Bias-Corrected Estimation for Spatial Autocorrelation - YANG, Zhenlin (Institutional Knowledge at Singapore Management University, 2010)
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.