论文标题
具有因子随机波动率的大贝叶斯var：识别，顺序不变性和结构分析
Large Bayesian VARs with Factor Stochastic Volatility: Identification, Order Invariance and Structural Analysis
论文作者
论文摘要
具有多元随机波动率的矢量自动加压（VAR）广泛用于结构分析。通常，通过经济有意义的限制（例如，标志限制）确定的结构模型应该独立于如何订购因变量。但是，由于降低的模型不是不变的顺序，因此结构分析的结果取决于变量的顺序。我们考虑一个基于因子随机波动率的VAR，该因子被构成为阶不变。我们表明，多元随机波动率的存在允许对模型进行统计鉴定。我们进一步证明，有了合适的符号限制，可以识别相应的结构模型。提出的方法的另一个吸引力是，它可以轻松处理大量因变量以及标志限制。我们通过结构分析证明了方法，在该结构分析中，我们使用具有符号限制的20变量VAR来识别5种结构性冲击。
Vector autoregressions (VARs) with multivariate stochastic volatility are widely used for structural analysis. Often the structural model identified through economically meaningful restrictions--e.g., sign restrictions--is supposed to be independent of how the dependent variables are ordered. But since the reduced-form model is not order invariant, results from the structural analysis depend on the order of the variables. We consider a VAR based on the factor stochastic volatility that is constructed to be order invariant. We show that the presence of multivariate stochastic volatility allows for statistical identification of the model. We further prove that, with a suitable set of sign restrictions, the corresponding structural model is point-identified. An additional appeal of the proposed approach is that it can easily handle a large number of dependent variables as well as sign restrictions. We demonstrate the methodology through a structural analysis in which we use a 20-variable VAR with sign restrictions to identify 5 structural shocks.
