Model averaging with averaging covariance matrix

This article studies optimal model averaging for linear models with heteroscedasticity. We choose weights by minimizing Mallows-type criterion. Because the covariance matrix of random error in the criterion is unknown, an averaging estimator of covariance matrix is plugged into the criterion. The resulting model averaging estimator is proved to be asymptotically optimal under some regularity conditions. Simulation experiments show that the proposed model averaging method is superior to its competitors.