Finitely Heterogeneous Treatment Effect in Event-study
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.
Date:
13 October 2023, 14:15 (Friday, 1st week, Michaelmas 2023)
Venue:
Manor Road Building, Manor Road OX1 3UQ
Venue Details:
Seminar Room A or https://zoom.us/j/93054414699?pwd=NEFiL2ZNc0t5N3ZIUTE2VEh5OXhZUT09
Speaker:
Myungkou Shin (University of Oxford)
Organising department:
Department of Economics
Part of:
Nuffield Econometrics Seminar
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Shreyasi Banerjee
