Statistical inference for Lévy-driven graph supOU processes: from short- to long-memory in high-dimensional time series
File(s)
Published version
Author(s)
Mehta, Shreya
Veraart, Almut
Type
Journal Article
Abstract
This article introduces Lévy-driven graph supOU processes, a parsimonious parametrisation for high-dimensional time series in which dependence between components is governed by a graph structure. Specifically, the model bridges short- and long-range dependence within a single parametric family while accommodating a wide range of marginal distributions. We further develop a generalised method of moments estimator, establish its consistency and asymptotic normality, and assess its finite-sample performance through a simulation study. Finally, we illustrate the practical relevance of our model and estimation method in an empirical study of wind capacity factors in a European electricity network context.
Date Issued
2026-01-01
Date Acceptance
2026-06-11
Citation
Electronic Journal of Statistics, 2026, 20 (2), pp.3360-3393
ISSN
1935-7524
Publisher
Institute of Mathematical Statistics
Start Page
3360
End Page
3393
Journal / Book Title
Electronic Journal of Statistics
Volume
20
Issue
2
Copyright Statement
Rights: Creative Commons Attribution 4.0 International License.
License URL
Identifier
10.1214/26-EJS2543
Subjects
MSC2020 subject classifications: Primary 62M10
62F99
60G10; secondary 62P99
60F05
60G57
60E07 Graph supOU process
Lévy basis
long memory
generalised method of moments
infinite divisibility
Publication Status
Published
Date Publish Online
2026-08-04