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Bayesian Nonparametric Estimation of the Spectral Density of a Long or Intermediate Memory Gaussian Process

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  • Judith Rousseau

    (Crest)

  • Nicolas Chopin

    (Crest)

  • Brunero Liseo

    (Crest)

Abstract

A stationary Gaussian process is said to be long-range dependent (resp., anti-persistent) if its spectral density f(?)f(?) can be written as f(?)=|?|-2dg(|?|)f(?)=|?|-2dg(|?|), where 0

Suggested Citation

  • Judith Rousseau & Nicolas Chopin & Brunero Liseo, 2010. "Bayesian Nonparametric Estimation of the Spectral Density of a Long or Intermediate Memory Gaussian Process," Working Papers 2010-38, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2010-38
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    References listed on IDEAS

    as
    1. John Geweke & Susan Porter‐Hudak, 1983. "The Estimation And Application Of Long Memory Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 4(4), pages 221-238, July.
    2. Offer Lieberman & Peter C. B. Phillips, 2004. "Error bounds and asymptotic expansions for toeplitz product functionals of unbounded spectra," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 733-753, September.
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    Cited by:

    1. Julyan Arbel & Ghislaine Gayraud & Judith Rousseau, 2013. "Bayesian Optimal Adaptive Estimation Using a Sieve prior," Working Papers 2013-19, Center for Research in Economics and Statistics.

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