A GENERAL FRAMEWORK FOR COMPARING PREDICTIONS AND MARGINAL EFFECTS ACROSS MODELS
A GENERAL FRAMEWORK FOR COMPARING PREDICTIONS AND MARGINAL EFFECTS ACROSS MODELS
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DOI：
10.1177/0081175019852763
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发表时间：
2019-01-01
期刊：
影响因子：
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通讯作者：
Long, J. Scott
中科院分区：
文献类型：
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作者：
Mize, Trenton D.;Doan, Long;Long, J. Scott
许多研究问题涉及比较多个模型的预测或影响。例如，在向模型中添加变量后，自变量的效应是否会发生变化可能会引起关注。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。在这样做时，边际效应是量化效应的一种有用方法，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。尽管取得了一些进展，使得计算几乎任何模型的边际效应成为可能，但还没有通用的方法来比较这些模型之间的效应。在这篇文章中，作者提供了一个通用的框架，用于使用看似不相关的估计来比较不同模型的预测和边际效应，以联合收割机组合来自多个模型的估计，这允许测试不同模型的预测和效应的相等性。作者说明了他们的方法来比较嵌套模型，比较不同的因变量或自变量的影响，比较不同样本或一个样本内的组的结果，并评估不同类型的模型的结果。
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.