Structural Dynamic Factor Analysis Using Prior Information From Macroeconomic Theory
提出一种贝叶斯方法，将动态随机一般均衡模型的信息作为先验，结合动态因子模型处理大数据的能力，用于分析政策干预对大量观测变量的影响，并以美国数据验证了模型在预测和识别货币冲击方面的有效性。
Dynamic factor models are becoming increasingly popular in empirical macroeconomics due to their ability to cope with large datasets. Dynamic stochastic general equilibrium (DSGE) models, on the other hand, are suitable for the analysis of policy interventions from a methodical point of view. In this article, we provide a Bayesian method to combine the statistically rich specification of the former with the conceptual advantages of the latter by using information from a DSGE model to form a prior belief about parameters in the dynamic factor model. Because the method establishes a connection between observed data and economic theory and at the same time incorporates information from a large dataset, our setting is useful to study the effects of policy interventions on a large number of observed variables. An application of the method to U.S. data shows that a moderate weight of the DSGE prior is optimal and that the model performs well in terms of forecasting. We then analyze the impact of monetary shocks on both the factors and selected series using a DSGE-based identification of these shocks. Supplementary materials for this article are available online.