Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey
Structural Vector Autoregressive Analysis in a Data Rich Environment: A Survey
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数据丰富环境中的结构向量自回归分析：调查
DOI：
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发表时间：
2014
期刊：
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通讯作者：
H. Luetkepohl
中科院分区：
文献类型：
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作者：
H. Luetkepohl
决策者在决定政策行动时使用大量的变量。因此，在经济分析模型中包括大量信息集是可取的。在这篇调查中，我们回顾了向量自回归（VAR）模型中大量变量的信息。这可以通过聚合变量或将参数空间减少到可管理的维度来完成。因子模型缩小了变量空间，而大型贝叶斯VAR模型和面板VAR缩小了参数空间。全球VAR使用混合方法。他们聚集变量并使用简约的参数化。这些方法都在本次调查中进行了讨论，虽然主要侧重于因素模型。
Large panels of variables are used by policy makers in deciding on policy actions. Therefore it is desirable to include large information sets in models for economic analysis. In this survey methods are reviewed for accounting for the information in large sets of variables in vector autoregressive (VAR) models. This can be done by aggregating the variables or by reducing the parameter space to a manageable dimension. Factor models reduce the space of variables whereas large Bayesian VAR models and panel VARs reduce the parameter space. Global VARs use a mixed approach. They aggregate the variables and use a parsimonious parametrisation. All these methods are discussed in this survey although the main emphasize is on factor models.