Factor‐Based Identification‐Robust Interference in IV Regressions
工具变量回归中基于因子的识别稳健推断AI 解读 ↓
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工具变量回归中基于因子的识别稳健推断
- 研究问题
- 当工具变量较弱或数量较多时，能否借助因子降维改善识别稳健检验的尺寸与功效权衡，并保持渐近及有限样本有效性？
- 方法与思路
- 作者以因子降维修改三种弱工具变量稳健统计量，推导渐近性质，并为 Anderson–Rubin 统计量给出不依赖因子结构的有限样本解析结果。
- 材料陈述的贡献
- 摘要报告模拟中的因子检验能控制尺寸，功效高于对应标准检验，并以新凯恩斯宏观模型说明方法在实证中的用途。
- 阅读关注点
- 重点核对因子如何提取、三种统计量如何修改，以及尺寸控制与功效比较各自的条件，特别是无需因子结构的有限样本结果。
- 阅读边界
- 题名写作“Interference”，摘要讨论推断，需核对；摘要未列有效性的完整条件，所述功效优势也限于其模拟设定。
原文摘要
Summary Robust methods for instrumental variable inference have received considerable attention recently. Their analysis has raised a variety of problematic issues such as size/power trade‐offs resulting from weak or many instruments. We show that information reduction methods provide a useful and practical solution to this and related problems. Formally, we propose factor‐based modifications to three popular weak‐instrument‐robust statistics, and illustrate their validity asymptotically and in finite samples. Results are derived using asymptotic settings that are commonly used in both the factor and weak‐instrument literature. For the Anderson–Rubin statistic, we also provide analytical finite‐sample results that do not require any underlying factor structure. An illustrative Monte Carlo study reveals the following. Factor‐based tests control size regardless of instruments and factor quality. All factor‐based tests are systematically more powerful than standard counterparts. With informative instruments and in contrast to standard tests: (i) power of factor‐based tests is not affected by k even when large; and (ii) weak factor structure does not cost power. An empirical study on a New Keynesian macroeconomic model suggests that our factor‐based methods can bridge a number of gaps between structural and statistical modeling. Copyright © 2015 John Wiley & Sons, Ltd.