Structural analysis with Multivariate Autoregressive Index models
Structural analysis with Multivariate Autoregressive Index models
复制标题
使用多元自回归指数模型进行结构分析
DOI：
10.1016/j.jeconom.2016.02.002
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发表时间：
2016
影响因子：
6.3
通讯作者：
Carriero A
中科院分区：
文献类型：
--
作者：
Carriero A
针对多变量向量自回归模型(VARs)中的参数降维问题，通过对系数矩阵施加特定降维限制，将VARs简化为多元自回归指数(MAI)模型。我们推导了MAIS所隐含的Wold表示，并表明它与动态因素模型的Wold表示密切相关。然后，将理论分析推广到VAR系数具有一般秩限制的情况。接下来，我们描述了大型MAI的经典估计和贝叶斯估计，并讨论了确定等级的方法。最后，在蒙特卡洛模拟和两个实证应用的背景下，比较了MAIS和大型贝叶斯VAR在货币政策传导机制和需求和供给冲击传播方面的表现。
We address the issue of parameter dimensionality reduction in Vector Autoregressive models (VARs) for many variables by imposing specific reduced rank restrictions on the coefficient matrices that simplify the VARs into Multivariate Autoregressive Index (MAI) models. We derive the Wold representation implied by the MAIs and show that it is closely related to that associated with dynamic factor models. Then, the theoretical analysis is extended to the case of general rank restrictions on the VAR coefficients. Next, we describe classical and Bayesian estimation of large MAIs, and discuss methods for rank determination. Finally, the performance of the MAIs is compared with that of large Bayesian VARs in the context of Monte Carlo simulations and two empirical applications, on the transmission mechanism of monetary policy and on the propagation of demand and supply shocks.