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Activity Number:
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120
- SPEED: Variable Selection and Networks
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Type:
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Contributed
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Date/Time:
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Monday, July 31, 2017 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistical Learning and Data Science
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Abstract #322522
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Title:
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Dynamic Latent Factor Modeling of UN Voting Networks
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Author(s):
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Bomin Kim* and Xiaoyue Niu and David Hunter and Xun Cao
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Companies:
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Pennsylvania State University and Penn State University and The Pennsylvania State University and The Pennsylvania State University
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Keywords:
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latent factor model ;
social network analysis ;
dynamic network model
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Abstract:
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The General Assembly of the United Nations serves as the key component of policymaking of the United Nations. 193 members vote on various of events each year. Nations' foreign policy preferences and positions can be revealed from those voting behaviors. In this paper, we analyze the UN voting data (Voeten 2013) using a dynamic latent factor network model. We introduce an extension of the multiplicative latent factor model (Hoff 2009), by incorporating the time-dependent covariance structure to the prior specifications of the parameters. By controlling for geographic distances and bilateral trade between countries, the model estimated latent positions and movement of positions reveal interesting and meaningful foreign policy positions and alliance of various countries.
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Authors who are presenting talks have a * after their name.
