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The Power of Cointegration Tests Versus Data Frequency and Time Spans

Listed author(s):
  • Su Zhou


    (Division of Economics and Finance, College of Business, University of Texas at San Antonio)

Registered author(s):

    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 non-zero 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.

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    Article provided by Southern Economic Association in its journal Southern Economic Journal.

    Volume (Year): 67 (2001)
    Issue (Month): 4 (April)
    Pages: 906-921

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    Handle: RePEc:sej:ancoec:v:67:4:y:2001:p:906-921
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