Asymptotic analysis of the squared estimation error in misspecified factor models

In this paper, we obtain asymptotic approximations to the squared error of the least squares estimator of the common component in large approximate factor models with possibly misspecified number of factors. The approximations are derived under both strong and weak factors asymptotics assuming that the cross-sectional and temporal dimensions of the data are comparable. We develop consistent estimators of these approximations and propose to use them for model comparison and for selection of the number of factors. We show that the estimators of the number of factors that minimize these loss estimators are asymptotically loss efficient in the sense of Shibata (1980), Li (1987), and Shao (1997).