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Estimation of spatial autoregressive models with boundary specification problem

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  • Zhang, Zhengyu
  • Tao, Ji

Abstract

This article examines the impact of the boundary specification problem upon the estimation of spatial autoregressive models within an instrumental variable (IV) framework. We show the usual IV estimator remains consistent and asymptotically normal, but incurs an asymptotic bias of order Op(n2/ν), where n2 is the number of boundary units and ν is the total sample size. Our results show some promise for IV based inference even in the presence of the boundary problem.

Suggested Citation

  • Zhang, Zhengyu & Tao, Ji, 2013. "Estimation of spatial autoregressive models with boundary specification problem," Economics Letters, Elsevier, vol. 118(1), pages 130-134.
    • Handle: RePEc:eee:ecolet:v:118:y:2013:i:1:p:130-134
    DOI: 10.1016/j.econlet.2012.10.001
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    References listed on IDEAS

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    1. Charles F. Manski, 1993. "Identification of Endogenous Social Effects: The Reflection Problem," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(3), pages 531-542.
    2. Kelejian, Harry H & Prucha, Ingmar R, 1998. "A Generalized Spatial Two-Stage Least Squares Procedure for Estimating a Spatial Autoregressive Model with Autoregressive Disturbances," The Journal of Real Estate Finance and Economics, Springer, vol. 17(1), pages 99-121, July.
    3. Lung-fei Lee, 2003. "Best Spatial Two-Stage Least Squares Estimators for a Spatial Autoregressive Model with Autoregressive Disturbances," Econometric Reviews, Taylor & Francis Journals, vol. 22(4), pages 307-335.
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    More about this item

    Keywords

    Spatial autoregression; Social network; Missing data; Instrumental variable;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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