Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/90545 
Year of Publication: 
2012
Series/Report no.: 
Discussion Papers No. 131
Publisher: 
Georg-August-Universität Göttingen, Courant Research Centre - Poverty, Equity and Growth (CRC-PEG), Göttingen
Abstract: 
We present a nonparametric method to decompose a times series into trend, seasonal and remainder components. This fully data-driven technique is based on penalized splines and makes an explicit characterization of the varying seasonality and the correlation in the remainder. The procedure takes advantage of the mixed model representation of penalized splines that allows for the simultaneous estimation of all model parameters from the corresponding likelihood. Simulation studies and three data examples illustrate the effectiveness of the approach.
Subjects: 
Penalized splines
Mixed model
Varying coefficient
Correlated remainder
Document Type: 
Working Paper

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