Generalized Least Squares Model Averaging
针对异方差线性回归模型，提出一种平均广义最小二乘估计量的方法，通过最小化类似Mallows Cp的准则选择权重，并证明其最优性。蒙特卡洛模拟和实证例子表明该方法有效。
In this article, 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 shown that this optimality holds even when the variances of the error terms are estimated and the feasible generalized least squares estimators are averaged. The variances can be estimated parametrically or nonparametrically. Monte Carlo simulation results are encouraging. An empirical example illustrates that the proposed method is useful for predicting a measure of firms’ performance.