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dc.contributor.authorVogt, Michaelen_US
dc.contributor.authorLinton, Oliveren_US
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_US
dc.publisher|aCentre for Microdata Methods and Practice (cemmap) |cLondonen_US
dc.relation.ispartofseries|acemmap working paper |xCWP23/12en_US
dc.subject.keywordnonparametric estimationen_US
dc.subject.keywordpenalized least squaresen_US
dc.subject.keywordperiodic sequenceen_US
dc.subject.keywordtemperature anomaly dataen_US
dc.titleNonparametric estimation of a periodic sequence in the presence of a smooth trenden_US
dc.typeWorking Paperen_US

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