Estimation in DID with Multiple Groups and Periods

Estimate the effect of a treatment on an outcome in sharp Difference-in-Difference designs with multiple groups and periods. It computes the DIDM estimator introduced in Section 4 of "Two-Way Fixed Effects Estimators with Heterogeneous Treatment Effects" (Chaisemartin, D'Haultfoeuille (2020) ), which generalizes the standard DID estimator with two groups, two periods and a binary treatment to situations with many groups,many periods and a potentially non-binary treatment. For each pair of consecutive time periods t-1 and t and for each value of the treatment d, the package computes a DID estimator comparing the outcome evolution among the switchers, the groups whose treatment changes from d to some other value between t-1 and t, to the same evolution among control groups whose treatment is equal to d both in t-1 and t. Then the DIDM estimator is equal to the average of those DIDs across all pairs of consecutive time periods and across all values of the treatment. Under a parallel trends assumption, DIDM is an unbiased and consistent estimator of the average treatment effect among switchers, at the time period when they switch. The package can also compute placebo estimators that can be used to test the parallel trends assumption. Finally, in staggered adoption designs where each group's treatment is weakly increasing over time, it can compute estimators of switchers' dynamic treatment effects, one time period or more after they have started receiving the treatment.


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0.1.0 by Shuo Zhang, a month ago

Browse source code at

Authors: Shuo Zhang [aut, cre] , Clément de Chaisemartin [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports dplyr, fixest, plotrix, stringr, sampling, stats, parallel, assertthat

Suggests wooldridge

See at CRAN