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dc.contributor.authorJochmans, Koenen
dc.date.accessioned2019-06-28T08:38:16Z
dc.date.available2019-06-28T08:38:16Z
dc.date.issued2018-09-05en
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/294126
dc.description.abstractThis paper considers inference in heteroskedastic linear regression models with many control variables. The slope coefficients on these variables are nuisance parameters. Our setting allows their number to grow with the sample size, possibly at the same rate, in which case they are not consistently estimable. A prime example of this setting are models with many (possibly multi-way) fixed effects. The presence of many nuisance parameters introduces an incidental-parameter problem in the usual heteroskedasticity-robust estimators of the covariance matrix, rendering them biased and inconsistent. Hence, tests based on these estimators are size distorted even in large samples. An alternative covariance-matrix estimator that is conditionally unbiased and remains consistent is presented and supporting simulation results are provided.en
dc.relation.ispartofseriesCambridge Working Papers in Economics
dc.rightsAll Rights Reserved
dc.rights.urihttp://www.rioxx.net/licenses/all-rights-reserved/
dc.titleHeteroskedasticity-robust inference in linear regression modelsen
dc.typeWorking Paper
prism.issueIdentifierCWPE1957en
prism.publicationDate2018en
dc.identifier.doi10.17863/CAM.41227
rioxxterms.versionofrecord10.17863/CAM.41227en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2018-09-05en
dc.contributor.orcidJochmans, Koen [0000-0002-3090-3003]
dc.publisher.urlhttp://www.econ.cam.ac.uk/research/cwpe-abstracts?cwpe=1957en
rioxxterms.typeWorking Paperen
pubs.funder-project-idEuropean Commission Horizon 2020 (H2020) ERC (715787)


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