Forecasting in nonlinear univariate time series using penalized splines

Michael Wegener () and Göran Kauermann ()
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Michael Wegener: Quantitative Products, DEKA Investment GmbH
Göran Kauermann: Ludwigs-Maximilians-University Munich

Statistical Papers, 2017, vol. 58, issue 3, No 1, 557-576

Abstract: Abstract In this article we discuss penalized splines for fitting and forecasting univariate nonlinear time series models. While penalized splines have been excessively used in smooth regression, their use in nonlinear time series models is less far developed. This paper focuses on univariate autoregressive processes and discuss different nonlinear (functional) time series models including parsimonious estimation and model selection ideas. Furthermore, in simulations and an application we show how this approach compares to common parametric nonlinear models.

Keywords: Time series; Penalized splines; Model selection; EONIA-rate; 62G08; 62M10 (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00362-015-0711-1

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