DID_IMPUTATION: Stata module to perform treatment effect estimation and pre-trend testing in event studies

Abstract

did_imputation estimates the effects of a binary treatment with staggered rollout allowing for arbitrary heterogeneity and dynamics of causal effects, using the imputation estimator of Borusyak, Jaravel, and Spiess (2021). The benchmark case is with panel data, in which each unit i that gets treated as of period Ei stays treated forever; some units may never be treated. Other types of data (e.g. repeated cross-sections) and other designs (e.g. triple-diffs) are also allowed.