用贝叶斯混频var识别高频冲击

Alessia Paccagnini, F. Parla
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引用次数: 6

摘要

我们通过引入一种创新的贝叶斯方法为混合频率回归的研究做出了贡献。基于一种新的“高频”识别方案,我们为识别美国经济的不确定性冲击提供了新的经验证据。作为主要发现,当我们采用常见的低频模型而不是估计混合频率框架时,我们记录了“时间聚集偏差”。当我们识别更高频率的冲击时,偏差会被放大。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Identifying High-Frequency Shocks with Bayesian Mixed-Frequency VARs
We contribute to research on mixed-frequency regressions by introducing an innovative Bayesian approach. Based on a new “high-frequency” identification scheme, we provide novel empirical evidence of identifying uncertainty shock for the US economy. As main findings, we document a “temporal aggregation bias” when we adopt a common low frequency model instead of estimating a mixed-frequency framework. The bias is amplified when we identify a higher frequency shock.
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