Convergence of spectral density estimators in the locally stationary framework

dc.contributor.author	Kawka, Rafael
dc.date.accessioned	2019-10-02T14:28:23Z
dc.date.available	2019-10-02T14:28:23Z
dc.date.issued	2019
dc.description.abstract	Locally stationary processes are characterised by spectral densities that are functions of rescaled time. We study the asymptotic properties of spectral density estimators in the locally stationary framework. In particular, we show that for a locally stationary process with time-varying spectral density function f(u; ) standard spectral density estimators consistently estimate the time-averaged spectral density R 1 0 f(u; ) du. This result is complemented by some illustrative examples and applications including HAC-inference in the multiple linear regression model and a simple visual tool for the detection of unconditional heteroskedasticity.	en
dc.identifier.uri	http://hdl.handle.net/2003/38260
dc.identifier.uri	http://dx.doi.org/10.17877/DE290R-20230
dc.language.iso	en	de
dc.relation.ispartofseries	Discussion Paper / SFB823;23/2019
dc.subject.ddc	310
dc.subject.ddc	330
dc.subject.ddc	620
dc.subject.rswk	Dichteschätzung	de
dc.subject.rswk	Zeitreihenanalyse	de
dc.title	Convergence of spectral density estimators in the locally stationary framework	en
dc.type	Text	de
dc.type.publicationtype	workingPaper	de
dcterms.accessRights	open access
eldorado.secondarypublication	false	de

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