Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/149081
Authors: 
MacKinnon, James G.
Webb, Matthew D.
Year of Publication: 
2016
Series/Report no.: 
Queen's Economics Department Working Paper 1355
Abstract: 
Inference using difference-in-differences with clustered data requires care. Previous research has shown that t tests based on a cluster-robust variance estimator (CRVE) severely over-reject when there are few treated clusters, that different variants of the wild cluster bootstrap can over-reject or under-reject severely, and that procedures based on randomization show promise. We demonstrate that randomization inference (RI) procedures based on estimated coefficients, such as the one proposed by Conley and Taber (2011), fail whenever the treated clusters are atypical. We propose an RI procedure based on t statistics which fails only when the treated clusters are atypical and few in number. We also propose a bootstrap-based alternative to randomization inference, which mitigates the discrete nature of RI P values when the number of clusters is small.
Subjects: 
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
DiD
randomization inference
JEL: 
C12
C21
Document Type: 
Working Paper

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