DSGE Models in a Data-Rich Environment
DSGE Models in a Data-Rich Environment
复制标题
数据丰富环境中的 DSGE 模型
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
M. Giannoni
中科院分区：
文献类型：
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作者：
Jean Boivin;M. Giannoni
估计动态随机一般均衡模型的标准做法坚持这样一个假设，即经济变量由单一指标适当衡量，估计的所有相关信息由少量数据系列充分概括，无论是否允许测量误差。然而，最近对因素模型的实证研究表明，大型数据集所包含的信息与重要宏观经济序列的演变有关。这表明，传统的模型估计和推断的基础上估计DSGE模型可能会被扭曲。在本文中，我们提出了一个实证框架估计DSGE模型，利用相关信息，从数据丰富的环境。该框架通过DSGE模型的镜头提供了对大型数据集中包含的所有信息的解释。估计涉及贝叶斯马尔可夫链蒙特-卡罗（MCMC）方法扩展，使估计可以，在某些情况下，继承经典的最大似然估计的性质。我们将这种估计方法应用于最先进的DSGE货币模型。处理模型的理论概念-如产出，通货膨胀和就业-作为部分观察，我们表明，从一个大的宏观经济指标集的信息是很重要的模型的准确估计。它还使我们能够改进对重要经济变量的预测
Standard practice for the estimation of dynamic stochastic general equilibrium (DSGE) models maintains the assumption that economic variables are properly measured by a single indicator, and that all relevant information for the estimation is adequately summarized by a small number of data series, whether or not measurement error is allowed for. However, recent empirical research on factor models has shown that information contained in large data sets is relevant for the evolution of important macroeconomic series. This suggests that conventional model estimates and inference based on estimated DSGE models are likely to be distorted. In this paper, we propose an empirical framework for the estimation of DSGE models that exploits the relevant information from a data-rich environment. This framework provides an interpretation of all information contained in a large data set through the lenses of a DSGE model. The estimation involves Bayesian Markov-Chain Monte-Carlo (MCMC) methods extended so that the estimates can, in some cases, inherit the properties of classical maximum likelihood estimation. We apply this estimation approach to a state-of-the-art DSGE monetary model. Treating theoretical concepts of the model --- such as output, inflation and employment --- as partially observed, we show that the information from a large set of macroeconomic indicators is important for accurate estimation of the model. It also allows us to improve the forecasts of important economic variables