Edgeworth expansions for spectral density estimates and studentized sample mean

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dc.contributor.author Velasco Gómez, Carlos
dc.contributor.author Robinson, Peter M.
dc.date.accessioned 2009-04-15T10:15:17Z
dc.date.available 2009-04-15T10:15:17Z
dc.date.issued 2001
dc.identifier.bibliographicCitation Econometric Theory, 2001, 17, 3, p. 497-539
dc.identifier.issn 1469-4360
dc.identifier.uri http://hdl.handle.net/10016/3970
dc.description.abstract We establish valid Edgeworth expansions for the distribution of smoothed nonparametric spectral estimates, and of studentized versions of linear statistics such as the sample mean, where the studentization employs such a nonparametric spectral estimate. Particular attention is paid to the spectral estimate at zero frequency and, correspondingly, the studentized sample mean, to reflect econometric interest in autocorrelation-consistent or long-run variance estimation. Our main focus is on stationary Gaussian series, though we discuss relaxation of the Gaussianity assumption. Only smoothness conditions on the spectral density that are local to the frequency of interest are imposed. We deduce empirical expansions from our Edgeworth expansions designed to improve on the normal approximation in practice and also deduce a feasible rule of bandwidth choice.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Cambridge University Press
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.title Edgeworth expansions for spectral density estimates and studentized sample mean
dc.type article
dc.type.review PeerReviewed
dc.description.status Publicado
dc.subject.eciencia Economía
dc.rights.accessRights openAccess
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