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Estimating the long memory granger causality effect with a spectrum estimator

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  • Wen-Den Chen

    (Tung Hai University, Taiwan)

Abstract

This paper discusses the Granger causality test by a spectrum estimator which allows the transfer function to have long memory properties. In traditional methodology the relationship among variables is usually assumed to be short memory or contemporaneous. Hence, we have to make sure they are of the same integrated order, else there might be a spurious regression problem. In practice, not all the variables are fractionally co-integrated in the economic model. They may have the same random resources, but under a different integrated order. This paper focuses on how to capture the long memory Granger causality effect in the transfer function. This does not necessarily assume the variables are of the same fractional integrated order. Moreover, by the transfer function we construct an estimator to test the long memory effect with the Granger causality sense. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • 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.
    • Handle: RePEc:jof:jforec:v:25:y:2006:i:3:p:193-200
    DOI: 10.1002/for.981
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    References listed on IDEAS

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    1. Leamer, Edward E., 1985. "Vector autoregressions for causal inference?," Carnegie-Rochester Conference Series on Public Policy, Elsevier, vol. 22(1), pages 255-304, January.
    2. 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.
    3. Sims, Christopher A, 1972. "Money, Income, and Causality," American Economic Review, American Economic Association, vol. 62(4), pages 540-552, September.
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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.
    3. 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.
    4. Anna B. Zaremba & Gareth W. Peters, 2022. "Statistical Causality for Multivariate Nonlinear Time Series via Gaussian Process Models," Methodology and Computing in Applied Probability, Springer, vol. 24(4), pages 2587-2632, December.
    5. Elie Bouri & Imad Kachacha & Donald Lien & David Roubaud, 2017. "Short- and long-run causality across the implied volatility of crude oil and agricultural commodities," Economics Bulletin, AccessEcon, vol. 37(2).
    6. Morana, Claudio, 2009. "On the macroeconomic causes of exchange rate volatility," International Journal of Forecasting, Elsevier, vol. 25(2), pages 328-350.
    7. Li, Yushu, 2012. "Estimating Long Memory Causality Relationships by a Wavelet Method," Working Papers 2012:15, Lund University, Department of Economics.

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