Jackknife Inference for Fixed Effects Models
Fuente:
arXiv
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866911613686120448 |
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| author | Higgins, Ayden |
| author_facet | Higgins, Ayden |
| contents | This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, (iii) tuning parameter-free, and (iv) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_21903 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Jackknife Inference for Fixed Effects Models Higgins, Ayden Econometrics This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, (iii) tuning parameter-free, and (iv) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained. |
| title | Jackknife Inference for Fixed Effects Models |
| topic | Econometrics |
| url | https://arxiv.org/abs/2602.21903 |