OptumInsight Life Sciences, Waltham, MA, USA.
Value Health. 2011 Dec;14(8):1078-84. doi: 10.1016/j.jval.2011.06.009. Epub 2011 Oct 1.
To examine the performance of instrumental variables (IV) and ordinary least squares (OLS) regression under a range of conditions likely to be encountered in empirical research.
A series of simulation analyses are carried out to compare estimation error between OLS and IV when the independent variable of interest is endogenous. The simulations account for a range of situations that may be encountered by researchers in actual practice-varying degrees of endogeneity, instrument strength, instrument contamination, and sample size. The intent of this article is to provide researchers with more intuition with respect to how important these factors are from an empirical standpoint.
Notably, the simulations indicate a greater potential for inferential error when using IV than OLS in all but the most ideal circumstances.
Researchers should be cautious when using IV methods. These methods are valuable in testing for the presence of endogeneity but only under the most ideal circumstances are they likely to produce estimates with less estimation error than OLS.
在可能遇到的各种实证研究条件下，考察工具变量（IV）和普通最小二乘法（OLS）回归的性能。
通过一系列模拟分析，比较了当感兴趣的自变量为内生变量时，OLS 和 IV 的估计误差。这些模拟考虑了研究人员在实际实践中可能遇到的各种情况，包括不同程度的内生性、工具强度、工具污染和样本量。本文的目的是为研究人员提供更多的直觉，了解从经验角度来看，这些因素有多么重要。
值得注意的是，模拟结果表明，除了最理想的情况外，在所有情况下，使用 IV 比 OLS 更有可能导致推断错误。
研究人员在使用 IV 方法时应谨慎。这些方法在检验内生性方面很有价值，但只有在最理想的情况下，它们才可能产生比 OLS 更少估计误差的估计值。
