Maximum Likelihood Estimation and Inference for Approximate Factor Models of High Dimension
研究了高维近似因子模型的最大似然估计方法，证明了估计量的一致性、收敛速度和极限分布，并通过蒙特卡洛模拟和美国收益率曲线应用展示了其优于主成分方法的效率。
An approximate factor model of high dimension has two key features. First, the idiosyncratic errors are correlated and heteroskedastic over both the cross-section and time dimensions; the correlations and heteroskedasticities are of unknown forms. Second, the number of variables is comparable or even greater than the sample size. Thus a large number of parameters exist under a high dimensional approximate factor model. Most widely used approaches to estimation are principal component based. This paper considers the maximum likelihood-based estimation of the model. Consistency, rate of convergence, and limiting distributions are obtained under various identification restrictions. Comparison with the principal component method is made. The likelihood-based estimators are more efficient than those of principal component based. Monte Carlo simulations show the method is easy to implement and an application to the U.S. yield curves is considered