A Wavelet Whittle estimator of the memory parameter of a non-stationary Gaussian time series

Abstract

We consider a time series $X=\{X_k,k\in \mathbb{Z}\}$ with memory parameter $d\in \mathbb{R}$. This time series is either stationary or can be made stationary after differencing a finite number of times. We study the "Local Whittle Wavelet Estimator" of the memory parameter $d$. This is a wavelet-based semiparametric pseudo-likelihood maximum method estimator. The estimator may depend on a given finite range of scales or on a range which becomes infinite with the sample size. We show that the estimator is consistent and rate optimal if $X$ is a linear process and is asymptotically normal if $X$ is Gaussian.