Structural Dynamic Factor Analysis Using Prior Information From Macroeconomic Theory

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.