{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T12:32:46Z","timestamp":1790166766284,"version":"4.1.0"},"reference-count":59,"publisher":"Oxford University Press (OUP)","license":[{"start":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T00:00:00Z","timestamp":1779148800000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/pages\/standard-publication-reuse-rights"}],"funder":[{"DOI":"10.13039\/100000001","name":"NSF","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>This article presents a theoretical case for replacement of conventional heteroskedasticity-consistent and cluster-robust variance estimators with jackknife variance estimators, in the context of linear regression with heteroskedastic and\/or cluster-dependent observations. We examine the bias of variance estimation and the coverage probabilities of confidence intervals. Concerning bias, we show that conventional variance estimators have full downward worst-case bias, while our jackknife variance estimator is never downward biased. Concerning confidence intervals, we show that intervals based on conventional standard errors have worst-case coverage equalling zero, while the jackknife-based confidence interval has coverage probability bounded by the Cauchy distribution, under the auxiliary assumption of normal errors. We also extend the Bell and McCaffrey (2002) student t approximation to our jackknife t-ratio, resulting in confidence intervals with improved coverage probabilities. Our theory holds under broad assumptions, allowing arbitrary cluster sizes, regressor leverage, within-cluster correlation, heteroskedasticity, regression with a single treated cluster, fixed effects, and delete-cluster invertibility failures. Our theoretical findings are consistent with the extensive simulation literature investigating heteroskedasticity-consistent and cluster-robust variance estimation.<\/jats:p>","DOI":"10.1093\/restud\/rdag050","type":"journal-article","created":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T08:36:31Z","timestamp":1779179791000},"source":"Crossref","is-referenced-by-count":1,"title":["Jackknife Standard Errors for Clustered Regression"],"prefix":"10.1093","author":[{"given":"Bruce E","family":"Hansen","sequence":"first","affiliation":[{"name":"University of Wisconsin ,","place":["USA"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2026,5,19]]},"reference":[{"key":"2026060110165377100_rdag050-B1","doi-asserted-by":"crossref","first-page":"307","DOI":"10.2307\/2938259","article-title":"Asymptotic Normality of Series Estimators for Nonparametric and Semiparametric Regression 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