Forecasting in nonlinear univariate time series using penalized splines

Forecasting in nonlinear univariate time series using penalized splines 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Statistical Papers Springer Journals

Forecasting in nonlinear univariate time series using penalized splines

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Publisher
Springer Berlin Heidelberg
Copyright
Copyright © 2015 by Springer-Verlag Berlin Heidelberg
Subject
Statistics; Statistics for Business/Economics/Mathematical Finance/Insurance; Probability Theory and Stochastic Processes; Economic Theory/Quantitative Economics/Mathematical Methods; Operations Research/Decision Theory
ISSN
0932-5026
eISSN
1613-9798
D.O.I.
10.1007/s00362-015-0711-1
Publisher site
See Article on Publisher Site

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.

Journal

Statistical PapersSpringer Journals

Published: Sep 15, 2015

References

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