A Quasi–Maximum Likelihood Approach for Large, Approximate Dynamic Factor Models
研究了最大似然法在大型时间序列截面因子模型中的适用性，证明其估计量在截面和时间维度趋于无穷时一致，且对异质性成分的设定错误具有稳健性。
Is maximum likelihood suitable for factor models in large crosssections of time series? We answer this question from both an asymptotic and an empirical perspective.We showthat estimates of the common factors based on maximum likelihood are consistent for the size of the cross-section (n) and the sample size (T), going to infinity along any path, and that maximum likelihood is viable for n large. The estimator is robust to misspecification of cross-sectional and time series correlation of the idiosyncratic components. In practice, the estimator can be easily implemented using the Kalman smoother and the EM algorithm as in traditional factor analysis. © 2012 by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.