AI 中文总结
本文引入因子过滤的混合因果-非因果VARX方法，结合GCov估计量，经模拟验证后对Stock-Watson货币政策（S）VAR的实证分析表明，过滤共同因子可消除非因果成分与价格谜团，为宏观金融VAR中非因果动态的来源提供了新证据。
AI 中文摘要
本文研究标准宏观金融VAR模型中非因果动态的存在性，并探究其反映的是真实非基础性，还是经济主体可用但计量经济学家未观测到的遗漏信息。为此，我们引入因子过滤的混合因果-非因果VARX方法，旨在纳入共同宏观经济信息。我们在模拟环境中评估其性能，同时证明使用多阶滞后时，广义协方差（GCov）估计量可正确恢复因果与非因果动态。实证层面，我们重新考察著名的Stock-Watson货币政策（S）VAR，发现基准设定中检测到的非因果成分在过滤共同因子后基本消失。最后，我们比较过滤后与原始数据的脉冲响应以评估货币政策冲击传导，结果显示过滤进一步消除了价格谜团。
英文摘要
This paper studies the presence of noncausal dynamics in standard macro-finance VAR models and asks whether they reflect genuine nonfundamentalness or omitted information available to economic agents but unobserved by the econometrician. To that end, we introduce a factor-filtering mixed causal-noncausal VARX approach designed to account for common macroeconomic information. We assess its performance in simulated settings, while showing also that the generalized covariance (GCov) estimator correctly recovers causal and noncausal dynamics when using several lags. Empirically, we revisit the well-known Stock-Watson monetary policy (S)VAR and show that the noncausal components detected in the baseline specification largely disappear once common factors are filtered out. Finally, we compare impulse responses from the filtered and original data to assess the transmission of monetary policy shocks and show that filtering further removes the price puzzle.
