  This article develops new closed-form variance expressions for power analyses
for commonly used difference-in-differences (DID) and comparative interrupted
time series (CITS) panel data estimators. The main contribution is to
incorporate variation in treatment timing into the analysis. The power formulas
also account for other key design features that arise in practice:
autocorrelated errors, unequal measurement intervals, and clustering due to the
unit of treatment assignment. We consider power formulas for both
cross-sectional and longitudinal models and allow for covariates. An
illustrative power analysis provides guidance on appropriate sample sizes. The
key finding is that accounting for treatment timing increases required sample
sizes. Further, DID estimators have considerably more power than standard CITS
and ITS estimators. An available Shiny R dashboard performs the sample size
calculations for the considered estimators.
