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An alternative approach for testing for linear association for two independent stationary AR(1) processes

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  • Christos Agiakloglou
  • Apostolos Tsimpanos

Abstract

Spurious correlations occur when two independent time series are found to be correlated according to the typical statistical procedure for testing the null hypothesis of zero correlation in the population. Using a Monte Carlo analysis, this study examines the spurious correlation phenomenon for two independent stationary AR(1) processes and it finds that if an alternative testing procedure is applied, spurious behaviour is eliminated using the variance of the sample correlation coefficient of these two series, suggested by Bartlett (1935).

Suggested Citation

  • Christos Agiakloglou & Apostolos Tsimpanos, 2012. "An alternative approach for testing for linear association for two independent stationary AR(1) processes," Applied Economics, Taylor & Francis Journals, vol. 44(36), pages 4799-4803, December.
    • Handle: RePEc:taf:applec:v:44:y:2012:i:36:p:4799-4803
    DOI: 10.1080/00036846.2011.595695
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    References listed on IDEAS

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    1. Clive Granger & Namwon Hyung & Yongil Jeon, 2001. "Spurious regressions with stationary series," Applied Economics, Taylor & Francis Journals, vol. 33(7), pages 899-904.
    2. Phillips, P.C.B., 1986. "Understanding spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 33(3), pages 311-340, December.
    3. Banerjee, Anindya & Dolado, Juan J. & Galbraith, John W. & Hendry, David, 1993. "Co-integration, Error Correction, and the Econometric Analysis of Non-Stationary Data," OUP Catalogue, Oxford University Press, number 9780198288107.
    4. Granger, C. W. J. & Newbold, P., 1974. "Spurious regressions in econometrics," Journal of Econometrics, Elsevier, vol. 2(2), pages 111-120, July.
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