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Bias-Corrected Estimation for Spatial Autocorrelation

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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.

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Bibliographic Info

Paper provided by Singapore Management University, School of Economics in its series Working Papers with number 12-2010.

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Length: 50 pages
Date of creation: Oct 2010
Date of revision:
Publication status: Published in SMU Economics and Statistics Working Paper Series
Handle: RePEc:siu:wpaper:12-2010

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Related research

Keywords: Third-order bias; Third-order variance; Bootstrap; Concentrated estimating equation; Monte Carlo; Quasi-MLE; Spatial layout.;

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