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Higher-order least squares inference for spatial autoregressions

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  • Rossi, Francesca
  • Robinson, Peter M.

Abstract

We develop refined inference for spatial regression models with predetermined regressors. The ordinary least squares estimate of the spatial parameter is neither consistent nor asymptotically normal, unless the elements of the spatial weight matrix uniformly vanish as sample size diverges. We develop refined testing of the hypothesis of no spatial dependence, without requiring such negligibility of spatial weights, by formal Edgeworth expansions. We also develop such higher-order expansions for both an unstudentized and a studentized transformed estimate, where the studentized one can be used to provide refined interval estimates. A Monte Carlo study of finite sample performance is included.

Suggested Citation

  • Rossi, Francesca & Robinson, Peter M., 2023. "Higher-order least squares inference for spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 244-269.
    • Handle: RePEc:eee:econom:v:232:y:2023:i:1:p:244-269
    DOI: 10.1016/j.jeconom.2022.01.010
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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 autoregression; Least squares estimation; Higher-order inference; Edgeworth expansion; Testing spatial independence;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • 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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