The power of cointegration tests versus data frequency and time spans.

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...

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Detalles Bibliográficos
Publicado en:Southern Economic Journal Vol. 67; no. 4; pp. 906 - 922
Autor principal: Zhou, Su
Formato: Artículo
Publicado: Southern Economic Association April 2001
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Acceso en línea:Ver este registro en EBSCOhost
Descripción
Sumario: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. Reprinted by permission of the publisher.