We develop a new algorithm for inference in structural vector autoregressions (SVARs) identified with sign restrictions that can accommodate big data and modern identification schemes. The key innovation of our approach is to move beyond the traditional accept-reject framework commonly used in sign-identified SVARs. We show that an elliptical slice within Gibbs sampler can deliver dramatic gains in computational speed and render previously infeasible applications tractable. We also prove that the algorithm is well-defined, in the sense that its stationary distribution coincides with the posterior distribution of interest. To illustrate the approach in the context of sign-identified SVARs, we use a tractable example. We further assess the performance of our algorithm through two applications: a well-known small-SVAR model of the oil market featuring a tight identified set, and a large SVAR model with more than ten shocks and 100 sign restrictions.

翻译：我们提出了一种用于结构向量自回归（SVAR）模型推断的新算法，该模型通过符号约束识别，能够适应大数据和现代识别方案。我们方法的关键创新在于突破了符号识别SVAR中常用的传统接受-拒绝框架。研究表明，吉布斯采样器中的椭圆切片法可大幅提升计算速度，并使先前不可行的应用场景变得易于处理。我们还证明了该算法的合理性——其稳态分布与目标后验分布一致。为展示该算法在符号识别SVAR中的应用，我们通过一个可处理的案例进行说明。进一步通过两个应用场景评估算法性能：一个是以紧致识别集著称的石油市场小型SVAR经典模型，另一个是包含十余种冲击和100个符号约束的大型SVAR模型。