10:30-11:30 Yuta Toyama (Waseda Univesity), “Designing Nonlinear Electricity Pricing with Misperception: Evidence from Free Electricity Policy” (with Ngawang Dendup)
11:35-12:35 Katalin Springel (HEC Montréal)), “Pass-through and Incidence of U.S. Electric Vehicle Purchase Incentives” (with David Scolari and Jing Li)

Abstract: This paper develops semiparametric Bayesian methods for estimating the average treatment effect on the treated (ATT) in difference-in-differences (DiD) designs. We propose two Bayesian procedures with frequentist validity. The first places a Gaussian process prior on the conditional mean function of the control group. The second is a double-robust Bayesian approach that adjusts the prior on the conditional mean function and then corrects the posterior distribution of the ATT. We establish a semiparametric Bernstein¨Von Mises theorem, showing the asymptotic equivalence between our Bayesian procedures and efficient frequentist estimators. For the second approach, the result holds under double-robust smoothness conditions: the lack of smoothness in the conditional mean function can be compensated by high regularity of the propensity score, and vice versa. Monte Carlo simulations and an empirical application demonstrate strong finite-sample performance of our Bayesian DiD methods. We also extend the Bayesian framework to staggered DiD designs.