Predictive, finite-sample model choice for time series under stationarity and non-stationarity

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dc.contributor.authorKley, Tobias
dc.contributor.authorPreuß, Philip
dc.contributor.authorFryzlewicz, Piotr
dc.date.accessioned2016-11-23T13:01:56Z
dc.date.available2016-11-23T13:01:56Z
dc.date.issued2016
dc.description.abstractIn statistical research there usually exists a choice between structurally simpler or more complex models. We argue that, even if a more complex, locally stationary time series model were true, then a simple, stationary time series model may be advantageous to work with under parameter uncertainty. We present a new model choice methodology, where one of two competing approaches is chosen based on its empirical finite-sample performance with respect to prediction. A rigorous, theoretical analysis of the procedure is provided. As an important side result we prove, for possibly diverging model order, that the localised Yule-Walker estimator is strongly, uniformly consistent under local stationarity. An R package, forecastSNSTS, is provided and used to apply the methodology to financial and meteorological data in empirical examples. We further provide an extensive simulation study and discuss when it is preferable to base forecasts on the more volatile time-varying estimates and when it is advantageous to forecast as if the data were from a stationary process, even though they might not be.en
dc.identifier.urihttp://hdl.handle.net/2003/35394
dc.identifier.urihttp://dx.doi.org/10.17877/DE290R-17435
dc.language.isoende
dc.relation.ispartofseriesDiscussion Paper / SFB823;78, 2016en
dc.subjectforecastingen
dc.subjectcovariance stationarityen
dc.subjectYule-Walker estimateen
dc.subject.ddc310
dc.subject.ddc330
dc.subject.ddc620
dc.titlePredictive, finite-sample model choice for time series under stationarity and non-stationarityen
dc.typeTextde
dc.type.publicationtypeworkingPaperde
dcterms.accessRightsopen access

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