save this paper

Testing Heteroskedasticity for Predictive Regressions With Nonstationary Regressors

Author

Listed:
  • Shaoxin Hong

    (Center for Economic Research, Shandong University, Jinan 250100, Shandong, China)

  • Zhengyi Zhang

    (International School of Economics and Management, Capital University of Economics and Business, Beijing, Beijing 100070, China)

  • Zongwu Cai

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

Abstract

In this paper, we propose the Cramer-von Mises type test statistic for testing heteroskedasticity in predictive regression when regressors are nonstationary. A Monte Carlo simulation study is conducted to illustrate the finite sample performance of the proposed test statistic and a real empirical example is examined.

Suggested Citation

  • Shaoxin Hong & Zhengyi Zhang & Zongwu Cai, 2021. "Testing Heteroskedasticity for Predictive Regressions With Nonstationary Regressors," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS 202101, University of Kansas, Department of Economics, revised Jan 2021.
  • Handle: RePEc:kan:wpaper:202101
    as

    Download full text from publisher

    File URL: http://www2.ku.edu/~kuwpaper/2021Papers/202101.pdf
    Download Restriction: no
    ---><---

    More about this item

    Keywords

    Cramer-von Mises test statistic; Heteroskedasticity; Nonstationarity; Predictive regressions; Specification test.;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

    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:202101. 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.