摘要翻译：
本文将大维向量自回归(VAR)模型分解为两个分量，第一个分量由小尺度VAR生成，第二个分量由白噪声序列生成。因此，通过VAR结构，减少了公共因素的数量，从而产生了大系统的整个动态。该模型将公共特征方法扩展到高维系统，它不同于动态因子模型，在动态因子模型中，异质成分也可以嵌入动态模式。我们给出了这种分解存在的条件。我们提供统计工具来检测它在数据中的存在，并估计底层小规模VAR模型的参数。基于我们的方法，我们提出了一种新的方法来识别冲击，这种冲击是对商业周期频率的大多数常见变化负责的。我们通过模拟以及对大量美国经济变量的实证应用来评估所提方法的实用价值。
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英文标题：
《Dimension Reduction for High Dimensional Vector Autoregressive Models》
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作者：
Gianluca Cubadda and Alain Hecq
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最新提交年份：
2021
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分类信息：

一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  This paper aims to decompose a large dimensional vector autoregessive (VAR) model into two components, the first one being generated by a small-scale VAR and the second one being a white noise sequence. Hence, a reduced number of common factors generates the entire dynamics of the large system through a VAR structure. This modelling extends the common feature approach to high dimensional systems, and it differs from the dynamic factor model in which the idiosyncratic component can also embed a dynamic pattern. We show the conditions under which this decomposition exists. We provide statistical tools to detect its presence in the data and to estimate the parameters of the underlying small-scale VAR model. Based on our methodology, we propose a novel approach to identify the shock that is responsible for most of the common variability at the business cycle frequencies. We evaluate the practical value of the proposed methods by simulations as well as by an empirical application to a large set of US economic variables.
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PDF链接：
https://arxiv.org/pdf/2009.03361