Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/64724 
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dc.contributor.authorVogt, Michaelen
dc.contributor.authorLinton, Oliveren
dc.date.accessioned2012-09-12-
dc.date.accessioned2012-10-16T13:08:35Z-
dc.date.available2012-10-16T13:08:35Z-
dc.date.issued2012-
dc.identifier.pidoi:10.1920/wp.cem.2012.2312en
dc.identifier.urihttp://hdl.handle.net/10419/64724-
dc.description.abstractIn this paper, we study a nonparametric regression model including a periodic component, a smooth trend function, and a stochastic error term. We propose a procedure to estimate the unknown period and the function values of the periodic component as well as the nonparametric trend function. The theoretical part of the paper establishes the asymptotic properties of our estimators. In particular, we show that our estimator of the period is consistent. In addition, we derive the convergence rates as well as the limiting distributions of our estimators of the periodic component and the trend function. The asymptotic results are complemented with a simulation study that investigates the small sample behaviour of our procedure. Finally, we illustrate our method by applying it to a series of global temperature anomalies.en
dc.language.isoengen
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen
dc.relation.ispartofseries|acemmap working paper |xCWP23/12en
dc.subject.ddc330en
dc.subject.keywordnonparametric estimationen
dc.subject.keywordpenalized least squaresen
dc.subject.keywordperiodic sequenceen
dc.subject.keywordtemperature anomaly dataen
dc.titleNonparametric estimation of a periodic sequence in the presence of a smooth trend-
dc.typeWorking Paperen
dc.identifier.ppn725566566en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen
dc.identifier.repecRePEc:ifs:cemmap:23/12en

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