 How cluster-robust inference is changing applied econometricsCanadian Journal of Economics, 2019, vol. 52, issue 3, 851-881 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. JEL-codes: C12 (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cje:issued:v:52:y:2019:i:3:p:851-881 Ordering information: This journal article can be ordered from Access Statistics for this article Canadian Journal of Economics is currently edited by Zhiqi Chen More articles in Canadian Journal of Economics from Canadian Economics Association Canadian Economics Association Prof. Werrner Antweiler, Treasurer UBC Sauder School of Business 2053 Main Mall Vancouver, BC, V6T 1Z2. Contact information at EDIRC. |
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