Instrumental variables regressions with uncertain exclusion restrictions: a Bayesian approach
用贝叶斯方法处理线性工具变量模型中排除限制不确定的问题，展示这种不确定性如何放大参数估计的不确定性，并指出在样本大或工具强时影响更明显。
SUMMARY The identification of structural parameters in the linear instrumental variables (IV) model is typically achieved by imposing the prior identifying assumption that the error term in the structural equation of interest is orthogonal to the instruments. Since this exclusion restriction is fundamentally untestable, there are often legitimate doubts about the extent to which the exclusion restriction holds. In this paper I illustrate the effects of such prior uncertainty about the validity of the exclusion restriction on inferences based on linear IV models. Using a Bayesian approach, I provide a mapping from prior uncertainty about the exclusion restriction into increased uncertainty about parameters of interest. Moderate prior uncertainty about exclusion restrictions can lead to a substantial loss of precision in estimates of structural parameters. This loss of precision is relatively more important in situations where IV estimates appear to be more precise, for example in larger samples or with stronger instruments. I illustrate these points using several prominent recent empirical papers that use linear IV models. An accompanying electronic table allows users to readily explore the robustness of inferences to uncertainty about the exclusion restriction in their particular applications. Copyright © 2010 John Wiley & Sons, Ltd.