Using invalid instruments on purpose: Focused moment selection and averaging for GMM
有意使用无效工具变量：GMM的聚焦矩选择与平均AI 解读 ↓
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AI 根据所提供的论文材料归类：摘要核心是通用GMM矩选择、均方误差权衡与选择后推断，应用仅称估计变量关系，未充分说明所列具体领域。
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有意使用无效工具变量：GMM的聚焦矩选择与平均
- 研究问题
- 当高度相关但略有内生性的工具变量可能降低有限样本均方误差时，如何针对目标参数选择矩条件并进行选择后推断？
- 方法与思路
- 作者提出聚焦矩选择准则，以目标参数估计的均方误差选择可疑矩条件，并将选择后估计纳入矩平均框架开展模拟推断。
- 材料陈述的贡献
- 摘要称在正确基准矩条件识别模型时，可渐近无偏估计渐近均方误差，并提出适用于多种矩选择和平均程序的推断方法。
- 阅读关注点
- 应核对可疑矩条件偏离有效性的渐近设定、基准矩的识别要求，以及数据依赖权重下模拟置信区间的有效性条件。
- 阅读边界
- 仅据所给出版社摘要，不能理解为任意无效工具都可改善估计；偏误设定、模拟优势幅度和应用关系的因果解释未说明。
原文摘要
In finite samples, the use of a slightly endogenous but highly relevant instrument can reduce mean-squared error (MSE). Building on this observation, I propose a novel moment selection procedure for GMM–the Focused Moment Selection Criterion (FMSC)–in which moment conditions are chosen not based on their validity but on the MSE of their associated estimator of a user-specified target parameter. The FMSC mimics the situation faced by an applied researcher who begins with a set of relatively mild “baseline” assumptions and must decide whether to impose any of a collection of stronger but more controversial “suspect” assumptions. When the (correctly specified) baseline moment conditions identify the model, the FMSC provides an asymptotically unbiased estimator of asymptotic MSE, allowing us to select over the suspect moment conditions. I go on to show how the framework used to derive the FMSC can address the problem of inference post-moment selection. Treating post-selection estimators as a special case of moment-averaging, in which estimators based on different moment sets are given data-dependent weights, I propose simulation-based procedures for inference that can be applied to a variety of formal and informal moment-selection and averaging procedures. Both the FMSC and confidence interval procedures perform well in simulations. I conclude with an empirical example examining the effect of instrument selection on the estimated relationship between malaria and income per capita.