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Asymptotic inference for nearly nonstationary AR(1) processes with possibly infinite variance

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  • Hwang, Kyo-Shin
  • Pang, Tian-Xiao

Abstract

In this article, the nearly nonstationary AR(1) processes, that is, Yt=[beta]Yt-1+[epsilon]t with [beta]=1-[gamma]/n and [gamma] being a fixed constant, are studied under the condition that the disturbances of the processes are a sequence of i.i.d. random variables, which is in the domain of attraction of the normal law with zero means and possibly infinite variances. Compared with the result in Chan and Wei (1987), a more robust statistics about the least squares estimate of [beta] is introduced.

Suggested Citation

  • Hwang, Kyo-Shin & Pang, Tian-Xiao, 2009. "Asymptotic inference for nearly nonstationary AR(1) processes with possibly infinite variance," Statistics & Probability Letters, Elsevier, vol. 79(22), pages 2374-2379, November.
    • Handle: RePEc:eee:stapro:v:79:y:2009:i:22:p:2374-2379
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    1. Jeganathan, P., 1999. "On Asymptotic Inference In Cointegrated Time Series With Fractionally Integrated Errors," Econometric Theory, Cambridge University Press, vol. 15(4), pages 583-621, August.
    2. Jeganathan, P., 1991. "On the Asymptotic Behavior of Least-Squares Estimators in AR Time Series with Roots Near the Unit Circle," Econometric Theory, Cambridge University Press, vol. 7(3), pages 269-306, September.
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    Cited by:

    1. Fu, Ke-Ang & Li, Yuechao & Ng, Andrew Cheuk-Yin, 2013. "Asymptotics for the residual-based bootstrap approximation in nearly nonstationary AR(1) models with possibly heavy-tailed innovations," Statistics & Probability Letters, Elsevier, vol. 83(11), pages 2553-2562.

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