IDEAS home Printed from https://ideas.repec.org/p/nan/wpaper/0608.html
   My bibliography  Save this paper

A New Approach to Detect Spurious Regressions using Wavelets

Author

Listed:
  • Chee Kian Leong

    (School of Humanities and Social Sciences, Nanyang Technological University, Singapore)

  • Weihong Huang

    (Division of Economics,School of Humanities and Social Sciences, Nanyang Technological University, Singapore)

Abstract

In this paper, we propose the use of wavelet covariance and correlation to detect spurious regression. Based on Monte Carlo simulation results and experiments with real exchange rate data, it is shown that the wavelet approach is able to detect spurious relationship in a bivariate time series more directly. Using the wavelet approach, it is sufficient to detect a spurious regression between bivariate time series if the wavelet covariance and correlation for the two series are significantly equal to zero. The wavelet approach does not rely on restrictive assumptions which are critical to the Durbin Watson test. Another distinct advantage of the graphical wavelet analysis of wavelet covariance and correlation to detect spurious regression is the simplicity and efficiency of the decision rule compared to the complicated Durbin-Watson decision rules.

Suggested Citation

  • Chee Kian Leong & Weihong Huang, 2006. "A New Approach to Detect Spurious Regressions using Wavelets," Economic Growth Centre Working Paper Series 0608, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
  • Handle: RePEc:nan:wpaper:0608
    as

    Download full text from publisher

    File URL: http://www3.ntu.edu.sg/hss2/egc/wp/2006/2006-08.pdf
    Download Restriction: no

    More about this item

    Keywords

    Wavelet analysis; spurious regression;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

    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:nan:wpaper:0608. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Magdalene Lim). General contact details of provider: http://edirc.repec.org/data/dentusg.html .

    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 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.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.