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Statistical estimation of nonstationaryGaussian processes with long-range dependence and intermittency

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  • Gao, jiti
  • Anh, vo
  • Heyde, christopher
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Abstract

This paper considers statistical inference for nonstationaryGaussian processes with long-range dependence and intermittency. The existence of such a process has been established by Anh et al. (J. Statist. Plann. Inference 80 (1999) 95–110). We systematically consider the case where the spectral densityof nonstationaryGaussian processes with stationaryincrements is of a general and

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File URL: http://mpra.ub.uni-muenchen.de/11972/
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Bibliographic Info

Paper provided by University Library of Munich, Germany in its series MPRA Paper with number 11972.

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Date of creation: 13 Dec 1999
Date of revision: 23 Oct 2001
Publication status: Published in Stochastic Processes and Their Applications 1.99(2002): pp. 295-323
Handle: RePEc:pra:mprapa:11972

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Keywords: Asymptotic theory; fractional Riesz–Bessel motion; nonstationary process; long-range dependence; statistical estimation;

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  1. Hardle, Wolfgang & LIang, Hua & Gao, Jiti, 2000. "Partially linear models," MPRA Paper 39562, University Library of Munich, Germany, revised 01 Sep 2000.
  2. Heyde, C. C. & Gay, R., 1993. "Smoothed periodogram asymptotics and estimation for processes and fields with possible long-range dependence," Stochastic Processes and their Applications, Elsevier, vol. 45(1), pages 169-182, March.
  3. Peter M. Robinson, 1997. "Large-sample inference for nonparametric regression with dependent errors," LSE Research Online Documents on Economics 302, London School of Economics and Political Science, LSE Library.
  4. Viano, M. C. & Deniau, C. & Oppenheim, G., 1994. "Continuous-time fractional ARMA processes," Statistics & Probability Letters, Elsevier, vol. 21(4), pages 323-336, November.
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Cited by:
  1. Gao, Jiti, 2002. "Modeling long-range dependent Gaussian processes with application in continuous-time financial models," MPRA Paper 11973, University Library of Munich, Germany, revised 18 Sep 2003.
  2. Leonenko, N.N. & Sakhno, L.M., 2006. "On the Whittle estimators for some classes of continuous-parameter random processes and fields," Statistics & Probability Letters, Elsevier, vol. 76(8), pages 781-795, April.
  3. Casas, Isabel, 2008. "Estimation of stochastic volatility with LRD," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 78(2), pages 335-340.
  4. Casas, Isabel & Gao, Jiti, 2008. "Econometric estimation in long-range dependent volatility models: Theory and practice," Journal of Econometrics, Elsevier, vol. 147(1), pages 72-83, November.
  5. Anh, V.V. & Leonenko, N.N. & Sakhno, L.M., 2007. "Statistical inference using higher-order information," Journal of Multivariate Analysis, Elsevier, vol. 98(4), pages 706-742, April.

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