I develop algorithms to facilitate Bayesian inference in structural vector autoregressions that are set-identified with sign and zero restrictions by showing that the system of restrictions is equivalent to a system of sign restrictions in a lower-dimensional space. Consequently, algorithms applicable under sign restrictions can be extended to allow for zero restrictions. Specifically, I extend algorithms proposed in Amir-Ahmadi and Drautzburg (2021) to check whether the identified set is nonempty and to sample from the identified set without rejection sampling. I compare the new algorithms to alternatives by applying them to variations of the model considered by Arias et al. (2019a), who estimate the effects of US monetary policy using sign and zero restrictions on the monetary policy reaction function. The new algorithms are particularly useful when a rich set of sign restrictions substantially truncates the identified set given the zero restrictions.

中文翻译：

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用符号和零限制标识的 SVAR 中的推理算法

我开发了一些算法来促进结构向量自回归中的贝叶斯推理，这些自回归通过显示限制系统等同于低维空间中的符号限制系统来进行集合标识和零限制。因此，可以扩展适用于符号限制的算法以允许零限制。具体来说，我扩展了 Amir-Ahmadi 和 Drautzburg (2021) 中提出的算法，以检查识别的集合是否为非空，并从识别的集合中进行抽样而不进行拒绝抽样。我通过将新算法应用于 Arias 等人考虑的模型的变体，将它们与替代方案进行比较。（2019a），他们使用货币政策反应函数的符号和零限制来估计美国货币政策的影响。