Identifying factor‐augmented vector autoregression models via changes in shock variances
提出一种利用冲击方差变化识别因子增强向量自回归模型的方法，无需对观测和未观测因子施加不相关或时序限制，并应用于美国货币政策对资产价格影响的实证分析。
Summary This study proposes a method to identify factor‐augmented vector autoregression models without imposing uncorrelatedness or any timing restrictions among observed and unobserved factors in the vector autoregression system. To this end, we utilize changes in unconditional shock variances following Rigobon (2003). The proposed method can incorporate both observed and unobserved factors in the structural vector autoregression system and allows the contemporaneous matrix to be fully unrestricted. We derive the asymptotic distribution of the impulse response estimator and consider a bootstrap inference method. We also provide two diagnostic tools: a test for the identification condition and a class of overidentifying restrictions tests. A Monte Carlo experiment shows that the asymptotic and bootstrap methods yield a satisfactory coverage rate when the shock of an observed factor is analyzed, although the bootstrap method is more accurate. We apply the proposed method to an empirical example for the effects of U.S. monetary policies on asset prices. A contractionary monetary policy shock induces positive and hump‐shaped interest rate responses along the maturity dimension and negative but insignificant stock price responses.