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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2023
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| Online Access: | https://arxiv.org/abs/2303.07631 |
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| _version_ | 1866912306025201664 |
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| author | Yang, Xinxin Du, Lilun |
| author_facet | Yang, Xinxin Du, Lilun |
| contents | Large-scale multiple testing under static factor models is widely used to detect sparse signals in high-dimensional data. However, static factor models are arguably too stringent because they ignore serial correlation, which seriously distorts error rate control in large-scale inference. In this manuscript, we propose a new multiple testing procedure under dynamic factor models that is robust to nonlinear serial dependence. The idea is to integrate a new sample-splitting strategy based on chronological order and a two-pass Fama--Macbeth regression to form a series of test statistics with marginal symmetry properties and then to use these properties to obtain a data-driven threshold. We show that our procedure can control the false discovery rate asymptotically under high-dimensional dynamic factor models. {As a byproduct of independent interest, we establish a new exponential-type deviation inequality for the sum of random variables over various functionals of linear and nonlinear processes.} Our numerical results, including a case study on hedge fund selection, demonstrate the advantage of our proposed method over several state-of-the-art methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2303_07631 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Multiple Testing under High-dimensional Dynamic Factor Model Yang, Xinxin Du, Lilun Statistics Theory Large-scale multiple testing under static factor models is widely used to detect sparse signals in high-dimensional data. However, static factor models are arguably too stringent because they ignore serial correlation, which seriously distorts error rate control in large-scale inference. In this manuscript, we propose a new multiple testing procedure under dynamic factor models that is robust to nonlinear serial dependence. The idea is to integrate a new sample-splitting strategy based on chronological order and a two-pass Fama--Macbeth regression to form a series of test statistics with marginal symmetry properties and then to use these properties to obtain a data-driven threshold. We show that our procedure can control the false discovery rate asymptotically under high-dimensional dynamic factor models. {As a byproduct of independent interest, we establish a new exponential-type deviation inequality for the sum of random variables over various functionals of linear and nonlinear processes.} Our numerical results, including a case study on hedge fund selection, demonstrate the advantage of our proposed method over several state-of-the-art methods. |
| title | Multiple Testing under High-dimensional Dynamic Factor Model |
| topic | Statistics Theory |
| url | https://arxiv.org/abs/2303.07631 |