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I've been so discombobulated lately that I don't keep track of what's in version of papers and what I include in lectures/teaching. So here's an update on what I've learned about DiD in 2022.
#jwdid (borrowing from @friosavila).
#jwdid (borrowing from @friosavila).
1. The pooled OLS method I proposed, which is the same as TWFE and random effects, is also equivalent to a version of imputation I proposed. That means it is consistent for various ATTs under weak assumptions (but those include no anticipation and parallel trends).
2. Because of this equivalence, POLS uses all possible control units in each time period for each cohort. Under standard assumptions, it is efficient.
3. I've only allowed time-constant covariates. But can see the "moderating effects" directly in the regression that gives ATTs.
3. I've only allowed time-constant covariates. But can see the "moderating effects" directly in the regression that gives ATTs.
