Statistically identified structural VAR model with potentially skewed and fat‐tailed errors
提出一种结构向量自回归模型，误差项采用偏斜广义t分布，利用非高斯性实现统计识别，并用于评估美国货币政策冲击的经济识别约束，发现紧缩政策在数月后显著抑制实际经济活动。
Abstract We introduce a structural vector autoregressive model in which the mutually independent errors follow skewed generalized t ‐distributions, whose flexibility compared with commonly considered Student's t ‐distributions diminishes the risk of misspecification and strengthens identification. Because of statistical identification due to non‐Gaussianity, the plausibility of economic identifying restrictions can be formally assessed. In an empirical application, the data support narrative sign restrictions in identifying the US monetary policy shock. In contrast to some of the previous literature, we find a strong negative response of real activity to contractionary monetary policy after a few months' delay.