Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/188899 
Year of Publication: 
2017
Series/Report no.: 
Queen's Economics Department Working Paper No. 1387
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard errors can be much too small if the number of treated (or control) clusters is small. Standard errors also tend to be too small when cluster sizes vary a lot, resulting in too many false positives. Bootstrap methods generally perform better than t-tests, but they can also yield very misleading inferences in some cases.
Subjects: 
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD regression
JEL: 
C15
C21
C23
Document Type: 
Working Paper

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