Dynamic Local Average Treatment Effects in Time Series
时间序列中的动态局部平均处理效应AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要研究时间序列中的动态 LATE 与工具变量识别，并应用于货币政策冲击效应。
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时间序列中的动态局部平均处理效应
- 研究问题
- 时间序列工具变量研究能否识别个体依从状态、检验排除限制，并在样本部分区段识别较弱时改善动态效应推断？
- 方法与思路
- 作者利用平滑性与局部处理比较识别依从者，通过动态规划选择识别最强的子样本，并建立识别稳健推断程序。
- 材料陈述的贡献
- 摘要称该方法提升工具变量解释性，并在部分样本识别失败时提高推断效率；货币政策应用有助于协调既有估计差异。
- 阅读关注点
- 应核对平滑性与局部比较的识别要求、非依从者比较为何能检验排除限制，以及子样本选择后的推断如何成立。
- 阅读边界
- 这是 arXiv 预印本，同行评审及期刊接收未核实；摘要未详述选择后推断条件，未拒绝排除限制不代表该限制已证实。
原文摘要
This paper discusses identification, estimation, and inference on dynamic local average treatment effects (LATEs) in instrumental variables (IVs) settings. First, we show that compliers--observations whose treatment status is affected by the instrument--can be identified individually in time series data using smoothness assumptions and local comparisons of treatment assignments. Second, we show that this result enables not only better interpretability of IV estimates but also direct testing of the exclusion restriction by comparing outcomes among identified non-compliers across instrument values. Third, we document pervasive weak identification in applied work using IVs with time series data by surveying recent publications in leading economics journals. However, we find that strong identification often holds in large subsamples for which the instrument induces changes in the treatment. Motivated by this, we introduce a method based on dynamic programming to detect the most strongly-identified subsample and show how to use this subsample to improve estimation and inference. We also develop new identification-robust inference procedures that focus on the most strongly-identified subsample, offering efficiency gains relative to existing full sample identification-robust inference when identification fails over parts of the sample. Finally, we apply our results to heteroskedasticity-based identification of monetary policy effects. We find that about 75% of observations are compliers (i.e., cases where the variance of the policy shifts up on FOMC announcement days), and we fail to reject the exclusion restriction. Estimation using the most strongly-identified subsample helps reconcile conflicting IV and GMM estimates in the literature.