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Edgeworth expansions for spectral density estimates and studentized sample mean

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dc.affiliation.dptoUC3M. Departamento de Economíaes
dc.contributor.authorVelasco, Carlos
dc.contributor.authorRobinson, Peter M.
dc.date.accessioned2009-06-08T14:11:18Z
dc.date.available2009-06-08T14:11:18Z
dc.date.issued2000-05
dc.description.abstractWe establish valid Edgeworth expansions for the distribution of smoothed nonparametric spectral estimates, and of studentized versions of linear statistics such as the same 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 a feasible rule of bandwidth choice.
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/10016/4373
dc.language.isoeng
dc.publisherLondon School of Economics
dc.relation.ispartofseriesEconometrics
dc.relation.ispartofseriesEM/00/390
dc.relation.publisherversionhttp://sticerd.lse.ac.uk/dps/em/em390.pdf
dc.rights.accessRightsopen access
dc.subjectEdgeworth expansions
dc.subjectNonparametric spectral estimates
dc.subjectStationary Gaussian series
dc.subjectStudentized sample mean
dc.subjectBandwidth choice
dc.subject.ecienciaEconomía
dc.titleEdgeworth expansions for spectral density estimates and studentized sample mean
dc.typeworking paper*
dspace.entity.typePublication
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