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A note on spatial–temporal lattice modeling and maximum likelihood estimation

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  • Zhang, Xiang
  • Zheng, Yanbing

Abstract

Spatial–temporal linear models and the corresponding likelihood-based statistical inference are important tools for the analysis of spatial–temporal lattice data. In this paper, we study the asymptotic properties of maximum likelihood estimates under a general asymptotic framework for spatial–temporal linear models. We propose mild regularity conditions on the spatial–temporal weight matrices and derive the asymptotic properties (consistency and asymptotic normality) of maximum likelihood estimates. A simulation study is conducted to examine the finite-sample properties of the maximum likelihood estimates.

Suggested Citation

  • Zhang, Xiang & Zheng, Yanbing, 2012. "A note on spatial–temporal lattice modeling and maximum likelihood estimation," Statistics & Probability Letters, Elsevier, vol. 82(12), pages 2145-2155.
  • Handle: RePEc:eee:stapro:v:82:y:2012:i:12:p:2145-2155
    DOI: 10.1016/j.spl.2012.07.019
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    References listed on IDEAS

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    1. Yu, Jihai & de Jong, Robert & Lee, Lung-fei, 2008. "Quasi-maximum likelihood estimators for spatial dynamic panel data with fixed effects when both n and T are large," Journal of Econometrics, Elsevier, vol. 146(1), pages 118-134, September.
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    5. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, May.
    6. Lung-Fei Lee, 2004. "Asymptotic Distributions of Quasi-Maximum Likelihood Estimators for Spatial Autoregressive Models," Econometrica, Econometric Society, vol. 72(6), pages 1899-1925, November.
    7. H. Kelejian, Harry & Prucha, Ingmar R., 2001. "On the asymptotic distribution of the Moran I test statistic with applications," Journal of Econometrics, Elsevier, vol. 104(2), pages 219-257, September.
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