Identification and Bayesian Estimation of Dynamic Factor Models
提出动态因子模型的最小识别条件，允许嵌入带测量误差的结构VAR，并给出贝叶斯估计算法，适用于脉冲响应分析和多级因子模型的溢出效应研究。
We consider a set of minimal identification conditions for dynamic factor models. These conditions have economic interpretations and require fewer restrictions than the static factor framework. Under these restrictions, a standard structural vector autoregression (SVAR) with measurement errors can be embedded into a dynamic factor model. More generally, we also consider overidentification restrictions to achieve efficiency. We discuss general linear restrictions, either in the form of known factor loadings or cross-equation restrictions. We further consider serially correlated idiosyncratic errors with heterogeneous dynamics. A numerically stable Bayesian algorithm for the dynamic factor model with general parameter restrictions is constructed for estimation and inference. We show that a square-root form of the Kalman filter improves robustness and accuracy when sampling the latent factors. Confidence intervals (bands) for the parameters of interest such as impulse responses are readily computed. Similar identification conditions are also exploited for multilevel factor models, and they allow us to study the “spill-over” effects of the shocks arising from one group to another. Supplementary materials for technical details are available online.