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

Michael Pfarrhofer and Hauzenberger Niko ()
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Hauzenberger Niko: University of Salzburg

No 2019-6, Working Papers in Economics from University of Salzburg

Abstract: Understanding disaggregate channels in the transmission of monetary policy to the real and nancial 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 nite Gaussian mixture model. The results suggest substantial heterogeneities in the responses of industries to surprise monetary policy shocks. Moreover, we nd 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 e ects can be attributed to network e ects; expansionary economic episodes show muted network e ects with magnitudes of roughly 20 to 30 percent.

Keywords: production networks; monetary policy shocks; high-frequency identi cation; spatio-temporal modeling (search for similar items in EconPapers)
JEL-codes: C23 C32 O47 R11 (search for similar items in EconPapers)
Pages: 17 pages
Date: 2019-11-15
New Economics Papers: this item is included in nep-mon
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Related works:
Journal Article: Bayesian State‐Space Modeling for Analyzing Heterogeneous Network Effects of US Monetary Policy (2021)
Working Paper: Bayesian state-space modeling for analyzing heterogeneous network effects of US monetary policy (2020)
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Persistent link: https://EconPapers.repec.org/RePEc:ris:sbgwpe:2019_006

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