中文摘要：

误差项不服从经典假设时,空间面板数据模型的Moran＇s Ⅰ检验存在较大的水平扭曲,导致空间相关性检验失效.本文采用改进的Bootstrap抽样方法,对空间面板数据模型的Moran＇s Ⅰ检验进行优化.蒙特卡洛模拟结果表明,在误差项正态分布条件下,渐近Moran＇s Ⅰ检验和Bootstrap Moran＇s Ⅰ检验均具有较优越的检验水平和检验功效表现;在误差项时间序列相关条件下,渐近Moran＇s Ⅰ检验存在严重的水平扭曲,而Bootstrap Moran＇s Ⅰ检验能够有效地矫正水平扭曲,且检验功效优于渐近Moran＇s Ⅰ检验,是更为有效的检验统计量.

英文摘要：

Moran＇s I test has been widely used for spatial autocorrelation. When the error term is non-normally distributed, Moran＇s I test turns out to be large size distortion, leading to a poor performance of the test. This study adopts the modified Bootstrap methods to improve the performance of Moran＇s I test. Monte Carlo simulation experiment resuhs suggest that, when the error term is normally distributed, both asymptotic Moran＇s I test and Bootstrap Moran＇s I test have superior size and power performance; when error term is serial correlated, asymptotic Moran＇s I test results in a serious size distortion, while bootstrap Moran＇s I test has a better performance, which has effectively reduced the size distortion and increased the power of asymptotic Moran＇s I test.