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Rosales, Luis Francisco
Krivobokova, Tatyana
Year of Publication: 
Series/Report no.: 
Discussion Papers No. 131
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.
Penalized splines
Mixed model
Varying coefficient
Correlated remainder
Document Type: 
Working Paper

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