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Forecasting Time Series:A Comparative Analysis Of Alternative Classes Of Time Series Models

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  • Phillip A. Cartwright

Abstract

. Performance of the state dependent model developed by Priestley is evaluated relative to that of bilinear and standard linear models using two well‐known time series. The results indicate the use of broader classes of time series models beyond the conventional ARMA class is likely to lead to significant reductions in forecasting error. However, there are difficult problems relating to the identification of the order of the model, estimation of the parameters, and determination of the correct nonlinear model.

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  • Phillip A. Cartwright, 1985. "Forecasting Time Series:A Comparative Analysis Of Alternative Classes Of Time Series Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(4), pages 203-211, July.
    • Handle: RePEc:bla:jtsera:v:6:y:1985:i:4:p:203-211
    DOI: 10.1111/j.1467-9892.1985.tb00410.x
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    Cited by:

    1. Guan, Bo & Silva, Emmanuel Sirimal & Hassani, Hossein & Heravi, Saeed, 2022. "Forecasting tourism growth with State-Dependent Models," Annals of Tourism Research, Elsevier, vol. 94(C).
    2. Modesto Gayo & Dominique Joye & Yannick Lemel, 2018. "Testing the universalism of Bourdieu's homology: Structuring patterns of lifestyle across 26 countries," Working Papers 2018-04, Center for Research in Economics and Statistics.

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