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Nonlinear Time Series Analysis

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XploRe: An Interactive Statistical Computing Environment

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Abstract

The recent development of nonlinear time series analysis is primarily due to the efforts to overcome the limitations of linear models such as the autoregressive moving-average models of Box and Jenkins (1976) in real applications. It is also attributed to the development of nonlinear/nonparamctric regression techniques which provides many useful tools. Advanced computational power and easy-to-use advanced software and graphics such as S-Plus and XploRe make all of these possible.

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Authors and Affiliations

  1. Department of Statistics, Texas A&M University, College Station, TX, 77843, USA

    Rong Chen

  2. Graduiertenkolleg “Angewandte Mikroökonomik”, Freie Universität Berlin, D-14195, Berlin, Germany

    Christian Hafner

Authors
  1. Rong Chen
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  2. Christian Hafner
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© 1995 Springer-Verlag New York, Inc.

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Chen, R., Hafner, C. (1995). Nonlinear Time Series Analysis. In: XploRe: An Interactive Statistical Computing Environment. Statistics and Computing. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-4214-7_14

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  • DOI: https://doi.org/10.1007/978-1-4612-4214-7_14

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-8699-8

  • Online ISBN: 978-1-4612-4214-7

  • eBook Packages: Springer Book Archive

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