Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy

Abstract

Understanding disaggregate channels in the transmission of monetary policy is of crucial importance for effectively implementing policy measures. We extend the empirical econometric literature on the role of production networks in the propagation of shocks along two dimensions. First, we set forth a Bayesian network panel state-space model that assumes time variation in the network dependence parameter. The framework is applied to a study of the network effects of Federal Reserve (Fed) monetary policy announcements on US stock market returns on the industry level using high-frequency data. Second, we account for cross-sectional heterogeneity and cluster impacts of monetary policy shocks to industries via a sparse finite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks in line with the recent literature. Moreover, we find that the impact of policy surprises on stock returns differes strongly over time. This time-variation stems mainly from changes in the strength network effects, with magnitudes of the effects during periods coinciding with recessions being almost twice as large than in expansionary phases.