The project seeks to develop new methods for modeling, estimation, and inference in panel data models with a factor error or approximate factor error structure. This error structure has roots in economics and finance, and the usual additive individual effects and time effects are special cases of this structure. Explanatory variables are correlated with unobserved factors and factor loadings.
Ignoring this correlation leads to inconsistent estimates. Professor Bai will develop a generalized Mundlak projection that is an alternative to the traditional Mundlak-Chamberlin projection for dealing with this correlation. The generalized projection is an improvement on the traditional method, and the result may be improvements in how to handle correlations between explanatory variables and unobserved effects.
The broader impacts of this research are largely from the potential use of these methods in more applied settings; panel data are widely used for policy evaluation.
