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King and Roberts (2015, KR) claim that a disagreement between robust andclassical standard errors exposes model misspecification. We emphasize thatKR's claim only generally applies to parametric models: models that assume arestrictive form of the distribution of the outcome. Many common models in usein political science, including the linear model, are not necessarilyparametric -- rather they may be semiparametric. Common estimators of modelparameters such as ordinary least squares have both robust (corresponding to asemiparametric model) and classical (corresponding to a more restrictive model)standard error estimates. Given a properly specified semiparametric model andmild regularity conditions, the classical standard errors are not generallyconsistent, but the robust standard errors are. To illustrate this point, weconsider the case of the regression estimate of a semiparametric linear modelwith no model misspecification, and show that robust standard errors maynevertheless systematically differ from classical standard errors. We show thata disagreement between robust and classical standard errors is not generallysuitable as a diagnostic for regression estimators, and that KR's reanalyses ofNeumayer (2003) and B\"uthe and Milner (2008) are predicated on strongassumptions that the original authors did not invoke nor require.

A Note on "How Robust Standard Errors Expose Methodological Problems They Do Not Fix, and What to Do About It"