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Marginal density estimation for linear processes with cyclical long memory

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  • Ould Haye, Mohamedou
  • Philippe, Anne

Abstract

Some convergence results on the kernel density estimator are proven for a class of linear processes with cyclic effects. In particular, we extend the results of Ho and Hsing (1996), Mielniczuk (1997) and Hall and Hart (1990) to the stationary processes for which the singularities of the spectral density are not limited to the origin. We show that the convergence rates and the limiting distribution may be different in this context.

Suggested Citation

  • Ould Haye, Mohamedou & Philippe, Anne, 2011. "Marginal density estimation for linear processes with cyclical long memory," Statistics & Probability Letters, Elsevier, vol. 81(9), pages 1354-1364, September.
    • Handle: RePEc:eee:stapro:v:81:y:2011:i:9:p:1354-1364
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    References listed on IDEAS

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    1. M. C. Viano & Cl. Deniau & G. Oppenheim, 1995. "Long-Range Dependence And Mixing For Discrete Time Fractional Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 16(3), pages 323-338, May.
    2. Javier Hidalgo & Philippe Soulier, 2004. "Estimation of the location and exponent of the spectral singularity of a long memory process," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(1), pages 55-81, January.
    3. Giraitis, L & Hidalgo, J & Robinson, Peter M., 2001. "Gaussian estimation of parametric spectral density with unknown pole," LSE Research Online Documents on Economics 297, London School of Economics and Political Science, LSE Library.
    4. Giraitis, Liudas & Koul, Hira L. & Surgailis, Donatas, 1996. "Asymptotic normality of regression estimators with long memory errors," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 317-335, September.
    5. Mielniczuk, Jan, 1997. "On the asymptotic mean integrated squared error of a kernel density estimator for dependent data," Statistics & Probability Letters, Elsevier, vol. 34(1), pages 53-58, May.
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