In this paper, we propose a method of averaging generalized least squares estimators for linear regression models with heteroskedastic errors. The averaging weights are chosen to minimize Mallows' Cp-like criterion. We show that the weight vector selected by our method is optimal. It is also...

This paper considers the problem of model averaging for regression models that can be nonlinear in their parameters and variables. We consider a nonlinear model averaging (NMA) framework and propose a weight-choosing criterion, the nonlinear information criterion (NIC). We show that up to a...

To avoid the risk of misspecification between homoscedastic and heteroscedastic models, we propose a combination method based on ordinary least-squares (OLS) and generalized least-squares (GLS) model-averaging estimators. To select optimal weights for the combination, we suggest two information...

This paper is concerned with the model averaging estimation for the conditional volatility model family. We propose a model averaging estimator for the conditional volatility under a framework of zero conditional mean and construct the corresponding weight choosing criterion. It is shown that...

We show that Mallows' model averaging estimator proposed by Hansen (2007) can be written as a least squares estimation with a weighted L₁ penalty and additional constraints. By exploiting this representation, we demonstrate that the weight vector obtained by this model averaging procedure has a...