Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/97480
Authors: 
Webb, Matthew D.
Year of Publication: 
2013
Series/Report no.: 
Queen's Economics Department Working Paper 1315
Abstract: 
Many empirical projects are well suited to incorporating a linear difference-in-differences research design. While estimation is straightforward, reliable inference can be a challenge. Past research has not only demonstrated that estimated standard errors are biased dramatically downwards in models possessing a group clustered design, but has also suggested a number of bootstrap-based improvements to the inference procedure. In this paper, I first demonstrate using Monte Carlo experiments, that these bootstrap-based procedures and traditional cluster-robust standard errors perform poorly in situations with fewer than eleven clusters - a setting faced in many empirical applications. With few clusters, the wild cluster bootstrap-t procedure results in p-values that are not point identified. I subsequently introduce two easy-to-implement alternative procedures that involve the wild bootstrap. Further Monte Carlo simulations provide evidence that the use of a 6-point distribution with the wild bootstrap can improve the reliability of inference.
Subjects: 
CRVE
grouped data
clustered data
panel data
cluster wild bootstrap
JEL: 
C15
C21
C23
Document Type: 
Working Paper

Files in This Item:
File
Size
462.51 kB





Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.