Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series

Shreya Mehta; Almut E. D. Veraart

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arXiv · 2502.08838

Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series

Shreya Mehta·Almut E. D. Veraart

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Abstract

This article introduces Levy-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.

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BibTeX RIS

Shreya Mehta, Almut E. D. Veraart. 2025-02-12. Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series. https://arxiv.org/abs/2502.08838

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