Instant Trend-Seasonal Decomposition of Time Series with Splines

Luis Francisco Rosales and Tatyana Krivobokova
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Luis Francisco Rosales: Georg-August-University Göttingen
Tatyana Krivobokova: Georg-August-University Göttingen

No 131, Courant Research Centre: Poverty, Equity and Growth - Discussion Papers from Courant Research Centre PEG

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 eff ectiveness of the approach.

Keywords: Penalized splines; Mixed model; Varying coecient; Correlated remainder (search for similar items in EconPapers)
Date: 2012-11-20
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Persistent link: https://EconPapers.repec.org/RePEc:got:gotcrc:131

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