  In this article, we study the limiting behavior of Bai (2009)'s interactive
fixed effects estimator in the presence of randomly missing data. In extensive
simulation experiments, we show that the inferential theory derived by Bai
(2009) and Moon and Weidner (2017) approximates the behavior of the estimator
fairly well. However, we find that the fraction and pattern of randomly missing
data affect the performance of the estimator. Additionally, we use the
interactive fixed effects estimator to reassess the baseline analysis of
Acemoglu et al. (2019). Allowing for a more general form of unobserved
heterogeneity as the authors, we confirm significant effects of democratization
on growth.
