-generation process, the least-squares estimators have smaller bias (in fact zero bias) but larger variances in the long regression than … in the short regression. But if the long regression is also misspecified, the bias may not be smaller. We provide bias … and mean squared error comparisons and study the dependence of the differences on the misspecification parameter. …

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 …

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 …

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 …

forecast bias. The new techniques incorporate and reflect market sentiment more accurately and prove to be of greater benefit …