Pitfalls when Estimating Treatment Effects Using Clustered Data

James MacKinnon and Matthew Webb

No 274713, Queen's Economics Department Working Papers from Queen's University - Department of Economics

Abstract: Inference for estimates of treatment effects with clustered data requires great care when treatment is assigned at the group level. This is true for both pure treatment models and difference-in-differences regressions. Even when the number of clusters is quite large, cluster-robust standard errors can be much too small if the number of treated (or control) clusters is small. Standard errors also tend to be too small when cluster sizes vary a lot, resulting in too many false positives. Bootstrap methods generally perform better than t tests, but they can also yield very misleading inferences in some cases.

Keywords: Financial Economics; Research Methods/Statistical Methods (search for similar items in EconPapers)
Pages: 19
Date: 2017-09
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Working Paper: Pitfalls When Estimating Treatment Effects Using Clustered Data (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:ags:quedwp:274713

DOI: 10.22004/ag.econ.274713

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