Statistically identified structural VAR model with potentially skewed and fat‐tailed errors
允许偏斜厚尾误差的统计识别结构 VAR
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…