Estimating average treatment effect by model averaging

- We propose to use a model average method to improve the estimation of average treatment effects.
- The proposed model average estimator selects weight optimally to minimize estimation mean squared errors.
- Simulation results show that the model average estimator exhibits smaller estimation mean squared errors in post-treatment prediction than AIC or AICC methods.

In this paper, we propose to use a model average method to improve the estimation performance of Hsiao et al. (2012) panel data approach for program evaluation. Instead of using the two-step model selection strategy which chooses one best model according to a criterion such as AIC or AICC, we average over a set of candidate models. Simulation results show that the model average estimator exhibits smaller estimation errors in post-treatment prediction than AIC or AICC method.