IS SPATIAL BOOTSTRAPPING A PANACEA FOR VALID INFERENCE?
通过蒙特卡洛模拟发现，基于受限残差的wild bootstrap检验在空间横截面数据中优于渐近检验和其他自助法检验。
ABSTRACT Bootstrapping methods have so far been rarely used to evaluate spatial datasets. Based on an extensive Monte Carlo study we find that also for spatial, cross‐sectional data, the wild bootstrap test proposed by Davidson and Flachaire ( ) based on restricted residuals clearly outperforms asymptotic as well as competing bootstrap tests, like the pairs bootstrap.