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Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension

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  • Gupta, A
  • Robinson, PM

Abstract

Pseudo maximum likelihood estimates are developed for higher-order spatial autoregres- sive models with increasingly many parameters, including models with spatial lags in the dependent variables and regression models with spatial autoregressive disturbances. We consider models with and without a linear or nonlinear regression component. Sufficient conditions for consistency and asymptotic normality are provided, the results varying ac- cording to whether the number of neighbours of a particular unit diverges or is bounded. Monte Carlo experiments examine nite-sample behaviour.

Suggested Citation

  • Gupta, A & Robinson, PM, 2015. "Pseudo Maximum Likelihood Estimation of Spatial Autoregressive Models with Increasing Dimension," Economics Discussion Papers 22698, University of Essex, Department of Economics.
    • Handle: RePEc:esx:essedp:22698
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    Cited by:

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    4. Jakub Olejnik & Alicja Olejnik, 2020. "QML estimation with non-summable weight matrices," Journal of Geographical Systems, Springer, vol. 22(4), pages 469-495, October.
    5. Gupta, Abhimanyu, 2018. "Nonparametric specification testing via the trinity of tests," Journal of Econometrics, Elsevier, vol. 203(1), pages 169-185.
    6. Federico Martellosio & Grant Hillier, 2019. "Adjusted QMLE for the spatial autoregressive parameter," Papers 1909.08141, arXiv.org.
    7. Gupta, Abhimanyu, 2023. "Efficient closed-form estimation of large spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 148-167.
    8. Huijuan Xiao & Sheng Bao & Jingzheng Ren & Zhenci Xu & Song Xue & Jianguo Liu, 2024. "Global transboundary synergies and trade-offs among Sustainable Development Goals from an integrated sustainability perspective," Nature Communications, Nature, vol. 15(1), pages 1-12, December.
    9. Rossi, Francesca & Lieberman, Offer, 2023. "Spatial autoregressions with an extended parameter space and similarity-based weights," Journal of Econometrics, Elsevier, vol. 235(2), pages 1770-1798.
    10. Ma, Yingying & Lan, Wei & Zhou, Fanying & Wang, Hansheng, 2020. "Approximate least squares estimation for spatial autoregressive models with covariates," Computational Statistics & Data Analysis, Elsevier, vol. 143(C).
    11. Higgins, Ayden & Martellosio, Federico, 2023. "Shrinkage estimation of network spillovers with factor structured errors," Journal of Econometrics, Elsevier, vol. 233(1), pages 66-87.
    12. Sylvain Barde & Rowan Cherodian & Guy Tchuente, 2023. "Moran's I Lasso for models with spatially correlated data," Papers 2310.02773, arXiv.org.
    13. Martellosio, Federico & Hillier, Grant, 2020. "Adjusted QMLE for the spatial autoregressive parameter," Journal of Econometrics, Elsevier, vol. 219(2), pages 488-506.
    14. Abhimanyu Gupta & Xi Qu, 2021. "Consistent specification testing under spatial dependence," Papers 2101.10255, arXiv.org, revised Aug 2022.

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    More about this item

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • 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
    • C36 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Instrumental Variables (IV) Estimation

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