When the error does not satisfy the assumption of normal independent distribution,the spatial correlation test based on the asymptotic distribution of Moran＇s I test statistic suffers weak power.In order to solve this pro-blem,Bootstrap methods are applied to the Moran＇s Ⅰ statistic for spatial correlation test in the spatial econometric model.The results of Monte Carlo simulation indicate that the Bootstrap test is as effective as（even better than） the asymptotic test when the error satisfies the i.i.d.normal distribution;that the Bootstrap test effectively improves the power of asymptotic test when the error does not satisfy the i.i.d.normal distribution and is heteroscedastic;that both the spatial correlation coefficient and the spatial contiguity structure significantly affect the power of Bootstrap test with small sample scale,especially,under the Queen matrix with dense spatial contiguity and the spatial correlation coefficient less than 0,the power of the Bootstrap test is obviously higher than that of the asymptotic test;and that,when the spatial weight matrix is the Queen matrix,the power curve of the Bootstrap test changes from ＂√＂type to ＂V＂ type,with the symmetry being increased and the influence of spatial contiguity structure on the power being weakened.