Abstract
AbstractWe extend the econometric literature on the role of production networks in the propagation of monetary policy shocks along two dimensions. First, we allow for time‐varying industry‐specific responses, reflecting non‐linearities and heterogeneity in direct transmission channels. Second, we allow for time‐varying network structures and dependence. This captures both variation in the structure of the production network and differences in cross‐industry demand elasticities. Spillover effects among industries appear to be important in periods of elevated economic and financial uncertainty, often coinciding with tight credit market conditions and financial stress. Cross‐sectional differentials can be explained by how close industries are to end‐consumers.
Highlights
A growing number of papers explores how shocks on the micro and macro level propagate through economic networks and how such shocks relate to aggregate fluctuations
We contribute to this literature by analyzing the transmission of monetary policy shocks through the granular US production network
Several studies find that returns respond much stronger to surprise monetary policy shocks during tight credit market conditions, or during bear markets
Summary
A growing number of papers explores how shocks on the micro and macro level propagate through economic networks and how such shocks relate to aggregate fluctuations (see, for instance, Gabaix, 2011; Acemoglu et al, 2012; Carvalho and Gabaix, 2013; Elliott et al, 2014; Acemoglu et al, 2015; Baqaee and Farhi, 2019). While Bernanke and Kuttner (2005) and Gurkaynak et al (2005) identify a significant and substantial impact of monetary surprises on aggregate stock market indices, Ozdagli and Weber (2020) decompose these estimates into direct effects and spillovers through the production network They use a conventional network panel model with homogenous parameters, and provide evidence for significant higher-order effects of monetary policy on stock market returns between 55 and 85 percent using disaggregate data on the industry-level. Several studies find that returns respond much stronger to surprise monetary policy shocks during tight credit market conditions, or during bear markets (see Chen, 2007; Basistha and Kurov, 2008; Kurov, 2010; Kontonikas et al, 2013) It is unclear, if these differences originate from changes in the covariance structure across industries reflecting network dependency and higherorder effects, or whether they stem from direct responses in the conditional mean of conventional regressions (captured, for instance, via time-varying parameters, TVPs).
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