Jackknife inference with two-way clustering
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910049823096832 |
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| author | MacKinnon, James G. Nielsen, Morten Ørregaard Webb, Matthew D. |
| author_facet | MacKinnon, James G. Nielsen, Morten Ørregaard Webb, Matthew D. |
| contents | For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-way CRVEs based on the cluster jackknife and prove that they yield valid inferences asymptotically. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a software package, twowayjack for Stata, that implements our recommended variance estimator. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_08880 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Jackknife inference with two-way clustering MacKinnon, James G. Nielsen, Morten Ørregaard Webb, Matthew D. Econometrics For linear regression models with cross-section or panel data, it is natural to assume that the disturbances are clustered in two dimensions. However, the finite-sample properties of two-way cluster-robust tests and confidence intervals are often poor. We discuss several ways to improve inference with two-way clustering. Two of these are existing methods for avoiding, or at least ameliorating, the problem of undefined standard errors when a cluster-robust variance matrix estimator (CRVE) is not positive definite. One is a new method that always avoids the problem. More importantly, we propose a family of new two-way CRVEs based on the cluster jackknife and prove that they yield valid inferences asymptotically. Simulations for models with two-way fixed effects suggest that, in many cases, the cluster-jackknife CRVE combined with our new method yields surprisingly accurate inferences. We provide a software package, twowayjack for Stata, that implements our recommended variance estimator. |
| title | Jackknife inference with two-way clustering |
| topic | Econometrics |
| url | https://arxiv.org/abs/2406.08880 |