Paper
Jackknife Inference for Fixed Effects Models
Authors
Ayden Higgins
Abstract
This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, and (iii) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a self-normalised jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained.
Metadata
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Raw Data (Debug)
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"raw_xml": "<entry>\n <id>http://arxiv.org/abs/2602.21903v1</id>\n <title>Jackknife Inference for Fixed Effects Models</title>\n <updated>2026-02-25T13:33:46Z</updated>\n <link href='https://arxiv.org/abs/2602.21903v1' rel='alternate' type='text/html'/>\n <link href='https://arxiv.org/pdf/2602.21903v1' rel='related' title='pdf' type='application/pdf'/>\n <summary>This paper develops a general method of inference for fixed effects models which is (i) automatic, (ii) computationally inexpensive, and (iii) highly model agnostic. Specifically, we show how to combine a collection of subsample estimators into a self-normalised jackknife $t$-statistic, from which hypothesis tests, confidence intervals, and $p$-values are readily obtained.</summary>\n <category scheme='http://arxiv.org/schemas/atom' term='econ.EM'/>\n <published>2026-02-25T13:33:46Z</published>\n <arxiv:primary_category term='econ.EM'/>\n <author>\n <name>Ayden Higgins</name>\n </author>\n </entry>"
}