We consider structural vector autoregressions that are identified through stochastic volatility under Bayesian estimation. Three contributions emerge from our exercise. First, we show that a non-centred parameterization of stochastic volatility yields a marginal prior for the conditional variances of structural shocks that is centred on homoskedasticity, with strong shrinkage and heavy tails -- unlike the common centred parameterization. This feature makes it well suited for assessing partial identification of any shock of interest. Second, Monte Carlo experiments on small and large systems indicate that the non-centred setup estimates structural parameters more precisely and normalizes conditional variances efficiently. Third, revisiting prominent fiscal structural vector autoregressions, we show how the non-centred approach identifies tax shocks that are consistent with estimates reported in the literature.

翻译：本文研究在贝叶斯估计框架下通过随机波动性实现识别的结构向量自回归模型。我们的研究得出三点贡献：首先，我们证明随机波动性的非中心化参数设定能够为结构冲击的条件方差生成一个以同方差性为中心的边缘先验分布，该分布具有强收缩性和厚尾特征——这与常见的中心化参数设定截然不同。这一特性使其特别适用于对任意关注冲击进行部分识别评估。其次，在小规模与大规模系统上进行的蒙特卡洛实验表明，非中心化设定能够更精确地估计结构参数，并有效实现条件方差的归一化。最后，通过重新审视经典的财政结构向量自回归模型，我们展示了非中心化方法如何识别出与文献报道估计结果相一致的税收冲击。