Uncertain identification
识别假设的不确定性AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要直接讨论因果研究中识别假设的不确定性，并以货币政策冲击和产出响应示例，支持因果推断与宏观冲击分类。
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识别假设的不确定性
- 研究问题
- 当研究者不确定应采用哪些识别假设，候选模型又同时包含点识别和集合识别情形时，如何综合推断并呈现结论的敏感性？
- 方法与思路
- 作者推广贝叶斯模型平均，将集合识别模型的多个后验与点识别或非教条假设模型的单一后验合并，输出后验集合及可信区域。
- 材料陈述的贡献
- 摘要称框架解释模型先验概率何时更新及其渐近行为，并以货币模型展示产出响应的方向如何依赖某项识别假设的先验概率。
- 阅读关注点
- 应核对候选模型及后验集合如何定义、模型概率更新的条件、后验均值集合与可信区域的计算，以及货币模型阈值的设定依赖。
- 阅读边界
- 仅依据所给 Crossref 摘要；具体模型、先验和渐近条件未说明，所报先验概率阈值仅属于该货币应用，也不推断版本变化。
原文摘要
Uncertainty about the choice of identifying assumptions is common in causal studies, but is often ignored in empirical practice. This paper considers uncertainty over models that impose different identifying assumptions, which can lead to a mix of point‐ and set‐identified models. We propose performing inference in the presence of such uncertainty by generalizing Bayesian model averaging. The method considers multiple posteriors for the set‐identified models and combines them with a single posterior for models that are either point‐identified or that impose nondogmatic assumptions. The output is a set of posteriors ( post‐averaging ambiguous belief ), which can be summarized by reporting the set of posterior means and the associated credible region. We clarify when the prior model probabilities are updated and characterize the asymptotic behavior of the posterior model probabilities. The method provides a formal framework for conducting sensitivity analysis of empirical findings to the choice of identifying assumptions. For example, we find that in a standard monetary model one would need to attach a prior probability greater than 0.28 to the validity of the assumption that prices do not react contemporaneously to a monetary policy shock, in order to obtain a negative response of output to the shock.