AI 中文总结
该研究针对SVAR变量选择的手动缺陷，提出贝叶斯方法构建信息集并保留最大系统，经实验验证其在产出、货币政策传导等SVAR分析中具有更优效果。
AI 中文摘要
每个SVAR（结构向量自回归）结果都取决于两个选择：识别冲击的约束条件，以及这些约束所作用的变量。现有文献对第一个选择有规范，而第二个选择则是手动确定的。我们开发了一种贝叶斯方法，用于构建信息集、使用样本外准则，并保留其能容纳的最大系统。在递归识别下，产出增长与住房生产相关，而非仅与家庭信贷相关；对于货币政策，采用无锚定的联合贝叶斯代理SVAR（结构向量自回归）并使用多个工具，强化了信贷利差渠道；添加选定企业利差的核心系统，将预期违约风险识别为强有力的传导边际。
英文摘要
Every SVAR result is conditional on two choices: the restrictions that identify the shock and the variables on which they operate. The literature disciplines the first; the second is chosen by hand. We develop a Bayesian methodology that constructs information sets, uses an out-of-sample criterion, and retains the largest system it admits. Under recursive identification, output rises with housing production rather than household credit alone. For monetary policy, an anchor-free joint Bayesian proxy SVAR with multiple instruments strengthens the credit spread channel. A core system augmented with the selected corporate spread identifies expected default risk as a potent transmission margin.
