In the practice of program evaluation, choosing the covariates and the functional form of the propensity score is an important choice for estimating treatment effects. This paper proposes data-driven model selection and model averaging procedures that address this issue for the propensity score...

This paper proposes a new model averaging estimator for the linear regression model with heteroskedastic errors. We address the issues of how to optimally assign the weights for candidate models and how to make inference based on the averaging estimator. We derive the asymptotic mean squared...

This paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework. We show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug-in averaging estimator that minimizes the...

This paper derives the limiting distributions of least squares averaging estimators for linear regression models in a local asymptotic framework. We show that the averaging estimators with fixed weights are asymptotically normal and then develop a plug-in averaging estimator that minimizes the...

In this paper we consider the problem of frequentist model averaging for quantile regression (QR) when all the M models under investigation are potentially misspecified and the number of parameters in some or all models is diverging with the sample size n. To allow for the dependence between the...

In this paper, a focused vector information criterion for model selection and model averaging is considered for the linear model with missing response. Based on the focused information criterion of Hjort and Claeskens (J Am Stat Assoc 98:879–945, <CitationRef CitationID="CR12">2003</CitationRef>) and imputation idea, a frequentist model...</citationref>

Model averaging (MA) estimators in the linear instrumental variables regression framework are considered. The obtaining of weights for averaging across individual estimates by direct smoothing of selection criteria arising from the estimation stage is proposed. This is particularly relevant in...

We discuss the impact of tuning parameter selection uncertainty in the context of shrinkage estimation and propose a methodology to account for problems arising from this issue: Transferring established concepts from model averaging to shrinkage estimation yields the concept of shrinkage...