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A Study Of The Application Of State‐Dependent Models In Non‐Linear Time Series Analysis

Author

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  • V. Haggan
  • S. M. Heravi
  • M. B. Priestley

Abstract

. The theory of state‐dependent models was developed by Priestley (1980), and a few simple applications were given in Priestley (1981). In this paper, an extensive study of the application of state‐dependent models to a wide variety of non‐linear time series data is carried out. Both real and simulated data are used in the study, and the problems encountered are highlighted. The method is demonstrated to be successful in practice in many cases, and suggestions for the further development of the algorithm are also given.

Suggested Citation

  • V. Haggan & S. M. Heravi & M. B. Priestley, 1984. "A Study Of The Application Of State‐Dependent Models In Non‐Linear Time Series Analysis," Journal of Time Series Analysis, Wiley Blackwell, vol. 5(2), pages 69-102, March.
    • Handle: RePEc:bla:jtsera:v:5:y:1984:i:2:p:69-102
    DOI: 10.1111/j.1467-9892.1984.tb00379.x
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    Cited by:

    1. Li, Yushu & Shukur, Ghazi, 2009. "Testing for Unit Root against LSTAR Model: Wavelet Improvement under GARCH Distortion," CAFO Working Papers 2009:6, Linnaeus University, Centre for Labour Market Policy Research (CAFO), School of Business and Economics.
    2. Criddle, Keith R. & Havenner, Arthur M., 1989. "An Encompassing Approach To Modeling Fishery Dynamics," Working Papers 225824, University of California, Davis, Department of Agricultural and Resource Economics.
    3. S. Heravi & J. Easaw & R. Golinelli, 2016. "Generalized State-Dependent Models: A Multivariate Approach," Working Papers wp1067, Dipartimento Scienze Economiche, Universita' di Bologna.
    4. Easaw, Joshy & Golinelli, Roberto & Heravi, Saeed, 2022. "Professionals Forecasting Inflation: The Role of Inattentiveness and Uncertainty," Cardiff Economics Working Papers E2022/7, Cardiff University, Cardiff Business School, Economics Section.
    5. 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).

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