Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/64724
Authors: 
Vogt, Michael
Linton, Oliver
Year of Publication: 
2012
Series/Report no.: 
cemmap working paper CWP23/12
Abstract: 
In 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.
Subjects: 
nonparametric estimation
penalized least squares
periodic sequence
temperature anomaly data
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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