Testing High-dimensional Nonstationary Time Series
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866908475835023360 |
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| author | Liu, Ruihan Wang, Chen |
| author_facet | Liu, Ruihan Wang, Chen |
| contents | In this article, we first establish the joint central limit theorem (CLT) for the extreme eigenvalues of the sample correlation matrix of high-dimensional random walks with cross-sectional dependence. We further investigate the asymptotic spectral properties of the sample correlation matrix of high-dimensional autoregressive processes. To apply our theoretical results, we propose a novel high-dimensional unit root test and develop a forward sequential test to determine the number of unit roots in high-dimensional time series data. Finally, we conduct an empirical study of the purchasing power parity (PPP) hypothesis in high-dimensional settings. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_06126 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Testing High-dimensional Nonstationary Time Series Liu, Ruihan Wang, Chen Methodology 62H15, 60B20, 62H10 In this article, we first establish the joint central limit theorem (CLT) for the extreme eigenvalues of the sample correlation matrix of high-dimensional random walks with cross-sectional dependence. We further investigate the asymptotic spectral properties of the sample correlation matrix of high-dimensional autoregressive processes. To apply our theoretical results, we propose a novel high-dimensional unit root test and develop a forward sequential test to determine the number of unit roots in high-dimensional time series data. Finally, we conduct an empirical study of the purchasing power parity (PPP) hypothesis in high-dimensional settings. |
| title | Testing High-dimensional Nonstationary Time Series |
| topic | Methodology 62H15, 60B20, 62H10 |
| url | https://arxiv.org/abs/2308.06126 |