Rolling difference-in-differences estimation for small and large panels

We introduce lwdid, a Stata command that implements the rolling difference-in-differences (DID) estimator proposed by Lee and Wooldridge (2025). The rolling approach transforms the panel-data DID problem into a sequence of cross-sectional treatment-effect estimation problems, allowing flexible estimation of treatment effects in settings with staggered adoption and treatment-effect heterogeneity. The command is further designed to accommodate both large and small panels. In particular, it also implements the extension developed in Lee and Wooldridge (2026) for small-panel settings, where the number of cross-sectional units is limited and conventional large-sample asymptotic inference may be unreliable. For large panels, a key feature of lwdid is that it provides computationally efficient inference using a multiplier bootstrap based on exact influence functions of the estimators. Because this approach avoids repeated model estimation, it substantially reduces computational cost. For settings with a small number of units, lwdid also provides valid inference procedures. Under normality, exact inference can be conducted using the t distribution, while HC3-based inference and randomization inference are provided as alternatives that require weaker assumptions. Overall, lwdid provides applied researchers with a practical and efficient tool for estimating treatment effects within the rolling DID framework.