When Do Covariates Matter? And Which Ones, and How Much?
指出依次添加协变量检验系数稳健性存在序列敏感性问题，提出一种基于遗漏变量偏差公式的条件分解方法，以评估各协变量对回归系数的影响，并提供一致协方差公式，用NLSY数据展示应用。
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.