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The local asymptotic normality of a class of generalized random coefficient autoregressive processes

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  • Hwang, S. Y.
  • Basawa, I. V.

Abstract

The local asymptotic normality for a class of generalized random coefficient autoregressive processes is established. This property implies the asymptotic optimality of the maximum likelihood estimator and the related test statistics. The model includes standard random coefficient autoregressive processes, Markovian bilinear models, and random coefficient exponential autoregressive processes as special cases.

Suggested Citation

  • Hwang, S. Y. & Basawa, I. V., 1997. "The local asymptotic normality of a class of generalized random coefficient autoregressive processes," Statistics & Probability Letters, Elsevier, vol. 34(2), pages 165-170, June.
    • Handle: RePEc:eee:stapro:v:34:y:1997:i:2:p:165-170
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    References listed on IDEAS

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    1. Young Hwang, Sun & Basawa, I. V., 1993. "Asymptotic optimal inference for a class of nonlinear time series models," Stochastic Processes and their Applications, Elsevier, vol. 46(1), pages 91-113, May.
    2. Paul D. Feigin & Richard L. Tweedie, 1985. "Random Coefficient Autoregressive Processes:A Markov Chain Analysis Of Stationarity And Finiteness Of Moments," Journal of Time Series Analysis, Wiley Blackwell, vol. 6(1), pages 1-14, January.
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    Cited by:

    1. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2019. "Random coefficient continuous systems: Testing for extreme sample path behavior," Journal of Econometrics, Elsevier, vol. 209(2), pages 208-237.
    2. Tao, Yubo & Phillips, Peter C.B. & Yu, Jun, 2017. "Random Coefficient Continuous Systems: Testing for Extreme Sample Path Behaviour," Economics and Statistics Working Papers 18-2017, Singapore Management University, School of Economics.
    3. Ke-Ang Fu & Ting Li & Chang Ni & Wenkai He & Renshui Wu, 2021. "Asymptotics for the conditional self-weighted M-estimator of GRCA(1) models with possibly heavy-tailed errors," Statistical Papers, Springer, vol. 62(3), pages 1407-1419, June.

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