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Adaptive inference on pure spatial models

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  • Lee, Jungyoon
  • Robinson, Peter M.

Abstract

In a general class of semiparametric pure spatial models (having no explanatory variables) allowing nonlinearity in the parameter and the weight matrix, we propose adaptive tests and estimates which are asymptotically efficient in the presence of unknown, nonparametric distributional form. Feasibility of adaptive estimation is verified and its efficiency improvement over Gaussian pseudo maximum likelihood is shown to be either less than, or more than, for models with explanatory variables, depending on properties of the spatial weight matrix. An adaptive Lagrange Multiplier testing procedure for lack of spatial dependence is proposed and this, and our adaptive parameter estimate, are extended to cover regression with spatially correlated errors.

Suggested Citation

  • Lee, Jungyoon & Robinson, Peter M., 2020. "Adaptive inference on pure spatial models," Journal of Econometrics, Elsevier, vol. 216(2), pages 375-393.
    • Handle: RePEc:eee:econom:v:216:y:2020:i:2:p:375-393
    DOI: 10.1016/j.jeconom.2019.10.006
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    References listed on IDEAS

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    More about this item

    Keywords

    Efficient test; Adaptive estimation; Spatial models;
    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
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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