phdid

Partial Homogeneity in Staggered Difference-in-Differences

In staggered difference-in-differences designs the treatment effect is a vector of cohort-time effects rather than a single number. Estimating each separately is unbiased but imprecise when some are equal, while pooling them all is precise but biased under genuine heterogeneity. This package treats the choice as a partition-selection problem on the cohort-time cells and provides two estimators for it: a Dirichlet process mixture fitted by a collapsed Gibbs sampler, whose posterior marginalises over the unknown partition and reports co-clustering probabilities, and an 'L0'-penalised estimator that returns a single partition and arises as the fixed-variance maximum a posteriori solution of the same model. Also provides tests for whether the cohort-time effects carry recoverable heterogeneity at all, sampler diagnostics including exact enumeration of the partition posterior for small designs, regularisation paths for both estimators, and a calibrated data-generating process. All estimators accept a vector of first-stage cohort-time effects with their joint covariance, so any heterogeneity-robust first-stage estimator may be used. Methods are described in Arora and Wagle (2026) <doi:10.2139/ssrn.7207083>.

Versions across snapshots

Dependencies (latest)