Structural FECM: Cointegration in large‐scale structural FAVAR models
从非平稳数据的动态因子模型出发，推导出因子增强误差修正模型及其移动平均表示，用于识别结构性冲击及其传导机制，并分析误差修正机制对脉冲响应估计偏差的影响。
Summary Starting from the dynamic factor model for nonstationary data we derive the factor‐augmented error correction model (FECM) and its moving‐average representation. The latter is used for the identification of structural shocks and their propagation mechanisms. We show how to implement classical identification schemes based on long‐run restrictions in the case of large panels. The importance of the error correction mechanism for impulse response analysis is analyzed by means of both empirical examples and simulation experiments. Our results show that the bias in estimated impulse responses in a factor‐augmented vector autoregressive (FAVAR) model is positively related to the strength of the error correction mechanism and the cross‐section dimension of the panel. We observe empirically in a large panel of US data that these features have a substantial effect on the responses of several variables to the identified permanent real (productivity) and monetary policy shocks.