Traditionally model averaging has been viewed as an alternative to model selection with the ultimate goal to incorporate the uncertainty associated with the model selection process in standard errors and confidence intervals by using a weighted combination of candidate models. In recent years, a new class of model averaging estimators has emerged in the literature, suggesting to combine models such that the squared risk, or other risk functions, are minimized. We argue that, contrary to popular belief, these estimators do not necessarily address the challenges induced by model selection uncertainty, but should be regarded as attractive complements for the machine learning and forecasting literature, as well as tools to identify causal parameters. We illustrate our point by means of several targeted simulation studies.

中文翻译：

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何时以及何时不使用最佳模型平均

传统上，模型平均被视为模型选择的替代方法，其最终目标是通过使用候选模型的加权组合将与模型选择过程相关的不确定性纳入标准误差和置信区间。近年来，文献中出现了一类新的模型平均估计量，建议组合模型以使平方风险或其他风险函数最小化。我们认为，与普遍看法相反，这些估计量不一定解决模型选择不确定性引起的挑战，但应被视为机器学习和预测文献的有吸引力的补充，以及识别因果参数的工具。我们通过几个有针对性的模拟研究来说明我们的观点。