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Forecasting in nonlinear univariate time series using penalized splines

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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.

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Authors and Affiliations

  1. Quantitative Products, DEKA Investment GmbH, Mainzer Landstrasse 16, Frankfurt (Main), Germany

    Michael Wegener

  2. Department of Statistics, Ludwigs-Maximilians-University Munich, Munich, Germany

    Göran Kauermann

Authors
  1. Michael Wegener
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  2. Göran Kauermann
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Correspondence to Göran Kauermann.

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Wegener, M., Kauermann, G. Forecasting in nonlinear univariate time series using penalized splines. Stat Papers 58, 557–576 (2017). https://doi.org/10.1007/s00362-015-0711-1

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  • DOI: https://doi.org/10.1007/s00362-015-0711-1

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