Cookies?
Library Header Image
LSE Research Online LSE Library Services

Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

Graves, Timothy, Franzke, Christian L.E., Watkins, Nicholas W., Gramacy, Robert B. and Tindale, Elizabeth (2017) Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models. Physica A: Statistical Mechanics and Its Applications, 473. pp. 60-71. ISSN 0378-4371

[img]
Preview
PDF - Accepted Version
Download (618kB) | Preview
Identification Number: 10.1016/j.physa.2017.01.028

Abstract

Long-Range Dependence (LRD) and heavy-tailed distributions are ubiquitous in natural and socio-economic data. Such data can be self-similar whereby both LRD and heavy-tailed distributions contribute to the self-similarity as measured by the Hurst exponent. Some methods widely used in the physical sciences separately estimate these two parameters, which can lead to estimation bias. Those which do simultaneous estimation are based on frequentist methods such as Whittle’s approximate maximum likelihood estimator. Here we present a new and systematic Bayesian framework for the simultaneous inference of the LRD and heavy-tailed distribution parameters of a parametric ARFIMA model with non-Gaussian innovations. As innovations we use the α-stable and t-distributions which have power law tails. Our algorithm also provides parameter uncertainty estimates. We test our algorithm using synthetic data, and also data from the Geostationary Operational Environmental Satellite system (GOES) solar X-ray time series. These tests show that our algorithm is able to accurately and robustly estimate the LRD and heavy-tailed distribution parameters.

Item Type: Article
Official URL: http://www.sciencedirect.com/science/journal/03784...
Additional Information: © 2017 Elsevier B.V
Divisions: Centre for Analysis of Time Series
Subjects: Q Science > QC Physics
Sets: Research centres and groups > Centre for the Analysis of Time Series (CATS)
Date Deposited: 12 Jan 2017 14:04
Last Modified: 20 May 2019 02:23
URI: http://eprints.lse.ac.uk/id/eprint/68837

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics