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.
翻译:传统的平均模型被视为替代模式选择的替代方法,其最终目的是通过对候选模型的加权组合,将与模式选择过程有关的不确定性纳入标准错误和信任间隔中;近年来,文献中出现了一种新的平均估计值模型类别,建议将各种模型结合起来,以尽可能降低平方风险或其他风险功能;我们争辩说,与普遍看法相反,这些估计值不一定能够解决模式选择不确定性引起的挑战,但应被视为对机器学习和预测文献的有吸引力的补充,以及查明因果参数的工具;我们通过若干有针对性的模拟研究来说明我们的观点。