  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
Cameron, Gelbach, and Miller (2011) and the cluster bootstrap of Menzel (2021).
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. 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.
