Dynamic Innovation Transmission Through Networks: Theory, Large T-Inference, and Business Cycles by Lagged Input-Output Conversion

Marko Mlikota

Papers from arXiv.org

Abstract: I develop an econometric framework that rationalizes the dynamics of a cross-sectional variable by lagged transmissions of innovations along bilateral links between units. While nesting the Spatial Autoregression and Spatial Error Model as limits and producing equivalent impulse-responses in the long run, the Network-Vector-Autoregression (NVAR) I propose can accommodate general transmission patterns over time and yields "networked" transition dynamics distinct from those implied by autocorrelated innovations. I discuss large $T$-inference conditional on a network. I then estimate an NVAR for US sectoral output, as derived under a Real Business Cycle economy with lagged input-output conversion. Under the preferred specification, lagged transmissions of productivity shocks along supply chains account for 85% of the persistence in aggregate output growth and reduce shock-variances relative to an economy with contemporaneous input-output conversion by 73% on average across sectors.

Date: 2022-11, Revised 2026-09
New Economics Papers: this item is included in nep-ecm and nep-net
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://arxiv.org/pdf/2211.13610 Latest version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2211.13610

Access Statistics for this paper

More papers in Papers from arXiv.org
Bibliographic data for series maintained by arXiv administrators ().