Is it a short-memory, long-memory, or permanently Granger-causation influence?
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.
Volume (Year): 27 (2008)
Issue (Month): 7 ()
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- Leamer, Edward E., 1985. "Vector autoregressions for causal inference?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 255-304, January.
- 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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- 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.
- Peter M. Robinson & Carlos Velasco, 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.
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