A simple and reliable method of inference for the spatial parameter in spatial autoregressive models is introduced, based on a statistic obtained by centering and rescaling the numerator of the concentrated Gaussian score function. The resulted tests and confidence intervals are robust against the distributional misspecifications and are insensitive to the spatial layouts and the error standard deviation. In contrast, the standard methods based on Gaussian score and information matrix may lead to inconsistent inference when errors are non normal, and can be quite sensitive to the spatial layouts and the error standard deviation even when errors are normally distributed. Extensive Monte Carlo results are reported and an empirical illustration is given.
Spatial dependence, Confidence interval, LM Tests, Centering, Rescaling, Finite sample performance, Robustness
YANG, Zhenlin and Shen, Y..
A Simple and Robust Method of Inference for Spatial Lag Dependence. (2011). Research Collection School Of Economics.
Available at: http://ink.library.smu.edu.sg/soe_research/1407
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