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A Study on "Spurious Long Memory in Nonlinear Time Series Models"

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  • Kuswanto, Heri
  • Sibbertsen, Philipp

Abstract

This paper discusses the existence of spurious long memory in common nonlinear time series models, namely Markov switching and threshold models. We describe the asymptotic behavior of the process in terms of autocovariance and autocorrelation function and support the theoretical evidences by providing Monte Carlo simulation. The existence of long memory in these nonlinear processes is induced by the nature of the process in certain conditions. In addition, GPH estimator itself introduces bias.

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File URL: http://diskussionspapiere.wiwi.uni-hannover.de/pdf_bib/dp-410.pdf
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Bibliographic Info

Paper provided by Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät in its series Hannover Economic Papers (HEP) with number dp-410.

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Length: 26 pages
Date of creation: Nov 2008
Date of revision:
Handle: RePEc:han:dpaper:dp-410

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Keywords: long memory; nonlinear time series; regime switching;

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References

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  1. Diebold, Francis X. & Inoue, Atsushi, 2001. "Long memory and regime switching," Journal of Econometrics, Elsevier, vol. 105(1), pages 131-159, November.
  2. Banerjee, Anindya & Urga, Giovanni, 2005. "Modelling structural breaks, long memory and stock market volatility: an overview," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 1-34.
  3. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-84, March.
  4. Granger, Clive W. J. & Ding, Zhuanxin, 1996. "Varieties of long memory models," Journal of Econometrics, Elsevier, vol. 73(1), pages 61-77, July.
  5. van Dijk, Dick & Teräsvirta, Timo & Franses, Philip Hans, 2000. "Smooth Transition Autoregressive Models - A Survey of Recent Developments," Working Paper Series in Economics and Finance 380, Stockholm School of Economics, revised 17 Jan 2001.
  6. Davidson, James & Sibbertsen, Philipp, 2005. "Generating schemes for long memory processes: regimes, aggregation and linearity," Journal of Econometrics, Elsevier, vol. 128(2), pages 253-282, October.
  7. Rohit Deo & Meng-Chen Hsieh & Clifford M. Hurvich & Philippe Soulier, 2007. "Long Memory in Nonlinear Processes," Papers 0706.1836, arXiv.org.
  8. Kuswanto, Heri & Sibbertsen, Philipp, 2007. "Can we distinguish between common nonlinear time series models and long memory?," Hannover Economic Papers (HEP) dp-380, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
  9. Breidt, F. Jay & Hsu, Nan-Jung, 2002. "A class of nearly long-memory time series models," International Journal of Forecasting, Elsevier, vol. 18(2), pages 265-281.
  10. Dominique Guegan, 2005. "How can we define the concept of long memory ? An econometric survey," Post-Print halshs-00179343, HAL.
  11. William R. Parke, 1999. "What Is Fractional Integration?," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 632-638, November.
  12. Gilles DUFRENOT & Dominique GUEGAN & Anne PEGUIN-FEISSOLLE, 2003. "A SETAR model with long-memory dynamics," Econometrics 0309002, EconWPA.
  13. Carrasco, Marine, 2002. "Misspecified Structural Change, Threshold, and Markov-switching models," Journal of Econometrics, Elsevier, vol. 109(2), pages 239-273, August.
  14. Leipus, Remigijus & Paulauskas, Vygantas & Surgailis, Donatas, 2005. "Renewal regime switching and stable limit laws," Journal of Econometrics, Elsevier, vol. 129(1-2), pages 299-327.
  15. Dominique Guegan & Stéphanie Rioublanc, 2005. "Regime switching models : real or spurious long memory ?," Post-Print halshs-00189208, HAL.
  16. Gourieroux, Christian & Jasiak, Joann, 2001. "Memory and infrequent breaks," Economics Letters, Elsevier, vol. 70(1), pages 29-41, January.
  17. Liu, Ming, 2000. "Modeling long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 99(1), pages 139-171, November.
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Citations

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Cited by:
  1. Delavari, Majid & Gandali Alikhani, Nadiya & Naderi, Esmaeil, 2012. "Do Dynamic Neural Networks Stand a Better Chance in Fractionally Integrated Process Forecasting?," MPRA Paper 45977, University Library of Munich, Germany.
  2. Abounoori, Abbas Ali & Naderi, Esmaeil & Gandali Alikhani, Nadiya & Amiri, Ashkan, 2013. "Financial Time Series Forecasting by Developing a Hybrid Intelligent System," MPRA Paper 45615, University Library of Munich, Germany.
  3. Kuswanto, Heri, 2009. "A New Simple Test Against Spurious Long Memory Using Temporal Aggregation," Hannover Economic Papers (HEP) dp-425, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.

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