Bayesian posteriors concentrate on pseudo-true parameters only under special prior sequences in linear minimum distance problems, but simple confidence intervals guarantee correct average coverage for the true parameter under all priors in the studied class regardless of misspecification size.
The purpose of an estimator is what it does: Misspecification, estimands, and over-identification
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True and Pseudo-True Parameters
Bayesian posteriors concentrate on pseudo-true parameters only under special prior sequences in linear minimum distance problems, but simple confidence intervals guarantee correct average coverage for the true parameter under all priors in the studied class regardless of misspecification size.