Sparse High-Dimensional Vector Autoregressive Bootstrap
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909607943733248 |
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| author | Adamek, Robert Smeekes, Stephan Wilms, Ines |
| author_facet | Adamek, Robert Smeekes, Stephan Wilms, Ines |
| contents | We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2302_01233 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Sparse High-Dimensional Vector Autoregressive Bootstrap Adamek, Robert Smeekes, Stephan Wilms, Ines Econometrics Statistics Theory Methodology We introduce a high-dimensional multiplier bootstrap for time series data based on capturing dependence through a sparsely estimated vector autoregressive model. We prove its consistency for inference on high-dimensional means under two different moment assumptions on the errors, namely sub-gaussian moments and a finite number of absolute moments. In establishing these results, we derive a Gaussian approximation for the maximum mean of a linear process, which may be of independent interest. |
| title | Sparse High-Dimensional Vector Autoregressive Bootstrap |
| topic | Econometrics Statistics Theory Methodology |
| url | https://arxiv.org/abs/2302.01233 |