摘要翻译：
我们提出了一个正则化因子增广向量自回归(FAVAR)模型，该模型允许因子加载的稀疏性。在这个框架中，因素可能只加载在变量的子集上，从而简化了因素的识别和经济解释。我们以数据驱动的方式识别因素，而不在未观察到的因素和潜在的时间序列之间强加特定的关系。利用我们的方法，可以研究结构冲击对经济上有意义的因素和包括在FAVAR模型中的所有观察到的时间序列的影响。我们证明了动态模型中因子负荷估计量、特质分量协方差矩阵、因子估计量以及自回归参数估计量的相合性。在实证应用中，我们考察了货币政策冲击对一系列经济相关变量的影响。我们使用因子模型和VAR模型中的结构创新的联合识别来识别这种冲击。我们在因子集合和观测时间序列水平上都找到了符合经济学原理的脉冲响应函数。
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英文标题：
《A Regularized Factor-augmented Vector Autoregressive Model》
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作者：
Maurizio Daniele, Julie Schnaitmann
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最新提交年份：
2019
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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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一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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英文摘要：
  We propose a regularized factor-augmented vector autoregressive (FAVAR) model that allows for sparsity in the factor loadings. In this framework, factors may only load on a subset of variables which simplifies the factor identification and their economic interpretation. We identify the factors in a data-driven manner without imposing specific relations between the unobserved factors and the underlying time series. Using our approach, the effects of structural shocks can be investigated on economically meaningful factors and on all observed time series included in the FAVAR model. We prove consistency for the estimators of the factor loadings, the covariance matrix of the idiosyncratic component, the factors, as well as the autoregressive parameters in the dynamic model. In an empirical application, we investigate the effects of a monetary policy shock on a broad range of economically relevant variables. We identify this shock using a joint identification of the factor model and the structural innovations in the VAR model. We find impulse response functions which are in line with economic rationale, both on the factor aggregates and observed time series level.
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PDF链接：
https://arxiv.org/pdf/1912.06049