During Michaelmas Term, OxTalks will be moving to a new platform (full details are available on the Staff Gateway).
For now, continue using the current page and event submission process (freeze period dates to be advised).
If you have any questions, please contact halo@digital.ox.ac.uk
Treatment effect estimation strategies in the event-study setup, namely panel data with variation in treatment timing, often use the parallel trend assumption that assumes mean independence across different treatment timings. In this paper, I relax the parallel trend assumption by including a latent type variable and develop a conditional two-way fixed-effects model. With a finite support assumption on the latent type variable, I show that an extremum classifier consistently estimates the type assignment. Then I solve the endogeneity problem of the selection into treatment by conditioning on the latent type, through which the treatment timing is correlated with the outcome. I also allow treatment to affect units of different types differently and thus directly model and estimate type-level heterogeneity in treatment effect.