Estimation in moderately misspecified models

Abstract

Suppose data are fitted to some parametric model but that the true model happens to be one with an additional parameter. When a parameter is to be estimated one can use likelihood estimation in the wider model or in the narrow model. Including the extra parameter in the model means less bias but larger sampling variability. Two basic questions are addressed in this article. (i) Just how much misspecification can the narrow model tolerate? In the context of a large-sample moderate misspecification framework we find a surprisingly simple, sharp, and general answer. There is effectively a `tolerance radius' around a given narrow model, inside of which narrow estimation is more precise than wide estimation for all estimands. This is computed in a selection of examples that also demonstrate the degree of robustness of important standard methods against moderate incorrectness of the model under which they are optimal. (ii) Are there other estimators that work well both under narrow and wide circumstances? We discuss several possibilities and propose some new procedures. All methods are compared in a broad large-sample performance study.