Dr. Fa Wang earned his Ph.D. in Economics from Syracuse University, USA. He is currently an Associate Professor and Ph.D. supervisor in the Department of Finance, School of Economics, Peking University. His research focuses on financial econometrics, factor models, and high-dimensional econometrics. He teaches courses including Time Series Analysis and Fixed Income Securities. His research has appeared in leading journals such as the Journal of Econometrics and Econometric Reviews. He serves as a referee for several top international economics journals and is the Principal Investigator of a grant from the National Natural Science Foundation of China for Young Scholars. 

This paper establishes the convergence rates, asymptotic normality and asymptotically equivalent representations for the least squares estimation of unbalanced heterogenous panel data models with interactive fixed effects. The asymptotic properties are established in a unified framework that covers both static and dynamic panels and both random-type and block-type missing patterns, such as completely exogenous missing, selection on regressors/factors/loadings and block missing/staggered missing/mixed frequency. These results allow us to account for heterogeneous regression coefficients, outcome dynamics and missing indicator dynamics in factor extraction, mean-group coefficients estimation, matrix completion and average treatment effect estimation. Our results do not require the covariates to have a factor structure. Interestingly, we find that the identification conditions imposed in the literature to establish consistency also ensure the well-behavior of the Hessian.