Cross-Sectional Dynamics Under Network Structure: Theory and Macroeconomic Applications

Marko Mlikota

Abstract: Many environments in economics feature a cross-section of units linked by bilateral ties. I develop a framework for studying dynamics of cross-sectional variables that exploits this network structure. The Network-VAR (NVAR) is a vector autoregression in which innovations transmit cross-sectionally via bilateral links and which can accommodate rich patterns of how network effects of higher order accumulate over time. It can be used to estimate dynamic network effects, with the network given or inferred from dynamic cross-correlations in the data. It also offers a dimensionality-reduction technique for modeling high-dimensional (cross-sectional) processes, owing to networks' ability to summarize complex relations among variables (units) by relatively few bilateral links. In a first application, consistent with an RBC economy with lagged input-output conversion, I estimate how sectoral productivity shocks transmit along supply chains and affect sectoral prices in the US economy. In a second application, I forecast monthly industrial production growth across 44 countries by assuming and estimating a network underlying the dynamics. This reduces out-of-sample mean squared errors by up to 23% relative to a factor model, consistent with an equivalence result I derive.

Date: 2022-11, Revised 2024-09
New Economics Papers: this item is included in nep-ecm and nep-net
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