American Economic Review · 2022 · David S. Lee、Justin McCrary、Marcelo J. Moreira、Jack Porter
中文摘要
在单个工具变量模型中，研究者通常依赖基于t比率的推断，尽管文献已经量化了其潜在的大样本严重扭曲。基于Stock和Yogo（2005），我们引入了tF临界值函数，从而得到一种标准误调整，该调整是第一阶段F统计量的平滑函数。对于61篇AER论文中四分之一的设定，在5%和1%的显著性水平上，修正后的标准误分别比传统2SLS标准误大至少49%和136%。只要两者均有界，tF置信区间的期望长度就短于Anderson和Rubin（1949）的置信区间。（JEL C13, C26）
Abstract
In the single-IV model, researchers commonly rely on t-ratio-based inference, even though the literature has quantified its potentially severe large-sample distortions. Building on Stock and Yogo (2005), we introduce the tF critical value function, leading to a standard error adjustment that is a smooth function of the first-stage F-statistic. For one-quarter of specifications in 61 AER papers, corrected standard errors are at least 49 and 136 percent larger than conventional 2SLS standard errors at the 5 percent and 1 percent significance levels, respectively. tF confidence intervals have shorter expected length than those of Anderson and Rubin (1949), whenever both are bounded. (JEL C13, C26)