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Asymptotic inference for a nearly unstable sequence of stationary spatial AR models

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  • Baran, Sándor
  • Pap, Gyula
  • van Zuijlen, Martien C. A.
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    Abstract

    A nearly unstable sequence of stationary spatial autoregressive processes is investigated, where the autoregressive coefficients are equal, and their sum tends to one. It is shown that the limiting distribution of the least-squares estimator for this coefficient is normal and, in contrast to the doubly geometric process, the typical rate of convergence is n-5/4.

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    Bibliographic Info

    Article provided by Elsevier in its journal Statistics & Probability Letters.

    Volume (Year): 69 (2004)
    Issue (Month): 1 (August)
    Pages: 53-61

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    Handle: RePEc:eee:stapro:v:69:y:2004:i:1:p:53-61

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    Related research

    Keywords: Autoregressive model Asymptotic normality Martingale central limit theorem;

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    Cited by:
    1. Baran, Sándor & Pap, Gyula, 2009. "On the least squares estimator in a nearly unstable sequence of stationary spatial AR models," Journal of Multivariate Analysis, Elsevier, vol. 100(4), pages 686-698, April.
    2. Martellosio, Federico, 2011. "Efficiency of the OLS estimator in the vicinity of a spatial unit root," Statistics & Probability Letters, Elsevier, vol. 81(8), pages 1285-1291, August.
    3. Ojeda, Silvia & Vallejos, Ronny & Bustos, Oscar, 2010. "A new image segmentation algorithm with applications to image inpainting," Computational Statistics & Data Analysis, Elsevier, vol. 54(9), pages 2082-2093, September.

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