Maximum likelihood estimation and inference for high dimensional generalized factor models with application to factor-augmented regressions

This paper reestablishes the main results in Bai (2003) and Bai and Ng (2006) for generalized factor models, with slightly stronger conditions on the relative magnitude of N (number of subjects) and T (number of time periods). Convergence rates of the estimated factor space and loading space and asymptotic normality of the estimated factors and loadings are established under mild conditions that allow for linear, Logit, Probit, Tobit, Poisson and some other single-index nonlinear models. The probability density/mass function is allowed to vary across subjects and time, thus mixed models are also allowed for. For factor-augmented regressions, this paper establishes the limit distributions of the parameter estimates, the conditional mean, and the forecast when factors estimated from nonlinear/mixed data are used as proxies for the true factors. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

This paper revisits the main results in Bai (2003) and Bai and Ng (2006) for generalized factor models, with slightly stronger conditions. It establishes convergence rates and asymptotic normality of the estimated factor space and loading space under mild conditions that allow for various single-index nonlinear models. Mixed models are also considered as the probability density/mass function can vary across subjects and time. For factor-augmented regressions, the limit distributions of parameter estimates, conditional mean, and forecast are derived when factors estimated from nonlinear/mixed data are used as proxies for the true factors.