A simple test for white noise in functional time series

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dc.contributor.authorBagchi, Pramita
dc.contributor.authorCharaciejus, Vaidotas
dc.contributor.authorDette, Holger
dc.date.accessioned2016-12-22T12:27:11Z
dc.date.available2016-12-22T12:27:11Z
dc.date.issued2016
dc.description.abstractWe propose a new procedure for white noise testing of a functional time series. Our approach is based on an explicit representation of the L2-distance between the spectral density operator and its best (L2-)approximation by a spectral density operator corresponding to a white noise process. The estimation of this distance can be easily accomplished by sums of periodogram kernels and it is shown that an appropriately standardized version of the estimator is asymptotically normal distributed under the null hypothesis (of functional white noise) and under the alternative. As a consequence we obtain a very simple test (using the quantiles of the normal distribution) for the hypothesis of a white noise functional process. In particular the test does neither require the estimation of a long run variance (including a fourth order cumulant) nor resampling procedures to calculate critical values. Moreover, in contrast to all other methods proposed in the literature our approach also allows to test for "relevant" deviations from white noise and to construct confidence intervals for a measure which measures the discrcepancy of the underlying process from a functional white noise process.en
dc.identifier.urihttp://hdl.handle.net/2003/35731
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17761
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;87, 2016en
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titleA simple test for white noise in functional time seriesen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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