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On the least squares estimator in a nearly unstable sequence of stationary spatial AR models

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  • Baran, Sándor
  • Pap, Gyula

Abstract

A nearly unstable sequence of stationary spatial autoregressive processes is investigated, when the sum of the absolute values of the autoregressive coefficients tends to one. It is shown that after an appropriate normalization the least squares estimator for these coefficients has a normal limit distribution. If none of the parameters equals zero then the typical rate of convergence is n.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:4:p:686-698
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    References listed on IDEAS

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    1. Baran, Sándor & Pap, Gyula & van Zuijlen, Martien C. A., 2004. "Asymptotic inference for a nearly unstable sequence of stationary spatial AR models," Statistics & Probability Letters, Elsevier, vol. 69(1), pages 53-61, August.
    2. Paulauskas, Vygantas, 2007. "On unit roots for spatial autoregressive models," Journal of Multivariate Analysis, Elsevier, vol. 98(1), pages 209-226, January.
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    Cited by:

    1. Badi H. Baltagi & Junjie Shu, 2024. "A Survey of Spatial Unit Roots," Mathematics, MDPI, vol. 12(7), pages 1-31, March.
    2. Baran, Sándor & Pap, Gyula, 2012. "Parameter estimation in a spatial unilateral unit root autoregressive model," Journal of Multivariate Analysis, Elsevier, vol. 107(C), pages 282-305.

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