发表机构
WU Vienna; Oesterreichische Nationalbank（维也纳经济大学; 奥地利国家银行）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文利用非线性混合频率VAR模型和贝叶斯加法回归树，结合高频政策意外识别，发现欧元区货币政策传导存在符号不对称：紧缩政策效果显著，宽松政策效果不显著，且峰值效应受初始条件影响。
AI 中文摘要
我们通过一个非线性混合频率向量自回归模型来回答标题中的问题，该模型使用贝叶斯加法回归树进行估计。该模型将月度宏观金融变量与季度银行贷款调查数据相结合，并从高频政策意外中识别动态响应。符号不对称占主导地位；紧缩政策产生的货币政策传导大多符合理论预测，而任何规模的宽松政策产生的响应大多不显著。峰值效应随初始条件而变化，而几种常见的、预定义的状态划分在冲击的传导中并未产生显著差异。
英文摘要
We answer the question posed in the title with a nonlinear mixed-frequency vector autoregression, estimated with Bayesian additive regression trees. The model combines monthly macro-financial variables with quarterly bank lending survey data, and identifies the dynamic responses from high-frequency policy surprises. Sign asymmetry dominates; a tightening produces monetary policy transmission mostly in line with the theoretical predictions, whereas easing of any size produces mostly insignificant responses. Peak effects vary with initial conditions, while several common, predefined regime splits do not yield considerable differences in the propagation of the shocks.
CommentsJEL: C32, E32, E52; keywords: credit channel, mixed frequency, Bayesian additive regression trees, nonlinear, nonparametric
