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Inference for Nonlinear Time Series Models

In: Non-Linear Time Series

Author

Listed:
  • Kamil Feridun Turkman

    (Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional)

  • Manuel González Scotto

    (Universidade de Aveiro, Departamento de Matemática)

  • Patrícia de Zea Bermudez

    (Faculdade de Ciências Universidade de Lisboa, Departmento de Estatística e Investigação Operacional)

Abstract

Suppose we have an observed time series $$x_{1},x_{2},\ldots,x_{n}$$ and want to know if a linear time series model is adequate for the data, or an alternative nonlinear model should be considered. Linear models are often taken as the null hypotheses against a nonlinear alternative due to the simplicity of inference. Often we know much about the underlying process which generate the data set. Therefore it is possible to decide if a linear model will be adequate and if not, what aspects of nonlinearity should be modeled as alternative.

Suggested Citation

  • Kamil Feridun Turkman & Manuel González Scotto & Patrícia de Zea Bermudez, 2014. "Inference for Nonlinear Time Series Models," Springer Books, in: Non-Linear Time Series, edition 127, chapter 0, pages 121-197, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-07028-5_4
    DOI: 10.1007/978-3-319-07028-5_4
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