We develop methods for robust Bayesian inference in structural vector autoregressions (SVARs) where the parameters of interest are set-identified using external instruments, or ‘proxy SVARs’. Set-identification in these models typically occurs when there are multiple instruments for multiple structural shocks. Existing Bayesian approaches to inference in proxy SVARs require researchers to specify a single prior over the model’s parameters, but, under set-identification, a component of the prior is never revised. We extend the robust Bayesian approach to inference in set-identified models proposed by Giacomini and Kitagawa in press[a] – which allows researchers to relax potentially controversial point-identifying restrictions without having to specify an unrevisable prior – to proxy SVARs. We provide new results on the frequentist validity of the approach in proxy SVARs. We also explore the effect of instrument strength on inference about the identified set. We illustrate our approach by revisiting Mertens and Ravn (2013) and relaxing the assumption that they impose to obtain point identification.

我们开发了在结构向量自回归 (SVAR) 中进行鲁棒贝叶斯推理的方法，其中感兴趣的参数是使用外部工具或“代理 SVAR”来设置识别的。这些模型中的集合识别通常发生在有多种工具用于多种结构冲击时。现有的在代理 SVAR 中进行推理的贝叶斯方法要求研究人员在模型参数上指定一个先验，但是在集合识别下，先验的一个组成部分永远不会被修改。我们将稳健的贝叶斯方法扩展到 Giacomini 和 Kitagawa 在 press [a] 中提出的集合识别模型中的推理——这允许研究人员放宽潜在有争议的点识别限制，而不必指定不可修改的先验——代理 SVAR。我们提供了关于代理 SVAR 方法的频率有效性的新结果。我们还探讨了仪器强度对已识别集合的推断的影响。我们通过重新审视 Mertens 和 Ravn (2013) 并放宽他们为获得点识别而施加的假设来说明我们的方法。