关于“稳健的标准误如何暴露它们无法解决的方法学问题，以及如何解决”的注记
A Note on "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It"
摘要：
King和Roberts（2015，KR）声称，稳健和经典标准误之间的不一致暴露了模型的错误设定。我们强调，KR的主张一般只适用于参数模型：假设结果分布的限制形式的模型。政治学中使用的许多常见模型，包括线性模型，不一定是参数模型，而是半参数模型。模型参数的常用估计量（如普通最小二乘）具有稳健（对应于半参数模型）和经典（对应于更严格的模型）标准误估计值。在适当指定的半参数模型和弱正则条件下，经典标准误一般不相容，但稳健标准误是相容的。为了说明这一点，我们考虑的情况下，回归估计的半参数线性模型，没有模型误设定，并表明，稳健的标准误，但可能系统地不同于经典的标准误误差。我们表明，稳健和经典标准误之间的不一致通常不适合作为回归估计的诊断，并且KR对Neumayer（2003）和Büthe and Milner（2008）的重新分析是基于原作者没有引用或要求的强假设。
英文摘要：
King and Roberts (2015, KR) claim that a disagreement between robust and classical standard errors exposes model misspecification. We emphasize that KR's claim only generally applies to parametric models: models that assume a restrictive form of the distribution of the outcome. Many common models in use in political science, including the linear model, are not necessarily parametric -- rather they may be semiparametric. Common estimators of model parameters such as ordinary least squares have both robust (corresponding to a semiparametric model) and classical (corresponding to a more restrictive model) standard error estimates. Given a properly specified semiparametric model and mild regularity conditions, the classical standard errors are not generally consistent, but the robust standard errors are. To illustrate this point, we consider the case of the regression estimate of a semiparametric linear model with no model misspecification, and show that robust standard errors may nevertheless systematically differ from classical standard errors. We show that a disagreement between robust and classical standard errors is not generally suitable as a diagnostic for regression estimators, and that KR's reanalyses of Neumayer (2003) and Büthe and Milner (2008) are predicated on strong assumptions that the original authors did not invoke nor require.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
