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Gaussian estimation of parametric spectral density with unknown pole

Author

Listed:
  • Giraitis, Liudas
  • Hidalgo, Javier
  • Robinson, Peter

Abstract

We consider a parametric spectral density with power-law behaviour about a fractional pole at the unknown frequency !. The case of known !, especially ! = 0, is standard in the long memory literature. When ! is unknown, asymptotic distribution theory for estimates of parameters, including the (long) memory parameter, is significantly harder. We study a form of Gaussian estimate. We establish n¡consistency of the estimate of !, and discuss its (non-standard) limiting distributional behaviour. For the remaining parameter estimates, we establish pn - consistency and asymptotic normality.

Suggested Citation

  • Giraitis, Liudas & Hidalgo, Javier & Robinson, Peter, 2001. "Gaussian estimation of parametric spectral density with unknown pole," LSE Research Online Documents on Economics 2182, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:2182
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    References listed on IDEAS

    as
    1. Robinson, P. M., 1978. "Alternative models for stationary stochastic processes," Stochastic Processes and their Applications, Elsevier, vol. 8(2), pages 141-152, December.
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    More about this item

    Keywords

    long range dependence; unknown pole;

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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