Virtual Room 3: Panel and Spatial Analysis


Wednesday, July 15, 2020, 12:00pm to 1:30pm


Chair: Ludovic Rheault (University of Toronto)


Co-Host: Regan Johnston (McMaster University)

A Bayesian Method for Modeling Dynamic Network Influence With TSCS Data

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Author(s): Xun Pang and Licheng Liu

Discussant: Matthew Blackwell (Harvard University)


With fast accumulations of network data, modeling time-varying network influence is necessary and important to relax the unrealistic constant-effect assumption and to deepen our understanding of the changing dynamic between networks and social behavior. However, even in static settings, the identification of network influence remains a challenging problem due to the complicated entanglement of network interdependence, homophily (selection), and common shocks. To identify and explain dynamic network influence, this paper proposes a multilevel Spatio-Temporal model with a multifactor error structure. Network influence is allowed to vary, and network structural features could enter the group-level regression and further explain the variation. The multifactor term is included to capture unobserved time-varying homophily and heterogeneous time trends. We apply Bayesian shrinkage for factor-selection to achieve sufficient bias-correction and avoid overfitting. The Bayesian Spatio-Temporal model is highly flexible and can accommodate a wide variety of network types. Monte Carlo experiments show the model performs well in recovering the true trajectory of network influence. Besides, the varying-influence specification actually helps identification and is robust to misspecification. The two empirical IR studies find interesting patterns of the time-varying influence of the migration flow network on terrorist attacks and the GATT/WTO institutional network on trade policies, which could inspire hypothesis-development and shed light on theoretical debates. An R package is developed for implementing the proposed method.

Predicting Dyadic and Geopolitical Interaction Between Spatially Moving Objects

Author(s): Howard Liu, Sangyeon Kim and Bruce Desmarais

Discussant: Scott J. Cook (Texas A&M University)


Political entities (countries, advocacy organizations, individual citizens) often interact spatially and depend on each other through spatial relations. This holds in both dyadic and monadic phenomena. For example, in much of the conflict literature, analyses concentrate on the interaction or relation between a pair or dyad of two political units. However, with a few exceptions, existing studies have analyzed spatial dependence in a monadic way, analyzing spatial contagion from one fixed geographic unit to another. This practice manifests two problems: First, actors---particularly source and target---and their mutual influence on spatial movement become ambiguous in the monadic analysis. Second, focusing on fixated geographic units in contagion studies prevents us from probing dependencies in spatial interaction between dynamic moving actors, which are commonly observed in sub-national conflict studies. In this article, we propose a simple data-driven method to account for spatial dependence in dyadic interactions between moving objects. We develop an algorithm that uses the spatiotemporal histories of dyadic interactions to project where interactions between two actors are likely to occur in the future and use these projected locations in models that predict dyadic interactions. Initial results show that including the dyadic projections yields noticeable improvement (almost twice as much) in out-of-sample forecasting of interaction location in comparison to a naive approach using monadic moving history only. The proposed method has wide application potential for researchers interested in studying interactions among dyadic actors.

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