A NEW STUDY ON ASYMPTOTIC OPTIMALITY OF LEAST SQUARES MODEL AVERAGING
提出一种新的渐近最优性证明方法，采用一般权重集和易于解释的假设，允许更多回归元，不限制最大选择风险，对模型平均理论有贡献。
In this article, we present a comprehensive study of asymptotic optimality of least squares model averaging methods. The concept of asymptotic optimality is that in a large-sample sense, the method results in the model averaging estimator with the smallest possible prediction loss among all such estimators. In the literature, asymptotic optimality is usually proved under specific weights restriction or using hardly interpretable assumptions. This article provides a new approach to proving asymptotic optimality, in which a general weight set is adopted, and some easily interpretable assumptions are imposed. In particular, we do not impose any assumptions on the maximum selection risk and allow a larger number of regressors than that of existing studies.