Clustering with Potential Multidimensionality: Inference and Practice
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arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866917842508578816 |
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| author | Xu, Ruonan Yap, Luther |
| author_facet | Xu, Ruonan Yap, Luther |
| contents | We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13372 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Clustering with Potential Multidimensionality: Inference and Practice Xu, Ruonan Yap, Luther Econometrics We show how clustering standard errors in one or more dimensions can be justified in M-estimation when there is sampling or assignment uncertainty. Since existing procedures for variance estimation are either conservative or invalid, we propose a variance estimator that refines a conservative procedure and remains valid. We then interpret environments where clustering is frequently employed in empirical work from our design-based perspective and provide insights on their estimands and inference procedures. |
| title | Clustering with Potential Multidimensionality: Inference and Practice |
| topic | Econometrics |
| url | https://arxiv.org/abs/2411.13372 |