Principal components at work: the empirical analysis of monetary policy with large data sets
Principal components at work: the empirical analysis of monetary policy with large data sets
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起作用的主成分：大数据集下的货币政策实证分析
DOI：
10.1002/jae.815
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发表时间：
2005
影响因子：
2.1
通讯作者：
F. Neglia
中科院分区：
文献类型：
--
作者：
Carlo A. Favero;Massimiliano Marcellino;F. Neglia
货币政策的实证分析需要构建针对未来预期通胀的工具。动态因素模型已被成功地应用于通货膨胀预测。事实上，最近已经开发出两种相互竞争的方法来估计大规模动态因素模型，分别基于静态和动态主成分。本文将计量经济学关于动态主成分的文献与货币政策的实证分析相结合。我们评估了两种相互竞争的提取因素的方法，基于它们在货币政策实证分析中对未来预期通胀的工具方面的成功。我们使用了美国和欧元区的两个宏观经济变量的大型数据集。我们的结果表明，估计的因素确实提供了设计货币政策所使用的信息的有用的简约总结。版权所有©2005 John Wiley&Sons，Ltd.
The empirical analysis of monetary policy requires the construction of instruments for future expected inflation. Dynamic factor models have been applied rather successfully to inflation forecasting. In fact, two competing methods have recently been developed to estimate large-scale dynamic factor models based, respectively, on static and dynamic principal components. This paper combines the econometric literature on dynamic principal components and the empirical analysis of monetary policy. We assess the two competing methods for extracting factors on the basis of their success in instrumenting future expected inflation in the empirical analysis of monetary policy. We use two large data sets of macroeconomic variables for the USA and for the Euro area. Our results show that estimated factors do provide a useful parsimonious summary of the information used in designing monetary policy. Copyright © 2005 John Wiley & Sons, Ltd.