How cluster-robust inference is changing applied econometrics - James G. MacKinnon (Queen's University, Department of Economics, 2019)
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.
Keywords: C15; C21; C23; 330; CRVE; grouped data; clustered data; panel data; wild cluster bootstrap; difference-in-differences; treatment model; fixed effects