论文标题
贝叶斯矢量自动遵循的结构限制的新算法
A new algorithm for structural restrictions in Bayesian vector autoregressions
论文作者
论文摘要
开发了一种使用符号和其他结构限制的媒介自动压力（VAR）推断的综合方法。减少形式的VAR干扰是由一些常见因素驱动的，结构识别限制可以以参数限制的形式纳入其载荷中。得出了Gibbs采样器，该采样器允许在一个步骤中有效地采样降低形式的参数和结构限制。提出方法的一个关键好处是，它允许将参数估计和结构推断视为关节问题。另一个好处是，该方法可以扩展到具有多个冲击的大型VAR，并且可以扩展以适应非线性，不对称性和许多其他有趣的经验特征。使用合成数据实验探索了新的推理算法的出色特性，并通过基于签名限制的识别来重新审视财务因素在经济波动中的作用。
A comprehensive methodology for inference in vector autoregressions (VARs) using sign and other structural restrictions is developed. The reduced-form VAR disturbances are driven by a few common factors and structural identification restrictions can be incorporated in their loadings in the form of parametric restrictions. A Gibbs sampler is derived that allows for reduced-form parameters and structural restrictions to be sampled efficiently in one step. A key benefit of the proposed approach is that it allows for treating parameter estimation and structural inference as a joint problem. An additional benefit is that the methodology can scale to large VARs with multiple shocks, and it can be extended to accommodate non-linearities, asymmetries, and numerous other interesting empirical features. The excellent properties of the new algorithm for inference are explored using synthetic data experiments, and by revisiting the role of financial factors in economic fluctuations using identification based on sign restrictions.
