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Inverse Gaussian Autoregressive Models

Author

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  • B. Abraham
  • N. Balakrishna

Abstract

A first‐order autoregressive process with one‐dimensional inverse Gaussian marginals is introduced. The innovation distributions are obtained in certain special cases. The unknown parameters are estimated using different methods and these estimators are shown to be consistent and asymptotically normal. Performance of the estimators is discussed using simulation experiments.

Suggested Citation

  • B. Abraham & N. Balakrishna, 1999. "Inverse Gaussian Autoregressive Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 20(6), pages 605-618, November.
    • Handle: RePEc:bla:jtsera:v:20:y:1999:i:6:p:605-618
    DOI: 10.1111/1467-9892.00161
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    Cited by:

    1. N. Balakrishna & Bovas Abraham & Ranjini Sivakumar, 2006. "Gamma stochastic volatility models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 153-171.
    2. Preve, Daniel, 2015. "Linear programming-based estimators in nonnegative autoregression," Journal of Banking & Finance, Elsevier, vol. 61(S2), pages 225-234.
    3. Alice B. V. Mello & Maria C. S. Lima & Abraão D. C. Nascimento, 2022. "A notable Gamma‐Lindley first‐order autoregressive process: An application to hydrological data," Environmetrics, John Wiley & Sons, Ltd., vol. 33(4), June.
    4. D. Moriña & P. Puig & J. Valero, 2015. "A characterization of the innovations of first order autoregressive models," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 78(2), pages 219-225, February.

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