Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/230563 
Year of Publication: 
2019
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1355
Publisher: 
Queen's University, Department of Economics, Kingston (Ontario)
Abstract: 
Inference using difference-in-differences with clustered data requires care. Previous research has shown that, when there are few treated clusters, t-tests based on cluster-robust variance estimators (CRVEs) severely overreject, and different variants of the wild cluster bootstrap can either overreject or underreject dramatically. We study two randomization inference (RI) procedures. A procedure based on estimated coefficients may be unreliable when clusters are heterogeneous. A procedure based on t-statistics typically performs better (although by no means perfectly) under the null, but at the cost of some power loss. An empirical example demonstrates that alternative procedures can yield dramatically different inferences.
Subjects: 
CRVE
grouped data
clustered data
panel data
randomization inference
difference-in-differences
wild cluster bootstrap
DiD
JEL: 
C12
C21
Document Type: 
Working Paper

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