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Default Priors Based on Pseudo-Likelihoods for the Poisson-GPD Model Read chapter Read first chapter

2013 | OriginalPaper | Chapter

12. Nonlinear Nonstationary Model Building by Genetic Algorithms

Authors : Francesco Battaglia, Mattheos K. Protopapas

Published in: Advances in Theoretical and Applied Statistics

Publisher: Springer Berlin Heidelberg

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Abstract

Many time series exhibits both nonlinearity and nonstationarity. Though both features have been often taken into account separately, few attempts have been proposed for modeling them simultaneously. We consider threshold models and present a general model allowing for several different regimes both in time and in levels, where regime transitions may happen according to self-exciting, or smoothly varying, or piecewise linear threshold modeling. Since fitting such a model involves the choice of a large number of structural parameters, we propose a procedure based on genetic algorithms, evaluating models by means of a generalized identification criterion. The proposed model building strategy is applied to a financial index.

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previous chapter Spectral Decomposition of the AR Metric
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Metadata
Title
Nonlinear Nonstationary Model Building by Genetic Algorithms
Authors
Francesco Battaglia
Mattheos K. Protopapas
Copyright Year
2013
Publisher
Springer Berlin Heidelberg
Book
Advances in Theoretical and Applied Statistics

Print ISBN: 978-3-642-35587-5

Electronic ISBN: 978-3-642-35588-2

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-642-35588-2_12

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