The method of model averaging has become an important tool to deal with model uncertainty, in particular in empirical settings with large numbers of potential models and relatively limited numbers of observations, as are common in economics. Model averaging is a natural response to model uncertainty in a Bayesian framework, so most of the paper deals with Bayesian model averaging. In addition, frequentist model averaging methods are also discussed. Numerical methods to implement these methods are explained, and I point the reader to some freely available computational resources. The main focus is on the problem of variable selection in linear regression models, but the paper also discusses other, more challenging, settings. Some of the applied literature is reviewed with particular emphasis on applications in economics. The role of the prior assumptions in Bayesian procedures is highlighted, and some recommendations for applied users are provided

中文翻译：

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模型平均及其在经济学中的应用

模型平均方法已成为处理模型不确定性的重要工具，尤其是在具有大量潜在模型且观测值相对有限的经验设置中，这在经济学中很常见。模型平均是对贝叶斯框架中模型不确定性的自然响应，因此，大多数论文都涉及贝叶斯模型平均。此外，还讨论了常客模型平均方法。解释了实现这些方法的数值方法，我向读者指出了一些免费的计算资源。主要关注线性回归模型中的变量选择问题，但本文还讨论了其他更具挑战性的设置。审查了一些应用文献，特别着重于经济学中的应用。