Semi-parametric Graphical Estimation Techniques for Long-memory Data
نویسندگان
چکیده
This paper reviews several periodogram-based methods for estimating the long-memory parameter H in time series and suggests a way to robustify them. The high frequencies tend to bias the estimates. Using only low frequencies eliminates the bias but increases the variance. We hence suggest plotting the estimates of H as a function of a parameter which balances bias versus variance and, if the plot attens in a central region, to use the at part for estimating H. We apply this technique to the periodogram regression method, the Whittle approximation to maximum likelihood and to the local Whittle method. We investigate its eeectiveness on several simulated fractional ARIMA series and also apply it to estimate the long-memory parameter H in computer network traac.
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