to hold. In this paper we propose a crude analytical approach to study the large sample bias of estimators when all … normalized weights leads to a smaller bias compared to a simple IPW estimator. To analyze the question of when the use of two … misspecified models are better than one we derive necessary and sufficient conditions for when the DR estimator has a smaller bias …

interaction bias and overfitting problems. This paper investigates the role of machine learning algorithms in stabilizing … estimates and demonstrates the possible regularization bias caused by common LASSO methods. To overcome the three problems …

We characterize the asymptotic bias that arises in ordinary least squares regression when control variables have … nonlinear effects on an outcome variable, but are assumed to enter the regression equation linearly. We show that this bias can … that under a natural assumption an upper bound to the magnitude of the bias may be estimated from the data, and consider …

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 …

misspecification, whereas the pre-test estimators reduce the risk in parts of the parameter space and increase it in other parts. To … data generating processes (DGPs) that model various degrees of local misspecification as well as global misspecification …

. We explain why this happens.We investigate an alternative relative accuracy measure which avoids this bias: the log of …