Estimation and Inference of FAVAR Models
研究了可观测与不可观测因子共同遵循向量自回归过程的FAVAR模型的识别限制，提出了两步似然估计法，并给出了估计因子、因子载荷和VAR动态参数的推断理论。
The factor-augmented vector autoregressive (FAVAR) model is now widely used in macroeconomics and finance. In this model, observable and unobservable factors jointly follow a vector autoregressive process, which further drives the comovement of a large number of observable variables. We study the identification restrictions for FAVAR models, and propose a likelihood-based two-step method to estimate the model. The estimation explicitly accounts for factors being partially observed. We then provide an inferential theory for the estimated factors, factor loadings, and the dynamic parameters in the VAR process. We show how and why the limiting distributions are different from the existing results. Supplementary materials for this article are available online.