Estimators of Long-Memory: Fourier versus Wavelets

By Gilles Faÿ, Eric Moulines, Franccois Roueff, Murad S. Taqqu. 2008

99 citations recorded by the source corpus.

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.

Subfields: Computer Vision and Pattern Recognition, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/2dfbfc038e67d7aeaa02ef94da3496903c43c2fd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. R. von Sachs
2. Franccois Roueff
3. Guy P. Nason

In Economics and Econometrics

1. R. von Sachs
2. Franccois Roueff
3. Efstathios Paparoditis
4. Guy P. Nason
5. Rainer Dahlhaus

In Finance

1. R. von Sachs
2. Franccois Roueff
3. Efstathios Paparoditis
4. Rainer Dahlhaus
5. Guy P. Nason

Citations in the corpus

• Locally stationary long memory estimation (2009, methodology): “MSC: primary 62M10; 62M15; 62G05; secondary 60G15 Keywords: Locally stationary process; Long memory; Semi-parametric estimation; Wavelets”