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Non-Gaussian Log-Periodogram Regression


  • Velasco, Carlos


We show the consistency of the log-periodogram estimate of the long memory parameter íor long range dependent linear, non necessarily Gaussian, time series when we make a pooling oí periodogram ordinates. Then, we study the asymptotic behaviour oí the tapered periodogram of long range dependent time series íor írequencies near the origin. Finally, we obtain the asymptotic distribution of the log-periodogram estimate íor possibly non-Gaussian observations when we use the tapered periodogram. For that result we rely on higher order asymptotic properties of a vector of periodogram ordinates of the linear innovations.
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  • Velasco, Carlos, 2000. "Non-Gaussian Log-Periodogram Regression," Econometric Theory, Cambridge University Press, vol. 16(01), pages 44-79, February.
  • Handle: RePEc:cup:etheor:v:16:y:2000:i:01:p:44-79_16

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    References listed on IDEAS

    1. Lobato, I. & Robinson, P. M., 1996. "Averaged periodogram estimation of long memory," Journal of Econometrics, Elsevier, vol. 73(1), pages 303-324, July.
    2. Robinson, P. M., 1986. "On the errors-in-variables problem for time series," Journal of Multivariate Analysis, Elsevier, vol. 19(2), pages 240-250, August.
    3. Rainer Sachs, 1994. "Estimating non-linear functions of the spectral density, using a data-taper," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 46(3), pages 453-474, September.
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