AI 中文总结
本文提出阈值随机波动率均值VAR模型，研究美国近150年数据中宏观经济尾部风险的结构性驱动因素，发现商业周期冲击主导中位数响应但尾部风险份额较小，宏观经济不确定性和金融不确定性在尾部风险中作用显著。
AI 中文摘要
宏观经济结果的尾部可能对其分布中心作出不同反应：对增长中位数或通胀影响较小的冲击，可能改变增长下行风险或通胀上行风险。我们开发了一个具有机制依赖杠杆的阈值随机波动率均值向量自回归模型，以研究其结构性驱动因素。该模型允许结果与波动率之间的内生交互作用、同期水平-波动率依赖性以及机制特定的传导。在近150年的美国数据中，预测模型选择支持三个以通胀定义的机制。我们识别了商业周期、金融、宏观经济不确定性和金融不确定性冲击，并分解了它们对增长风险和通胀风险的贡献。尾部风险的结构组成不同于预测中位数的组成。商业周期冲击主导了GNP增长的中位数响应，但在增长风险中所占份额要小得多。宏观经济不确定性对增长风险和通胀风险均作出实质性贡献，其增长风险份额随正向宏观经济不确定性冲击的幅度增加而上升，尽管其在中位数中的作用有限。在高通胀状态下，金融不确定性对通胀风险的贡献随正向金融不确定性冲击幅度的增加而上升。
英文摘要
The tails of macroeconomic outcomes can respond differently from the centre of their distribution: shocks with modest effects on median growth or inflation can shift downside growth or upside inflation risk. We develop a threshold stochastic-volatility-in-mean VAR with regime-dependent leverage to study their structural drivers. The model allows endogenous interactions between outcomes and volatility, contemporaneous level-volatility dependence, and regime-specific propagation. In nearly 150 years of U.S. data, predictive model selection supports three inflation-defined regimes. We identify business-cycle, financial, macroeconomic-uncertainty, and financial-uncertainty shocks and decompose their contributions to growth- and inflation-at-risk. The structural composition of tail risk differs from that of the predictive median. Business-cycle shocks dominate the median response of GNP growth but account for a substantially smaller share of growth-at-risk. Macroeconomic uncertainty makes a material contribution to both growth- and inflation-at-risk, with its share of growth-at-risk increasing with the magnitude of a positive macroeconomic-uncertainty impulse, despite its limited role at the median. In high-inflation states, the contribution of financial uncertainty to inflation-at-risk rises with the magnitude of positive financial-uncertainty impulses.
