Empirical spectral processes for locally stationary time series

dc.creatorDahlhaus, Rainer
dc.creatorPolonik, Wolfgang
dc.date2009-02-09
dc.date.accessioned2026-07-07T12:39:29Z
dc.date.available2026-07-07T12:39:29Z
dc.descriptionA time-varying empirical spectral process indexed by classes of functions is defined for locally stationary time series. We derive weak convergence in a function space, and prove a maximal exponential inequality and a Glivenko--Cantelli-type convergence result. The results use conditions based on the metric entropy of the index class. In contrast to related earlier work, no Gaussian assumption is made. As applications, quasi-likelihood estimation, goodness-of-fit testing and inference under model misspecification are discussed. In an extended application, uniform rates of convergence are derived for local Whittle estimates of the parameter curves of locally stationary time series models.
dc.descriptionPublished in at http://dx.doi.org/10.3150/08-BEJ137 the Bernoulli (http://isi.cbs.nl/bernoulli/) by the International Statistical Institute/Bernoulli Society (http://isi.cbs.nl/BS/bshome.htm)
dc.identifierhttps://arxiv.org/abs/0902.1448
dc.identifierhttp://arxiv.org/abs/0902.1448
dc.identifierBernoulli 2009, Vol. 15, No. 1, 1-39
dc.identifierdoi:10.3150/08-BEJ137
dc.identifier.urihttp://salesiana.dossiersoluciones.com/handle/123456789/219129
dc.subjectStatistics Theory
dc.titleEmpirical spectral processes for locally stationary time series
dc.typetext

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