A New Approach to Detect Spurious Regressions using Wavelets

Chee Kian Leong and Weihong Huang ()

No 608, Economic Growth Centre Working Paper Series from Nanyang Technological University, School of Social Sciences, Economic Growth Centre

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.

Keywords: Wavelet analysis; spurious regression (search for similar items in EconPapers)
JEL-codes: C19 C65 (search for similar items in EconPapers)
Pages: 23 pages
Date: 2006-08
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Persistent link: https://EconPapers.repec.org/RePEc:nan:wpaper:0608

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