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The Power of Cointegration Tests Versus Data Frequency and Time Spans

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  • Su Zhou

Abstract

Using Monte Carlo methods, this study illustrates the potential benefits of using high frequency data series to conduct cointegration analysis. The study also provides an account of why the results are different from those reported by Hakkio and Rush (1991). The simulation results show that when the studies are restricted by relatively short time spans of 30 to 50 years, increasing data frequency may yield considerable power gain and less size distortion, especially when the cointegrating residual is not nearly nonstationary, and/or when the models with nonzero lag orders are required for testing cointegration. The study may help clarify some misconceptions and misinterpretations surrounding the role of data frequency and sample size in cointegration analysis.

Suggested Citation

  • Su Zhou, 2001. "The Power of Cointegration Tests Versus Data Frequency and Time Spans," Southern Economic Journal, John Wiley & Sons, vol. 67(4), pages 906-921, April.
    • Handle: RePEc:wly:soecon:v:67:y:2001:i:4:p:906-921
    DOI: 10.1002/j.2325-8012.2001.tb00380.x
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    References listed on IDEAS

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    1. Hakkio, Craig S. & Rush, Mark, 1991. "Cointegration: how short is the long run?," Journal of International Money and Finance, Elsevier, vol. 10(4), pages 571-581, December.
    2. Shiller, Robert J. & Perron, Pierre, 1985. "Testing the random walk hypothesis : Power versus frequency of observation," Economics Letters, Elsevier, vol. 18(4), pages 381-386.
    3. Johansen, Soren & Juselius, Katarina, 1990. "Maximum Likelihood Estimation and Inference on Cointegration--With Applications to the Demand for Money," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 52(2), pages 169-210, May.
    4. Cheung, Yin-Wong & Lai, Kon S, 1995. "Lag Order and Critical Values of the Augmented Dickey-Fuller Test," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(3), pages 277-280, July.
    5. Masih, Abul M. M. & Masih, Rumi, 1996. "Empirical tests to discern the dynamic causal chain in macroeconomic activity: new evidence from Thailand and Malaysia based on a multivariate cointegration/vector error-correction modeling approach," Journal of Policy Modeling, Elsevier, vol. 18(5), pages 531-560, October.
    6. Cheung, Yin-Wong & Lai, Kon S, 1993. "Finite-Sample Sizes of Johansen's Likelihood Ration Tests for Conintegration," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 55(3), pages 313-328, August.
    7. Horvath, Michael T.K. & Watson, Mark W., 1995. "Testing for Cointegration When Some of the Cointegrating Vectors are Prespecified," Econometric Theory, Cambridge University Press, vol. 11(5), pages 984-1014, October.
    8. Johansen, Soren, 1988. "Statistical analysis of cointegration vectors," Journal of Economic Dynamics and Control, Elsevier, vol. 12(2-3), pages 231-254.
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