Estimators of long-memory: Fourier versus wavelets

Abstract

Semi-parametric estimation methods of the long-memory exponent of a time series have been studied in several papers, some applied, others theoretical, some using Fourier methods, others using a wavelet-based technique. In this paper, we compare the Fourier and wavelet approaches to the local regression method and to the local Whittle method. We provide an overview of these methods, describe what has been done and indicate the available results and the conditions under which they hold. We discuss their relative strengths and weaknesses both from a practical and a theoretical perspective. We also include a simulation-based comparison. The software written to support this work is available on demand and we illustrate its use at the end of the paper.Murad S. Taqqu would like to thank Telecom Paris Tech for their hospitality. This research was partially supported by the NSF Grants DMS-0505747 and DMS-0706786 at Boston University. (DMS-0505747 - NSF; DMS-0706786 - NSF)First author draf