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Refined Tests For Spatial Correlation

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  • Robinson, Peter M.
  • Rossi, Francesca

Abstract

We consider testing the null hypothesis of no spatial correlation against the alternative of pure first order spatial autoregression. A test statistic based on the least squares estimate has good first-order asymptotic properties, but these may not be relevant in small- or moderate-sized samples, especially as (depending on properties of the spatial weight matrix) the usual parametric rate of convergence may not be attained. We thus develop tests with more accurate size properties, by means of Edgeworth expansions and the bootstrap. Although the least squares estimate is inconsistent for the correlation parameter, we show that under quite general conditions its probability limit has the correct sign, and that least squares testing is consistent; we also establish asymptotic local power properties. The finite-sample performance of our tests is compared with others in Monte Carlo simulations.

Suggested Citation

  • Robinson, Peter M. & Rossi, Francesca, 2015. "Refined Tests For Spatial Correlation," Econometric Theory, Cambridge University Press, vol. 31(6), pages 1249-1280, December.
    • Handle: RePEc:cup:etheor:v:31:y:2015:i:06:p:1249-1280_00
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    Cited by:

    1. Francesca Rossi & Peter M. Robinson, 2020. "Higher-Order Least Squares Inference for Spatial Autoregressions," Working Papers 04/2020, University of Verona, Department of Economics.
    2. Rossi, Francesca & Robinson, Peter M., 2023. "Higher-order least squares inference for spatial autoregressions," Journal of Econometrics, Elsevier, vol. 232(1), pages 244-269.
    3. Lee, Jungyoon & Robinson, Peter M., 2020. "Adaptive inference on pure spatial models," Journal of Econometrics, Elsevier, vol. 216(2), pages 375-393.
    4. Robinson, Peter M. & Rossi, Francesca, 2015. "Refinements in maximum likelihood inference on spatial autocorrelation in panel data," Journal of Econometrics, Elsevier, vol. 189(2), pages 447-456.
    5. Carlo Grillenzoni, 2024. "Forecasting Lattice and Point Spatial Data: Comparison of Unilateral and Multilateral SAR Models," Forecasting, MDPI, vol. 6(3), pages 1-18, August.
    6. Taşpınar, Süleyman & Doğan, Osman & Bera, Anil K., 2017. "GMM gradient tests for spatial dynamic panel data models," Regional Science and Urban Economics, Elsevier, vol. 65(C), pages 65-88.
    7. Bera, Anil K. & Doğan, Osman & Taşpınar, Süleyman, 2018. "Simple tests for endogeneity of spatial weights matrices," Regional Science and Urban Economics, Elsevier, vol. 69(C), pages 130-142.
    8. Maria Kyriacou & Peter C. B. Phillips & Francesca Rossi, 2017. "Indirect inference in spatial autoregression," Econometrics Journal, Royal Economic Society, vol. 20(2), pages 168-189, June.
    9. Maria Kyriacou & Peter C. B. Phillips & Francesca Rossi, 2017. "Indirect inference in spatial autoregression," Econometrics Journal, Royal Economic Society, vol. 20(2), pages 168-189, June.
    10. Liu, Xiaodong & Prucha, Ingmar R., 2018. "A robust test for network generated dependence," Journal of Econometrics, Elsevier, vol. 207(1), pages 92-113.
    11. Zhenlin Yang, 2021. "Joint tests for dynamic and spatial effects in short panels with fixed effects and heteroskedasticity," Empirical Economics, Springer, vol. 60(1), pages 51-92, January.
    12. Jungyoon Lee & Peter M Robinson, 2018. "Adaptive Inference on Pure Spatial Models," STICERD - Econometrics Paper Series 596, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
    13. Chaonan Jiang & Davide La Vecchia & Elvezio Ronchetti & Olivier Scaillet, 2023. "Saddlepoint Approximations for Spatial Panel Data Models," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 118(542), pages 1164-1175, April.
    14. Abhimanyu Gupta & Xi Qu, 2021. "Consistent specification testing under spatial dependence," Papers 2101.10255, arXiv.org, revised Aug 2022.

    More about this item

    JEL classification:

    • J1 - Labor and Demographic Economics - - Demographic Economics

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