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Alternative asymptotics and the partially linear model with many regressors

Author(s): Cattaneo, MD; Jansson, M; Newey, WK

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dc.contributor.authorCattaneo, MDen_US
dc.contributor.authorJansson, Men_US
dc.contributor.authorNewey, WKen_US
dc.date.accessioned2021-10-11T14:17:23Z-
dc.date.available2021-10-11T14:17:23Z-
dc.date.issued2018-04-01en_US
dc.identifier.citationCattaneo, MD, Jansson, M, Newey, WK. (2018). Alternative asymptotics and the partially linear model with many regressors. Econometric Theory, 34 (2), 277 - 301. doi:10.1017/S026646661600013Xen_US
dc.identifier.issn0266-4666en_US
dc.identifier.urihttp://arks.princeton.edu/ark:/88435/pr1fs1b-
dc.description.abstract© Cambridge University Press 2016. Many empirical studies estimate the structural effect of some variable on an outcome of interest while allowing for many covariates. We present inference methods that account for many covariates. The methods are based on asymptotics where the number of covariates grows as fast as the sample size. We find a limiting normal distribution with variance that is larger than the standard one. We also find that with homoskedasticity this larger variance can be accounted for by using degrees-of-freedom-adjusted standard errors. We link this asymptotic theory to previous results for many instruments and for small bandwidth(s) distributional approximations.en_US
dc.format.extent277 - 301en_US
dc.relation.ispartofEconometric Theoryen_US
dc.titleAlternative asymptotics and the partially linear model with many regressorsen_US
dc.typeJournal Article-
dc.identifier.doidoi:10.1017/S026646661600013Xen_US
dc.identifier.eissn1469-4360en_US
pu.type.symplectichttp://www.symplectic.co.uk/publications/atom-terms/1.0/journal-articleen_US

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