Inference Based on Time-Varying SVARs Identified with Sign Restrictions
提出一种贝叶斯推断方法，用于符号约束识别的时变结构向量自回归模型，通过旋转不变性类和新算法，从均匀分布抽取正交矩阵序列，并分析货币政策在近期通胀飙升中的作用。
Abstract We propose an approach for Bayesian inference in TV structural vector autoregressions (SVARs) identified with sign restrictions. The linchpin of our approach is a class of rotation-invariant TV SVARs in which the prior and posterior densities of any sequence of structural parameters belonging to the class are invariant to {orthogonal transformations} of the sequence. Our methodology is new to the literature. In contrast to existing algorithms for inference based on sign restrictions, our algorithm is the first to draw from a uniform distribution over the sequences of orthogonal matrices given the reduced-form parameters. We illustrate our procedure for inference by analyzing the role played by monetary policy during the latest inflation surge.