Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/85178 
Authors: 
Year of Publication: 
2002
Series/Report no.: 
CoFE Discussion Paper No. 02/04
Publisher: 
University of Konstanz, Center of Finance and Econometrics (CoFE), Konstanz
Abstract: 
This paper focuses on developing a new data-driven procedure for decomposing seasonal time series based on local regression. Formula of the asymptotic optimal bandwidth hA in the current context is given. Methods for estimating the unknowns in hA are investigated. A data-driven algorithm for decomposing seasonal time series is proposed based on the iterative plug-in idea introduced by Gasser et al. (1991). Asymptotic behaviour of this algorithm is investigated. Some computational aspects are discussed in detail. Practical performance of the proposed algorithm is illustrated by simulated and data examples. The results here also provide some insights into the iterative plug-in idea.
Subjects: 
Time series decomposition
Local regression
Iterative plug-in
Bandwidth selection
Persistent Identifier of the first edition: 
Document Type: 
Working Paper

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