Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866910040604016640 |
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| author | Mehta, Shreya Veraart, Almut E. D. |
| author_facet | Mehta, Shreya Veraart, Almut E. D. |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_08838 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series Mehta, Shreya Veraart, Almut E. D. Methodology Statistics Theory Applications 62M10, 62F99, 60G10, 62P99, 60F05, 60G57, 60E07 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. |
| title | Statistical inference for Levy-driven graph supOU processes: From short- to long-memory in high-dimensional time series |
| topic | Methodology Statistics Theory Applications 62M10, 62F99, 60G10, 62P99, 60F05, 60G57, 60E07 |
| url | https://arxiv.org/abs/2502.08838 |