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Can We Trust Cluster-Corrected Standard Errors? An Application of Spatial Autocorrelation with Exact Locations Known

Author

Listed:
  • John Gibson

    (University of Waikato)

  • Bonggeun Kim

    (Seoul National University)

  • Susan Olivia

    (Monash University)

Abstract

Standard error corrections for clustered samples impose untested restrictions on spatial correlations. Our example shows these are too conservative, compared with a spatial error model that exploits information on exact locations of observations, causing inference errors when cluster corrections are used.

Suggested Citation

  • John Gibson & Bonggeun Kim & Susan Olivia, 2010. "Can We Trust Cluster-Corrected Standard Errors? An Application of Spatial Autocorrelation with Exact Locations Known," Working Papers in Economics 10/07, University of Waikato.
    • Handle: RePEc:wai:econwp:10/07
    as

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    File URL: https://repec.its.waikato.ac.nz/wai/econwp/1007.pdf
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    References listed on IDEAS

    as
    1. Thomas Barrios & Rebecca Diamond & Guido W. Imbens & Michal Kolesár, 2012. "Clustering, Spatial Correlations, and Randomization Inference," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 578-591, June.
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    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
    • C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data; Data Access

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