美联储：通过EM算法对大近似动态因子模型进行准最大似然估计和推断（英文版）

We study estimation of large Dynamic Factor models implemented through the Expectation Maximization (EM) algorithm, jointly with the Kalman smoother. We prove that as both the cross-sectional dimension, n, and the sample size, T, diverge to infinity: (i) the estimated loadings are √ T-consistent, asymptotically normal and equivalent to their Quasi Maximum Likelihood estimates; (ii) the estimated factors are √ nconsistent, asymptotically normal and equivalent to their Weighted Least Squares es