Allowing for misspecification in the linear conditional quantile function, this paper provides a new interpretation and … attractive features of ordinary least squares: interpretability and semiparametric efficiency under misspecification. …

Generalized Information Matrix Tests (GIMTs) have recently been used for detecting the presence of misspecification in … developed for the purpose of identifying, classifying, and deriving novel model misspecification tests for finite …

error times a critical value that takes into account the potential bias from misspecification of the moment conditions. In … order to optimize performance under potential misspecification, the weighting matrix for this GMM estimator takes into …

cannot be correctly specified unless we know the true model. Structural changes in time series and misspecification in …

misspecification in the presence of incomplete data where maximum likelihood estimates are obtained by maximizing the observable … simultaneous presence of both missing data and model misspecification. These results are then used to derive robust hypothesis …) missing data. Second, we introduce a method for the detection of model misspecification in missing data problems using …

, making them susceptible to model misspecification. This paper investigates how misspecification affects the results of … inference in these models. Specifically, we consider a local misspecification framework in which specification errors are … assumed to vanish at an arbitrary and unknown rate with the sample size. Relative to global misspecification, the local …

zero, while the asymptotic upper bound is zero uniformly over any degree of misspecification. The results apply to …