标题： 分布向量自回归：揭示宏观和金融依赖性 显示英文标题

标题： Distributional Vector Autoregression: Eliciting Macro and Financial Dependence

摘要： 向量自回归是实证宏观经济学和金融学中理解多变量时间序列之间动态相互依赖关系的重要工具。 在本研究中，我们通过结合多变量分布回归框架并引入分布冲击响应函数，扩展了向量自回归的范围，提供了动态异质性的全面视角。 我们提出了一种简单但灵活的估计方法，并在弱依赖假设下建立了其渐近性质。 我们的实证分析考察了美国国内生产总值增长和金融状况的条件联合分布，重点在于全球金融危机。 我们的结果表明，紧缩的金融状况导致国内生产总值增长和金融状况的多模态条件联合分布，而放松金融状况对长期国内生产总值增长有显著影响，而在全球金融危机期间改善国内生产总值增长对金融状况的影响有限。 显示英文摘要

摘要： Vector autoregression is an essential tool in empirical macroeconomics and finance for understanding the dynamic interdependencies among multivariate time series. In this study, we expand the scope of vector autoregression by incorporating a multivariate distributional regression framework and introducing a distributional impulse response function, providing a comprehensive view of dynamic heterogeneity. We propose a straightforward yet flexible estimation method and establish its asymptotic properties under weak dependence assumptions. Our empirical analysis examines the conditional joint distribution of GDP growth and financial conditions in the United States, with a focus on the global financial crisis. Our results show that tight financial conditions lead to a multimodal conditional joint distribution of GDP growth and financial conditions, and easing financial conditions significantly impacts long-term GDP growth, while improving the GDP growth during the global financial crisis has limited effects on financial conditions.