  We consider the construction of confidence intervals for treatment effects
estimated using panel models with interactive fixed effects. We first use the
factor-based matrix completion technique proposed by Bai and Ng (2021) to
estimate the treatment effects, and then use bootstrap method to construct
confidence intervals of the treatment effects for treated units at each
post-treatment period. Our construction of confidence intervals requires
neither specific distributional assumptions on the error terms nor large number
of post-treatment periods. We also establish the validity of the proposed
bootstrap procedure that these confidence intervals have asymptotically correct
coverage probabilities. Simulation studies show that these confidence intervals
have satisfactory finite sample performances, and empirical applications using
classical datasets yield treatment effect estimates of similar magnitudes and
reliable confidence intervals.
