Censored Regression for Modelling Small Arms Trade Volumes and Its 'Forensic' Use for Exploring Unreported Trades. Issue 4 (7th August 2021)

• Record Type: Journal Article
• Authors: Lebacher, Michael; Thurner, Paul W.; Kauermann, Göran
• Is Part Of: Journal of the Royal Statistical Society. Volume 70:Issue 4(2021)
• Page Start: 909
• Page End: 933
• Publication Date: 2021-08-07
• Subjects: gravity model -- latent variable -- Monte Carlo EM algorithm -- network analysis -- spatial autocorrelation -- zero inflated data; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12491
• Languages: English
• ISSNs: 0035-9254

Abstract

Abstract: In this paper, we use a censored regression model to investigate data on the international trade of small arms and ammunition provided by the Norwegian Initiative on Small Arms Transfers. Taking a network-based view on the transfers, we do not only rely on exogenous covariates but also estimate endogenous network effects. We apply a spatial autocorrelation gravity model with multiple weight matrices. The likelihood is maximized employing the Monte Carlo expectation maximization algorithm. Our approach reveals strong and stable endogenous network effects. Furthermore, we find evidence for a substantial path dependence as well as a close connection between exports of civilian and military small arms. The model is then used in a 'forensic' manner to analyse latent network structures and thereby to identify countries with higher or lower tendency to export or import than reflected in the data. The approach is also validated using a simulation study.