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Strategies for Modelling Nonlinear Time Series Relationships

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  • Granger, Clive W. J.

Abstract

Building models of nonlinear relationships are inherently more difficult than linear ones There are more possibilities, many more parameters and thus more mistakes can be made. It is suggested that a strategy be applied when attempting such modelling involving testing for linearity, considering just a few model types of parsimonious form and then performing post‐sample evaluation of the resulting models compared to a linear one. The strategy proposed is a ‘simple‐to‐general’ one and the application of a heteroskedasticity correction is not recommended
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Suggested Citation

  • Granger, Clive W. J., "undated". "Strategies for Modelling Nonlinear Time Series Relationships," Department of Econometrics and Business Statistics Working Papers 267405, Monash University, Department of Econometrics and Business Statistics.
    • Handle: RePEc:ags:monebs:267405
    DOI: 10.22004/ag.econ.267405
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    Cited by:

    1. is not listed on IDEAS
    2. Boero, Gianna & Marrocu, Emanuela, 2004. "The performance of SETAR models: a regime conditional evaluation of point, interval and density forecasts," International Journal of Forecasting, Elsevier, vol. 20(2), pages 305-320.
    3. Gary Madden & Joachim Tan, 2008. "Forecasting international bandwidth capacity using linear and ANN methods," Applied Economics, Taylor & Francis Journals, vol. 40(14), pages 1775-1787.

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