Bootstrap-Based Improvements for Inference with Clustered Errors
提出聚类自助t法来改进小样本（5-30个聚类）下聚类稳健标准误的推断，蒙特卡洛模拟显示该方法能将10%的拒绝率降至名义水平5%。
Researchers have increasingly realized the need to account for within-group dependence in estimating standard errors of regression parameter estimates. The usual solution is to calculate cluster-robust standard errors that permit heteroskedasticity and within-cluster error correlation, but presume that the number of clusters is large. Standard asymptotic tests can over-reject, however, with few (five to thirty) clusters. We investigate inference using cluster bootstrap-t procedures that provide asymptotic refinement. These procedures are evaluated using Monte Carlos, including the example of Bertrand, Duflo, and Mullainathan (2004). Rejection rates of 10% using standard methods can be reduced to the nominal size of 5% using our methods. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.