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Is it a short-memory, long-memory, or permanently Granger-causation influence?

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  • Wen-Den Chen

    (Tung Hai University, PO Box 5-0885, No. 181, Section 3, Taichung-Kan Road, Taichung, Taiwan 407)

Abstract

Exploring the Granger-causation relationship is an important and interesting topic in the field of econometrics. In the traditional model we usually apply the short-memory style to exhibit the relationship, but in practice there could be other different influence patterns. Besides the short-memory relationship, Chen (2006) demonstrates a long-memory relationship, in which a useful approach is provided for estimation where the time series are not necessarily fractionally co-integrated. In that paper two different relationships (short-memory and long-memory relationship) are regarded whereby the influence flow is decayed by geometric, or cutting off, or harmonic sequences. However, it limits the model to the stationary relationship. This paper extends the influence flow to a non-stationary relationship where the limitation is on −0.5 ≤ d ≤ 1.0 and it can be used to detect whether the influence decays off (−0.5 ≤ d < 0.5) or is permanent (0.5 ≤ d ≤ 1.0). Copyright © 2008 John Wiley & Sons, Ltd.

Suggested Citation

  • Wen-Den Chen, 2008. "Is it a short-memory, long-memory, or permanently Granger-causation influence?," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(7), pages 607-620.
    • Handle: RePEc:jof:jforec:v:27:y:2008:i:7:p:607-620
    DOI: 10.1002/for.1075
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    References listed on IDEAS

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    2. White, Halbert, 1980. "A Heteroskedasticity-Consistent Covariance Matrix Estimator and a Direct Test for Heteroskedasticity," Econometrica, Econometric Society, vol. 48(4), pages 817-838, May.
    3. Wen-Den Chen, 2006. "Estimating the long memory granger causality effect with a spectrum estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 193-200.
    4. Peter M Robinson & Carlos Velasco, 2000. "Whittle Pseudo-Maximum Likelihood Estimation for Nonstationary Time Series - (Now published in Journal of the American Statistical Association, 95, (2000), pp.1229-1243.)," STICERD - Econometrics Paper Series 391, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    5. Robinson, Peter M. & Velasco, Carlos, 2000. "Whittle pseudo-maximum likelihood estimation for nonstationary time series," LSE Research Online Documents on Economics 2273, London School of Economics and Political Science, LSE Library.
    6. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
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    Cited by:

    1. Kunal Saha & Vinodh Madhavan & Chandrashekhar G. R. & David McMillan, 2020. "Pitfalls in long memory research," Cogent Economics & Finance, Taylor & Francis Journals, vol. 8(1), pages 1733280-173, January.
    2. Yushu Li, 2015. "Estimate Long Memory Causality Relationship by Wavelet Method," Computational Economics, Springer;Society for Computational Economics, vol. 45(4), pages 531-544, April.

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