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Inference for Nonlinear Time Series Models

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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.

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Notes

  1. 1.

    Bauwens and Lubrano (1998) use trapezoidal rule of integration.

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Authors and Affiliations

  1. Departmento de Estatística e Investigação Operacional, Faculdade de Ciências Universidade de Lisboa, Lisboa, Portugal

    Kamil Feridun Turkman & Patrícia de Zea Bermudez

  2. Departamento de Matemática, Universidade de Aveiro, Aveiro, Portugal

    Manuel González Scotto

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  1. Kamil Feridun Turkman
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  2. Manuel González Scotto
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  3. Patrícia de Zea Bermudez
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Turkman, K.F., Scotto, M.G., de Zea Bermudez, P. (2014). Inference for Nonlinear Time Series Models. In: Non-Linear Time Series. Springer, Cham. https://doi.org/10.1007/978-3-319-07028-5_4

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