AI 中文总结
本文提出贝叶斯结构矩阵自回归（BSMAR）框架，解决大型多国SVAR扩展难题，应用于15个经济体数据发现国际冲击传导异质性，需求冲击跨国溢出作用更突出。
AI 中文摘要
理解宏观经济冲击如何在各国间传导，需要能够联合识别特定国家冲击及其国际传导的结构模型。然而，将结构向量自回归（SVAR）扩展到大型多国系统颇具挑战，原因在于维度快速增长、计算成本高昂以及识别约束激增。本文提出一种贝叶斯结构矩阵自回归（BSMAR）框架，该框架利用国际宏观经济数据的天然矩阵结构，通过分离经济变量间的依赖关系与国家间的依赖关系，提供了一种简约表示，大幅降低了大型结构系统的维度。我们开发了一种用于后验推断的贝叶斯抽样算法，该算法可容纳零约束、符号约束和排序（量级）约束，允许将已有的SVAR识别方案与一种识别同期国际溢出效应的新方法相结合。将该模型应用于15个经济体的季度数据，我们发现国际冲击传导存在显著异质性，其中需求冲击在产生跨国溢出效应方面比供给冲击发挥着更突出的作用。
英文摘要
Understanding how macroeconomic shocks propagate across countries requires structural models that can jointly identify country-specific shocks and their international transmission. Yet extending structural vector autoregressions (SVARs) to large multi-country systems is challenging due to rapidly increasing dimensionality, computational costs, and the proliferation of identifying restrictions. This paper develops a Bayesian Structural Matrix Autoregression (BSMAR) framework that exploits the natural matrix structure of international macroeconomic data. By separating dependence across economic variables from dependence across countries, the framework provides a parsimonious representation that substantially reduces the dimensionality of large structural systems. We develop a Bayesian sampling algorithm for posterior inference that accommodates zero, sign, and ranking (magnitude) restrictions, allowing established SVAR identification schemes to be combined with a novel approach to identifying contemporaneous international spillovers. Applying the model to quarterly data for 15 economies, we find substantial heterogeneity in international shock transmission, with demand shocks playing a more prominent role than supply shocks in generating cross-country spillovers.
