When Do Covariates Matter? And Which Ones, and How Much?
When Do Covariates Matter? And Which Ones, and How Much?
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DOI：
10.1086/683668
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发表时间：
2016-04-01
影响因子：
3.8
通讯作者：
Gelbach, Jonah B.
中科院分区：
文献类型：
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作者：
Gelbach, Jonah B.
作者经常顺序地将协变量添加到基础模型中，以测试特定系数的稳健性或考虑添加协变量对该系数的影响。这是有问题的，因为当添加的协变量相互关联时，序列敏感性。利用省略变量偏差公式，构造了一个条件分解，该分解考虑了各种协变量在移动基回归系数中的作用。我还提供了一个一致的协方差公式。我用NLSY数据在一个具有序列敏感性的应用程序中说明了这种条件分解。相关的扩展包括工具变量，我的分解嵌套Oaxaca-Blinder分解的事实，以及Hausman测试结果。
Authors often add covariates to a base model sequentially either to test a particular coefficient's robustness or to account for the effects on this coefficient of adding covariates. This is problematic, due to sequence sensitivity when added covariates are intercorrelated. Using the omitted variables bias formula, I construct a conditional decomposition that accounts for various covariates' role in moving base regressors' coefficients. I also provide a consistent covariance formula. I illustrate this conditional decomposition with NLSY data in an application that exhibits sequence sensitivity. Related extensions include instrumental variables, the fact that my decomposition nests the Oaxaca-Blinder decomposition, and a Hausman test result.