  This paper addresses the estimation of locally stationary long-range
dependent processes, a methodology that allows the statistical analysis of time
series data exhibiting both nonstationarity and strong dependency. A
time-varying parametric formulation of these models is introduced and a Whittle
likelihood technique is proposed for estimating the parameters involved. Large
sample properties of these Whittle estimates such as consistency, normality and
efficiency are established in this work. Furthermore, the finite sample
behavior of the estimators is investigated through Monte Carlo experiments. As
a result from these simulations, we show that the estimates behave well even
for relatively small sample sizes.
