Statistical Power for Estimating Treatment Effects Using Difference-in-Differences and Comparative Interrupted Time Series Designs with Variation in Treatment Timing
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This article develops new closed-form variance expressions for power analyses for commonly used panel model estimators. The main contribution is to incorporate variation in treatment timing into the analysis, but the variance 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 to improve precision. An illustrative power analysis provides guidance on appropriate sample sizes for various model specifications. An available Shiny R dashboard performs the sample size calculations.
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