Investigating some estimators of the fractional degree of differencing, in long memory time series
Abstract
We investigated three estimators of the fractional parameter d, in long memory time series.
Discussed in [4], the rst estimator is based on a regression analysis using the periodogram
of the long memory series; the second estimator which is discussed in [11, 10], is based
on a regression analysis using a lag-window spectral density estimator; the third estimator
performs a maximum likelihood estimation (MLE) of d using the fast and accurate method
of [8]. To conduct our investigation, we generated synthetic ARFIMA(p; d; q) samples where
d = :25 and d = :45. Computational results showed that in general the MLE method
performs better in large samples whereas the [4] proposed estimator performs better in small
samples. Yet, the estimator in [11, 10] behaves better than that proposed by [4] in large
samples. While the MLE has the smallest standard errors in both small and large samples,
the standard errors of the [4] approach are the largest.
URI
http://purl.galileo.usg.edu/uga_etd/kiogou_sebastien_d_201105_mshttp://hdl.handle.net/10724/27190