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A New Approach to Detect Spurious Regressions using Wavelets


  • 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)


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

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    More about this item


    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


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