Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/230566
Authors: 
MacKinnon, James G.
Year of Publication: 
2019
Series/Report no.: 
Queen’s Economics Department Working Paper No. 1413
Abstract: 
In many fields of economics, and also in other disciplines, it is hard to justify the assumption that the random error terms in regression models are uncorrelated. It seems more plausible to assume that they are correlated within clusters, such as geographical areas or time periods, but uncorrelated across clusters. It has therefore become very popular to use "clustered" standard errors, which are robust against arbitrary patterns of within-cluster variation and covariation. Conventional methods for inference using clustered standard errors work very well when the model is correct and the data satisfy certain conditions, but they can produce very misleading results in other cases. This paper discusses some of the issues that users of these methods need to be aware of.
Subjects: 
CRVE
grouped data
clustered data
panel data
wild cluster bootstrap
difference-in-differences
treatment model
fixed effects
JEL: 
C15
C21
C23
Document Type: 
Working Paper

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