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

Understanding disaggregate channels in the transmission of monetary policy to the real and financial sectors 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 spatial panel state-space model that assumes time variation in the spatial dependence parameter, and apply the framework to a study of measuring network effects of US monetary policy on the industry level. Second, we account for cross-sectional heterogeneity and cluster impacts of monetary policy shocks to production industries via a sparse finite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks. Moreover, we find that the role of network effects varies strongly over time. In particular, US recessions tend to coincide with periods where between 40 to 60 percent of the overall effects can be attributed to network effects; expansionary economic episodes show muted network effects with magnitudes of roughly 20 to 30 percent.