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Bias-Corrected Estimation for Spatial Autocorrelation

Author

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  • Zhenlin Yang

    (School of Economics, Singapore Management University)

Abstract

The biasedness issue arising from the maximum likelihood estimation of the spatial autoregressive model (SAR) is further investigated under a broader set-up than that in Bao and Ullah (2007a). A major difficulty in analytically evaluating the expectations of ratios of quadratic forms is overcome by a simple bootstrap procedure. With that, the corrections on bias and variance of the spatial estimator can easily be made up to third-order, and once this is done, the estimators of other model parameters become nearly unbiased. Compared with the analytical approach, the new approach is much simpler, and can easily be extended to other models of a similar structure. Extensive Monte Carlo results show that the new approach performs excellently in general.

Suggested Citation

  • Zhenlin Yang, 2010. "Bias-Corrected Estimation for Spatial Autocorrelation," Working Papers 12-2010, Singapore Management University, School of Economics.
    • Handle: RePEc:siu:wpaper:12-2010
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    File URL: https://mercury.smu.edu.sg/rsrchpubupload/17434/ZLYangOct10.pdf
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    Cited by:

    1. Yang, Zhenlin, 2015. "A general method for third-order bias and variance corrections on a nonlinear estimator," Journal of Econometrics, Elsevier, vol. 186(1), pages 178-200.

    More about this item

    Keywords

    Third-order bias; Third-order variance; Bootstrap; Concentrated estimating equation; Monte Carlo; Quasi-MLE; Spatial layout.;
    All these keywords.

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models

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