Many research questions involve comparing predictions or effects across multiple models. For example, it may be of interest whether an independent variable’s effect changes after adding variables to a model. Or, it could be important to compare a variable’s effect on different outcomes or across different types of models. When doing this, marginal effects are a useful method for quantifying effects because they are in the natural metric of the dependent variable and they avoid identification problems when comparing regression coefficients across logit and probit models. Despite advances that make it possible to compute marginal effects for almost any model, there is no general method for comparing these effects across models. In this article, the authors provide a general framework for comparing predictions and marginal effects across models using seemingly unrelated estimation to combine estimates from multiple models, which allows tests of the equality of predictions and effects across models. The authors illustrate their method to compare nested models, to compare effects on different dependent or independent variables, to compare results from different samples or groups within one sample, and to assess results from different types of models.

中文翻译：

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比较模型间预测和边际效应的通用框架

许多研究问题涉及比较多个模型的预测或效果。例如，将变量添加到模型后，自变量的效果是否会发生变化可能很有趣。或者，比较变量对不同结果或不同类型模型的影响可能很重要。这样做时，边际效应是量化效应的有用方法，因为它们处于因变量的自然度量中，并且在比较 logit 和 probit 模型的回归系数时可以避免识别问题。尽管取得了进步，几乎可以计算任何模型的边际效应，但没有通用的方法来比较模型之间的这些效应。在本文中，作者提供了一个通用框架，用于比较模型之间的预测和边际效应，使用看似无关的估计来组合来自多个模型的估计，这允许测试跨模型的预测和效果的相等性。作者举例说明了比较嵌套模型、比较对不同因变量或自变量的影响、比较来自不同样本或同一样本中的组的结果以及评估来自不同类型模型的结果的方法。