Estimation of the long-range dependence parameter of fractional ARIMA processes

In this paper, several methods for estimating long-range dependence parameters have been proposed. By far, the wavelet method is the most widely used. When a process is assumed to be second-order self-similar, a new method was introduced that uses the structure of the covariance function to estimate the Hurst parameter. The method was shown to be much faster and yield smaller confidence intervals than the wavelet method. The case was consider in this paper when the process is assumed to be fractional ARIMA and show that the new method still processes the aforementioned qualities.