Harold D. Chiang, Bruce E. Hansen, Yuya Sasaki
Standard Errors for Two-Way Clustering with Serially Correlated Time Effects
December 2023
Abstract:
We propose improved standard errors and an asymptotic distribution theory for two-way clustered panels.
Our proposed estimator and theory allow for arbitrary serial dependence in the common time effects, which is excluded by existing two-way methods, including the popular two-way cluster standard errors of \citet*{CGM2011} and the cluster bootstrap of \cite*{menzel2021bootstrap}.
Our asymptotic distribution theory is the first which allows for this level of inter-dependence among the observations.
Under weak regularity conditions, we demonstrate that the least squares estimator is asymptotically normal, our proposed variance estimator is consistent, and t-ratios are asymptotically standard normal, permitting conventional inference.
The main results extend to two-way fixed-effect models.
We present simulation evidence that confidence intervals constructed with our proposed standard errors obtain superior coverage performance relative to existing methods.
We illustrate the relevance of the proposed method in an empirical application to a standard Fama-French three-factor regression.
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Some of the above material is based upon work supported by the National Science Foundation under Grants No. SES-9022176, SES-9120576, SBR-9412339, and SBR-9807111.
Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the author(s), and do not necessarily reflect the views of the NSF.