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Non‐Linear Time Series Analysis Of Blowfly Population

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  • Ruey S. Tsay

Abstract

. In recent years there has been a growing interest in studying non‐linear time series and various non‐linear models have been proposed in the literature. In this paper, I consider non‐linear time series modelling via a case study. Several important issues concerning non‐linear time series models and data analysis emerge from the study.

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  • Ruey S. Tsay, 1988. "Non‐Linear Time Series Analysis Of Blowfly Population," Journal of Time Series Analysis, Wiley Blackwell, vol. 9(3), pages 247-263, May.
    • Handle: RePEc:bla:jtsera:v:9:y:1988:i:3:p:247-263
    DOI: 10.1111/j.1467-9892.1988.tb00469.x
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    Cited by:

    1. Chan Wai-Sum & Hung King-Chi, 2011. "On Robust Testing and Modelling of Threshold-Type Non-Linearity in ASEAN Foreign Exchange Markets," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 5(2), pages 1-16, July.
    2. Kiani, Khurshid M., 2016. "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, vol. 53(C), pages 179-186.
    3. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    4. Khurshid M. Kiani & Prasad V. Bidarkota, 2004. "On Business Cycle Asymmetries in G7 Countries," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 66(3), pages 333-351, July.
    5. King Chi Hung & Siu Hung Cheung & Wai-Sum Chan & Li-Xin Zhang, 2009. "On a robust test for SETAR-type nonlinearity in time series analysis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(5), pages 445-464.
    6. Khurshid Kiani, 2005. "Detecting Business Cycle Asymmetries Using Artificial Neural Networks and Time Series Models," Computational Economics, Springer;Society for Computational Economics, vol. 26(1), pages 65-89, August.

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