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Inference For A Special Bilinear Time-Series Model

Author

Listed:
  • Shiqing Ling
  • Liang Peng
  • Fukang Zhu

Abstract

type="main" xml:id="jtsa12092-abs-0001"> It is well known that estimating bilinear models is quite challenging. Many different ideas have been proposed to solve this problem. However, there is not a simple way to do inference even for its simple cases. This article proposes a generalized autoregressive conditional heteroskedasticity-type maximum likelihood estimator for estimating the unknown parameters for a special bilinear model. It is shown that the proposed estimator is consistent and asymptotically normal under only finite fourth moment of errors.

Suggested Citation

  • Shiqing Ling & Liang Peng & Fukang Zhu, 2015. "Inference For A Special Bilinear Time-Series Model," Journal of Time Series Analysis, Wiley Blackwell, vol. 36(1), pages 61-66, January.
    • Handle: RePEc:bla:jtsera:v:36:y:2015:i:1:p:61-66
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    File URL: http://hdl.handle.net/10.1111/jtsa.12092
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    References listed on IDEAS

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    5. Jian Liu, 1989. "A Simple Condition For The Existence Of Some Stationary Bilinear Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(1), pages 33-39, January.
    6. Francesco Giordano & Cosimo Vitale, 2003. "CLS asymptotic variance for a particular relevant bilinear time series model," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 12(2), pages 169-185, December.
    7. Granger, C. W. J. & Andersen, Allan, 1978. "On the invertibility of time series models," Stochastic Processes and their Applications, Elsevier, vol. 8(1), pages 87-92, November.
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    Cited by:

    1. Predrag M. Popović & Hassan S. Bakouch, 2020. "A bivariate integer-valued bilinear autoregressive model with random coefficients," Statistical Papers, Springer, vol. 61(5), pages 1819-1840, October.
    2. Philip Hans Franses, 2019. "Model‐based forecast adjustment: With an illustration to inflation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(2), pages 73-80, March.

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