Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230574
Authors: 
MacKinnon, James G.
Webb, Matthew
Year of Publication: 
2019
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1421
Abstract: 
We discuss when and how to deal with possibly clustered errors in linear regression models. Specifically, we discuss situations in which a regression model may plausibly be treated as having error terms that are arbitrarily correlated within known clusters but uncorrelated across them. The methods we discuss include various covariance matrix estimators, possibly combined with various methods of obtaining critical values, several bootstrap procedures, and randomization inference. Special attention is given to models with few treated clusters and clusters that vary in size, where inference may be problematic. Two empirical examples and a simulation experiment illustrate the methods we discuss and the concerns we raise.
Subjects: 
clustered data
cluster-robust variance estimator
CRVE
wild cluster bootstrap
robust inference
JEL: 
C15
C21
C23
Document Type: 
Working Paper

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