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Estimating a spatial autoregressive model with an endogenous spatial weight matrix

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  • Qu, Xi
  • Lee, Lung-fei

Abstract

The spatial autoregressive (SAR) model is a standard tool for analyzing data with spatial correlation. Conventional estimation methods rely on the key assumption that the spatial weight matrix is strictly exogenous, which would likely be violated in some empirical applications where spatial weights are determined by economic factors. This paper presents model specification and estimation of the SAR model with an endogenous spatial weight matrix. We provide three estimation methods: two-stage instrumental variable (2SIV) method, quasi-maximum likelihood estimation (QMLE) approach, and generalized method of moments (GMM). We establish the consistency and asymptotic normality of these estimators and investigate their finite sample properties by a Monte Carlo study.

Suggested Citation

  • Qu, Xi & Lee, Lung-fei, 2015. "Estimating a spatial autoregressive model with an endogenous spatial weight matrix," Journal of Econometrics, Elsevier, vol. 184(2), pages 209-232.
    • Handle: RePEc:eee:econom:v:184:y:2015:i:2:p:209-232
    DOI: 10.1016/j.jeconom.2014.08.008
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    References listed on IDEAS

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    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Spatial autoregressive model; Endogenous spatial weight matrix; 2SIV; QMLE; GMM;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation

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