PROBLEMS WITH INSTRUMENTAL VARIABLES ESTIMATION WHEN THE CORRELATION BETWEEN THE INSTRUMENTS AND THE ENDOGENOUS EXPLANATORY VARIABLE IS WEAK
PROBLEMS WITH INSTRUMENTAL VARIABLES ESTIMATION WHEN THE CORRELATION BETWEEN THE INSTRUMENTS AND THE ENDOGENOUS EXPLANATORY VARIABLE IS WEAK
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DOI：
10.2307/2291055
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发表时间：
1995-06-01
影响因子：
3.7
通讯作者：
BAKER, RM
中科院分区：
文献类型：
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作者：
BOUND, J;JAEGER, DA;BAKER, RM
我们提请注意与工具变量（IV）的使用相关的两个问题，其对于实证工作的重要性尚未得到充分认识。首先，即使工具与结构方程中的误差之间仅存在微弱的关系，使用对内生解释变量的变化解释甚少的工具也可能导致 IV 估计值的巨大不一致。其次，在有限样本中，IV 估计值与普通最小二乘 (OLS) 估计值的偏差方向相同。随着工具和内生解释变量之间的 R(2) 接近 0，IV 估计的偏差大小接近 OLS 估计的偏差。为了说明这些问题，我们重新审视了 Angrist 和 Krueger 最近发表的一篇论文的结果，他们使用美国人口普查的大样本来估计工资方程，其中四分之一的出生率被用作教育程度的工具。我们发现证据表明，尽管样本量巨大，但他们的 IV 估计可能会受到有限样本偏差的影响，并且也可能不一致。这些发现表明，有效的工具可能比以前想象的更难找到。他们还表明，使用大型数据集并不一定能让研究人员免受定量重要的有限样本偏差的影响。我们建议第一阶段估计中识别工具的部分 R(2) 和 F 统计量是 IV 估计质量的有用指标，应定期报告。
We draw attention to two problems associated with the use of instrumental variables (IV), the importance of which for empirical work has not been fully appreciated. First, the use of instruments that explain little of the variation in the endogenous explanatory variables can lead to large inconsistencies in the IV estimates even if only a weak relationship exists between the instruments and the error in the structural equation. Second, in finite samples, IV estimates are biased in the same direction as ordinary least squares (OLS) estimates. The magnitude of the bias of IV estimates approaches that of OLS estimates as the R(2) between the instruments and the endogenous explanatory variable approaches 0. To illustrate these problems, we reexamine the results of a recent paper by Angrist and Krueger, who used large samples from the U.S. Census to estimate wage equations in which quarter of birth is used as an instrument for educational attainment. We find evidence that, despite huge sample sizes, their IV estimates may suffer from finite-sample bias and may be inconsistent as well. These findings suggest that valid instruments may be more difficult to find than previously imagined. They also indicate that the use of large data sets does not necessarily insulate researchers from quantitatively important finite-sample biases. We suggest that the partial R(2) and the F statistic of the identifying instruments in the first-stage estimation are useful indicators of the quality of the IV estimates and should be routinely reported.d