Mean shift least squares model averaging
This paper proposes a new estimator for selecting weights to average over least squares estimates obtained from a set of models. Our proposed estimator builds on the Mallows model average (MMA) estimator of Hansen (2007) but, unlike MMA, simultaneously controls for location bias and regression error through a common constant. We show that our proposed estimator the mean shift Mallows model average (MSA) estimator is asymptotically optimal to the original MMA estimator in terms of mean squared error. A simulation study is presented, where we show that our proposed estimator uniformly outperforms the MMA estimator.