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Motivated by a study of United Nations voting behaviors, we introduce aregression model for a series of networks that are correlated over time. Ourmodel is a dynamic extension of the additive and multiplicative effects networkmodel (AMEN) of Hoff (2019). In addition to incorporating a temporal structure,the model accommodates two types of missing data thus allows the size of thenetwork to vary over time. We demonstrate via simulations the necessity ofvarious components of the model. We apply the model to the United NationsGeneral Assembly voting data from 1983 to 2014 (Voeten (2013)) to answerinteresting research questions regarding international voting behaviors. Inaddition to finding important factors that could explain the voting behaviors,the model-estimated additive effects, multiplicative effects, and theirmovements reveal meaningful foreign policy positions and alliances of variouscountries.

A Dynamic Additive and Multiplicative Effects Model with Application to the United Nations Voting Behaviors