登入選單

返回Google圖書搜尋

Bootstrap-based Improvements for Inference with Clustered Errors

Adrian Colin CameronJonah B. GelbachDouglas Lee Miller

出版	National Bureau of Economic Research, 2007
URL	http://books.google.com.hk/books?id=df0mnQEACAAJ&hl=&source=gbs_api

註釋

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 (5-30) 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 ten percent using standard methods can be reduced to the nominal size of five percent using our methods.