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Separable and semiparametric network-based counting processes applied to the international combat aircraft trades

Published online by Cambridge University Press:  20 September 2021

Cornelius FritzOpen the ORCID record for Cornelius Fritz[Opens in a new window],
Paul W. Thurner and
Göran Kauermann
Cornelius Fritz*
Affiliation:
Department of Statistics, LMU Munich, Munich, Germany (e-mail: goeran.kauermann@stat.uni-muenchen.de)
Paul W. Thurner
Affiliation:
Geschwister Scholl Institute of Political Science, LMU Munich, Munich, Germany (e-mail: paul.thurner@gsi.uni-muenchen.de)
Göran Kauermann
Affiliation:
Department of Statistics, LMU Munich, Munich, Germany (e-mail: goeran.kauermann@stat.uni-muenchen.de)
*
*Corresponding author. Email: cornelius.fritz@stat.uni-muenchen.de
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Abstract

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We propose a novel tie-oriented model for longitudinal event network data. The generating mechanism is assumed to be a multivariate Poisson process that governs the onset and repetition of yearly observed events with two separate intensity functions. We apply the model to a network obtained from the yearly dyadic number of international deliveries of combat aircraft trades between 1950 and 2017. Based on the trade gravity approach, we identify economic and political factors impeding or promoting the number of transfers. Extensive dynamics as well as country heterogeneities require the specification of semiparametric time-varying effects as well as random effects. Our findings reveal strong heterogeneous as well as time-varying effects of endogenous and exogenous covariates on the onset and repetition of aircraft trade events.

Keywords

arms trade networkcombat aircraftlongitudinal network analysisrelational event model

Information

Type
Research Article
Information
Network Science , Volume 9 , Issue 3 , September 2021, pp. 291 - 311
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s), 2021. Published by Cambridge University Press

Footnotes

Action Editor: Stanley Wasserman

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